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Television Versus The Internet

Permanent Link: http://ufdc.ufl.edu/UFE0024881/00001

Material Information

Title: Television Versus The Internet A Comparative Analysis Of Traditional And New Video Platforms In Substitutability, Perceptions, And Displacement Effects
Physical Description: 1 online resource (231 p.)
Language: english
Creator: Cha, Jiyoung
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: adoption, attributes, diffusion, displacement, online, technology, television, theory, video, webcasting
Journalism and Communications -- Dissertations, Academic -- UF
Genre: Mass Communication thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: In today's multiplatform video environment, this study sheds light on the use of the Internet as a video platform. When a new medium emerges in the marketplace, one of the prevailing goals of related research is to identify the factors that predict consumers' decisions to adopt the medium. However, most of these types of studies tend to focus on the new medium alone. In reality, though, the new medium coexists with traditional media; consumers' use of, or attitude toward, traditional media may influence their decision to adopt the new medium. Furthermore, the introduction of the new medium might also influence consumers' use of traditional media. Recognizing the fact that that new and old media coexist in the market, the overarching aim of this study is to examine how the Internet and television, as video platforms, are interrelated with respect to consumer demand and time. To that end, this study employed mail surveys of a random sample of Internet users throughout the United States. The findings indicated that both actual users of online video platforms and people who are likely to adopt online video platforms expect different things from online video platforms than from television. The perceived substitutability between online video platforms and television negatively affects the intention to use online video platforms. This study also revealed that the perceived compatibility of online video platforms has the strongest impact on the intention to use online video platforms ? but the compatibility was perceived as being very low. Meanwhile, the relative advantage and compatibility of online video platforms decrease the likelihood of using television. With respect to the displacement effect of online video platforms on television, this study found that, overall, the time spent using the Internet to watch video content reduces the time spent watching television. However, the presence of the displacement effect actually depends on what type of video content consumers watch online, how much of that content overlaps between online video platforms and television, and what types of online video venues consumers use.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Jiyoung Cha.
Thesis: Thesis (Ph.D.)--University of Florida, 2009.
Local: Adviser: Chan-Olmsted, Sylvia M.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0024881:00001

Permanent Link: http://ufdc.ufl.edu/UFE0024881/00001

Material Information

Title: Television Versus The Internet A Comparative Analysis Of Traditional And New Video Platforms In Substitutability, Perceptions, And Displacement Effects
Physical Description: 1 online resource (231 p.)
Language: english
Creator: Cha, Jiyoung
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: adoption, attributes, diffusion, displacement, online, technology, television, theory, video, webcasting
Journalism and Communications -- Dissertations, Academic -- UF
Genre: Mass Communication thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: In today's multiplatform video environment, this study sheds light on the use of the Internet as a video platform. When a new medium emerges in the marketplace, one of the prevailing goals of related research is to identify the factors that predict consumers' decisions to adopt the medium. However, most of these types of studies tend to focus on the new medium alone. In reality, though, the new medium coexists with traditional media; consumers' use of, or attitude toward, traditional media may influence their decision to adopt the new medium. Furthermore, the introduction of the new medium might also influence consumers' use of traditional media. Recognizing the fact that that new and old media coexist in the market, the overarching aim of this study is to examine how the Internet and television, as video platforms, are interrelated with respect to consumer demand and time. To that end, this study employed mail surveys of a random sample of Internet users throughout the United States. The findings indicated that both actual users of online video platforms and people who are likely to adopt online video platforms expect different things from online video platforms than from television. The perceived substitutability between online video platforms and television negatively affects the intention to use online video platforms. This study also revealed that the perceived compatibility of online video platforms has the strongest impact on the intention to use online video platforms ? but the compatibility was perceived as being very low. Meanwhile, the relative advantage and compatibility of online video platforms decrease the likelihood of using television. With respect to the displacement effect of online video platforms on television, this study found that, overall, the time spent using the Internet to watch video content reduces the time spent watching television. However, the presence of the displacement effect actually depends on what type of video content consumers watch online, how much of that content overlaps between online video platforms and television, and what types of online video venues consumers use.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Jiyoung Cha.
Thesis: Thesis (Ph.D.)--University of Florida, 2009.
Local: Adviser: Chan-Olmsted, Sylvia M.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0024881:00001


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1 TELEVISION VERSUS THE INTERNET: A COMPARATIVE ANALYSIS OF TRADITIONAL AND NEW VIDEO PLATFORMS IN SUBSTITUTABILITY, PERCEPTIONS, AND DISPLACEMENT EFFECTS By JIYOUNG CHA A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF TH E UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2009

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2 2009 Jiyoung Cha

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3 To my parents who walked with me every step of this journey

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4 ACKNOWLEDGMENTS Whenever I had a difficult moment during my doctoral studies, I used to imagine this moment when I would write down my acknowledgments. At last, it is not my imagination anymore. I am exhilarated and grateful for this moment In retrospect, I had a lot of joyful and blissful times during my days as a doctoral student. On the flip side, I had heartbreaking and challenging moments as well. A lot of people around me shared my joy and concerns. They made this dissertation comple te. I am not sure how well words can convey how grateful I feel to all of them, but I would like to try. Very special thanks to my advisor and the dissertation chair, Dr. Sylvia Chan Olmsted. I am very fortunate to have her as my advisor, dissertation chair, and mentor. She admitted me to the program, and supported, encouraged, and mentored me above and beyond my academic life. I remember when she treated me to lunch after Id just arrived to Gainesville about 4 years ago. I also remember how she talked a bout her life and research at one of the gatherings she brought me to in the first year of my doctoral studies. She often challenged me to come up with more solid ideas and theoretical rationales for my research. I will never forget her dedicated and thorough guidance regarding my research, teaching, and career throughout my doctoral studies. Thank you so much, Dr. Chan Olmsted, for giving me so many wonderf ul opportunities to learn and explore more about research, teaching, and life. I also want to expres s my special and sincere gratitude to Dr. David Ostroff. Despite his busy schedule as a department chair, he allowed me to share my worries about my dissertation, teaching, and career He encouraged me to think about research problems from various angles a nd provided me with valuable input. Whenever I left his office after talking with him, I had a much clearer idea as to what to do; I gained courage. I definitely benefited from his support, advice, and encouragement.

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5 Dr. Barton Weitz and Dr. Marilyn Rober ts deserve special words of thanks. Dr. Weitz made it possible for me to minor in marketing during my doctoral studies. He inspired me to advance my knowledge in marketing. His thoughtful guidance and warm heart relieved my tension. His openness and generosity also allowed me to ask him research questions that I had been initially afraid of asking due to my lack of knowledge. I would also like to thank Dr. Roberts for her support and willingness to spare her time with me under difficult circumstances. Her e ncouragement and kindness helped me get through all of this. I also owe my special appreciation to Dr. Fiona Chew who is my life -long mentor Thanks to her, I decided to advance my studies to the doctoral level when I was a masters student. Without her strong support, I might not have pursued my doctoral -level studies. She shared my triumphs and concerns throughout my graduate studies. Her warm -hearted personality dispelled my fear of talking with professors. She encouraged and supported me endlessly ove r the past years. I look forward to our reunion at one of the conferences and hearing her happy laughter again. I also want to express my sincere gratitude to several other faculty members. I thank Dr. Madelyn Lockhart and other faculty members at The As sociation for Academic Women for their financial and emotional support of my dissertation. I also thank Dr. Ronald Ward, Dr. Carlos Jauregui, and Dr. James Algina, who taught me econometrics and structural equation modeling. They welcomed my countless ques tions about statistics and clarified them. I am truly amazed at how dedicated they are to their students. I also want to thank Dr. Johanna Cleary, Dr. Linda Kaid, Dr. Hyojin Kim, Dr. Amy Coffey, and Professor Tim Sorel for their support and detailed guidan ce. They helped me realize what I could do during my doctoral studies. I also owe a note of special thanks to Dr. Haekyung Han, Dr. Changseop Choi, Director Jieho Lee, and Director

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6 Jiyeon Nam. They made it possible for me to resume my graduate studies in m edia, so different a subject than my undergraduate major. During my stay in the United States, many of my friends in Korea continued to stand by me. I in particular single out Haejin, Joyo ung, J u young, Jiyoon, Jungeun, and Jungran. I also met a wonderful circle of friends during my graduate studies. I was very fortunate to meet Fei, Jasmine, Mic, Cari, Brad, Jieun, Becky, Chunsik, Dave, Seth, Jun, Mijung, Yeuseung, Miao, Hyunjung, Tayo, Jinseong, Jiyoung, Jamie, Jaejin, Kyungseop, Khelia, Eunsoo, Wanseop, Jungmin, Hanna, Eunh w a, and Jae. Thank you very much for all the good memories and for responding to my SOS. I also thank Jody, Kim, Diana, and Ms. Hunter for their kindness and support. I would also like to express my sincere and very special thanks to Seungyeon who shared every single joy and sorrow with me over the past years. Seungyeon without your consistent support and encouragement I would have not able to make it. I would also like thank Dole for listening to me and always suggesting smart solutions. Dole thanks to your strong urging, I was able to refocus on my dissertation. My very special thanks also go to AJ, who has stood by me no matter where I am and what I go through. AJ, thank you very much for making me laugh louder, dancing crazily w ith me, and helping me sustain my strength. Last, but not least, I express my tremendous gratitude to my family. My doctoral studies allowed me to redefine what my family means to me in my life. My brother has been one of my biggest supporters throughout my whole life. My brother, I will always be one of your biggest supporters. I also owe my very special thanks to my parents, who made me and made this happen. Their love, support, patience, and trust helped me get through all of this and to grow as a

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7 huma n being. Their love truly made me stand up eight times or more even though I sometimes fell down seven times. Dear family members and AJ, we finally did it!

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8 TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................................................................................................... 4 LIST OF TABLES .............................................................................................................................. 11 LIST OF FIGURES ............................................................................................................................ 13 ABSTRACT ........................................................................................................................................ 14 CHAPTER 1 INTRODUCTION ....................................................................................................................... 16 Environmental Change of the Television Industry ................................................................... 16 Purpose of the Study ................................................................................................................... 20 2 THEORETICAL FRAMEWORK ............................................................................................. 23 Integration of Different Theories ............................................................................................... 23 Theory of Planned Behavior ............................................................................................... 23 Technology Acceptance Model and Innovation Diffusion Theory .................................. 25 A Comparative Study .................................................................................................................. 27 3 LITERATURE REVIEW AND RESEARCH QUESTIONS .................................................. 32 Perceived Characteristics of Online Video Platforms .............................................................. 32 Perceived Substitutability .................................................................................................... 32 Relative Advantage .............................................................................................................. 37 Content related attributes ............................................................................................. 39 Technology related attributes ...................................................................................... 41 Cost related attributes .......................................................................................................... 42 Perceived Ease of Use ......................................................................................................... 47 Compatibility ....................................................................................................................... 48 Consumer Characteristics ........................................................................................................... 49 Online Flow Experience ...................................................................................................... 49 Viewing Orientation ............................................................................................................ 51 Subjective Norm .................................................................................................................. 54 Perceived Behavioral Control ............................................................................................. 57 Displacement or Complementation ............................................................................................ 62 Viewership Overlap .................................................................................................................... 69 4 METHOD .................................................................................................................................... 72 Survey .......................................................................................................................................... 72 Pretests ......................................................................................................................................... 75

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9 First Pretest ........................................................................................................................... 75 Second Pretest ...................................................................................................................... 77 Main Test ..................................................................................................................................... 80 Instrument Development ............................................................................................................. 82 Definitions ............................................................................................................................ 82 Measures ............................................................................................................................... 83 Intention to use online video platforms and television .............................................. 85 Actual use of online video platforms and television .................................................. 85 D isplacement effect ...................................................................................................... 86 Motives behind video content consumption ............................................................... 86 Perceived substitutability ............................................................................................. 87 Relative advantage ....................................................................................................... 87 Perceived ease of use ................................................................................................... 87 Compatibility ................................................................................................................ 87 Flow experience online ................................................................................................ 88 Subjective norm ............................................................................................................ 88 Perceived behavioral control ....................................................................................... 89 Orientation behind video content consumption.......................................................... 89 Fourteen attributes of online video platforms and television .................................... 90 Demographic information and media use ................................................................... 91 Response Rates ............................................................................................................................ 92 Participants .................................................................................................................................. 92 Reliability .................................................................................................................................... 94 Validity ........................................................................................................................................ 95 Statistical Analysis ...................................................................................................................... 96 Motivations behind Video Content Consumption ............................................................. 96 Perceived Substitutability between Online Video Platforms and Television .................. 96 Intention to Use Online Video Platforms and Television ................................................. 97 Relative Advantage ............................................................................................................ 100 Users versus Non -Users of Online Video Platforms ....................................................... 101 Displacement Effect .......................................................................................................... 102 Viewership Overlap ........................................................................................................... 103 5 RESULTS .................................................................................................................................. 105 Simple Model for Intention to Use Different Types of Video Platforms .............................. 105 Measurement Model .......................................................................................................... 106 Structural Model ................................................................................................................ 113 Intention to Use Online Video Platfor ms ......................................................................... 114 Intention to Use Television ............................................................................................... 116 Complex Model for Intention to Use Different Types of Video Platforms ........................... 120 Intention to Use Online Video Platforms ......................................................................... 121 Intention to Use Television ............................................................................................... 123 Motivations Behind Video Content Consumption .................................................................. 127 Perceived Substitutability ......................................................................................................... 129 Relative Advantage ................................................................................................................... 130 Users Versus Non -Users of Online Vi deo Platforms .............................................................. 133

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10 Motivations behind Video Content Consumption ........................................................... 134 Fourteen Attributes of Online Video Platforms and Television ..................................... 134 Perceived Characteristics of Online Video Platforms ..................................................... 141 Consumer Characteristics .................................................................................................. 142 Displacement Effect of Online Video Platforms on Television ............................................. 144 Viewership Overlap .................................................................................................................. 148 6 DISCUSSION AND CONCLUSION ...................................................................................... 160 Summary of Findings for Int ention to Use and Actual Use of Video Platforms .................. 160 Perceptions of the Internet as a Video Platform .............................................................. 161 Consumer Characteristics .................................................................................................. 165 Summary of Findings for Displacement Effect ....................................................................... 167 Summary of Findings for Viewership Overlap ....................................................................... 168 Theoretical Implications ........................................................................................................... 170 Benefits of the Integrated Model and Comparative -Study Approach ............................ 170 Fundamental Functional Similarity and Functional Uniqueness .................................... 172 Different Gratifications between Online Video Platforms and Television .................... 175 Importance of the Per ceived Characteristics as Predictors of the Intention to Use Online Video Platforms and the Intention to Use Television ..................................... 176 The Influence of Subjective Norm and Perceived Behavioral Control on the Intention to Use Online Video Platforms ..................................................................... 180 Skepticism of the Influence of Flow Experience on Intention ....................................... 181 Instrumental Viewing Orientation as a Critical Determinant of Television Use ........... 182 Displacement and Complementary Effects of Online Video Platforms on Television 185 Practical Implications ................................................................................................................ 188 Managerial Implications for the Online Video Industry ................................................. 188 Managerial Implications for the Television Industry ...................................................... 193 Limitations of the Study ........................................................................................................... 198 Future Research ......................................................................................................................... 200 APPENDIX: QUESTIONNAIRE ................................................................................................. 204 LIST OF REFERENCES ................................................................................................................. 210 BIOGRAPHICAL SKETCH ........................................................................................................... 231

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11 LIST OF TABLES Table page 3 1 List of hypotheses ................................................................................................................... 59 4 1 Reliability check for constructs (Pr etest 1) ........................................................................... 76 4 2 Reliability check for constructs (Pretest 2) ........................................................................... 77 4 3 Exploratory factor analysis for motivations behind video con tent consumption (Pretest 2) ................................................................................................................................ 79 4 4 Constructs and operational definitions .................................................................................. 83 4 5 Response rates ........................................................................................................................ 92 4 6 The comparison of the sample profile with adult Internet users in the U.S. ...................... 94 5 1 Confirmatory factor analysis ............................................................................................... 109 5 2 Correlation matrix for validity of constructs ...................................................................... 112 5 3 Reliability of constructs ....................................................................................................... 113 5 4 Descripti ve statistics ............................................................................................................ 113 5 5 Correlation matrix for exogenous variables ....................................................................... 114 5 6 Summary of hypothesis testing for the intention to use online video platforms .............. 115 5 7 Summary of hypothesis testing for the intention to use television ................................... 117 5 8 Parameter estimates of complex model for the intention to use online video platforms 122 5 9 Parameter estimates of complex model for the intention to use television ...................... 124 5 10 Exploratory factor analysis for motives behind video content consumption ................... 128 5 11 Descriptive statistics of motives behind video content consumption ............................... 129 5 12 Regression for predictors of the perceived substitutability of online video platforms and television ........................................................................................................................ 130 5 13 Repeated measures of ANOVA f or perceptions of online video platforms and television with respect to content, technology, and cost related attributes ...................... 132 5 14 Regression for the perceived attributes of online video platfo rms that affect the overall relative advantage of online video platforms ......................................................... 133

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12 5 15 Comparison of users and nonusers of online videos in motivations behind video content consumption ............................................................................................................ 134 5 16 T tests for differences between users and non users of online video platforms with respect to the perceived attributes of the online video platforms ...................................... 135 5 17 T tests for differences between users and non users of online video platforms with respect to the perceived attributes of television ................................................................. 136 5 18 Repeated measures of A NOVA for the perceived attributes of online video platforms and television among users of online video platforms ....................................................... 137 5 19 Repeated measures of ANOVA for the perceived attributes of online vide o platforms and television among non users of online video platforms ............................................... 139 5 20 Summary of the perceived attributes of online video platforms and television ............... 140 5 21 T tests for differences between users and non users of online video platforms with respect to the perceived characteristics of online video platforms ................................... 141 5 22 T tests for differences between users and non users with respect to consumer characteristics ....................................................................................................................... 143 5 23 Correlation matrix for the amount of time change with television ................................... 145 5 24 Multiple regression for the amount of time change with television ................................. 147 5 25 Differences of viewership overlap by television subscription type .................................. 151 5 26 Result summary for hypotheses .......................................................................................... 152 5 27 Result summary for research questions .............................................................................. 154

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13 LIS T OF FIGURES Figure page 3 1 Proposed model for the intention to use online video platforms and television ................ 61 5 1 Simple model for intention to use online video platforms and intention to use television ............................................................................................................................... 119 5 2 Complex model for intention to use online video platforms and intention to use television ............................................................................................................................... 126 5 3 Viewership overlap between Internet and television as video platforms among all of the respondents ..................................................................................................................... 149 5 4 Vi ewership overlap between Internet and television as video platforms among users of online videos .................................................................................................................... 150 5 5 Viewership overlap between Internet and television as video platforms among television users ..................................................................................................................... 150 5 6 Visual depiction of the results of hypothesis testing.......................................................... 153 5 7 Visual depiction of the results of RQ 1b: how motiva tions behind video content consumption affect the perceived substitutability between online video platforms and television ............................................................................................................................... 158 5 8 Visual depiction of the results of RQ 2a: How the perceived s pecific attributes of online video platforms affect the overall relative advantage of online video platforms 159

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14 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Pa rtial Fulfillment of the Requirements for the Degree of Doctor of Philosophy TELEVISION VERSUS THE INTERNET: A COMPARATIVE ANALYSIS OF TRADITIONAL AND NEW VIDEO PLATFORMS IN SUBSTITUTABILITY, PERCEPTIONS, AND DISPLACEMENT EFFECTS By Jiyoung Cha Decembe r 2009 Chair: Sylvia M. Chan Olmsted Major: Mass Communication In todays multiplatform video environment, this study sheds light on the use of the Internet as a video platform. When a new medium emerges in the marketplace, one of the prevailing goals o f related research is to identify the factors that predict consumers decisions to adopt the medium. However, most of these types of studies tend to focus on the new medium alone. In reality, though, the new medium coexists with traditional media; consumer s use of, or attitude toward, traditional media may influence their decision to adopt the new medium. Furthermore, the introduction of the new medium might also influence consumers use of traditional media. Recognizing the fact that that new and old medi a coexist in the market, the overarching aim of this study is to examine how the Internet and television, as video platforms, are interrelated with respect to consumer demand and time. To that end, this study employed mail surveys of a random sample of In ternet users throughout the United States. The findings indicated that both actual users of online video platforms and people who are likely to adopt online video platforms expect different things from online video platforms than from television. The perce ived substitutability between online video platforms and television negatively affects the intention to use online video platforms. This study

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15 also revealed that the perceived compatibility of online video platforms has the strongest impact on the intentio n to use online video platforms but the compatibility was perceived as being very low. Meanwhile, the relative advantage and compatibility of online video platforms decrease the likelihood of using television. With respect to the displacement effect of online video platforms on television, this study found that, overall, the time spent using the Internet to watch video content reduces the time spent watching television. However, the presence of the displacement effect actually depends on what type of vide o content consumers watch online, how much of that content overlaps between online video platforms and television, and what types of online video venues consumers use.

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16 CHAPTER 1 INTRODUCTION The television industry has experienced significant changes in recent decades. This chapter describes how that industry has transformed over time and explains the emergence of the Internet as a video platform. In addition, it describes the purposes of this study and briefly introduces the research topics o n which it focuses. In doing so, this chapter also discusses how this study is different from previous research and how important it is to the television industry as well as to online video venues. Environmental Change of the Television Industry The envir onment for video content viewing has changed markedly over the past few decades. Several decades ago, television was the sole video platform and broadcast networks were the primary sources of television programming. The addition of cable and satellite tele vision to the market ultimately changed televisions initial limited capabilities into that of a multichannel environment. According to Nielsen, the average number of channels received by U.S. TV households in 2007 was 118.6 (Television Broadcast, 2008). A nother report indicates that 58% of U.S. TV households received more than 100 channels and another 26% received between 60 and 99 channels. Even though most U.S. households received more than 100 television channels, people tuned primarily to 16 channels f or at least 10 minutes per week in 2007 (Television Broadcast, 2008). Given this fierce competition in the television industry, the surging popularity of the Internet as a video platform in recent years is noteworthy. When the Internet was introduced to the marketplace, some industry practitioners and researchers were concerned about the possibility that the Internet in general would have a cannibalistic effect on television and, more specifically, that the Internet as a video platform would replace television altogether.

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17 However, it actually took much longer than these industry experts had anticipated for U.S. television networks, web proprietors, and Internet users to embrace the Internet as an alternative video platform. The slow adoption of online vi deo platforms was largely attributed to technical restrictions such as Internet transmission speed and bandwidth (Lin, 2004). However, with recent technological advancements, the video viewing environment is rapidly changing. As Internet and video technol ogies evolved, it became more common for television networks and web proprietors to provide video content online. At the same time, broadband Internet and personal video facilities became increasingly pervasive in the U.S., with more and more people using the Internet to watch and share video content. Industry reports released in 2006 and 2007 indicated that more than half of U.S. Internet users had watched video on the Internet (Holahan, 2006; Holahan, 2007). A more recent report that comScore released in 2008 stated that 74% of the total Internet audience had watched online videos (CNN Money, 2008). The total number of video streams accessed was nearly 4 billion between 2001 and 2002 (International Webcasting Association, 2004). In less than a decade, the number of videos viewed online had reached staggering new heights. According to comScore Media Matrix, during May 2008 alone, U.S. Internet users watched approximately 12 billion online videos (CNN Money, 2008). As numerous reports indicate, online video platforms seem to have taken off and to have entered a new and rapid stage of growth. Online video viewing typically takes places on two types of venues. The venues can be categorized according to the types of content providers and content, which are actua lly interrelated. One group of online video viewing providers is video sharing sites or video aggregators (pure plays) where both media firms and individual Internet users are allowed to post video content. Thus, video sharing sites deliver not only branded -video

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18 content, which is originally produced by media firms, but also unbranded user -generated video content, which is created by individual Internet users. Examples of this type of venue include YouTube, Yahoo Video, Daily motion, Veoh, and social netw orking sites that have video posting and sharing functions. The exponential growth of video sharing sites is a phenomenon that has gained a lot of attention in recent years. The market share of the top 10 most -visited Internet video sites increased by 164% between February 2006 and May 2006 (Hitwise, 2006). Along with the popularity of video sharing sites, user generated videos have emerged as a new type of video content. However, an industry report noted that users of online video sites prefer content fro m major media brands over user -generated content (Holahan, 2007). If the present popularity of video sharing websites is based on branded videos originally produced by media companies, the growth of video sharing websites might be threatened because of the possibility that some branded videos currently available on video sharing sites might infringe on television networks copyrights. In fact, in March 2007, Viacom sued YouTube and its parent company, Google, for more than $1 billion, arguing that the compa nies are infringing on Viacoms copyrights by making almost 160,000 unauthorized video clips available for viewing on YouTube (Dauman, 2007). Veoh was also sued by lo Group, which produces adult content, for infringement on its copyrights (Sandoval, 2008). Websites affiliated with television networks (the clicks and -bricks scenario) constitute a nother group of online video providers Unlike video sharing sites, television network websites do not allow individual Internet users to upload videos. Rather, television networks webcast only their own branded video content. Television networks post, repurpose, or simulcast their own original offline programs through webcasting, and some post previews or behind -the -scenes

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19 stories of their original television pr ograms. Individual television network websites such as ESPN.com and Hulu.com, a joint venture of News Corp and NBC Universal, fall into this group. Although television for the past several decades has been the predominant medium in reaching mass audiences, some noteworthy trends have emerged in recent years concerning the competitiveness of television as a distribution channel. Despite televisions decades of success in attracting mass audiences, primetime audiences of broadcast networks have steadily decli ned since the early 1980s, according to Nielsen Media Research (Gorman, 2008). This downturn has been attributed to a variety of factors, such as the emergence of multi television channels and video platforms. Use of the Internet as a video platform might be a likely contributor to this trend. As one survey conducted during the third quarter of 2008 found, approximately 20% of American households with Internet access use the Internet to watch television programs (Albanesius, 2008). The migration of advertis ers from traditional mass media to online media is also a concern for television networks. The top 25 companies that spent the most on advertising over the last 5 years cut their spending in traditional media by about $767 million in 2006, according to Adv ertising Age and TNS Media Intelligence (Story, 2007). Now, advertisers increasingly invest their money in online commercials. Nike is a prime example of this shift among advertisers. In 2005, Nike posted an ad exclusively online, staring Brazilian soccer player Ronaldinho. According to the company, more than 17 million viewers watched the ad on YouTube (Story, 2007). E -Marketer projected that the spending of advertisers online video platforms will account for approximately $3 billion by 2010 (Knight, 2006) Given the obstacles that both television networks and video sharing websites encounter, investigating whether the Internet as a video platform cannibalizes television is crucial. As Porter

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20 (2001) stated, the Internet is widely assumed to be cannibalis tic. Thus, for years scholars have questioned whether traditional medias online counterparts have the power to cannibalize traditional media. In particular, many experts argued that the main culprits of this cannibalism would be online news and magazines sources (Deleersnyder, Geyskens, Gielens, & Dekimpe, 2002; Pauwels & Dans, 2001). As technical barriers, such as bandwidth and speed, are lowered and more people adopt online video platforms, the focus of the Internets cannibalistic effect is now turning to the relationship between television and the Internet as a video platform. However, neither theoretical nor empirical research has barely addressed the channel cannibalization effect Some previous studies investigated the driving force behind consumer s adoption of advanced television technologies such as interactive cable television or webcasting services (e.g., Lin, 2004; 2008; Li, 2004). The majority of existing studies, however, treated the new market established by the new technologies as an isola ted entity completely separated from the traditional television market. Yet, in reality, the new video market coexists with the traditional television market. Television networks might compete with similar types of firms, but presumably they also compete w ith different types of video content services that rely on various technologies. By the same token, there is a possibility that video sharing websites might either complement or compete against television networks. Therefore, the models that treat television and the Internet as separate video platforms fail to capture the dynamic interaction between the two platforms ( Viswanathan, 2000; Viswanathan, 2005) To gain a better understanding of the underlying reasons for online video platform adoption in a multi platform environment, this study does not isolate television. Rather, it instead takes it into account. Purpose of the Study Given the fierce competition and the rise of online video platforms in the video content market, this study suggests that it is p ivotal to have an understanding of why certain people

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21 prefer either the Internet or television as a video platform, why the number of people using online video platforms in relation to television is increasing, and whether online video platforms displace t elevision viewing. To address these issues, this study breaks down and examines the relationship between television and the Internet as video platforms. Specifically, this study investigates 1) what motives behind video content consumption increase or decrease the perceived substitutability between online video platforms and television, 2) what content, technology, and cost related attributes of online video platforms are perceived as better or worse than television, 3) how the perceived characteristics of online video platforms and consumer related factors affect consumers intention to use the Internet and television to watch video content, 4) whether differences exist between users and nonusers of online video platforms with respect to perceptions of vi deo platforms and consumer characteristics, and 5 ) whether and how online video platforms displace television with respect to time investment and viewership. In general, the fear of financial losses due to the cannibalistic effect of the Internet on tradi tional media has deterred many firms from deploying the Internet as a distribution channel (Deleersnyder, Geyskens, Gielens, & Dekimpe, 2002). However, firms should not overlook or disregard this rising distribution channel. They should instead carefully c onsider ways to efficiently and effectively exploit the distribution opportunities provided by this platform while balancing different forms of it. To that end, this study will offer insights into how the television industry employs different types of dist ribution channels and leverages the value of online platforms. This study will also provide the online video industry with managerial implications concerning values, strategies, and risks as a web -based system. Theoretically, this study integrates several bodies of literature to help validate potential emergent theoretical elements and

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22 reveal a more comprehensive picture of the use of online video platforms and television. This study constrains the definition of online video viewing to watching video conte nt on the computer through the Internet in real time. Thus, this study excludes viewing video content after the video content has been downloaded to computers, television, or portable devices capable of accessing the Internet One of the reasons for these distinctions is that the purpose of this study is to examine how the unique and inherent characteristics of online video viewing in real time affect both video content consumption and television viewing. Viewing downloaded video content is intrinsically di fferent from viewing streaming video content, and thus would provide consumers with a different type of viewing experience. For the purpose of this study, watching video content on mobile phones is also excluded, because the content available on mobile pho nes as well as the viewing experience would also be different from streaming video.

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23 CHAPTER 2 THEORETICAL FRAMEWOR K This study integrates several theories that have been used to explain technology adoption. The following chapter will discuss ea ch of the individual constructs that this study focuses on This chapter provides a holistic explanation of the theoretical framework and the research approach. The primary theoretical foundations of this study include the theory of planned behavior (TPB), the technology acceptance model (TAM), and the innovation diffusion theory (IDT). This chapter briefly introduces the theories, explains why this research project integrates them into a comparative -study approach, and discusses the importance of examining this studys research topics from a comparative -study approach. Integration of Different Theories Theory of Planned Behavior Numerous researchers have explored the link between attitudes and behavioral intentions in consumers use of technology (Adams Nelson, & Todd, 1992; Bagozzi & Kimmel, 1995; Dabholkar, 1992, 1996; Davis, 1989, 1993; Davis, Bagozzi, & Warshaw, 1989; Hill, Smith, & Mann, 1987; Mathieson, 1991; Taylor & Todd, 1995). The theory of reasoned action (TRA) (Fishbein & Ajzen, 1975), suppo rted mostly by the social psychology literature, has been applied successfully to identify key elements of consumer decision-making (Taylor & Todd, 1995; Sheppard, Hartwick, & Warshaw, 1988). This theory proposes that behavior is determined by an individua ls intention to perform the behavior, with intention being a function of two determinants: attitudes and subjective norms (Ajzen & Fishbein, 1980). Research using the theory of reasoned action has proved to be successful across a number of disciplines and has been lauded as having been designed to explain virtually any human behavior (Ajzen &

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24 Fishbein, 1980, p. 4). With this success in mind, researchers developed a model for better predictive power. An extension of the theory of reasoned action is the t heory of planned behavior (TPB), proposed by Ajzen (Ajzen, 1991; Ajzen & Fishbein, 1980). This theory, like the theory of reasoned action, posits that actual behaviors result from behavioral intention. While the theory of reasoned action suggests that inte ntion is a function of two determinants (attitudes toward the behavior and subjective norms), the theory of planned behavior adds another independent variable perceived behavioral control to predict behavioral intention. The theory of reasoned behavior assumes that the majority of human behaviors are controlled by volition. However, there are situations where peoples intentions and actual behaviors are influenced by factors over which they have no control (Ajzen, 2002). Thus, this study employs the the ory of planned behavior with the perceived behavioral control construct instead of solely relying on the theory of reasoned action. The theory of planned behavior is widely accepted in a variety of disciplines as an effective means of predicting individua ls intention and actual behaviors. However, the theory has a critical weakness when studying the adoption of media systems. The theory was not specifically designed for examining technology adoption, and thus cannot fully illuminate the impact that techno logy characteristics have on the adoption decision regarding a technological medium. However, the technology acceptance model (TAM), which was specifically designed for the adoption of an information system (Chung & Nam, 2007), can augment the theory of pl anned behavior, thereby providing a solution to this dilemma. While the theory of planned behavior tends to be applied successfully across disciplines to a wide range of subjects, including information technology, society, sports, consumerism, community, a nd health psychology

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25 (Notani, 1998; Davis, Bagozzi, & Warshaw, 1989; Taylor & Todd, 1995; Venkatesh, Morris, Davis, & Davis, 2003), the technology acceptance model has been specifically modified to predict consumers intention to accept a technology. Tech nology Acceptance Model and Innovation Diffusion Theory The technology acceptance model, developed by Davis (1989) and Davis et al. (1989), is another adaptation of the theory of reasoned action. This model focuses primarily on the attributes of a system as the factors that affect intention and actual behavior in terms of using a technological system. According to this model, perceived usefulness and ease of use are the two core predictors of the intention to use and actual behavioral use of the technology (Davis, 1989; Davis, Bagozzi, & Warshaw, 1989). Davis (1989) found that both perceived usefulness and perceived ease of use are significantly correlated with actual ongoing usage and future usage of a technology. The technology acceptance model is widely used to predict the adoption of a technology in both organizational and individual settings (Schepers & Wetzels, 2007). Innovation diffusion theory (IDT), which is widely used to explain consumers adoption of a technology, is similar to the technology a cceptance model in that it focuses on the perceived characteristics of a technology. This theory has been applied extensively in studies on various topics such as marketing, management, communication and education. Innovation diffusion theory postulates th at the important characteristics that influence the adoption of an innovation are relative advantage, complexity, compatibility, trialability, and observability (Rogers, 1995). Although the theory originally suggested the five characteristics of an innovat ion as the key determinants of the adoption interest of the technology, a meta analysis of empirical diffusion studies found that the consistent variables that are related to the adoption of an innovation are relative advantage, complexity, and compatibili ty (Tornatzky & Klein, 1982; Agarwal & Prasa, 1998). Note that the constructs of relative advantage and complexity are similar to perceived

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26 usefulness and perceived ease of use, respectively, in the technology acceptance model (Moore & Benbasat, 1991). For that reason, this study includes an innovations compatibility, along with relative advantage (perceived usefulness) and complexity (perceived ease of use). However, trialability and observability are excluded in the conceptual model of this study because the impact of those variables were inconsistent. The technology acceptance model is considered parsimonious, predictive, and robust (Venkatesh & Davis, 2000), and thus is the most widely accepted and applied model for explaining the adoption of a technolo gy (Yi, Jackson, Park, & Probst, 2006). Nevertheless, research that meta analyzed the technology acceptance model criticized the technology acceptance model for disregarding human and social change variables such as subjective norms (Legris, Ingham, & Coll erette, 2003), and exclusively focusing on audiences perceptions of the technology. Innovation diffusion theory also has the same shortcoming. Psychology literature indicates that individuals perceptions of a certain thing can depend on the social context (Robertson, 1989). It is not difficult to imagine situations where an individuals decision is often influenced by referents (Scherpers & Wetzels, 2007). Considering the impact of social contexts and social interactions on perceptions as well as a social interaction motivation behind video content consumption, the inclusion of social influences in the study of technology adoption seems necessary. Another criticism of the technology acceptance model is that it focuses only on the positive benefits of a sys tem, whereas the theory of planned behavior examines both positive and negative beliefs that influence intention (Horst, Kuttschurutter, & Gutteling, 2007; Tassabehji & Elliman, 2006 ). When it comes to the decision to adopt a system, it is legitimate to pr opose that audiences will consider both benefits and risks.

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27 Efforts to increase the applicability of the theory of planned behavior and the technology acceptance model to various topics and settings have made the theories parsimonious and robust. Nonethel ess, because this study aims to provide managerial implications for the television industry and online video industry, a model that integrates the aforementioned theories is required. In terms of the theoretic aspects of this study, the goal is to find a m ore comprehensive model that identifies specific factors that affect the intention to use a particular type of video platform. Previous research also pointed out that other factors need to be incorporated into the theory of planned behavior and the technol ogy acceptance model in order for them to be more comprehensive (Igbaria, Zinatelli, Cragg, & Cavaye, 1997; Jackson, Chow, & Leitch, 1997). A meta analysis of the theory of planned behavior found that the theory explains 27% and 39% of the variance of act ual behavior and intention, respectively (Armitage & Conner, 2001). Legris, Ingham, and Collerette (2003) found in their meta analysis that the technology acceptance model accounts for scarcely more than 40% of the variance in the use of a system. As a res ult, researchers have used various combinations of the technology acceptance model, the theory of planned behavior, and innovation diffusion theory (e.g., Keen, Wetzels, Ruyter, & Feinberg, 2004; Chen, Gillenson, & Sherrell, 2002; Wu & Wang, 2005). Others have added more constructs, such as flow and media use orientation, to the integrated model (Koufaris, 2002). Considering the aforementioned weaknesses of each theory, the present study integrates and extends the theory of planned behavior, the technology acceptance model, and innovation diffusion theory with other factors to examine the relationship between television and the Internet as a video platform. A Comparative Study This study aims to provide a greater understanding of how online video platforms and television influence one another with respect to consumers decisions to use each of these

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28 platforms, and consumers time spent using the platforms. To do so, this study not only integrates different theories but also takes a comparative-study approach. Therefore, this study does not merely focus on why consumers use online video platforms. It also compares the Internet as a relatively new video platform in the marketplace with television, which has been the predominant video platform since its introduc tion. Specifically, this study compares both predictors of online video platform use with predictors of television use, and the perceived characteristics of the Internet as a video platform with the perceived characteristics of television. Additionally, this study examines whether online video platforms compete with television with respect to the amount of time consumers spend on them and whether the users of online video platforms and television overlap. When a new medium emerges in the marketplace, one of the prevailing goals of research about this medium is to identify the factors that predict consumers decisions to adopt it. In recent years, researchers in the field of mass communication have focused on discovering which factors predict consumers ado ptions of satellite radio, webcasting, Internet protocol television, mobile phone, digital television, mobile video, and so forth. However, the majority of these types of studies tended to focus solely on the new medium. In reality, though, the new medium coexists with traditional media, and consumers use of or attitude toward traditional media may influence their decision to adopt the new medium. Furthermore, the introduction of the new medium might also influence consumers use of traditional media. Reco gnizing the fact that that new and old media coexist in the market, some researchers started to focus on how the new medium and traditional media influence one another with respect to consumers choices. Some researchers have compared the factors that aff ect the use of a new medium versus traditional media. For instance, Childers and Krugman (1987) examined the factors that

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29 influence consumers decisions regarding VCRs, pay cable, and pay -per -view. LaRose and Atkin (1991) investigated consumers adoption of pay per -view, taking into account another competing distribution modality: movie theaters. Another group of studies primarily focused on whether a new medium substitutes or displaces traditional media (e.g., Grotta & Newsom, 1983; Webster, 1983, 1986; Be cker, Dunwoody, & Rafaeli, 1983). Ever since the Internet was introduced to the public sphere in the 1990s, many researchers have studied its displacement effect in general on the use of traditional media (e.g., Kayany & Yelsma, 2000; Tsao & Sabley, 2004; Lee & Leung, 2008). However, little research been done on whether and how the factors that affect consumers decisions to adopt online and offline distribution platforms differ. Most of the research in the field of mass communication still tends to isolat e online distribution platforms from existing traditional offline platforms, and to disregard the ways in which traditional platforms might influence consumers decisions to use online distribution platforms (Tse & Yim, 2001; Viswanathan, 2005). Shopping is one area where researchers have started to vigorously investigate consumer preferences between online and offline platforms (Levin, Levin, & Weller, 2005; Andrews & Currim, 2004). Tse and Yim (2001) identified how factors affecting the choice of online and offline shopping channels are different. Shankar, Smith, and Rangaswamy (2003) addressed how consumers satisfaction differs when a service is chosen online versus offline. Kaufman Scarborough and Lindquist (2002) investigated how preference of in-stor e purchases differs between people who browse and purchase online and people who browse online without purchasing online. Ahn, Ryu, and Han (2004) examined how features of both online and offline shopping malls predict consumers acceptance of online shopp ing. Ward (2001) investigated whether online shopping competes with traditional retailing or catalog shopping.

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30 Viswanathan (2000, 2005) suggested that the models that treat either offline distribution channels or online channels as unrelated entities do not capture the dynamic interaction between the two platforms. Empirical evidence supports the value of the comparative -study approach. Previous studies that took a comparative -study approach found that differences exist with respect to the factors that af fect consumers intention or choice of using online platforms and offline platforms. Cha (2008b) found that consumers choose online (i.e., the Internet) or traditional movie platforms (i.e., television, DVD rentals, and video on demand) for different reaso ns. The same study also found that competition exists among online and offline movie platforms, even though movies are not distributed to the distribution channels simultaneously but are instead sequentially distributed. In the context of shopping, Tse an d Yim (2001) discovered that the determinants of choosing online and offline shopping channels are very different. Ahn, Ryu, and Han (2004) found that comparing the features of both online and offline shopping malls better explains why consumers shop onlin e, rather than focusing on the features of online shopping malls alone. In short, the previous studies emphasize that the models that take into account traditional distribution platforms achieve a more in -depth understanding of why consumers use the newer online platform. It was also found that the models that consider traditional distribution platforms are capable of determining whether online distribution platforms compete against conventional offline platforms for consumer demands. Therefore, this study implements a similar approach; it compares various aspects of online video platforms with television instead of limiting the focus to online video platforms alone. This study investigates how the perceived characteristics of online video platforms and c onsumer characteristics influence the likelihood of us ing online video platforms and television.

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31 Unlike many previous studies, this study utilizes the perceived characteristics of online video platforms to predict both the intention to use online video pla tforms as well as the intention to use television. Considering that the perceived characteristics of a particular technology are designed as the potential predictors of a corresponding technology in most of the literature about adoption, it is atypical to employ the perceived characteristics of online video platforms to predict the intention to use television Given that television is used in about 99% of U.S. households, the aim of the prediction of the intention to use television is to determine how the emergence of online video platforms affects consumers decisions to use television when they want to watch video content. With the high diffusion rate of television in the U.S., examining how the perceived characteristics of online video platforms affect c onsumers intention to use television will result in a better understanding of the type of characteristics that attract or repel consumers. Overall, the application of the perceived characteristics of online video platforms to determine both the intention to use online video platforms and the intention to use television will yield more interesting insights into the factors that drive consumers decision to watch video content on online video platforms and on television. .

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32 CHAPTER 3 LITERATURE REVIEW AN D RE SEARCH QUESTIONS While the previous chapter provides a holistic description of the theoretical framework and approach of this study, this chapter introduces individual constructs that this study focuses on to explain consumers use of online video platform s and television In doing so, this chapter illustrates the links between this study and previous studies conducted by other researchers. This chapter also introduces the research questions and hypotheses of this study and the reasons behind their selectio n. Perceived Characteristics of Online Video Platforms Perceived Substitutability Whenever a new medium is introduced to the market, a debate over whether the new medium supplements or substitutes for the older one is not uncommon. Some of the research on the issue assumes that audiences have finite time for media and non -media activities. Thus, the research with that assumption expects that the addition of a new medium will encourage the audiences to spend less time on the existing media or to disregard t he new medium regardless of the functional similarity between the new and traditional media. This is based on a zero -sum relationship for the amount of time invested for each of the media (Kayany & Yelsma, 2000). Another group of research argues that a new medium displaces traditional media as long as the new medium is functionally similar to the traditional media. As the example of the relationship between a computer and printer demonstrates, the key point is not the zero-sum relationship for the amount of time for media activities; instead, the important construct in research that investigates the relationship between a new medium and old medium is whether the new medium is functionally similar, functionally more desirable, or complementary to the old m edium (Lin, 1994; Lin, 2001a). To find out how a new medium changes the use of the existing

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33 one, Ferguson and Perse (2000) asserted that the first step in the process should be whether consumers perceive the new medium to be substitutable for or to be a functional alternative to the existing one. In microeconomics, substitution is defined as consumers tendency to switch from one product to another that fulfills the same purpose (Nicholson, 1995). Nicholson (1995) suggested that two goods are substitute s (complements) if a rise of price for one medium increases (decreases) demand for the other. Cross -price elasticity of demand is a mathematical measure of the degree of product substitution (Boyes & Melvin, 1996). Cross price elasticity of demand is referred to as the percentage change in the demand for one good divided by the percentage change in the price of a related good, other things being equal. In the media industry, it is, however, difficult to employ the cross -price elasticity of demand as a measu re for substitution because the condition of all other things being equal can hardly occur in the real media environment (Chyi & Lasorsa, 2002). Given that it is challenging to measure the degree of substitution of traditional media by a new medium in p ractice, some of the previous research tends to translate the extent of time displacement effect of a new medium on traditional media into the degree of the substitution. Considering this definition of substitution consumers tendency to switch from one product to another that fulfills the same purpose (Nicholson, 1995) the current dissertation does not view the notion of substitution to be interchangeable with the notion of time displacement effect. Another reason why the current study does not use substitution and time displacement effect interchangeably is because it is unclear whether the displacement effect occurs when old and new media have functional similarity, or if it is a mere consequence of a zero -sum relationship.

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34 Instead of translating the time diffusion effect of a new medium on traditional media to the substitution, other prior studies inspected the perceived substitutability between traditional media and a new medium by comparing the motives behind the use of the new medium and tradition al media (e.g., Flavian & Gurrea, 2007). Althaus and Tewksbury (2000) suggested that the core assumption of this group of research is that consumers are active rather than passive. Uses and gratifications posit that people select and use a medium over othe rs to gratify specific needs and purposes (Levy & Windahl, 1984; Perse, 1990a; Rosengren & Windahl, 1972; Rubin, 1994; Althaus & Tewksbury, 2000). Applying a uses and gratifications perspective, Perse and Courtright (1993) found that cable television and V CRs can be viewed as substitutes for broadcast television in that cable television and VCRs gratify the relaxing entertainment need people seek from broadcast television. Since the emergence of the Internet, there has been an array of research that examin ed how the Internet is functionally similar to television. Previous studies identified pass time, relaxation, companionship, social interaction, habit, entertainment, information, arousal, and escape as motives behind television consumption (Rubin, 1983; A belman, 1987; Abelman, Atkin, & Rand, 1997). Researchers consistently found that relaxation and entertainment are the primary motives for watching television. Pass time and information seeking are also motivations for watching television, but not as domina nt as the relaxing entertainment motive (Rubin, 1981, 1984). Some of the motives for Internet use are similar to the ones for television use. They included information, entertainment, escape, and social interaction needs (DAmbra & Rice, 2001; Kaye, 1998; Lin, 2001b), which are also the motivations for watching television. Like television, entertainment and recreation are the major types of information that people search for online (Spink, Dietmar, & Saracevic, 2001). Ferguson and Perse (2000) examined whe ther the

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35 Web can be a functional alternative to television by examining what television -related motives influence the use of the Web and television. They concluded that the Web can serve as a functional alternative to television because entertainment is a salient motive for visiting it; entertainment is also the primary gratification sought from television (Rubin, 1981, 1983). Entertainment, pass time, relaxation, and information -seeking motivations, which are the motives of television use, also predicted various activities on the Web (Ferguson & Perse, 2000). Even though the Internet satisfies some of the same needs that people seek from television, it is difficult to say that the Internet completely substitutes for television. This is because the extent t o which the Internet gratifies these needs might be different from the extent to which television can satisfy them. As a matter of fact, researchers argue that the Internet is not identical to television, although it might have some commonalities with tele vision with respect to gratifications (Kaye & Johnson, 2003). For instance, researchers found convenience to be a gratification sought from Internet use (Kaye & Johnson, 2004; Papacharissi & Rubin, 2000), but the convenience gratification is not found in t elevision use. The unique characteristics and inherent nature of the Internet mean consumers get different gratifications from it than from traditional media. While some previous studies examined substitutability between different media types that deliver different types of content (e.g., television and the Internet), there are other studies that specifically focused on substitutability between different modalities that deliver the same or similar type of content (e.g., print newspaper and online newspaper ). Specifically focusing on the effect of different modality types, Vincent and Basil (1997) found that the surveillance motivation is the most common and reliable predictor when explaining the consumption of news across different modalities, i.e. newspape rs, news magazines, and television news. It dominated

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36 entertainment, escape, and boredom relief motivations. However, their study also interestingly indicated that the motives behind news consumption differ by modalities of news. The findings revealed that the entertainment motive is an important determinant, particularly for television news viewing. By the same token, Flavian and Gurrea (2007) attempted to identify specific motives that affect the degree of substitutability between online newspapers and t raditional newspapers. They found that motivations commonly satisfied by both offline and online channels increase the level of perceived substitutability between digital and traditional newspapers. However, the discrepancy between online newspapers and tr aditional newspapers with respect to motives decreases the perceived substitutability. The findings indicated that the motivations of seeking current news and of habit positively affect the perceived substitutability between traditional newspapers and onli ne newspapers. On the other hand, search for specific information, search for updated news, and entertainment motivations decrease the perceived substitutability between the two media. The findings are similar to those of Althaus and Tewksbury (2000), who found that different motivations sought from each media modality reduce substitutability and serve as differentiation points. With the rise of the Internet as a video platform, it is important to know the extent to which consumers perceive online video p latforms as a substitute for television. Thus, this study attempts to identify the specific underlying motivations for watching video content that increase or decrease the degree to which consumers perceive substitutability between online video platforms a nd television. Doing so provides insights into the motivational differences between the two modalities, and how online video platforms and television can establish points of

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37 differentiation or parity based on the discrepancy of the gratifications consumers seek from each platform. Some of the prior studies suggested that a new medium that is functionally similar to an old medium tends to replace the old medium. With that in mind, this study proposes that the perceived substitutability between online video platforms and television is positively related to the intention to use online video platforms. On the other hand, it is expected that the perceived substitutability is negatively related to the intention to use television. The specific research questions a nd hypotheses are as follows: RQ1a. What motivations do consumers have for watching video content? RQ1b. What specific motivations for watching video content affect consumers perceived substitutability between online video platforms and television? RQ1 c. Are there differences between users and non users of online video platforms with respect to motivations for watching video content? RQ1d. Do users and nonusers of online video platforms differ in how they perceive the substitutability between online video platforms and television? H1a. Perceived substitutability between online video platforms and television will be positively related to the intention to use online video platforms. H1b. Perceived substitutability between online video platforms and t elevision will be negatively related to the intention to use television. Relative Advantage Numerous previous studies proposed that when the new medium is more functionally desirable or has a relatively higher advantage over the older one, consumers are more likely to choose the new medium over the older one (Heikinnen & Reese, 1986; Levy & Windahl, 1984; Robinson & Jeffres, 1979; Rosengren, Windahl, 1972; William, Rice, & Rogers, 1988; Lin,

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38 1994). Innovation diffusion literature emphasizes the role of th e relative advantage in the adoption of a new technology. Rogers (1995) defined the relative advantage as the degree to which an innovation is perceived as being better than the idea it supersedes (p. 212). Rogers (2003) suggested that relative advantage relates to politics, economics, convenience, social prestige and satisfaction. In innovation diffusion literature, Rogers (1995, 2002) originally suggested that five perceived innovation attributes -relative advantage, complexity, compatibility, observ ability, and trialability -are important factors that affect the adoption of a new technology. Empirical studies found that relative advantage is the most reliable factor that affects technology adoption in different domains. Previous studies indicated t hat relative advantage, along with complexity, is particular ly salient in predicting the communication technology adoption (Lin, 1998, 2001). In the context of television, Li (2004) also found that the relative advantage is the strongest predictor of the a doption of interactive cable television services in Taiwan. The role of the relative advantage can also be found in the technology acceptance model, which postulates that the perceived usefulness along with the perceived ease of use is the core determina nt of both the intention to adopt a technology and the actual use of the technology (Davis, 1989; Peters, Amato, & Hollenbeck, 2007). The perceived usefulness is defined as the degree to which an individual believes that using a particular system would en hance his/her job performance (p. 320). As seen in the conceptual definitions, perceived usefulness in the technology acceptance model is conceptually interchangeable with the relative advantage in innovation diffusion theory. While relative advantage is important for the adoption of a new technology, conversely, relative disadvantage might act as a deterrent. Indeed, the theory of perceived risk has been

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39 applied to explain consumer behavior in decision -making since the 1960s (Taylor, 1974). In the past, perceived risks were primarily regarded as fraud and product quality. Now that online transactions have become popular, the definition of perceived risk has changed. Today, perceived risk refers to certain types of financial, product performance, social, p sychological, physical, or time risks when consumers make transactions online (Ben-Ur & Winfield, 2000; Forsythe & Shi, 2003) Some researchers integrated risks or disadvantages involved with the system in identifying the factors that affect the adoption o f a technology. Applying innovation diffusion theory to the adoption of interactive cable television in Taiwan, Li (2004) deconstructed Rogers relative advantage construct into relative advantage and relative disadvantage. The finding indicates that both relative advantage and relative disadvantage have significant impacts on the intention to adopt interactive television. Lin (2001a) suggested that the relative advantage is typically discussed in aspects of 1) superior content, 2) technological benefits, a nd 3) cost efficiency. The present study classifies the relative advantage and disadvantage from these three aspects. Content -related attributes There is no doubt that delivering superior content is important for media products. Previous studies have shown the impact of content in explaining the adoption of videorelated technologies or entertainment services. When television was introduced to the market, superior content was the primary reason why it displaced radio albeit radio later positioned itsel f as a niche medium to differentiate itself from television (Lin, 2001a). The decreasing primetime audience of broadcast networks can be understood in the same vein. The capability of cable networks to provide more content selectivity is a plausible reason why the primetime audience of broadcast networks has been decreasing since the early 1980s, whereas the ratings of adsupported basic cable networks gradually increased from 1984 to 2007 (Gorman, 2008).

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40 Cha (2008b) explored the role of content variety on the intention to use different types of movie distribution channels. The findings indicated that selectivity of movies is one of the salient factors that affected college students decisions to choose DVD rentals over other movie distribution channels such as movie theaters, the Internet, and video on demand. The importance of content variety in DVD rentals also played a role in Watermans study (1985), which suggested that great product diversity is a substantial advantage for home videos ability to compe te with other media it offers. LaRose and Atkin (1991) also suggested that pay per cable has the relative advantage of content selectivity and quality. Given the increasing quantities of advanced services on mobile phones, Anil, Ting, Moe and Jonathan (20 03) singled out lack of content along with other technological characteristics as the main deterrents to adopting value added services on mobile phones. Specifically focusing on entertainment type of services movies, sports news, entertainment television programs, music videos, and recaps and previews for mobile phones, Cha and ChanOlmsted (2007) discovered that content variety is an important factor that affects the intention to adopt the entertainment service on mobile phones. Vlachos, Vrechopoulos, and Doukidis (2003) also found that respondents evaluated the variety and quality of content as having more importance than the price when making a decision to use a music service on a mobile phone. The importance of content is no exception in discussing the relative advantage of online media. In a comparison between traditional media and their online counterparts (e.g., newspapers and online news), Simon and Kadiyali (2007) pointed out the character that websites can hold unlimited amounts of content as a substantial advantage of online media over traditional offline media. While some of the previous studies examined the role of content from a diversity or variety perspective, Cha (2008a) distinguished between content variety and overall content

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41 quality. C has findings indicated that more college students think that content of video sharing sites provides variety, and thus they use video sharing sites more often. Meanwhile, there was no impact of the overall content quality on the frequency of using video s haring sites. Technology -related attributes While content is viewed as software of a medium, technological attributes of a new system can be viewed as hardware. The benefits from technological advancements are a key factor that leads consumers to the adoption of a new system. Atkin (2002) asserted that the technologys tangible features (i.e., transmission speed, storage capacity, audiovisual quality, transferability) make consumers consider a new medium as a substitute for an old medium. Compared with o ffline media, the Internet has many unique advantages. In a specific comparison of online counterparts with traditional media, online media allow people to update content on an almost continuous basis; online media offer superior search capabilities; and o nline media allow interaction. Moreover, the Internets interactive features make consumers buying experiences much easier. They can simply point and click to order products (Simon & Kadiyali, 2007). Deleersnyder, Geyskens, Gielens, and Dekimpe (2002) p ointed out easy search facilities, speed of delivery, and customization options as the advantages of delivering content through online distribution channels over traditional distribution channels. Similarly, Chyi and Sylvie (2000) summarized that online ne wspapers are technically capable of producing interactive, multimedia content such as online forums, searchable news archives, links to related stories, frequent updates, and webcasting. The aforementioned studies focused on the advantages of online count erparts compared with traditional media, but it also is necessary to think about the disadvantages of online media compared with traditional offline media. Even though the intrinsic natures of online media and mobile phones are different, the disadvantages of mobile phones as a content distribution

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42 platform provide opportunities to think about other technological attributes of online media. In the context of value added services on mobile phones, Wu and Wang (2005) studied the negative impact of the perceiv ed disadvantage on consumers intention to use mobile commerce. They revealed that three of the technological attributes of mobile commerce, along with price, keep consumers from adopting mobile commerce. The technological attributes included inconvenience of mobile device, transmission quality, and transmission speed. Meanwhile, Smith (2001) identified transaction security problems (33%), difficult navigation (11%), and low access speed (9%) as potential deterrents of adopting mobile commerce from a technology standpoint Additionally, Anil, Ting, Moe and Jonathan (2003) singled out difficulty in establishing connection, screen limitation, slow loading speed, difficulty in inputting data, no standard means of payment, security and privacy concerns as deterr ents to adopting value added services on mobile phones. They found that slow loading speed and high usage cost are the most critical impediments to adopting mobile commerce, followed by high cost of Internet -enabled handsets and difficulty in establishing connection. Cost -related attributes Considering the perceived advantages of a new medium with respect to content and technology related attributes, a new medium has a better chance of replacing an older medium if the new one provides consumers with a fin ancial benefit. Cost has almost always been a driving force behind the adoption of an innovation (Reagan, 2002). Porter (1985) maintained that a crucial determinant of the substitution threat is the relative price performance of substitutes. Prior studies found empirical evidence that supports the importance of the cost of a medium or system in influencing consumers decisions to adopt the medium or system. For instance, researchers pointed out that video rentals offer the relative advantage of lower admiss ion cost over theaters (Childers & Krugman, 1987; Lin, 1993). Focusing on different

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43 types of movie platforms, Cha (2008b) empirically found that economic benefits of the Internet and video on demand as movie platforms increased the likelihood of using thes e platforms. Even though a new medium may have advantages with respect to content and technological attributes, cost may overshadow these advantages in media adoption decisions if the cost is a burden to consumers. Smith (2001) discovered that high acce ss cost is the most critical deterrent to mobile commerce adoption, followed by other technological characteristics such as transaction security problems (33%), difficult navigation (11%), and low access speed (9%) Anil, Ting, Moe and Jonathan (2003) also pointed out that high costs of service usage and handsets deter consumers from using mobile commerce. When a new medium is introduced to the market, consumers have three choices: 1) disregard the new medium, 2) occasional use of the new medium, or 3) comp letely switch from a traditional medium to the new medium. Economic theory dealing with consumer switching costs predicts that consumers reluctance to switch to a new or competing product stems from explicit or implicit switching costs (Hoeffler, 2003; Kl emperer, 1995; Klemperer, 1987a, 1987b, 1992; Beggs & Klemperer, 1992; Klemperer & Padilla, 1997) Consumers must deal with nonnegligible costs in switching between relative services in various markets (Chen & Hitt, 2002; Plouffe, Hulland, & Vandenbosch, 2001). Klemperer (1987a, b) identified three types of switching costs: 1) transaction costs, 2) learning costs, and 3) artificial or contractual costs. Transaction costs are incurred to begin service with a provider and/or to terminate service with a previ ous provider. Learning costs are required to become comfortable with a new product or service. Artificial switching costs are created by firms through marketing or contractual terms, such as longterm contracts, to lock in a consumer to the firms produc t. In addition to explicit

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44 costs, implicit switching costs also exist, particularly based on a lack of knowledge about a substitute service. From a managerial perspective for both television and online video platforms, it is important to know which type of video platform has a competitive advantage over the other in terms of cost. I n the context of video platforms, much attention can be paid to transaction costs. Transaction costs consist of 1) price, and 2) other non-pecuniary costs, such as time spent on search for product and mental effort in choosing (Ward & Morganosky, 2003). Perceptions of price and non -pecuniary costs can play important roles in consumers decisions to adopt a new medium. Perceived price is defined as the consumers perceptual repr esentation or subjective perception of the objective price of the product (Jacoby & Olson, 1977). With respect to the perceived price of content available online in general, there has been a tendency to believe that consumers think that everything online is free. Few of the online news sites currently charge for subscription or access. This is one of the driving forces behind the increasing use of online news over print newspapers and magazines. Similarly, video sharing sites often do not charge for acces s to videos. Numerous television networks do not charge for online viewing either, except for a few television networks such as ESPN. The price disparity between online and offline platforms could prompt customers to switch (Ahlers, 2006). Thus, it is plau sible that some consumers who seek certain video content might prefer online video platforms to television because of their perceived price benefit from online viewing. Online video platforms might benefit from perceived price even in a situation where they require payment for certain video content. Consumers might think that it is more reasonable to pay for only what they watch rather than paying for bundled video content, widely used by the

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45 current cable and satellite television services, since consumers never watch some of the content in the bundle. Search costs are another dimension of transaction cost to be taken into account regarding the choice of video platforms. S earch cost determines consumer behavior and eventually market structures (Bakos, 1997). Search costs include the opportunity cost of time spent searching, as well as associated expenditures such as driving, telephone calls, computer fees, magazine subscriptions, etc. Compared with traditional distribution platforms, the online distribution platform lowered search costs by allowing consumers to access product features easily in the context of shopping (Bakos, 1998). It is relatively clear that online distribution channels reduced time and effort spent compared with traditional channels in th e shopping context. Yet little research has empirically explored whether consumers actually perceive online video platforms to be better than television with respect to search costs. Based on the previous studies that conceptually emphasized the importance of transaction costs on consumers decision to adopt a new medium, this study empirically investigates how transaction costs -perceived price and search costs -of using online video platforms and television influence consumers overall perceptions of the relative advantage of each video platform. Even though some of the content, technology, and cost related attributes such as interactivity, search capabilities, storage capabilities, personalization, content variety, and cost of online video plat forms might be intuitively expected to be better than those attributes of television, no empirical research has examined whether consumers actually perceive these attributes of online video platforms to be better than those of television. Therefore, this s tudy empirically compares how consumers perceive online video platforms and television with respect to the content, technology, and cost related attributes. Those perceptions of content,

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46 technology, and cost related attributes of online video platforms mig ht also affect consumers overall perception of the relative advantage of online video platforms over television. Thus, this study also raises the question of how the specific attributes of online video platforms actually contribute to establishing the ove rall relative advantage of online video platforms. Further, this study investigates the differences between users and non users of online video platforms with respect to the perceived attributes and the overall relative advantage of online video platforms compared with television. Thus, the following research questions are addressed: RQ2a. How do consumers perceive online video platforms to be better or worse than television with respect to specific content, technology, and cost attributes? What spe cific content, technology, and cost attributes of online video platforms affect the overall relative advantage of online video platforms? RQ2b. Do users and nonusers of online video platforms differ in how they perceive online video platforms with res pect to specific content, technology, and cost attributes? RQ2c. Do users and nonusers of online video platforms differ in how they perceive television with respect to specific content, technology, and cost attributes? RQ2d. Do users and nonusers of on line video platforms perceive online video platforms differently from television with respect to specific content, technology, and cost attributes? RQ2e. Do users and nonusers of online video platforms differ in how they perceive the overall relative adv antage of online video platforms? Based on the findings of the previous studies, it is also legitimate to propose a positive relationship between the relative advantage of online video platforms and the intention to use online video platforms. In contrast the relative advantage of online video platforms may act as a

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47 relative disadvantage of television. Therefore, this study proposes a negative relationship between the relative advantage of online video platforms and the intention to use television. H2a. Relative advantage of online video platforms will be positively related to the intention to use online video platforms. H2b. Relative advantage of online video platforms will be negatively related to the intention to use television. Perceived Ease of U se The technology acceptance model postulates that the perceived ease of use, along with the perceived usefulness, is the core determinant of the intention to adopt a technology and actual use of that technology (Davis, 1989; Peters, Amato, & Hollenbeck, 2007). Perceived ease of use refers to the degree to which an individual believes that using a particular system would be free of physical and mental efforts (David, 1989, p. 323). It is exactly the opposite of the notion of complexity in innovation dif fusion theory. Innovation diffusion literature defines complexity as the degree to which an innovation is difficult to understand and use (Rogers, 2003, p. 16). The perceived ease of use and innovation theorys concept of complexity essentially measure t he same perception, but from diametrically opposed directions. Numerous studies have provided empirical evidence that the perceived ease of use positively predicts the adoption of a new system. For instance, recent studies have found that in addition to pe rceived usefulness, the perceived ease of use is a significant factor that affects consumers intentions to use e commerce (Lee, Park, & Ahn 2001; Gefen & Straub 2000; Gefen, Karahanna, & Straub, 2003). Perceived ease of use was also found to be a predictor of the adoption of mobile Internet technologies (Cheng & Park, 2005; Hong, Thong, & Tam, 2006). Because perceived ease of use has a significant influence on the decision to adopt a new system, this study questions whether users and nonusers of online video platforms differ in how

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48 they perceive the ease of use of online video platforms. Based on the findings from previous studies, this study also proposes a positive relationship between the perceived ease of use of online video platforms and the intenti on to use online video platforms. However, perceived ease of use of online video platforms may reduce the likelihood to use television, because most consumers select just one of the video platforms to watch video content. RQ3. Do users and nonusers of o nline video platforms differ in how they perceive the ease of use of online video platforms? H3a. The perceived ease of use of online video platforms will be positively related to the intention to use the Internet to watch video content. H3b. The percei ved ease of use of online video platforms will be negatively related to the intention to use television. Compatibility In addition to relative advantage and complexity, the innovation diffusion theory literature indicates that compatibility of an innovati on is also consistently related to adoption of the innovation (Tornatzky & Klein, 1982). Compatibility is defined as the degree to which the adoption of a technology is compatible with existing values, past experiences, and needs of potential adopters (R ogers, 2003, p. 15). Perceived compatibility is a crucial factor in a consumers decision, particularly when adopting an Internet based service or system. This is because online service adoption is usually considered incompatible with non Internet based ad option (Lin, 2001). Previous studies also revealed that compatibility has been successfully integrated with the technology acceptance model and the theory of planned behavior, and has positively affected the adoption of new technologies (Chen, Gillenson, & Sherrell, 2002; Wu & Wang, 2005). Thus, this study suggests a positive relationship between the perceived compatibility of online video

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49 platforms and the intention to use these platforms. Considering that the Internet based system is not compatible with non Internet based adoption (Lin, 2001a), the people who perceive online video platforms as compatible may think that television is incompatible with their existing values and past experiences. Thus, this study proposes a negative relationship between the compatibility of online video platforms and the intention to use television. Furthermore, this study examines the differences between users and nonusers of online video platforms with respect to perceptions about the compatibility of online video platform s. RQ4. Do users and nonusers of online video platforms differ in how they perceive the compatibility of online video platforms ? H4a. The perceived compatibility of online video platforms will be positively related to the intention to use online video platforms. H4b. The perceived compatibility of using online video platforms will be negatively related to the intention to use television. Consumer Characteristics Online Flow Experience Flow experience has arisen as a key to understanding consumers onl ine behaviors (Hoffman & Novak, 1996; Novak, Hoffman, & Yung, 2000). Csikszentmihalyis flow theory views flow as a state in which individuals might be oblivious to the world around them and lose track of time and even self awareness as a result of high level engagement in an activity (1975, 1990, 2000). Csikszentmihalyi (1975) defined flow as the holistic sensation that people feel when they act with total involvement (p. 36). People in a state of flow are depicted as intrinsically motivated, interes ted in the challenging tasks at hand, being unconscious of themselves while performing the tasks, feeling a unity between consciousness and activities, and often losing sense of physical time (Csikszentmihalyi, 1990, pp. 4866). Hoffman and Novak

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50 (1996) al so defined online flow as the state occurring during network navigation which is characterized by a seamless sequence of responses facilitated by machine interactivity, intrinsically enjoyable, accompanied by a loss of self -consciousness, and self reinfor cing (p. 57). Some individuals may be able to remain in the state of flow longer than others because of their inherent characteristics, such as their openness to flow experiences, their perception of the skills presented by the task, their upbringing, etc (Csikszentmihalyi & Csikszentmihalyi, 1988). The notion of flow has been applied to a variety of contexts, including shopping, sports, hobbies, computer use and media consumption (Csikszentmihalyi, 1975, 1990, 1997; Csikszentmihalyi & LeFevre, 1989). Hof fman and Novak (1997) specifically focused on how the experience of flow online predicts website visits. They found that both the frequency and duration of website visits increase as websites facilitate the flow experience. Shin (2006) empirically detected a positive correlation between the flow and satisfaction levels with a virtual course. Hsu and Lu (2004) employed a model that integrated the technology acceptance model with subjective norm and flow experience in order to identify the determinants of onl ine game adoption, and found that flow experience online has a positive effect on the intention to adopt online games. Given that the Internet typically requires more involvement from users than does television, people would need to experience some degre e of flow if opting to use the Internet to watch video content. Thus, this study proposes that people who have a high level of online flow experience are more likely to adopt the Internet as a video platform, whereas people who experience a low level of fl ow online are more apt to use television because, in part, they are less inclined to engage in activities that would provide opportunities to use online video

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51 platforms as an alternative to television for watching video content. Hence, the following rese arch question and hypotheses are suggested: RQ5. Are there any differences between users and non users of online video platforms with respect to flow experience online? H5a. Flow experience online will be positively related to the intention to use onli ne video platforms. H5b. Flow experience online will be negatively related to the intention to use television. Viewing Orientation The theory of uses and gratifications posit that the audiences use specific media and content to gratify certain needs (Blu mler, 1979; Kim & Rubin, 1997; Levy 1987; Levy & Windahl, 1984, 1985; Perse 1990a; Rubin & Perse, 1987a, 1987b). Based on the belief that audience members are variably active in their choice -making activities (Blumler, 1979), researchers have explored how audience activities and involvement with various platforms mediate outcomes. This exploration has resulted in a distinction between ritualistic media use and instrumental media use. Ritualistic media use tends to center on the medium rather than on partic ular content (Rubin & Perse, 1987a). Thus, it involves diversionary motives, such as habit or pass ing time (Hearn, 1989; Kim & Rubin, 1997; Perse, 1990a, 1990b, 1998; Perse & Rubin, 1988; Rubin, 1984, 1993; Rubin & Perse, 1987a, 1987b). Rubin and Perse (1987a) defined ritualistic media use as a less intentional and non -selective orientation, a time -filling activity and a tendency to use media regardless of content (p. 59). Instrumental media use, on the other hand, is more intentional and selective of c ontent, and reflects purposive exposure to specific content (Rubin & Perse, 1987a, p. 59). That is, instrumental orientation is associated with more goal -directed motivations such as information -

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52 seeking, behavioral guidance, or arousal (Hearn, 1989; Kim & Rubin, 1997; Perse, 1990a, 1990b, 1998; Perse & Rubin, 1988; Rubin, 1984, 1993; Rubin & Perse, 1987a, 1987b). In general, instrumental media use has been linked to increased audience activity and media involvement (Perse, 1990b). Previous studies indicat e that media use orientation can differ between television and the Internet. Prior studies consistently showed that people primarily watch television with ritualistic orientation. Focusing on the relationship between media types and media use orientation, Metzger and Flanagin (2002) found that television use in general is more motivated by ritualistic orientation than by instrumental orientation. The habit motivation, which represents ritualistic viewing orientation, has been occasionally questioned concerning its role as an independent motivation behind media use (Grant & Rosenstein, 1997). Nevertheless, empirical studies repeatedly showed that habit is a crucial motivation for television viewing. Greenberg (1974) found that habit is the primary reason for television viewing among British adolescents. Blumler and McQuail (1968) found that a group of audiences watch election broadcasts as a matter of habit rather than with political interest. Herzog (1944) and Rubin (1981) also found that habit is one of the primary reasons why people tune into daytime serial dramas on both radio and television. Furthermore, Hawkins, Reynolds and Pingree (1991) discovered that children watch cartoons and adventure programs out of habit rather than because of specific interests Likewise, Stone and Stone (1990) concluded that people identified habit as their primary reason for watching evening television dramas regardless of age, gender, education, income, or race. When it comes to orientation for Internet use, previous studies have yielded mixed results. Papacharissi and Rubin (2000) found that the Internet is more oriented toward instrumental use rather than ritualistic use. The primary motives for Internet use can be summarized into

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53 information -seeking, interpersonal utility, pass time, convenience, and entertainment. That said, scholars found that the most salient use of the Internet is for information -seeking, which reflects instrumental orientation. In contrast to the findings of Papacharissi and Rubin (2000), Metzger and Flanagin (2002) found no significant difference between ritualistic and instrumental orientation for Internet use. The finding of Metzger and Flanagin (2002) might imply that people use the Internet to pass time or as a habit as much as they use it to acco mplish certain tasks. The advance in the technological and diversification of the Internet and content services might help to explain why Metzger and Flanagin (2002) did not find a relevant difference between ritualistic and instrumental orientation behin d Internet use. Broadband has been one of the dominant bandwidths used in U.S. households since 2005 (Arbitron, 2006). Additionally, online content and services have been continuously proliferating and diversifying as the data storage capability and transm ission speed of the Internet improve. In the U.S., daily use of the Internet has become a familiar part of everyday life. As a result, it appears that the Internet is becoming a more balanced medium that gratifies both ritualistic and instrumental orientat ions; it had been more of an instrumental orientation driven medium in its earlier stages. Even though U.S. consumers have become very familiar with the Internet in general, their familiarity with online video platforms might be lagging behind. Online vid eo platforms tend to provide audiences with more control over content and schedule than does television. They also require more involvement and activity than television in terms of viewing video content. Thus, this study proposes that the more people watch video content with instrumental orientation, the more likely they are to use online video platforms; whereas the more people watch video content with ritualistic orientation, the less likely they use online video platforms. Given that many of the previous studies consistently found that ritualistic viewing orientation explains television use,

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54 this study postulates a positive relationship between ritualistic viewing orientation and the intention to use television. It is also proposes a negative relationship between instrumental viewing orientation and the intention to use television. Moreover, this study addresses the differences between users and nonusers of online video platforms with respect to viewing orientation. The research questions and hypotheses a re as follows: RQ6. Are there any differences between users and non users of online video platforms with respect to viewing orientation? H6a. Instrumental viewing orientation will be positively related to the intention to use online video platforms. H6b. Ritualistic viewing orientation will be negatively related to the intention to use online video platforms. H6c. Instrumental viewing orientation will be negatively related to the intention to use television. H6d. Ritualistic viewing orientation wi ll be positively related to the intention to use television. Subjective Norm The theory of reasoned action and the theory of planned behavior both posit that the subjective norm is a direct determinant of behavioral intention (Fishbein & Ajzen 1975; Ajze n 1991). Subjective norm is defined as the perceived social pressure that most people who are important to him/her think he/she should or should not perform the behavior in question (Fishbein & Ajzen, 1975, p. 302). A persons subjective norm is composed of normative beliefs (i.e., what others think about the behavior) and his or her motivation to comply with a particular type of behavior or set of societal conventions (Chung & Nam, 2007). If others who are relevant to an individual view that persons behavior as positive, and the individual is motivated to meet

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55 the expectations of those relevant others, then a positive subjective norm is expected (Chung & Nam, 2007). Studies found that the higher the subjective norm, the higher the behavioral intention (T aylor & Todd, 1995; De Vos, Ter Hofte, & De Poot, 2004). Some researchers who attempted to integrate the subjective norm with the technology acceptance model found mixed results. Comparing the technology acceptance model with the theory of reasoned actio n, Davis, Bagozzi, and Warshaw (1989) failed to detect a significant impact of subjective norm on intentions. This led them to remove the subjective norm construct from the original technology acceptance model, but they maintained that there is still a nee d for further research to investigate the conditions and mechanisms governing the impact of social influences on usage behavior (p. 999). Yet neither Mathieson (1991) nor Chau and Hu (2002), who attempted a similar integration, found a significant effect of the subjective norm on intention. Earlier studies were not successful in detecting the impact of the subjective norm in the extended models of the original technology acceptance model. However, some later research lent support to the notion that the s ubjective norm does have an impact on technology adoption. Venkatesh and Davis (2000) initiated an expansion of the technology acceptance model to a second version by proposing that the subjective norm has positive direct effects on both the perceived usef ulness and the intention to use a particular technology. In addition, numerous empirical studies have found that social factors positively impact individuals usage of IT technologies, such as a short message service (SMS) on mobile phones and instant mess engers (Lucas & Spitler, 2000; Taylor & Todd, 1995; Venkatesh & Morris, 2000; To, Liao, Chiang, Shih, & Chang, 2008; Muk, 2007; Chung & Nam, 2007). Yi, Jackson, Park, and Probst (2006) tested an integrated theory of planned behavior and the technology acce ptance model. They

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56 found that the subjective norm is statistically significant, along with the other constructs (namely, perceived behavioral control, usefulness, and ease of use), in predicting the intention to adopt a Personal Assistant Device (PDA). Em pirical studies have also supported the positive impact of social influences on an individuals behavioral intention outside the context of information technologies (Karahanna & Straub, 1999; Liao, Shao, Wang, & Chen 1999; Liker & Sindi, 1997). In the context of movie distribution channels, Cha (2008b) expanded on the theory of planned behavior by adding specific service evaluation factors. Cha (2008b) found a positive impact of the subjective norm across all four types of movie distribution channels mov ie theaters, DVD rentals, the Internet and video on demand. Some of the previous studies did not find a significant influence of subjective norm on the adoption intention of a technology when the theory of planned behavior and technology acceptance model were integrated. But in recent years, an increasing number of empirical studies support a positive effect of the subjective norm on the intention to adopt a technology in the integrated model of technology acceptance model and theory of planned behavior. T he effect of the subjective norm has been quite stable, particularly in examining the adoption of computer mediated technologies. Social influence on the adoption decision is highly plausible in the video consumption context, especially because viewing video content has been, and continues to be, a popular source for social interactions and social activities throughout most of society. Therefore, this study posits that the subjective norm of using online video platforms is positively related to the intentio n to use online video platforms. In terms of television, however, this study postulates that the social influence on using online video platforms would be negatively related to the intention to use television to view

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57 video content. This study also attempt s to examine whether there are differences between users and non users of online video platforms with respect to the subjective norm of using online video platforms. The following research question and hypotheses are suggested: RQ7. Are there differences between users and nonusers of online video platforms with respect to subjective norm of using online video platforms? H7a. The subjective norm of using online video platforms will be positively related to the intention to use online video platforms. H7 b. The subjective norm of using online video platforms will be negatively related to the intention to use television. Perceived Behavioral Control Perceived behavioral control refers to the degree to which people believe that they are able to engage in a particular behavior. It is presumably more influential than actual behavioral control on behavioral intent and action (Ajzen, 1991). Ajzens perceived behavioral control is closely related to Banduras concept of perceived self -efficacy, which is the beli ef in ones capabilities to organize and execute the courses of action required to produce given attainments (Bandura, 1986, p. 3). Ajzen (1991) argues that the perceived behavioral control and self -efficacy constructs are interchangeable in that both c onstructs are concerned with the perception of ones ability to perform a behavior (Ajzen, 2002). In contrast, Bandura (1986) has argued that perceived behavioral control and self -efficacy are quite different concepts. Other researchers (e.g. Terry, 1993; Ho, Lee, & Hameed, 2008; Armitage & Conner, 1999a, 1999b) have also maintained that perceived behavioral control and self -efficacy are not entirely synonymous. Scholars have emphasized that one of the distinctions between perceived behavioral control and self -efficacy is that while the former is more oriented toward control over external

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58 factors, the latter is more oriented toward control over internal processes. In other words, perceived behavioral control is tied to more general, external factors, wherea s self -efficacy is more concerned with cognitive perceptions of control based on internal control factors. Perceived behavioral control pertains to an individuals belief about whether he or she possesses the requisite resources and opportunities that are essential to perform a certain behavior (Ajzen & Madden, 1986; Yi, Jackson, Park, & Probst, 2006). Perceived self -efficacy refers to peoples beliefs about their capabilities to exercise control over their own level of functioning and over events that aff ect their lives (Bandura, 1991, p. 257). Unlike television, online video platforms require individuals to have resources and knowledge in order to watch video content. Thus, instead of focusing on self -efficacy, the inquiries of this study focus on perce ived behavioral control, which measures the degree to which the individuals believe that they have the abilities required to accomplish their tasks (i.e., watching video content through online video platforms). When considering the perceived behavioral control within the video platform context, some people may feel they have less control over television unless they have a digital video recorder, they have to arrange their schedules around the airing times of the programs they want to watch. Yet others may think that they have less control over viewing video content through the Internet if they do not have knowledge or resources that can help them figure out how and where they can find the video content they want. Thus, this study proposes a positive relati onship between the perceived behavioral control of using online video platforms and the intention to use online video platforms. This study also posits that the more people think that they have behavioral control over using online video platforms, the les s likely they are to use television, because possessing a

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59 strong belief about ones ability to exert a satisfactory level of control when using online video platforms might reduce that persons use of television. This study also questions whether there are differences between users and nonusers of online video platforms with respect to the perceived behavioral control of using online video platforms. The following research question and hypotheses are addressed: RQ8. Are there any differences between use rs and non users of online video platforms with respect to perceived behavior control? H8a. Perceived behavioral control of using online video platforms will be positively related to the intention to use online video platforms. H8b. Perceived behavioral control of using online video platforms will be negatively related to the intention to use television. Table 3 1 summarizes the hypotheses in this study. Figure 3 1 describes the proposed model to predict the intention to use online video platforms and t elevision. Table 3 1. List of hypotheses Hypotheses regarding online video platforms Hypotheses regarding television H1a. Perceived substitutability between online video platforms and television will be positively related to the intention to use online video platforms. H1b. Perceived substitutability between online video platforms and television will be negatively related to the intention to use television. H2a. Relative advantage of online video platforms will be positively related to the intent ion to use online video platforms. H2b. Relative advantage of online video platforms will be negatively related the intention to use television. H3a. Perceived ease of use of online video platforms will be positively related to the intention to use onli ne video platforms. H3b. Perceived ease of use of online video platforms will be negatively related to the intention to use television. H4a. Compatibility of online video platforms will be positively related to the intention to use online video platform s. H4b. Compatibility of online video platforms will be negatively related to the intention to use television. H5a. Flow experience online will be positively related to the intention to use online video platforms. H5b. Flow experience online will be ne gatively related to the intention to use television.

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60 Table 3 1. Continued H6a. Instrumental viewing orientation will be positively related to the intention to use online video platforms. H6c. Instrumental viewing orientation will be negatively rela ted to the intention to use television. H6b. Ritualistic viewing orientation will be negatively related to the intention to use online video platforms. H6d. Ritualistic viewing orientation will be negatively related to the intention to use television. H7a. Subjective norm of using online video platforms will be positively related to the intention to use online video platforms. H7b. Subjective norm of using online video platforms will be negatively related to the intention to use television. H8a. Perce ived behavioral control of using online video platforms will be positively related to the intention to use online video platforms. H8b. Perceived behavioral control of using online video platforms will be negatively related to the intention to use telev ision.

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61 Figure 3 1. Proposed model for the intention to use online video platforms and television Flow experience online Perceived characteristics of online video platforms Perceived substitutability Intention to use online video platforms Intention to use television Relative advantage Perceived ease of use Compatibility Instrumental orientation Ritualistic orientation Subjective norm Perceived behavioral control Consumer characteristics

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62 Displacement or Complementation Whether a new emerging medium displaces an existing one has historically been de bated. When radio emerged in the market, the focus of the debate was on whether radio would erode music sales. It turned out that radio increased record sales (Sterling & Kittross 2001). Similar types of debates concerning the cannibalistic effect of a new medium on older media continue. More recently, debate continues in the economics literature about the relationships between free file -sharing services and recorded music (Zentner, 2006; Oberholzer & Strumpf, 2007; Rob & Waldfogel, 2004), online and offline retailing (Goolsbee 2001; Sinai & Waldfogel, 2004), and online and print newspapers (Chyi & Sylvie, 2000; 2001; Deleersnyder, Geyskens, Gielens, & Dekimpe, 2002). At the Internets nascent stage, research tended to focus on whether the introduction of the Internet is related to traditional media use. That is, the majority of research did not address whether the time spent using traditional media decreased as a consequence of Internet use. Rather, it concentrated on a simple relationship between time sp ent on the Internet and time spent with traditional media without investigating the possibility of a causal relationship. The findings from the research are inconclusive. A survey conducted in mid1994 found that time spent reading newspapers remained th e same during the introductory stage of the Internet (Bromley & Bowles, 1995). Lin (1999) also found that there is largely no link between television viewing and intention to use online services. Shapiro (1998) revealed that frequent Internet users tend to watch television programs more often. The research that attempted to investigate the direct influence of Internet use on time spent with traditional media showed more consistent results. These studies discovered a displacement effect of the Internet on t raditional media at some levels. Lee and Kuo (2002) found that Internet usage negatively affected time spent watching television. Kayany and Yelsma (2000) also found

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63 that time spent on the Internet most critically reduced time spent watching television, followed by reduced telephone use, newspaper use, and domestic conversations in that order. An industry survey by Arbitron (2002) showed that 37% of Americans said that they spent less time watching television due to their time spent online as of 2002. Thirt y one percent, 27%, and 20% of Americans said that they spent less time with newspapers, magazines, and radio, respectively, according to the same survey. A more recent survey released in 2006 revealed that 33% of Americans said that they spent less time w atching television due to Internet consumption. The percentages of people who said that they spend less time with newspapers and magazines as a result of their time on the Internet increased to 30% for both media (Arbitron, 2006). These studies examined t he displacement effect of the Internet in general on traditional media. But as the Internet becomes increasingly pervasive, some researchers began to investigate the impact of specific online services on their offline counterparts. The results are inconclu sive. Some studies supported a complementary relationship between the two different modalities (Simon & Kadiyali, 2007), which means the consumption of online content actually increases the consumption of its offline counterpart. In contrast, Lee and Leung (2008) found that the use of the Internet for entertainment is negatively correlated with the time spent watching television. The same study revealed that Internet use for news and information is negatively correlated with time spent reading print newspap ers. The relationship between print newspapers or magazines and their online counterparts has been a hot issue in the past decade. Most of the studies mentioned above examined the displacement effect of the Internet from the aspect of time spent with eac h medium, whereas the following studies focused on the displacement effect whether newspaper demands change

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64 among consumers or advertisers due to their online counterparts. The studies did not show consistent results as to whether online newspapers or ma gazines cannibalize their print versions. Deleersnyder, Geyskens, Gielens, and Dekimpe (2002) surveyed 85 British and Dutch newspapers that have news websites. They found that the addition of online distribution channels for print newspapers overall did not have a cannibalistic effect either on the print newspapers circulation revenue growth or on the print newspapers advertising revenue growth, except for a few newspapers. Kaiser (2006) assessed the impact of a magazines website on the demand for its p rint content and advertising space in a small sample of German womens magazines. The findings indicated that having a website has no effect on the demand for either product (Kaiser, 2006). Meanwhile, Kaiser and Kongsted (2005) studied a sample of 42 Germa n magazines and found that website visits actually increased offline magazine circulation. In contrast Gentzkow (2007) examined whether the online version of the Washington Post cannibalized its offline version based on the individual level data, and foun d that the online edition reduced print readership by 27,000 per day. The finding parallels that of Filistrucchi (2005), who found evidence of substantial cannibalization in the cases of four Italian newspapers that offered websites. Note that the array of research focused on different aspects with respect to cannibalization. Some focused on advertising revenue, while others concentrated on circulation revenue. An interesting point is that some of the newspaper studies disregarded the degree of consumers actual visits of, or use of, those newspaper websites in examining the consumers and advertisers demand changes for the corresponding print versions due to the emergence of their online versions. In other words, some of the studies merely focused on how the existence of newspaper websites influenced circulation or advertising revenues of their print versions (e.g.,

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65 Deleersnyder, Geyskens, Gielens, & Dekimpe, 2002; Kaiser, 2006). Thus, it is not legitimate to directly compare the findings of the studies one on one. Why has the displacement effect of online newspapers or magazines on their offline counterparts been such a hotly debated issue? The reason is partly because of the revenue structure of print media. The revenues of print media mainly come from subscriptions in conjunction with advertising. On the other hand, the online versions of newspapers and magazines tend to be offered for free From the standpoint of print media, it is thus pivotal to know whether online versions of newspapers or magazine s cannibalize consumers demand for their print versions, which also influences advertisers demand for the print versions (Chiyi & Sylvie, 2000). On the surface, the revenu e structure of television networks appears somewhat different from the structure of print media, because broadcast networks and basic cable networks rely on revenues from advertising no revenue is generated directly from audiences. It is, however, noteworthy that the audience ratings determine the advertising prices. In addition, premi um cable networks rely on audience subscription. Thus, whether online viewing displaces or complements television viewing is also a crucial issue for the television industry. The displacement effect of online video platforms on television can be a particu larly serious pitfall for television networks if consumers online viewing occurs on video sharing sites instead of television network websites. This is because some of the branded video content on video sharing sites might be illegally uploaded by individual users. Thus, the viewing of such branded video content on video sharing sites may deprive television networks of opportunities to generate more revenues from their original aired programs unless television networks officially promote their branded vi deos under contracts with video sharing sites. Even though we could assume that a vast number of audience members of a television network will migrate to its

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66 related websites instead of video sharing sites to watch the network branded videos, the migratio n will still presumably hurt the revenues of television networks due to the discrepancy of advertising prices between ads on television and on television network websites. Given that there is little research on channel cannibalization in general (Deleersn yder, Geyskens, Gielens, Dekimpe, 2002), theoretical research that examined specifically whether online video platforms complements or displaces television is scarce There are a few industry reports that address the issue. For example, one report conducte d by Time Warner AOL and the Associated Press revealed that 52% of the respondents who watched videos via the Internet said that the time spent on this had no effect on the time spent watching television. Interestingly, 32% said that online video viewing a ctually spurred them to watch more television. The other 15 % of the respondents said that they watched less television as a result of online viewing (Holahan, 2006). Although this industry report revealed the percentage of people who watch more or less t elevision as a result of online video viewing, it does not indicate how the level of the video content consumption online alters consumers time spent with television. T he use of the dichotomous category to measure the use of online video platforms (i.e., yes or no) would be insufficient to accurately illustrate whether online video platforms complement or displace television, because the answer might also depend on the level of online video platform use. There is also a need to update the investigation as the adoption of online video platforms continues to increase. Thus, the present study examines how the extent of using online video platforms changes the time spent with television. This study further addresses how the use of different types of online vide o venues influences the time spent with television.

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67 RQ9a. How does the time spent using online video platforms affect the time spent watching television? RQ9b. How does the time spent using different types of online video venues (i.e., television networ k websites and video sharing sites ) affect the time spent watching television? While the degree to which individuals use online video platforms is a plausible factor that might affect the displacement effect of online video platforms on television, anot her factor to be considered is the degree of content overlap between online video platforms and television. In the newspaper industry, Deleersnyder, Geyskens, Gielens, and Dekimpe (2002) found that print newspapers that provide more overlap with their onli ne counterparts in terms of content coverage are more likely to be cannibalized by their online versions. Chyi and Sylvie (1998) also found that print newspapers and online counterparts compete when both versions are available within the same geographic ma rket, if the two versions deliver similar content. In other words, consumers are more likely to choose the online version of the newspaper over its print version, or vice versa, when the content between the two platforms are highly overlapped. Simon and K adiyali (2007) discovered that online content cannibalizes print magazine sales on average by 3 4%, but they suggested that the level of the cannibalization varies according to the content overlap between online and offline versions. They found that the ca nnibalization effect increases as the content overlap between online and offline version increases. The findings indicated that allowing readers to read the entire content of the current issue online reduces print sales by more than twice as much. The same study discovered that offering digital content that overlaps less with the current print issue cannibalizes print sales, although the effects are smaller and the results are less robust. Strikingly, this study further revealed that even distinctively diff erent or preview online content does not promote the

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68 circulation of printed version of magazines. Note that Chyi and Sylvie (1998), Deleersnyder, Geyskens, Gielens, and Dekimpe (2002) and Simon and Kadiyali (2007) investigated only whether specific online newspapers displace their print counterparts. The studies did not focus on the displacement effect of online newspapers on the print newspaper industry in general. The degree of video platform content overlap can be understood from two aspects: 1)what ty pes of video content do online video platforms deliver? and 2) how long is there an overlap between online video platforms and television? The first aspect focuses on how the content type overlap between television and online video platforms influences ove rall television consumption. There are two types of video content available online: branded videos, which are originally produced by media companies; and unbranded videos, which are produced by individual Internet users (i.e., user -generated videos). The content overlap between branded videos online and television programs on television is extensive. On the other hand, user -generated videos online and television programs on television do not have content overlap. Noting the growth of the Internet as a vid eo platform, some of the television networks allow audiences to watch their television programs on their websites. An industry report also revealed that consumers actually prefer branded videos online to user -generated videos online (Holahan, 2007). Nevert heless, the popularity of video sharing sites has made it possible for user generated videos to attract millions of Internet users around the world. The degree to which consumers spend their time watching these branded and user -generated videos through onl ine video platforms might influence the time spent watching television. Thus, the present study addresses how the consumption of branded or user generated video content affects the time spent watching television.

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69 RQ10a. How does the time spent watching br anded video content and user -generated video content, respectively, through online video platforms affect the time spent watching television? The second aspect of content overlap deals with how much time -wise do the video content that television and onlin e video platforms deliver overlap. Some television networks might upload an entire episode of their original program online after that episode is initially aired on television. Other television networks may post a short clip of the programs online, but not the entire episode. This type of practice is becoming very common in the television industry, but there is no empirical study that actually investigates how the consumption of an entire episode, or clips of television programs, through online video platforms changes the time spent watching television. From a television networks managerial perspective, to learn the consequence of putting a clip or entire episode of its programs online is important in order to maximize the profit from different types of dis tribution platforms. Therefore, this study addresses how the use of online video platforms to watch clips or an entire episode of television programs affects the time spent watching television. RQ10b. How does the time spent watching clips or an entire ep isode of television program s respectively, through online video platforms affect the time spent watching television? Viewership Overlap An array of research questions and hypotheses were developed from various aspects to see how online video platforms a nd television influence one another for consumer demand. Another interesting issue behind the emergence of online video platforms is whether consumers use either online video platforms or television exclusively. Some researchers in the newspaper context in vestigated whether print newspapers and their online versions reached mutually exclusive audiences (Chyi & Lasorsa, 1999; Chyi & Sylvie, 2001; Chyi & Lasorsa, 2002). Chyi and Sylvie (2001) found that half of the readers of national and regional newspapers in the online forms also

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70 tended to read their print versions. Duplication of readership between print versions and online versions of newspapers were particularly high for local dailies. More than 80% of online local newspaper readers read the print versi ons as well. The results parallel those found by Chyi and Lasorsa (1999), who also examined the readership overlap in the context of newspapers. They found that national newspapers were more likely to have readers who choose one platform over the other. On the other hand, print and online versions of local dailies tended to reach the same readers. The investigation on how the users of online video platforms and television can overlap show the state of the Internet as a video platform. If the user overlap between online video platforms and television is low, that partly implies that online video platforms and television compete. In other word, consumers tend to choose one platform over another exclusively for watching video content. On the other hand, if the user overlap between online video platforms and television is high, that means the video platforms may complement each other. Another variation of the inquiry is whether the user overlap varies according to television subscription type. The rise of the Internet as a video platform is of particular concern to cable and satellite television system operators. Recent news articles have pointed out that the use of the Internet to watch video content may lead consumers to eventually cancel paid subscriptions s uch as cable television or satellite television (e.g., Arango, 2009). An industry report by the Sanford C. Bernstein research group projected that about 35 % of people who watch videos online might cancel their cable subscription within five years (Arango, 2009). The use of online video platforms may also shrink the audiences of broadcast networks. The primetime audience of broadcast networks has already been decreasing over the years. People who receive broadcast networks over the air without subscribing t o any fee -based television services do not have as

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71 many channels as do cable or satellite television subscribers. Thus, they might heavily rely on the Internet to watch video content. Given that there is little research that addresses whether online video platforms and television reach mutually exclusive audiences, this study inspects the user overlap between online video platforms and television. Further, this study addresses whether television subscription type makes a difference in the user overlap betw een online video platforms and television. RQ11a. Do television and online video platforms reach mutually exclusive viewers? RQ11b. Does the viewership overlap between television and online video platforms differ according to types of television subscri ption (broadcasting over the air only, cable television, and satellite television)?

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72 CHAPTER 4 METHOD This chapter describes the research procedures used in this study pretest, main survey procedures, operational definitions of constructs, and statistica l analyses. In addition, this chapter elaborates on the issues pertinent to the main test, including mail survey, response rates, sampling, and survey participants. Before the main survey was conducted, two pretests were carried out to aid in the developme nt and modification of the instruments for the main survey. For the main test, a paper pencil mail survey was administered to Internet users across the United States. Survey The overarching goals of this study are to identify underlying determinants behind consumers use of online video platforms and television from the consumers perspective, and to investigate consumption patterns of the two different video platforms. To accomplish these goals, it is essential to investigate how consumers perceive the In ternet and television as video platforms, and how users and non users of online video platforms differ. The investigation should thus be based on the data that reflect consumers perceptions of the video platforms and consumption of the video platforms. Therefore, this study used a survey method to collect data. The survey approach is one of the most appropriate data collection methods to describe a situation or phenomenon (Fowler, 1995). Two of the biggest merits of the survey data collection method are t hat survey research allows researchers to investigate problems in realistic settings, and also enables the researchers to generalize the results of a survey to a large population since surveys are not limited to geographic boundaries (Wimmer & Dominick, 2003). Mail Survey : Surveys can be administered to people in remote areas through the mail, telephone, e -mail, or Web. Specifically, the data for the main test was obtained through a

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73 national mail survey due to several advantages. The written presentation o f structured questions and response options in mail surveys enables researchers to more easily control data collection. In addition, mail surveys allow researchers to collect data from a large, dispersed, and carefully selected sample of participants (Vaux & Briggs, 2006). Research questions and hypotheses in this study are not limited to a certain group of people in a limited geographic area. Therefore, plausible options for the data collection other than mail surveys include telephone, e -mail, and we b -based surveys. A telephone survey was not chosen because the questionnaire for this study contains a large number of questions; telephone surveys are more appropriate for a study with a short list of questions (Babbie, 2001). In this study, there were al so terms that should be defined to the participants before, or while, they fill out the questionnaire. A telephone survey is less feasible than a mail survey in such a scenario. Another reason this study eschewed a telephone survey was the cost of long -dis tance calls. Another alternative for data collection in this study was an online survey. Considering the fact that the research questions and hypotheses of the current study focus on whether and how the Internet as a video platform interacts with television, the online survey method was appealing. Participation in an online survey implies that the participants are at least Internet users, regardless of whether or not they actually use the Internet to watch video content. Thus, the use of an online survey means that there is no process necessary to screen Internet users among the participants of the survey. Online surveys are also inexpensive compared with mail surveys in general. In addition, online surveys require less time to develop, implement, and col lect responses (Spizziri, 2000). Nevertheless, this study did not choose an online survey for data collection for two main reasons.

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74 First, online surveys have problems regarding representativeness of the samples. Sampling frames that list U.S. adults who use the Internet are unavailable for online surveys (Dillman, 2000). As a result, studies that employ online surveys tend to use a non-probability sampling method, which prevents the researcher from generalizing the study findings to the population of the study. Even though researchers can use various websites to recruit participants of online surveys, there will be bias involving the data collection. Compared with mail surveys, there are more possibilities for online surveys to over represent or under -rep resent certain groups of people (Bradley, 1999). For example, it is difficult to detect multiple submissions from a single individual (Schmidt, 1997). Given that Internet users occasionally change their Internet service providers and their e -mail addresses and that a single individual can hold multiple e -mail addresses, multiple submissions from an individual is a plausible concern (Bradley, 1999). Representativeness of the sample is pivotal when generalizing the results of a study to the population of a study. Election polls clearly show that the representativeness of samples is much more important than the response rate. Although election polls employ only a small fraction of the U.S. population, the predictions and estimates are quite accurate (Cook, He ath, & Thomson, 2000). Therefore, the lack of representativeness in the samples is a critical shortcoming of online surveys. Given that the current study strives to generalize the findings to the population, the representativeness of the sample is crucial. The second reason this study did not choose an online survey for data collection is because prior research indicates that response rates of mail surveys are much higher than those of electronic surveys (Kaplowitz, Hadlock, & Levine, 2004). Consumers conc erns about online security and junk mail, or spam, reduce response rates of online surveys (Sills & Song, 2002).

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75 Kaplowitz, Hadlock, and Levine (2004) found that the response rates of mail surveys are statistically significantly higher than the response ra tes of e -mail surveys. The same study found that the difference of the response rates between the two distribution modes was about 10%. Over the past decade, response rates of surveys have been declining for all manner of surveys (Bickart & Schmittlein, 19 99). Given that there is scarce previous research investigating how the Internet and television influence each other as video platforms, the current study desires to generalize the results to common Internet users. In summary, the following reasons explai n the choice of mail surveys over other alternatives: 1) the mail surveys enable the researcher to achieve representativeness of the population for the study and reasonable response rates, 2) the questionnaire employed for this study has a relatively long list of questions, and 3) mail surveys are more feasible when definitions of terms are necessary for the purpose of this study. Pretests Two pretests were carried out before the main test was conducted. The purpose of the pretests was to ensure the vali dity and reliability of constructs that were measured with multiple items. Another objective was to check the flow and wording of the questions in order to eliminate confusion or misunderstanding in the finalized mail survey. First Pretest A total of 18 adults who use the Internet were selected for the first pretest. The age of the participants for the first pretest ranged from 17 to 58. The mean age was 30.35 ( SD = 9.60). With respect to gender, females were dominant. Out of the 18 participants who submi tted complete surveys, 77% were females (n = 13); 24% of the participants were males (n = 4). The participants live in different regions across the United States.

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76 Some of the constructs in the questionnaire were measured with multiple items on a 7 point Likert scale. The constructs with multiple indicators include 1 ) intention to use online video platforms, 2 ) intention to use television, 3 ) perceived substitutability, 4 ) relative advantage, 5 ) perceived ease of use, 6 ) compatibility, 7 ) subjective norm, 8 ) perceived behavioral control, 9 ) ritualistic orientation behind video content consumption, 10) instrumental orientation behind video content consumption, and 11) flow experience online. For those constructs, Cronbachs alpha was estimated to check the reliability of the constructs in particular, internal consistency. Cronbachs alpha represents the inter -correlations among items that are used to measure the same construct. Table 4 1 shows the Chronbachs alpha for each of the constructs. Table 4 1. Rel iability check for constructs (Pretest 1) Construct No. of item Cronbachs alpha Intention to use online video platforms 2 .895 Intention to use television 2 .869 Perceived substitutability 5 .792 Relative advantage 3 .890 Perceived ease of use 3 .644 Compatibility 3 .931 Flow experience online 2 .844 Ritualistic viewing orientation 5 .905 Instrumental viewing orientation 7 .464 Subjective norm 3 .815 Perceived behavioral control 4 .956 The common rule of thumb for reliability coeff icients suggests that having Cronbchs alpha around .90 is considered excellent, values around .80 are very good, and values around .70 are adequate (Hair, Anderson, Tatham, & Black, 1998; Kline, 2005). The acceptance value of Cronbachs alpha for r eliability is lowered to .60 in exploratory research (Hair Anderson, Tatham, & Black, 1998). The Cronbachs alpha values from the first pretest show that all of the constructs, except for instrumental orientation, are reliable, (Cronbachs alpha = .464). Compared with the rule of thumb and the other constructs, instrumental

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77 orientation has a relatively low Cronbachs alpha. Therefore, the items for instrumental orientation were modified and a few items for the construct were replaced based on previous stu dies. Second Pretest After revising the questionnaire, the second pretest was carried out. A total of 68 college undergraduate students participated in the second pretest. The participants were recruited from the introductory mass communication course a t a large university located in the southeastern part of the country. The course is open to non-majors, so students with various majors participated in the second pretest. The age of the participants for the second pretest ranged from 18 to 33, and the mea n age of the participants was 21 ( SD = 2.36). The gender breakdown was quite even. Out of the 68 participants who submitted complete surveys, 52% of the participants were females (n = 35) and 47 % were males (n = 32). Table 4 2. Reliability check for cons tructs (Pretest 2) Construct No. of item Cronbachs alpha Intention to use online video platforms 2 .890 Intention to use television 2 .932 Perceived substitutability 5 .736 Relative advantage 3 .829 Perceived ease of use 3 .877 Compatibility 3 .879 Online flow experience 2 .900 Ritualistic orientation 5 .684 Instrumental orientation 7 .820 Subjective norm 3 .650 Perceived behavioral control 4 .573 As in the first pretest, reliability of the constructs that were measured using multi ple items was checked through Cronbachs alpha. Table 4 2 presents Cronbachs alpha values for each of the constructs. The results of the second pretest indicated that the internal consistency of the instrumental orientation, which was problematic in the f irst pretest, improved from .464 in the

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78 first pretest to .820 in the second pretest. However, the Cronbachs alpha for perceived behavioral control (Cronbachs alpha = .573) was below the acceptable value. The reliability check was rerun with the part of t he items that measure perceived behavioral control. The items that critically lowered Cronbachs alpha were removed. These few question items were replaced with the items that previous studies proved had high internal consistency. Another purpose of the s econd pretest was to test whether the constructs that measure motivations for watching video content have validity. Based on previous studies, items that represent nine motivations for watching video content were selected. The motivations include: 1) updat ing latest event information, 2) companionship, 3) relaxation, 4) learning, 5) entertainment, 6) pass time, 7) social interaction, 8) escape, and 9) habit. Even though the selection of the individual items was based on prior studies, the items are rooted i n different studies. It was necessary to integrate different studies because the items that measure the motivations should be updated according to recent literature and should reflect the characteristics of the Internet as a video platform. Therefore, an e xploratory factor analysis, rather than a confirmatory factor analysis, was performed to check the validity of the motivation constructs in the second pretest. Specifically, an exploratory factor analysis with varimax rotation was performed. The factor an alysis yielded eight factors, explaining 76.53% of the variance in motivations for watching video content. The results of the factor analysis showed that one item that was supposed to measure social interaction motivation was loaded onto another factor to which it is not theoretically related. Another item had high factor loadings across multiple factors. Therefore, the two items were removed from the factor analysis. Table 4 3 shows the results of the factor analysis after the two items were removed.

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79 Tabl e 4 3. Exploratory factor analysis for motivations behind video content consumption (Pretest 2) Item Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8 Updating latest event information 1 .877 .083 .037 .050 .075 .042 .182 .038 2 .803 .183 .069 .161 .072 .028 .126 .028 3 .797 .102 .322 .022 .189 .022 .182 .096 4 .511 .003 .006 .245 .015 .434 .171 .320 Companionship 1 .003 .901 .059 .007 .072 .108 .154 .007 2 .009 .871 .044 .027 .039 .126 .179 .086 3 .072 .755 .255 .041 .166 .254 .128 .031 Relaxation 1 .010 .229 .830 .126 .139 .001 .033 .067 2 .059 .043 .756 .112 ..202 .014 .314 .127 3 .014 .091 .704 .324 .134 .313 .064 .083 Learning 1 .522 .074 .548 .279 .226 .115 .133 .223 2 .419 .007 .408 .189 .280 .097 .112 .606 3 .297 .120 .362 .384 .288 .092 .225 .406 Entertainment 1 .179 .084 .233. .851 .226 .083 .027 .053 2 .119 .003 .302 .827 .146 .016 .039 .129 3 .051 .133 .021 .777 .045 .152 .240 .034 Pass time 1 .009 .124 .221 .114 .919 .005 .052 .016 2 .144 .080 .097 .143 .889 .062 .098 .070 3 .016 .192 .106 .494 .609 .179 .075 .034 Social interaction 1 .127 .055 .043 .047 .139 .811 .046 .267 2 .084 .076 .275 .111 .023 .778 .130 .146 Escape 1 .026 .560 .131 .000 .155 .091 .682 .001 2 .056 .567 .028 .023 .282 .129 .632 .230 Habit 1 .354 .084 .154 .229 .084 .059 .651 .392 2 .240 .091 .078 .060 .282 .053 .022 .756 Eigenvalue 6.312 3.443 2.650 1.759 1.677 1.401 1.321 1.057 % of variance explained 25.250 % 13.771 % 10.601 % 7.035 % 6.707 % 5.606 % 5.285 % 4.228 % Most of the motivation constructs had validity. The items for each of the constructs were properly differentiated from the items that intend to measure different motivations. However, the items that are supposed to measure learning motivation did not have a separate factor; instead, they had high factor loadings on two factors: updating latest event information motivation and relaxation motivation. The updating latest event information moti vation was newly added to the motivation constructs from a recent study (Flavian & Gurrea, 2007). Theoretically, it makes sense that the learning motive and the updating latest event information motive are highly correlated. When the reliability of the thr ee items for learning motivation was tested, Cronbachs

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80 alpha (.817) validated the internal consistency of the three items. Therefore, the three items that measure learning motive were retained for the main survey. Of the two items that are supposed to me asure habit motivation, one item had a high factor loading on escape motivation. A high factor loading of the item on escape motivation is not uncommon. Some researchers frequently conflated habitual motivations for viewing with escapism (Stone & Stone, 19 90). The other item that was intended to measure habit motivation had a high factor loading on a factor, but no other items had high factor loadings on the factor. Therefore, those two items that measure habit motivation were also retained. Main Test The two pretests helped to validate the constructs and question wordings for the main test. Before a main survey is administered, the selection of a sample is a necessary and important step for scientific research. In the United States, 98.9 % of households owned a television as of 2009 (Television Bureau of Advertising, 2009). A recent report indicates that 74.7 % of the U.S. population used the Internet as of the first quarter of 2009 (Internet World Stats, 2009). Because how consumers perceive online video platforms is important to answer the research questions and test hypotheses, this study chose Internet users as the population of the main survey. A total of 1,500 adults throughout the country who use the Internet was employed for the sample of the main survey. Specifically, a mailing list of 1,500 Internet users was purchased through the mailing list brokerage firm InfoUSA.com. This firm collects names and addresses from a variety of sources, including telephone directories, mail order buyers/subscriber s directories, real estate brokers, voter registration data sites, magazine subscription directories, and survey respondents. The sample for the main test was randomly selected from the list of 150 million adults nationwide who use the Internet. Considerin g that 227 million people used the Internet as of the first quarter of 2009 and that those 227 million Internet users do not exclude

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81 either children or adolescents the size of the database seems to reasonably be representative of U.S. Internet users. Along with ensuring representativeness of the sample, it is important for survey methods to increase response rates ( Rose Sidle & Griffith 2007). Fowle r (1984) and Roth and BeVier (1998) argue that increasing response rates is important because responses obtained from only a portion of a sample may not accurately represent the full sample, due to problems involving selective returns and volunteer bias. H igh response rates may also contribute to improving the accuracy of the prediction. Previous studies identified the factors that can raise response rates: monetary incentive, unconditional incentives, short questionnaires, personalized questionnaires, the use of colored ink, postage by recorded delivery and first class post, provision of a stamped return envelope, contacting participants before sending questionnaires, follow up contact, and providing a second questionnaire (Edwards, Roberts, Clarke, DiGuise ppi, Pratap, Wentz R, et al., 2003; Linsky, 1975; Yu & Cooper, 1983; Church, 1993; Warriner, Goyder, Gjertsen, Hohner & McSpurren, 1996). To increase response rates, the present study strived to use as many of the above methods as possible. Previous studi es found that prepaid monetary incentives have the strongest effect on increasing response rates when compared with other types of incentives, including prepaid nonmonetary incentives, postpaid monetary incentives, and postpaid non -monetary incentives (Linsky, 1975, Yu & Cooper, 1983; Church, 1993; Warriner, Goyder, Gjertsen, Hohner & McSpurren, 1996). Therefore, a $1 bill was enclosed with the questionnaire as a small token of appreciation in its first mailing. Monetary incentive is a common means to enco urage more people to respond to the survey in market research (Aaker, 1997). Business reply envelopes for returns were also enclosed with the questionnaire. A follow up mailing was conducted two

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82 weeks after the initial mailing, along with a questionnaire a nd a business reply envelope. Instead of bulk and meter mailing, first class delivery was used for both the first and follow up mailings. Instrument Development Definitions Even though the terms videos, video content, Internet, and online videos are frequently used, it is possible that individuals interpret those terms differently depending on situational contexts. To avoid confusion, this study defined a few of these terms for the questionnaire participants. First, the term video content was de fined at the beginning of the questionnaire. Video can mean different things. According to Dictionary.com (2009), videos refer to the visualized portion of a televised broadcast. Videos can also mean television, videocassettes and videotapes, or music vi deos. To avoid confusion due to these multiple definitions, this study used the term video content instead of the term video. In the questionnaire, the term video content was defined as any type of content that is based on the combination of audio an d video. Examples of video content were also given to help respondents understand the definition. Examples included television programs, music videos, movies, and YouTube clips. The second term defined for the purpose of this study is watching video cont ent through the Internet. This study limited the case of watching video content through the Internet to viewing video content on the computer through the Internet in real time. To aid the participants understanding of the definition, cases that are not considered watching video content through the Internet were given as examples in the questionnaire. They include using the Internet to download video content or watching video content on a mobile phone. The perceived characteristics and experience might be different for watching video content through downloading and through streaming in real time, even though both methods use the Internet as a

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83 part of, or as a whole procedure, to watch video content. For the same reason, this study made a distinction bet ween watching video content on computers and on mobile phones. Measures Table 4 4 summarizes the constructs included and their operational definitions. Table 4 4. Constructs and operational definitions Construct Operational Definition Source Per ceived substitutability The Internet and television offer different services for watching video content. r Flavian & Gurrea (2007) The Internet and television offer content in the same way for watching video content. The Internet and television satis fy different needs for watching video content. r Audiences consult the Internet and television in different situations for watching video content. r The Internet and television can be considered different media for watching video content. r Relat ive advantage Using the Internet to watch video content is better than television. Chan Olmsted & Chang (2006) Using the Internet to watch video content fulfills my needs for video content consumption better than television. Using the Internet to watch video content improves my lifestyle. Perceived ease of use It is easy to use the Internet for watching video content. Davis (1989); Davis et al. (1989); Davis et al.(1992); Wu & Wang (2005) It is easy for me to become skilled at using the Inter net to watch video content. Learning to use the Internet to watch video content is easy for me. Compatibility Using the Internet to watch video content fits my lifestyle. Taylor & Todd (1995); Chen, Gillenson, & Sherrell (2002); Eastin (2002); ChanOlmsted & Chang (2006) Using the Internet to watch video content fits well with the way I like to engage in video content viewing. Using the Internet to watch video content is compatible with most aspects of my video content viewing. Ritualistic o rientation Because it passes time when I am bored Rubin (1983) When I have nothing better to do Because it gives me something to do to occupy my time Because its a habit, just something to do Just because its there

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84 Table 4 4. Co ntinued Instrumental orientation To find constantly updated event information Rubin (1983); Flavian & Gurrea (2007) Because I am interested in current events Because it extends my mind To find breaking news events Because it lets me explore new things Because it opens me up to new ideas Because I am interested in the immediacy with which information can be obtained Subjective norm People important to me support my use of the Internet to watch video content. Mathieson (1991) Peop le who influence my behavior want me to use the Internet to watch video content. People whose opinions I value prefer that I use the Internet to watch video content. Perceived behavioral control I feel free to use the Internet to watch what I want to watch. Taylor & Todd (1995); Armitage et al.(1999) Whether or not I use the Internet to watch video content is entirely up to me. I have the necessary means and resources to use the Internet to watch video content. Whether I use the Internet t o watch video content or not is completely within my control. Flow experience online In general, how frequently would you say you have experience flow when you use the Internet? Novak, Hoffman, & Yung (2000) Most of time I use the Internet I feel th at I am in flow. Intention to use online video platforms I intend to use the Internet to watch video content. Venkatesh & Davis (2000); Wu & Wang (2005) I predict that I will use the Internet to watch video content in the future. Intention to use television I intend to use the Internet to watch video content. Venkatesh & Davis (2000); Wu & Wang (2005) I predict that I will use the Internet to watch video content in the future Displacement effect If you use the Internet to watch video conten t, please indicate how this has changed the amount of time you have spent watching television since you started using the Internet to watch video content. Kayany & Yelsma (2000) Note: r indicates the items that are reversely coded. All of the theoretica l constructs that were used in the current study were conceptualized and operationalized with items that were validated from prior studies. This study used multiple items to measure each of the constructs. The use of multiple indicators was intended to avo id problems involving the use of a single item for a construct. The use of a single item for each construct forces the researcher to choose among alternative items. Another problem of using a single indicator for constructs is that any single indicator is susceptible to measurement error

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85 (Kline, 2005). By using multiple indicators for each construct, this study intended to reduce measurement errors. Intention to use online video platforms and television Two sets of items measured the intention to use the Internet and television to watch video content. The items were adapted from Davis (1989) and Davis, Bagozzi, and Warshaw (1989). These items have been frequently adopted by recent studies (Venkatesh & Davis, 2000; Wu & Wang, 2005). Respondents were asked t o indicate their level of agreement with each of the statements using a seven point Likert scale (1 = strongly disagree, 7 = strongly agree). The Cronbachs alpha of the items ranged from .82 to .97 across previous studies. Actual use of online video plat forms and television Each respondent was first asked whether or not he/she uses each of the video platforms, using yes or no answer categories. Regardless of whether the respondent uses the Internet or television to watch video content, all of the resp ondents were asked to further indicate how many days they use the Internet and television to watch video content during a typical week. To measure the amount of time spent using each of the video platforms, the respondents were also asked to specify the nu mber of hours they use the Internet and television to watch video content during a typical week. A dditional questions regarding using the Internet to watch video content were asked to collect more detailed information. The questions focus on the types of video content (i.e., user generated video content and branded -video content), types of online video venues (i.e., video sharing sites and television network sites), and video content overlap (clips of television programs and an entire episode of television programs). Specifically, the respondents were asked how many hours they use the Internet to watch branded -video content and user -generated video content, respectively, during a typical week. They were also asked to indicate how many hours

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86 they spend on vi deo sharing sites and television network sites to watch video content, respectively, during a typical week. For video content overlap, the respondents were asked how many hours they use the Internet to watch clips of television programs and an entire episode of television programs, respectively, during a typical week. The questions that asked the number of days and amount of time were open -ended. Displacement effect Some of the research questions addressed the displacement effect of using online video pla tforms on television use. To measure the displacement effect, the respondents were asked about the change in time spent watching television since they started using online video platforms. One item was adapted from Kayany and Yelsma (2000), Bagozzi, Dholak ia, Pearo (2007), and Lin (2004). Respondents were directly asked whether the amount of time they have spent watching television had changed since they started to use the Internet to watch video content, using a seven-point scale (1 = decreased a lot, 7 = increased a lot). Motives behind video content consumption To measure motives for video content consumption, 25 items were employed. Some of the items came from literature that focuses on television use. Motives for Internet use were also integrated to illustrate the possible differences between television and online video platforms. The items that focus on television use were adapted from Abelman (1987) and Rubin (1981; 1983; 1984). The measures adapted from Rubin (1981; 1984) were successful in examini ng whether the Internet serves as a functional alternative to television (Ferguson & Perse, 2000). The items reflecting Internet use were adapted from Flavian and Gurrea (2007), Papacharissi and Rubin (2000), Ferguson and Perse (2000), and Yang and Kang (2006). Respondents were asked how much they agree with each of the statements that describe their motives behind video content consumption, using a seven-point Likert scale (1 = strongly disagree, 7 = strongly agree).

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87 Perceived substitutability Substituta bility is defined as the tendency of people to switch from one product to another that fulfills the same purpose (Nicholson, 1995; Boyes & Melvin, 1996). This study measured perceived substitutability instead of actual substitutability. Perceived substitut ability between online video platforms and television was measured with five items adapted from Flavian and Gurrea (2007). Respondents were asked to indicate their level of agreement with each of the statements on a seven point Likert scale (1 = strongly disagree, 7 = strongly agree). Relative advantage Relative advantage is defined as the degree to which to an innovation is perceived as being better than the idea it supersedes (Rogers, 1995, p. 212). Three items were adapted from Chan -Olmsted and Chang ( 2006) to measure the relative advantage of online video platforms compared with television. Respondents were asked to indicate their level of agreement with each of the statements on a seven -point Likert scale (1 = strongly disagree, 7 = strongly agree). T he Cronbachs alpha for the items was .84, as per Chan Olmsted and Chang (2006). Perceived ease of use Perceived ease of use is defined as the degree to which an individual believes that using a particular system would be free of physical and mental ef forts (David, 1989, p. 323). Three items were adapted from previous studies to measure perceived ease of use of the online video platforms (Davis 1989; Davis, Bagozzi, & Warshaw 1989; Wu & Wang, 2005). Respondents were asked to indicate their level of a greement with each of the statements on a seven -point Likert scale (1 = strongly disagree, 7 = strongly agree). Compatibility Compatibility is defined as the degree to which the adoption of a technology is compatible with existing values, past experience s, and needs of potential adopters (Rogers,

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88 2003, p. 15). For the compatibility of using online video platforms, measures were borrowed from Taylor and Todd (1995), Chen, Gillenson, and Sherrell (2002), Eastin (2002), and ChanOlmsted and Chang (2006). Re spondents were asked to indicate their level of agreement with each of the statements on a seven -point Likert scale (1 = strongly disagree, 7 = strongly agree). Flow experience online Flow experience online is defined as the degree to which individuals might be oblivious to the world around them and lose track of time and even self as a result of high -level engagement in an activity online (Csikszentmihalyi, 1975, 1990, 2000). To measure flow experience online, the des cription and definition of flow were first given. The description and definition came from Novak, Hoffman, and Yung (2000). Following the description and definition, the two question items borrowed from Novak, Hoffman, and Yung (2000) were asked to measure flow experience online. One it em asked about the frequency of flow experience online using a seven-point scale (1 = never 7 = all the time ). The other item asked the respondents to indicate their level of agreement with the statement asking whether the respondent felt that he/she feel s flow online most of the time using a seven-point Likert scale (1 = strongly disagree 7 = strongly agree ) Subjective norm Subjective norm refers to the perceived social pressure that most people who are important to him/her think he/she should or shou ld not perform the behavior in question (Fishbein & Ajzen, 1975, p. 302). The subjective norm of using online video platforms was measured through three items adapted from Mathieson (1991). The Cronbachs alpha for the items was .86 in Mathieson (1991). A seven -point Likert scale was used for the respondents to evaluate their level of agreement with each of the statements (1 = strongly disagree 7 = strongly agree )

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89 Perceived behavioral control Perceived behavioral control refers to the individuals p erceptions of the presence or absence of requisite resources and opportunities (Ajzen & Madden 1986, p. 457) necessary to perform the behavior (Mathieson, 1991, p. 176). Perceived behavioral control of using online video platforms was measured with four i tems borrowed from Taylor and Todd (1995) and Armitage, Conner, Loach, and Willetts (1999). A seven-point Likert scale was used for the respondents to evaluate their level of agreement with each of the statements (1 = strongly disagree 7 = strongly agree ). Orientation behind video content consumption Previous studies classified orientation behind media use into ritualistic and instrumental orientations (Rubin, 1984; Metzger & Flanagain, 2002; Perse, 1990a, 1990b, 1998). Ritualistic orientation refers t o a time -filling activity and a tendency to use media regardless of content (Jeffres, 1978). By contrast, instrumental orientation is defined as the activity that involves intentionally and selectively using media for goal -directed motives (Hearn, 1989; Ki m & Rubin, 1997; Perse, 1990a, 1990b, 1998; Perse & Rubin, 1988; Rubin, 1984, 1993; Rubin & Perse, 1987a, 1987b). While most researchers agree with the conceptual definitions of ritualistic and instrumental orientation for media use, it was found that ma ny researchers used different operational definitions to measure orientation. For instance, Metzger and Flanagin (2002) included items that reflect entertainment and relaxation motives to measure ritualistic orientation. On the other hand, Perse (1990a, 1990b, 1998) used those items to measure instrumental orientation. Meanwhile, the findings from Hawkins, Reynolds, and Pingree (1991), Stone and Stone (1990), and Greenberg (1974) restricted the meaning of ritualistic orientation to habit and pass time motivations.

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90 The inclusion of entertainment and relaxation motives to the ritualistic orientation may conflict with the conceptual definitions mentioned previously. Entertainment and relaxation motives should be distinguished from habit and pass time motives, because people tend to select specific content types to entertain and relax. Thus, the present study used narrow definitions of ritualistic orientation and instrumental orientation. In this study, ritualistic orientation focuses on pass time and habit moti ves for watching video content, while the instrumental orientation focuses on learning and updating latest event information motives. Seven items were used to measure instrumental orientation. Five items were employed to measure ritualistic orientation. Th e items were adapted from Rubin (1983) and Flavian and Gurrea (2007). A seven-point Likert scale was used for the respondents to evaluate their level of agreement with each of the statements (1 = strongly disagree 7 = strongly agree ) Fourteen attribu tes of online video platforms and television Following the suggestion by Lin (2001a), this study examined how consumers perceive the specific attributes of online video platforms and television from three aspects: content, technology, and cost. Respondent s were asked to evaluate a total of 14 attributes that reflect content, technology, and cost of using each of the video platforms (the Internet and television). With respect to content, respondents were asked how they perceive a) video content variety and b) video content quality of online video platforms and television, respectively. The items used to measure technological attributes of online video platforms and television came from varying television and Internet related literature. Items regarding tec hnological attributes include a) interactivity, b) timely updates, c) navigation, d) personalization, e) storage capabilities, f) cumbersomeness of advertisements during viewing, g) usefulness of reviews and ratings, and h) overall reliability (Chyi & Sylv ie, 2000; Smith, 2001; Viswanathan, 2005; Simon & Kadiyali, 2007).

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91 To evaluate the perceived cost of using online video platforms and television, three items focusing on perceived overall financial benefit and searching costs were asked. To measure search ing cost, the respondents were asked to evaluate a) time efficiency and b) effort efficiency in searching. The measures for searching costs were adapted from Teo and Yu (2005), Srinivasan and Ratchford (1991), and Liang and Huang (1998). The respondents we re asked how they perceive each of the attributes listed above on a 7 -point Likert scale (1 = strongly disagree 7 = strongly agree ). Demographic information and media use To predict consumers intention to use online video platforms and television, thi s study employed two models. One is a simple model that contains theoretical constructs only. The other one is a simple model that contains control variables along with the theoretical constructs. The control variables are classified into basic demographic information and media use. With respect to the basic demographic information, participants were asked to identify their gender, age, income, education, marital status, and ethnicity. With respect to age, the respondents were asked to specify their age usi ng an open -ended question. When it comes to media use, respondents were asked to indicate whether they own a digital video recorder (DVR) and have an Internet connection, respectively, using dichotomous yes or no response categories. In terms of Internet c onnection, the respondents were further asked to indicate whether they subscribe to dial up or high -speed Internet. Respondents were also asked to check off all of the television subscription types they have from a list. The categories included over -the ai r broadcasting only, basic cable, premium cable, satellite television, and others. Basic cable and premium cable were combined into cable television subscription for data analysis. In cases where the respondent chose both premium cable and satellite televi sion, the respondent was classified as a satellite television subscriber for the data analysis.

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92 Response Rates The national mail surveys collected a total of 504 responses out of 1,500 mailed surveys. Thirty -five surveys were returned due to undeliverabl e addresses. The 35 returns were eliminated for the data analysis. Another 35 responses were removed from the data analysis due to refusal to respond. An additional 46 responses were also removed for the data analysis due to incomplete responses or an unac ceptably large number of missing responses. For the analysis to test hypotheses and to answer research questions, 388 responses of 504 responses were used. All of the 388 responses were either fully completed or had only a few missing responses. Those retu rned questionnaires with a few missing responses were retained for the data analysis. The response rates of the survey ranged from 25.9% to 29.6%. The contact rates ranged from 31.3% to 32.0% (see Table 4 5). Table 4 5. Response rates Type of response r ates Formula Application Response rates RR1 Complete / Total *Total = (Complete + Incomplete + Refusal + Nondeliverable + Others) 388/1500 25.9% RR2 Complete / (Total Non deliverable) 388/(1500 35) 26.5% RR3 (Complete + Incomplete) / Total (388 +46)/1500 28.9% RR4 (Complete + Incomplete) / (Total Non deliverable) (388+46)/(1500 35) 29.6% Contact rates 1 (Complete + Incomplete + Refusal) / Total (388+46+35) /1500 31.3% Contact rates 2 (Complete + Incomplete + Refusal) / (Total Non deliver able) (388+46+35) /(1500 35) 32.0% Source: American Association for Public Opinion Research (AAPOR, 2003) Contact rate measures the proportion of all cases in which some responsible member of the housing unit was reached by the survey. Participants A s mentioned above, 388 responses were used for the data analysis. Before discussing research questions and testing hypotheses, the profile of the participants in this survey is described briefly below, followed by a comparison with the U.S. population.

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93 Th e mean age of the respondents is 52.69 (SD = 12.58). Males (n = 221) account for 57.0% of the participants, whereas 43.0% of the participants were females (n = 167). Out of 388 respondents, 47.4% of the participants completed college (n = 184). Another 27. 1 % and 23.2% hold graduate degrees (n = 105) and high school diplomas (n = 90), respectively. With respect to income, 26.3 % (n = 102) of the respondents said that they earn $100,000 or more. Another 21.2 % (n = 82) said that their income ranges from $40, 000 to $59,999. The me dian income ranges from $60,000 to $79,999. Approximately 87.1 % of the respondents were nonHispanic Caucasians (n = 338). Another 4.5% (n = 17) and 2.8% (n = 12) of the respondents were African Americans and Asians, respectively. When it comes to media use, 57% of the respondents (n = 221) said that they use the Internet to watch video content, whereas 43% of the respondents (n = 167) said that they do not use the Internet for watching video content. Even though over half the respo ndents use the Internet to watch video content, 90.2% of the respondents (n = 350) said that they use television as a primary means to watch video content. Only 9.3% (n = 36) chose the Internet as the main medium by which they watch video content. T his st udy briefly compares the demographic characteristics of the participants in this study with the characteristics of adult Internet users in the U.S. reported by recent industry reports Table 4 6 presents the profile of the participants in this study and th e demographic information of adult Internet users in the U.S Note that the data for the current study was gathered in 2009, while the industry reports are based on the data collected in 2007 and 2008. The sample of the current study has a higher percentage of males (57.0%) than the overall percentage of males in the U.S. who are Internet users (49. 0 %). While Internet users age between 18 and 34 years old account for 7% of the participants in this study, the people in that age group

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94 account for 33.1% of adult Internet users in the U.S. The proportion of whites (87.1%) in this study is higher than the proportion of U.S. Internet users (74. 0 %). The participants of the current study included more married people (68.3%) than the proportion of married people amon g the adult Internet users (58.2%) described by industry reports. In terms of education, 74.5% of the participants in this survey attended college or higher whereas only 60. 2 % of the U.S. Internet user population were at this same level of education. The biggest difference between the profile of the participants in the current study and the profile of adult Internet users reported by industry reports lies in age composition. The use of mail surveys may account for the difference. Table 4 6. The comparison of the sample profile with adult Internet users in the U.S. Variable Category Current study Year of 2009 Industry reports Year of 2007 or 2008 Gender Male Female 57.0% 43.0% 49.0% 1) 51.0% 1) Age 18 to 34 years old 7.0% 33.1% 2) 35 to 54 y ears old 48.2% 41.4% 2) 55 years old and over 42.8% 25.5% 2) Ethnicity White African American Hispanic Others 87.1% 4.4% 2.3% 4.4% 74.0% 1) 9.0% 1) 11.0% 1) 6.0% 1) Marital status Single 20.6% 26.0% 2) Married 68.3% 58.2% 2) Others 10.1% 1 9.4% 2) Education Did not attend college Attended college Graduated college plus 25.5% 47.4% 27.1% 39.8% 2) 30.4% 2) 29.8% 2) 1 ) Source: U.S. Census Bureau (2007). See http://www.census.gov/compendia/statab/tables/09s1120.pdf Original source: Mediamark Research Inc., NewYork, NY, CyberStats, fall 2007 (copyright). See . 2 ) Source: Ezine Articles (2008). See < http://ezinearticles.com/?Internet -UsersDemographics&id=2189063 >. Original source: Clickz. Reliability Befor e the statistical analysis was conducted to test hypotheses and to answer research questions, reliability and validity tests were performed for the constructs that were measured

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95 with multiple items. The constructs for the reliability check include: intention to use online video platforms, intention to use television, perceived substitutability, relative advantage, perceived ease of use, compatibility, flow experience online, ritualistic orientation, instrumental orientation, subjective norm, and perceived behavioral control. Cronbachs alpha was consulted to check internal consistency of the items for each of the constructs. All of the constructs with multiple indicators were assessed based on whether the Cronbachs alpha values were above the acceptable Cro nbachs alpha value .70 (Hair, Anderson, Tatham, & Black, 1998; Kline, 2005). Validity Validity refers to the extent to which a measurement adequately reflects the real meaning of what it is supposed to measure. There are three widely accepted ways to dete rmine validity: content, criterion, or construct. Among those, construct validity is the most complex and most important form of validity (Davis, 1997). Construct validity is theory -based and is concerned with the extent to which a particular measure rela tes to other measures consistent with theoretically derived hypotheses concerning the concepts (or constructs) being measured (Carmines & Zeller, 1979, p. 23). Therefore, this study focused on construct validity. Construct validity has two sub categories: discriminant validity and convergent validity. Discriminant validity refers to the degree to which measures that should not be related are, in reality, not related. Convergence validity refers to the degree to which measures that should be related are, in reality, related. Discriminant validity and convergence validity of constructs were assessed by examining the correlations of the latent variable scores with the measurement items. The correlations of items and their respective construct were consulted to see if items have high correlations with their own theoretically assigned constructs than on other constructs in the model (Gefen & Straub, 2005).

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96 Statistical Analysis To answer the research questions and test hypotheses, this study employed several dif ferent statistical analyses. Before explaining the results of the analyses, it is necessary to explain what statistical procedures were used for each of the research questions and hypotheses. Motivations behind Video Content Consumption RQ 1a gauged what motivations consumers have for watching video content. To address this research question, an exploratory factor analysis was first performed using all of the question items that asked about motives behind video content consumption. Before the results of t he factor analysis were interpreted, the adequacy of the data for the factor analysis was examined. To that end, the Kaiser -Meyer Olkin (KMO) measure of sampling adequacy and Bartletts test of sphericity were consulted. After the adequacy of the data was confirmed, this study examined whether the individual items have high loadings on the motivation that the items are supposed to reflect. After the theoretical examination, items were averaged to create composite motivation variables. The mean of each resul tant motivation variable was calculated to learn the highest or lowest motivations behind video content consumption. The standard deviation was also calculated for each motivation variable to examine how clustered the data are around the mean. Perceived S ubstitutability between Online Video Platforms and Television RQ 1b asked what specific motivations behind video content consumption affect consumers perceived substitutability between online video platforms and television. To answer the research questio n, multiple regression was used. The independent variables are different motives behind video content consumption, which resulted from the exploratory factor analysis. The dependent variable is perceived substitutability between online video platforms and television. Prior to reporting the results of the multiple regression, m uliticollinearity among

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97 independent variables was evaluated. Variance Influence Factor (VIF) was used to check to see if multicollinearity exists among the independent variables. Inte ntion to Use Online Video Platforms and Television Hypotheses 1 through 8 were designed to identify the critical predictors of consumers intention to use online video platforms and television. Structural equation modeling (SEM) was used to test all of t he hypotheses. M -plus version 5.2 was the statistics software used. The proposed model contains eight exogenous variables and two endogenous variables. The endogenous variables are a) intention to use the Internet to watch video content and b) intention to use television to watch video content. The exogenous variables include a) perceived substitutability between online video platforms and television, b) relative advantage of online video platforms, c) compatibility of online video platforms, d) perceived e ase of use of online video platforms, e) flow experience online, f) subjective norm of using online video platforms, g) perceived behavioral control of using online video platforms, h) ritualistic orientation behind video content consumption, and i) instru mental orientation behind video content consumption. A simple model without control variables (i.e., demographic information and media usage variables) was performed first to test hypotheses. There were two issues to determine in using SEM in this study. The first issue was how to handle missing values. The incomplete data can cause the analysis based on covariance matrix to be biased (Byrne, 2001). As mentioned in the response rates, there are several missing values across observations in this study. Ther e are two approaches of dealing with missing values. One is a listwise deletion technique. Another is full information analysis. The listwise deletion technique simply removes the observations with incomplete data and makes the data set complete. This is s imple and easy to employ, but this approach critically reduces the sample size. The reduction of the sample size influences significance tests. In contrast, the full information analysis uses every available score without

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98 throwing away any observation. Ove rall, there is an agreement that full information analysis is considered one of the better methods available for analyzing data sets with incomplete data. Thus, the present study chose to employ the full information analysis method. Another issue to consi der in the use of SEM is how to treat ordinal variables. All of the constructs in the simple model without control variables were measured on a 7 point Likert scale. In a strict sense, variables measured with a Likert scale are ordinal variables because t he equality of the interval between categories is somewhat arbitrary. Meanwhile, it is true that the variables measured with a Likert scale are readily considered interval in social sciences. By far the most common method of estimation within confirmatory factor analysis (CFA) is maximum likelihood (ML), a technique which assumes that the observed variables are continuous and normally distributed (e.g., Bollen, 1989, pp. 131 134). These assumptions are not met when the observed data are discrete as occurs w hen using ordinal scales; thus, significant problems can result when fitting CFA models for ordinal scales using ML estimation (e.g., Muthen & Kaplan, 1985). The use of ordinary structural equation modeling with ML estimation can bias parameters and signi ficance. There is growing consensus in the literature that the best approach to analysis of ordinal variables in SEM is a robust weighted least squares (WLS) approach. The robust WLS method is well -suited for a variety of non normal distributions that might be expected in practice (Flora & Curran, 2004). Therefore, this study employed robust weighted least square estimator using a diagonal weight matrix (WLSM) in M Plus, which is specifically designed to treat the models containing ordinal and binary variables (Brown, Zablah, & Bellenger, 2008). SEM essentially involves two fundamental concepts: measurement and structural modeling. The measurement modeling examines relationships between latent and multiple observed variables (indicators or items). Latent va riables are the operationalization of constructs that are

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99 not directly measured but are measured by one or more observed variables (indicators) (Hair, Anderson, Tatham, & Black, 1998). While measurement modeling examines the relationship between latent var iables and observed variables (indicators or items), structural modeling tests dependent relationships linking the hypothesized models constructs. To test the hypotheses in this study, two steps were involved. First, the measurement model was specified. T he specification of the measurement model aimed to define the indicators for each construct and assess the reliability of each construct for estimating the causal relationships. To that end, c onfirmatory factor analysis was conducted. Second, the structura l model was tested to examine the hypothesized relationships among latent variables. Before the measurement model and structural model are evaluated, assessing overall model fit is a necessary procedure. To assess the overall model fit, several measures were consulted, including the Chi -square goodness of fit test. The weakness of the Chi -square goodness of fit test is that the estimate is sensitive to sample size. The other fit indices are used to compromise the weakness of Chi -square goodness of fit tes t. The other fit indices include the Comparative Fit Index (CFI), the Turker Lewis Index (TLI), the Root Mean Square Error of Approximation (RMSEA), and the Weighted Root Mean Square Residual (WRMR). Therefore, this study consulted Chi -Square goodness of f it test, CFI, TIL, RMSEA, and WRMR to examine the model fit. Once the overall model fit was evaluated, the measurement of each construct was assessed for unidimensionality and reliability. After the unidimensionality and reliability were examined, the str uctural model was specified. In testing the specified structural model, multicollinearity (i.e., high correlation among exogenous variables) can affect the results of SEM, as it does in regression. Therefore, correlations among the exogenous variables (i.e ., perceived

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100 substitutability, relative advantage, perceived ease of use, compatibility, subjective norm, perceived behavioral control, instrumental orientation, ritualistic orientation, and flow experience online) were first examined to detect multicollin earity. The specified structural model was examined with the significance of estimated coefficients. In addition to the simple model that contains the exogenous and endogenous variables, this study ran a more complex model. The complex model includes control variables that reflect consumers demographic information and media use. The purpose of evaluating the complex model is twofold: first, the complex model examines the effects of exogenous variables on endogenous variables (i.e., intention to use the Internet to watch video content and intention to use television to watch video content) after the demographic and media use variables are controlled. Second, the complex model allows the researcher to learn how demographic and media use variables are rel ated to the endogenous variables. This study employed nine control variables, including a) gender, b) age, c) ethnicity, d) education, e) income, f) marital status, g) television subscription type, h) Internet subscription type, and i) DVR ownership. For c ategorical variables such as gender, education, income, marital status, Internet subscription type, and DVR ownership, dummy variables were created. As in the simple model, the complex model was evaluated through the significance of the estimated coefficie nts. Relative Advantage RQ 2a asked what specific content, technology, and cost attributes of online video platforms are perceived as better or worse than television. RQ 2a further addressed what specific content, technology, and cost related attributes influence consumers overall perception of the relative advantage of an online video platforms when compared with television. To answer the first part of RQ 2a, repeated measures of ANOVA were performed. To answer the second part of RQ 2a, multiple regres sion was conducted. In performing multiple regression, the relative

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101 advantage of online video platforms was defined as the dependent variable. The independent variables are 14 attributes of online video platforms. The attributes include 1) video content va riety, 2) video content quality, 3) financial benefit, 4) effort efficiency in search, 5) time efficiency in search, 6) interactivity, 7) personalization, 8) timeliness, 9) usefulness of reviews and ratings, 10) time shifting, 11) cumbersomeness of adverti sing, 12) storage capability, 13) instant replay, and 14) reliability. Prior to reporting the results of multiple regression, m uliticollinearity among independent variables was assessed by consulting VIF values. Users versus Non -Users of Online Video Platf orms Some of the research questions addressed the differences between users and nonusers of online video platforms. Specifically, RQ 1c asked whether there are any differences between users and nonusers motivations behind video content consumption. RQ 1d addressed whether there are differences between users and non users of online video platforms with respect to perceived substitutability between online video platforms and television. RQ 2b asked if there are any differences between users and non users of online video platforms with respect to content, technology, and cost related attributes of using online video platforms. RQ 2c sought to determine whether there are differences between users and non users of online video platforms with respect to how they perceive content, technical characteristics, and the cost of television. RQ 2e asked whether there are any differences between users and non users of online video platforms with respect to the overall relative advantage of online video platforms. RQ3, RQ4, RQ5, RQ6, RQ7, and RQ8 addressed whether there are differences between users and nonusers of online video platforms with respect to perceived ease of use, compatibility, flow experience online, orientation for video content consumption, subjective norm, and perceived behavioral control, respectively. For these questions concerning the differences between users and non -

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102 users, independent sample t -tests were used. For each of the research questions, Levenes test was consulted to check to see if the dat a violated equal variance assumption. RQ 2d addressed whether users and nonusers of online video platforms perceive online video platforms and television differently in terms of content, technical characteristics, and cost. Two separate, repeated measure of ANOVA were conducted. The first repeated measure of ANOVA was used to examine how users of online video platforms perceive online video platforms and television with respect to content, technical characteristics, and cost. The second repeated measure o f ANOVA was conducted to examine how nonusers of online video platforms perceive online video platforms and television with respect to content, technical characteristics, and cost. To answer RQ2d, the results of users and nonusers were compared. Disp lacement Effect This study has four research questions that address displacement effects of online video platforms on television. Specifically, RQ 9a asked how the amount of time using online video platforms has affected time spent watching television sin ce consumers started to use an online video platform. Correlation analysis and OLS regression were conducted to answer RQ 9a. For the regression, the change in amount of time watching television since the use of online platforms was regressed on the amount of time using online video platforms. While RQ 9a focused on the complementation or displacement effect of online video platforms on television in general, the rest of the research questions were designed to explore the specific contributor of the comple mentation or displacement effect. RQ 9b asked how the amount of time using television network websites and video sharing sites has affected time spent watching television since the use of online video platforms. To find the answer to RQ 9b, correlation and OLS regression were performed. When it comes to the regression for RQ 9b, the dependent variable is the change in amount of time watching television. The two independent

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103 variables are time spent using television network sites and video sharing sites to wa tch video content, respectively. RQ 10a addressed how the amount of time using the Internet to watch branded-video content and user -generated video content has affected the time spent watching television. Correlation and OLS regression were run. For the re gression, the dependent variable is the amount of time change watching television. The two independent variables are time spent using the Internet to watch branded-video content and user -generated video content, respectively. RQ 10b asked how the amount o f time using the Internet to watch an entire episode of television programs and clips of television programs has affected time spent watching television. Correlation and OLS regression were performed. For the regression, the dependent variable is the amoun t of time change watching television. The two independent variables are time spent using the Internet to watch an entire episode of television programs and clips of television programs, respectively. After individual tests were run, another multiple regr ession with a backward elimination technique was performed to examine how the consumption of different types of video content through the Internet and online video venues affected the time spent watching television. The dependent variable is the change in amount of time spent watching television since the use of online video platforms. The six independent variables are the consumption of 1) television network sites, 2) video sharing sites, 3) branded video content, 4) user generated video content, 5) an ent ire episode of television programs, and 6) clips of television programs through the Internet. Viewership Overlap The last group of research questions in this study focused on how users of online video platforms and television overlap. RQ 11a asked whethe r television and online video platforms reach mutually exclusive viewers. To answer this research question, frequencies and percentages

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104 were calculated for various groups of video platform users (e.g., people who use the Internet only to watch video conten t, people who use television only, people who use both the Internet and television to watch video content). RQ 11b addresses whether viewership overlap between television and online video platforms differs by television subscription types. To answer this q uestion, the respondents were first categorized into four groups depending on their television subscription types. The groups include a) over the air broadcasting only, b) cable television subscribers, c) satellite television subscribers, and d) other television service subscribers. For each group, the frequencies and percentage of users of online video platforms and television were calculated. The frequencies and percentage were compared across different types of television subscribers.

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105 CHAPTER 5 RESULTS This chapter presents the results of the hypothesis testing and the answers to the research questions. The procedures of statistical analyses will be also briefly described. Specifically, the results are grouped into the following topics: 1) simple model for the intention to use online video platforms and television, 2) complex model for the intention to use online video platforms and television, 3) motivations behind video content consumption, 4) factors that affect the degree of the perceived subs titutability between online video platforms and television, 5) relative advantage of online video platforms, 6) differences between users versus nonusers of online video platforms, 7) displacement effect of online video platforms on television, and 8) vie wership overlap between online video platforms and television. Simple Model for Intention to Use Different Types of Video Platforms Hypotheses 1a to 8b in this study examine how perceived characteristics of online video platforms or consumer characteri stics affect the intention to use the Internet or television to watch video content. In this study, two models were tested to predict the intention to use online video platforms and television. One is a simple model that contains theoretical constructs onl y. The simple model is composed of nine exogenous variables perceived substitutability, relative advantage, compatibility, perceived ease of use, subjective norm, perceived behavioral control, flow experience online, ritualistic orientation, and instrume ntal orientation. The two endogenous variables in the simple model are the intention to use online video platforms and the intention to use television. Another model to predict the intention to use online video platforms and television is a complex model that contains control variables along with exogenous and endogenous variables. The exogenous and endogenous variables for the complex models are the same as the ones of the

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106 simple models. The only difference between the simple and complex models is that th e complex model contains nine control variables gender, age, education, ethnicity, income, marital status, Internet connection type, television subscription types, and DVR ownership. Given that the majority of prior studies did not employ models with control variables for hypothesis testing, the simple model that focuses only on theoretical constructs will be used for hypothesis testing. In this section, the results of the simple model will be presented first. The results of the complex model will be pres ented in the following section. Structural equation modeling was used to test all of the hypotheses. With respect to the statistics software, Mplus version 5.2 was used. In dealing with missing data, the present study employed the full information analysi s method for the simple model. To handle the ordinal variables that are measured using 7 -point Likert scales, a robust weighted least squares (WLS) was used for model estimation. Specifically, it is a robust weighted least square estimator using a diagonal weight matrix (WLSM), which is specifically designed to treat the models containing ordinal and binary variables in M -Plus (Brown, Zablah, & Bellenger, 2008), was employed for model estimation. Measurement Model Before testing the structural model to examine the relationship between exogenous and endogenous variables, the measurement model was first specified. This measurement model procedure allows the researcher to specify which observed variables (items) define each construct (Kline, 2005). The items that are supposed to measure 11 constructs were specified in the measurement model. The constructs specified in the measurement models are: perceived substitutability, relative advantage, compatibility, perceived ease of use, subjective norm, perceiv ed behavioral control, ritualistic orientation, instrumental orientation, flow experience

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107 online, the intention to use online video platforms, and the intention to use television. To specify the measurement model, confirmatory factor analysis was performed The model fit of the measurement model was first assessed. There are several different indices to test the model fit, of which one is the chi -square goodness of fit test. The weakness of Chi -square goodness of fit test is the fact that the estimate is s ensitive to sample size. The other indices that compromise the weakness of Chi -square goodness of fit test include Comparative Fit Index (CFI), Turker Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Weighted Root Mean Square Residua l (WRMR). Notwithstanding, the non-significant chi square statistic is the least used as a goodness -of -fit index, as it is the most difficult to achieve. This is because it accounts for all possible relationships between constructs and constructs, between constructs and indicators and between indicators and indicators. Thus, the more the constructs and indicators in a model, the lower the p -value (i.e. the less non -significant) of the chi -square statistic, resulting in a poor model fit (Cheng, 2001). There is general agreement that the effective measures of fit are when TLI or CFI are greater than .90 and the RMSEA is below .08 (Hoyle, 1995; Hoyle & Duvall, 2004). WRMR values less than 1.0 are viewed as a good fitting model (Hancock & Mueller, 2006). Others are more conservative. Hu and Bentler (1999) suggested a cutoff value of .95 or more for CFI, whereas the value of RMSEA should be close to .06. These cutoffs are somewhat arbitrary and thus should serve as a rule of thumb rather than fixed criteria (Boll en, 1989). When the 2979.256. CFI and TLI were .986 and .984, which indicate the reasonable model fit. However, the values of RMSEA and WRMR called for caution. RMSEA a nd WRMR were .096 and 1.207, respectively. As a result, both standardized residuals and modification indices were further

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108 consulted for identifying model misspecification. More important, the assessment of model fit/misfit was guided by theoretical meaning fulness of the models and by previous research findings. The standardized residuals and modification indices indicated that four items have high correlated errors with other constructs. The first problematic item with the highest modification index is I f eel free to use the Internet to watch what I want to watch. Another three items that have high correlated errors with other constructs are 1) People important to me support my use of the Internet to watch video content 2) The Internet and television offer content in the same way for watching video content and 3) Because it opens me up to new ideas. Each of the items was removed one by one to see how much the model fit is improved. When all of the four items were removed from the measurement model, the model finally reached a good fit. When all of the four items were removed from the measurement model, the model fit =.993. RMSEA was .072 and WRMR was .891. All of the goodness of fit indices indicated that the model has an adequate fit. RMSEA value was slightly higher than the value suggested for an indication of a good fit by some researchers. However, the RMSEA value still presents a reasonable fit. Hu and Ben tler (1995) classified RMSEA into four categories: close fit (.00 .05), fair fit (.05 .08), mediocre fit (.08 .10), and poor fit (over .10). Other authors proposed that the RMSEA .08 is acceptable (Kline, 2005; Vandenberg & Lance, 2000). Note that the weakness of RMSEA is that the estimate of RMSEA highly depends on model size (Kenny & McCoach, 2003; Fan & Sivo, 2007). In other words, the RMSEA can be different depending on whether the model size is small or large. In relation to model size, it is noteworth y that the fits for CFI and

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109 TLI worsen as more variables are added to the model, whereas RMSEA is improved with more variables. A critical concern involving goodness of fit indices occurs when RMSEA indicates a poor fit and CFI and TLI also reflect a poor fit (Kenny & McCoach, 2003). Therefore, it is conclusive that the measurement model of this study has an acceptable fit based on the goodness of fit indices. Table 5 1 presents the results of the confirmatory factor analysis. Table 5 1. Confirmatory factor analysis Variable Standardized Factor Loading Perceived Substitutability The Internet and television offer different services for watching video content. r .752*** The Internet and television satisfy different needs for watching video content. r 795*** Audiences consult the Internet and television in different situations for watching video content. r .798*** The Internet and television can be considered different media for watching video content. r .749*** Relative Advantage Using t he Internet to watch video content is better than television. .936*** Using the Internet to watch video content fulfills my needs for video content consumption better than television. .969*** Using the Internet to watch video content improves my lifestyle. .868*** Perceived Ease of Use It is easy to use the Internet for watching video content. .954*** It is easy for me to become skilled at using the Internet to watch video content. .968*** Learning to use the Internet to watch video content is easy for me. .970*** Compatibility Using the Internet to watch video content fits my lifestyle. .973*** Using the Internet to watch video content fits well with the way I like to engage in video content viewing. .963*** Using the I nternet to watch video content is compatible with most aspects of my video content viewing. .909*** Ritualistic Orientation Because it passes time when I am bored .881*** When I have nothing better to do .803*** Because it gives me something to do to occupy my time .786*** Because its a habit, just something to do .676*** Just because its there .663*** Instrumental Orientation To find constantly updated event information .871*** Because I am interested in current events .897***

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110 Table 5 1. Continued Because it extends my mind .755*** To find breaking news events .859*** Because it lets me explore new things .590*** Because I am interested in the immediacy with which information can be obtained .841*** Subjective Norm People who influence my behavior want me to use the Internet to watch video content. .930*** People whose opinions I value prefer that I use the Internet to watch video content. .935*** Perceived Behavioral Control W hether or not I use the Internet to watch video content is entirely up to me. .975*** I have the necessary means and resources to use the Internet to watch video content. .888*** Whether I use the Internet to watch video content or not is completely within my control. .716*** Online Flow Experience In general, how frequently would you say you have experienced flow when you use the Internet? 1.064*** Most of time I use the Internet I feel that I am in flow. .825*** Intention to use online v ideo platforms I intend to use the Internet to watch video content. .989*** I predict that I will use the Internet to watch video content in the future. .918*** Intention to use television I intend to use the Internet to watch video content 1.050*** I predict that I will use the Internet to watch video content in the future .854*** Note: r indicates the items that are reversely coded. p < .05, ** p < .01, *** p < .001 (twotailed) Goodness of Fit Indices: Chi square = 1507.659 (df = 505, p =.00), CFI = .994, TLI = .993, RMSEA = .072, WRMR = .891 After confirming the adequate model fit, the factor loadings of individual items were consulted. The results of the confirmatory factor analysis showed that the items statistically significa ntly measure the constructs as intended. The factor loading of each item is also very high on the corresponding construct that each item is supposed to measure. Validity and reliability : The convergent ( the degree to which an operation is similar to other operations that it theoretically should be) and discriminant validity (the degree to which items differentiate among constructs or measure distinct concepts) of the measures were assessed

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111 by examining the correlations of the latent variables with the meas urement items. Items should load more strongly on their own theoretically assigned constructs than on other constructs in the model (Gefen & Straub, 2005). The constructs for the validity check included all of the constructs that serve as exogenous variab les perceived substitutability, relative advantage, perceived ease of use, compatibility, flow experience online, ritualistic orientation, instrumental orientation, subjective norm, and perceived behavioral control. Table 5 2 shows the correlations betwe en measurement items and the corresponding latent variables. All of the items ha ve the highest correlations with the latent variables that each is supposed to measure. The constructs in the model have convergent and discriminant validity. Reliabilities of all of the constructs in the model were also tested. Specifically, Cronbachs alpha for all of the constructs was consulted. The Cronbachs alpha values ranged from .773 to .967 (see Table 5 3). The Cronbachs alpha values surpassed the criterion .70 whic h is recommended for applied research (Nunally, 1978). Another way to check reliabilities of individual items is to check factor loadings on their respective construct. Reliabilities of individual items are considered adequate when items loading on their respective constructs is higher than 0.5 (Rivard, 1988). The confirmatory factor analysis showed that the factor loadings of individual items ranged from .590 to 1. 064 (see Table 5 1). Both Cronbachs alpha and factor loadings confirm that the measuremen t items for all of the constructs have reliabilities. Table 5 4 presents the means and standard deviations of the exogenous and endogenous variables in the model.

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112 Table 5 2. Correlation matrix for validity of constructs Variable Item PS RA PEU COM FLOW IO RO SN PBC Perceived Substitutability (PS) 1 .77*** .19*** .32*** .22*** .13* .19*** .08 .19*** .20*** 2 .82*** .19*** .33*** .29*** .08 .21*** .09 .25*** .18*** 3 .83*** .10* .32*** .16** .15** .16** .05 .12* .27*** 4 .8 1*** .08 .27*** .13** .14** .13* .07 .09 .22*** Relative advantage (RA) 1 .10* .93*** .32*** .71*** .24*** .02 .05 .42*** .03 2 .15** .95*** .34*** .72*** .23*** .02 .08 .43*** .04 3 .24*** .89*** .35*** .67*** .27*** .10 .05 .47*** .04 Perceived ease of use (PEU) 1 .39** .35*** .96*** .49*** .16** .08 .02 .34*** .31*** 2 .35** .34*** .97*** .47*** .18** .05 .04 .31*** .33*** 3 .36** .37*** .97*** .53*** .15** .06 .04 .35*** .34*** Compatibility (COM) 1 .21*** .70*** .46*** 94*** .27*** .12* .07 .41*** .09 2 .27*** .74*** .52*** .97*** .33*** .14** .06 .47*** .05 3 .23*** .74*** .49*** .96*** .314*** .15** .10 .48*** .03 Flow experience online (FLOW) 1 .21*** .28*** .20*** .33*** .95*** .11* .14** .20*** .07 2 .09 .24*** .12* .28*** .96*** .06 .13** .15** .04 Instrumental orientation (IO) 1 .18** .04 .07 .15** .06 .65*** .14** .17** .02 2 .14** .02 .03 .10 .03 .82*** .10 .05 .03 3 .21*** .07 .06 .10 .12* .84*** .16** .06 .08 4 .17** .00 .00 .08 .09 .88* ** .11* .04 .03 5 .22*** .07 .07 .17** .10 .79*** .05 .11* .00 6 .16** .03 .10 .11* .02 .86*** .01 .05 .02 Ritualistic orientation (RO) 1 .06 .07 .09 .05 .12* .09 .75*** .06 .05 2 .08 .04 .04 .01 .08 .19*** .75*** .08 .01 3 .08 .09 .04 .10 .08 .03 .81*** .06 .00 4 .09 .11* .03 .11 .12* .14** .81*** .10 .06 5 .07 .05 .01 .06 .16** .08 .85*** .06 .06 Subjective norm (SN) 1 .19*** .46*** .31*** .46*** .16** .07 .08 .96*** .04 2 .21*** .45*** .34*** .45*** .17** .11* .10 96*** .04 Perceived behavioral control (PBC) 1 .16** .06 .164** .00 .03 .02 .04 .01 .83*** 2 .29*** .01 .34*** .07 .05 .06 .02 .03 .89*** 3 .25*** .01 .40*** .10 .07 .07 .01 .07 .81***

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113 Table 5 3. Reliability of constructs Cons truct No. of item Cronbachs alpha Intention to use online video platforms 2 .924 Intention to use television 2 .910 Perceived substitutability 4 .817 Relative advantage 3 .915 Perceived ease of use 3 .967 Compatibility 3 .951 Online flow exper ience 2 .900 Instrumental viewing orientation 7 .907 Ritualistic viewing orientation 5 .851 Subjective norm 2 .911 Perceived behavioral control 3 .773 Table 5 4. Descriptive statistics Variable Minimum Maximum Mean SD Intention to use online vi deo platforms 1.00 7.00 4.165 2.030 Intention to use television 1.00 7.00 6.249 1.219 Perceived substitutability 1.00 7.00 2.898 1.093 Relative advantage 1.00 7.00 2.497 1.575 Perceived ease of use 1.00 7.00 4.660 1.845 Compatibility 1.00 7.00 2 .981 1.759 Online flow experience 1.00 7.00 3.082 1.668 Instrumental orientation 1.40 7.00 5.244 1.297 Ritualistic orientation 1.00 6.80 3.702 1.481 Subjective norm 1.00 7.00 2.981 1.471 Perceived behavioral control 1.00 7.00 5.864 1.450 Structural Model After the adequate fit of the measurement model was confirmed, correlations among exogenous variables were calculated to see if the structural model has a multicollinearity problem, because multicollinearity can affect the results of SEM as did it in regression (Hair, Anderson, Tatham, & Black, 1998). Table 5 5 shows the correlation matrix. Some of the exogenous variables have statistically significant correlations with other exogenous variables. However, the correlation values were not high enou gh to raise major concerns about multicollinearity. Only correlation values above .90 should cause concern. In many cases, correlations exceeding .80 can be also considered indicative of multicollinearity problems (Hair,

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114 Anderson, Tatham, & Black, 1998). A ccording to these guidelines, the results of the correlations indicate that there is no multicollinearity problem in the structural model. Table 5 5. Correlation matrix for exogenous variables 1 2 3 4 5 6 7 8 9 1 -2 .18*** -3 .3 8*** .36*** -4 .25*** .76*** .51*** -5 .15*** .27*** .17*** .32*** -6 .09 .07 .03 .08 .15*** -7 .22*** .05 .07 .14*** .09 .12*** -8 .21*** .48*** .34*** .47*** .17*** .09 .10 -9 .26*** .04 .34*** .06 .05 .12 .04 .04 -Note: 1: Perceived substitutability; 2: Relative advantage; 3: Perceived ease of use; 4: Compatibility; 5: Flow experience online; 6: Ritualistic orientation; 7: Instrumental orientation; 8: Subjective norm; 9: Perceived behavioral control p < .05, ** p < .01, *** p < .001 (twotailed) After the correlations among exogenous variables ensured that there was no multicollinearity, the structural model was finally performed to test hypotheses 1 to 8. The goodness of fit indices were exactly ident = 505). CFI and TLI showed that the model has a good fit with the estimates .994 and .993, respectively. RMSEA was .072 and WRMR was .891. These goodness of fit indices confirmed that the structural mode l has a good fit. The good model fit indicates that the proposed hypotheses in this study can be tested with the data To test the proposed hypotheses, a one tailed test of the significant tests was employed because the direction of the hypotheses has been specified as part of the study design. Intention to Use Online Video Platforms The hypotheses in this study predict either the intention to use online video platforms or the intention to use television. This section first presents the results of the in tention to use online video platforms. Specifically, hypotheses 1a, 2a, 3a, and 4a examine how perceived characteristics of online video platforms predict the intention to use online video platforms.

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115 Hypotheses 5a, 6a, 6b, 7a, and 8a address the relationsh ip between consumer characteristics and the intention to use online video platforms. Table 5 6 summarizes the results of the hypothesis testing for the intention to use online video platforms. Table 5 6. Summary of hypothesis testing for the intention to use online video platforms Exogenous variable Standardized path coefficient SE Outcome H1a Perceived substitutability (+) .196*** .054 Not supported H2a Relative advantage (+) .202* .096 Supported H3a Perceived ease of use (+) .116* .066 Suppor ted H4a Compatibility (+) .389*** .105 Supported H5a Flow experience online (+) .018 .034 Not supported H6a Instrumental orientation (+) .051 .040 Not supported H6b Ritualistic orientation ( ) .024 .041 Not supported H7a Subjective norm (+) .131* 062 Supported H8a Perceived behavioral control (+) .122* .074 Supported p < .05, ** p < .01, *** p < .001 (one -tailed) The results of the structural model are also presented in Figure 5 1 as a path diagram form. With the standardized path coefficients, the relationships between the exogenous and endogenous variables can be found there. Specifically, hypothesis 1a proposed that perceived substitutability between online video platforms and television is positively related to the intention to use online vi deo platforms. Hypothesis 1a was not supported. The results of the structural equation model indicate that the perceived substitutability is statistically significant in predicting the intention to use online video platforms. Contradicting the proposed hyp othesis 1a, .196, p < .001, one tailed). Hypothesis 2a posited that relative advantage of online video platforms is positively p < .05, one -tailed). Hypothesis 3a postulated that perceived ease of use of online video platforms is .116, p < .05, one tailed). Hypothesis 4a, which predic ted a positive relationship between

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116 compatibility of online video platforms and intention to use online video platforms, was also p < .001, one -tailed). While hypotheses 1a, 2a, 3a, and 4a examine the effect of the perceived character istics of online video platforms on the intention to use them, hypotheses 5a, 6a, 6b, 7a, and 8a address the relationship between consumer characteristics and the intention to use online video platforms. Specifically, hypothesis 5a stated that flow experie nce online is positively related to the intention to use online video platforms. The results of the structural model indicate that flow experience online has no statistically significant relationship with the intention to use online video platforms. Theref ore, hypothesis 5a was not supported. Hypothesis 6a predicted that flow experience online is positively related to the intention to use online video platforms. On the other hand, hypothesis 6b expected a negative relationship between ritualistic orientatio n and the intention to use online video platforms. Neither hypothesis 6a nor hypothesis 6b was supported. Hypothesis 7a predicted that subjective norm is positively related to the intention to use online video platforms. p < .05, one tailed). Hypothesis 8a, which posited that perceived behavioral control is positively related to the intention to use online p < .05, one tailed). Intention to Use Television Another group of hypotheses predict the intention to use television. Standardized path coefficients, standard errors, and p -values are reported in Table 5 7. The results of the structural equation modeling and corresponding standardized pat h coefficients are also presented as a path diagram form in Figure 5 1. Hypothesis 1b expected that the perceived substitutability between online video platforms and television is negatively related to the intention to use television. The hypothesis was not supported. Hypothesis 2b expected that relative advantage of online video platforms is

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117 -.401, p < .01, one -tailed). Hypothesis 3b predicted a negative relationship between perceived ease of use of online video platforms and the intention to use television. There was a statistically significant relationship between the perceived ease of use and the intention to use television, but the 175, p < .05, one tailed). Therefore, hypothesis 3b was not supported. Hypothesis 4b posited that compatibility of online video platforms is negatively .242, p < .05, one tailed) Table 5 7. Summary of hypothesis testing for the intention to use television Exogenous variable Standardized path coefficient SE Outcome H1b Perceived substitutability ( ) .076 .092 Not supported H2b Relative advantage ( ) .401** .157 Suppor ted H3b Perceived ease of use ( ) .175* .111 Supported H4b Compatibility ( ) .242* .169 Not supported H5b Flow experience online ( ) .005 .048 Not supported H6c Instrumental orientation ( ) .284*** .057 Not supported H6d Ritualistic orientation (+) .093* .062 Supported H7b Subjective norm ( ) .000 .079 Not supported H8b Perceived behavioral control ( ) .011 .109 Not supported p < .05, ** p < .01, *** p < .001 (one -tailed) While hypotheses 1b, 2b, 3b, and 4b concern the relationship between the perceived characteristics of online video platforms and the likelihood to use television, hypotheses 5b, 6c, 6d, 7b, and 8b address how consumer characteristics predict the intention to use television. Specifically, hypothesis 5b expected that flow experi ence online is negatively related to the intention to use television. The hypothesis was not supported. Hypothesis 6c expected that instrumental orientation behind video content consumption is negatively related to the intention to use television. The rela tionship between the two variables was statistically significant, but the p < .001, one -tailed) instead of negative. Therefore,

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118 hypothesis 6c was not supported. Hypothesis 6d, which predicted that ritualistic orientatio n behind video content consumption is positively related to the intention to use television, was p < .05, one tailed). Hypothesis 7b predicted that subjective norm of using online video platforms is negatively related to the intention to use television, but hypothesis 7b was not supported. Hypothesis 8b, which posited that perceived behavioral control of online video platforms is negatively related to the likelihood to use television, was not supported.

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119 Intention to use television .79 *** .68*** .76 *** Figure 5 1. Simple model for intention to use online vi deo platform s and intention to use television 80 *** 75 *** 94*** Subjective norm p < .05, ** p < .0 1, *** p < .001 (one tailed) Ritualistic orientation intend 97 *** .87*** .84 *** .90 *** .59 *** 80 *** .85 *** 1.05 ** Instrumental orientation improve aspect learn easy explore breaking current immed iacy update extend habit pass occup nothing intend predict 196 *** .284*** .093* .011 .000 .175* .242* .401** most .005 91 *** 97 *** 96 *** 75 *** 87 *** 97*** 95*** 97 *** 94 *** opinion 93 *** 72 *** 98 *** 89 *** Perceived behavioral l 1 06 *** Flow .83 *** .80 *** .88 *** .66 *** ..86 *** .99 *** .92*** Intention to use online video platforms there Perceived s ubstitutability skill control often upto Relative advantage better fulfill predict differ satisfy consider consult lifes tyle fits Compatibility Perceived ease of use influence necessary .389*** .116* .131* .018 .122 .024 .076 .202*

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120 Complex Model for Intention to Use Different Types of Video Platforms Because the simple model contains theoretical variables only, this study tests another model that contains control variables along with the theoretical variables, cal led a complex model. Testing the complex model aims to investigate how the relationship between an exogenous and an endogenous variable changes when the control variables are included. Control variables added to the complex model are demographic and media use variables. Even though the relationship between the control variables and endogenous variables are not hypothesized, it is also valuable to see how the demographic and media use variables are related to the intention to use online video platforms and t elevision for watching video content. Specifically, the control variables are 1) gender, 2) age, 3) income, 4) education, 5) ethnicity, 6) marital status, 7) television subscription type, 8) Internet connection type, and 9) DVR ownership. The exogenous va riables are the same as the simple model. The exogenous variables are the perceived substitutability between online video platforms and television, relative advantage of online video platforms, compatibility of online video platforms, perceived ease of use of online video platforms, flow experience online, ritualistic orientation behind video content consumption, instrumental orientation behind video content consumption, subjective norm of using online video platforms, and perceived behavioral control of us ing online video platforms. The two endogenous variables are the intention to use online video platforms and the intention to use television. To execute this investigation, a structural equation modeling was employed. Because some variables are ordinal, W LSM, which is specifically designed to treat the models containing ordinal and binary variables (Brown, Zablah, & Bellenger, 2008), was used for the model estimation as for the simple model. WLSM handles non-normality of the data, which is a commonly obser ved violation of assumption in practice. With respect to dealing with missing

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121 observations, the simple model took the full information approach, whereas the complex model employed the listwise deletion approach for model estimation. The reason for this is that there will always be listwise deletion of cases with missing values on control variables. This is because the model estimation is conditioned on the control variables. Unlike theoretical variables, all of the control variables were measured using a s ingle indicator. Hence, there was no need to evaluate the model fit of the measurement model again. As a result, the model fit of the structural model was examined. The model fit indices were consulted to examine the adequacy of the model to the data. The model fit indices indicated a good model fit. Chi -Square value was 2518.379 (df = 1198). CFI and TLI were .985 and .984, respectively. RMSEA and WRMR were .058 and 1.111. Figure 5 2 shows the standardized path coefficients after controlling for the demogra phic and media use variables. Intention to Use Online Video Platforms After age, gender, ethnicity, income, education, DVR ownership, Internet connection type, television subscription type, and marital status are controlled, the predictors of the intentio n to use online video platforms are still very similar to the ones without controlling for the demographic and media use variables. The perceived substitutability, compatibility, relative advantage, subjective norm, and perceived behavioral control still have statistically significant effects on the intention to use online video platforms. Table 5 8 shows the parameter estimates of the complex model to predict the intention to use online video platforms. Figure 5 2 presents the standardized path coefficient s of the theoretical variables in the complex model. The more consumers think that online video platforms and television are substitutable, the .163, p < .001, one tailed). Relative 65, p < .05, one -p < .001, one -tailed), subjective p < .05, one p < .05, one -

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122 tailed) increase the likelihood to use online video platforms. While perceived ease of use was statistically significant in predicting the intention to use online video platforms in the simple model, the effect of the perceived ease of use did not reach the statistical significance in the complex model. Table 5 8. Parameter esti mates of complex model for the intention to use online video platforms Unstandardized path coefficient SE Standardized Path coefficient Perceived substitutability .254 .074 .163*** Relative advantage .198 .111 .165* Compatibility .443 .117 .351*** Perceived ease of use .112 .068 .097 Flow experience online .027 .045 .025 Ritualistic orientation .025 .051 .020 Instrumental orientation .011 .059 .008 Subjective norm .160 .072 .131* Perceived behavioral control .169 .094 .108* Age .02 8 .006 .295*** Gender (Female) d .354 .149 .157** African American d .194 .289 .037 Asian d .463 .362 .072 Hispanic d .939 .481 .131* Other race d .202 .614 .022 Less than $20,000 d .302 .329 .051 $20,000 $39,999 d .104 .252 .029 $40,000 $59,999 d .069 .197 .026 $60,000 $79,999 d .142 .203 .051 $80,000 $99,999 d .315 .202 .096 Marital status (Married) .174 .183 .072 Marital status (Others) .289 .226 .078 Less than high school d .536 .736 .059 High school d .022 .208 .00 8 College d .048 .152 .021 DVR ownership d .041 .142 .018 Internet connection type d .767 .299 .162** Cable subscription d .012 .332 .005 Satellite subscription d .199 .352 .081 Other TV service d .266 .587 .029 Note: d stands for dummy variables. References: Gender: Males; Race: Non Hispanic Caucasian; Income: $100,000 or more; Marital Status: Single; Education Level: Graduate School; DVR Ownership: Dont Own DVR; Internet Connection Type: Dial -Up; Television Subscription: Broadcasting over t he Air Only. p < .05, ** p < .01, *** p < .001 (one tailed)

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123 Some of the control variables were found to be significant in predicting the intention to use online video platforms. The structural equation modeling revealed that Hispanics are less likely than non-.131, p < .05, one tailed). High -speed Internet subscribers are more likely than dial up Internet subscribers to use online p < .01, one -tailed). The younger individual s are, the more likely they .295, p < .001, one -tailed). Females are .157, p < .01, one tailed). The remainder of the control var iables -education, marital status, income, television subscription type, and DVR ownership -did not have statistically significant effects on the likelihood to use online video platforms. Intention to Use Television Table 5 9 shows the parameter esti mates of the complex model to predict the intention to use television. Figure 5 2 presents the standardized coefficients of the theoretical variables after controlling for the demographic and media use variables. When the demographic and media use variable s are controlled, then relative advantage, instrumental orientation behind video content consumption, and perceived substitutability have statistically significant effects on the intention to use television. The more people think that online video platform s have relative advantage over television, the less likely they are to use .350, p < .01, one -tailed). The more people watch video content for instrumental orientation, the more likely they are to use television p < .001, one tailed). The more people think that online video platforms and television are .120, p < .05, one tailed). Although the simple model found that compatibility, perceived ease of use, and ritualistic viewing orientation have statistically significant effects on the intent ion to use television, the effects of those variables in the complex model were not detected.

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124 Table 5 9. Parameter estimates of complex model for the intention to use television Unstandardized path coefficient SE Standardized path coefficient Perceive d substitutability .187 .108 .012* Relative advantage .421 .155 .350** Compatibility .236 .180 .187 Perceived ease of use .068 .103 .059 Flow experience online .017 .058 .016 Ritualistic orientation .092 .072 .073 Instrumental orientatio n .304 .074 .229*** Subjective norm .016 .093 .013 Perceived behavioral control .028 .120 .018 Age .001 .007 .013 Gender (Female) d .424 .185 .187* African American d .546 .329 .103* Asian d 1.044 .442 .162** Hispanic d .533 .598 .074 O ther race d .896 .660 .099 Less than $20,000 d .582 .389 .099 $20,000 $39,999 d .387 .298 .107 $40,000 $59,999 d .281 .224 .105 $60,000 $79,999 d .009 .226 .003 $80,000 $99,999 d .128 .249 .039 Marital status (Married) .028 .224 .012 Marital status (Others) .197 .301 .053 Less than high school d .356 1.115 .039 High school d .212 .238 .079 College d .082 .186 .037 DVR ownership d .197 .165 .085 Internet connection type d .208 .360 .044 Cable subscription .537 .366 .232 Satellite subscription .491 .374 .199 Other TV service .637 .870 .071 Note: d stands for dummy variables. References: Gender: Males; Race: Non Hispanic Caucasian; Income: $100,000 or more; Marital Status: Single; Education Level: Graduate School; D VR Ownership: Dont Own DVR; Internet Connection Type: Dial -Up; Television Subscription: Broadcasting over the Air Only. p < .05, ** p < .01, *** p < .001 (one tailed) The complex model showed that some of the control variables have statistically signific ant effects on the likelihood to use television. Specifically, this study found that females are more likely than males to use television p < .05, one -.162, p < .01, one -

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125 .103, p < .05, one tailed) are less likely than Caucasians to use television.

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126 Ritualistic orientation .79 *** .84 *** .76*** .68 *** Figure 5 2. Complex model for intention to use online video platforms and intention to use television Instrumental orientation 97 *** .90 *** .59 *** .87 *** .120* .85*** 1.05 ** improve aspect learn easy explore breaking current immediacy update extend habit pass occup y nothing intend predict 163*** .165* .351*** .097 .13 1* .108* .020 .008 .229*** .073 .018 .013 .059 .187 .350** most .025 .016 91 *** 97 *** 96 *** 75 *** 80*** 75 *** 87 *** 94 *** 97 *** 95 *** 97 *** 94 *** opinion 93 *** 72 *** 98*** 89 *** Subjective norm Perceived behavioral l 1 06 *** Flow .83*** .80 *** .88 *** .66 *** ..86 *** .99*** .92 *** Intention to use online video platforms p < .05, ** p < .0 1, *** p < .001 (one tailed) there skill control often upto Relative adv antage better fulfill predict differ satisfy consider consult lifestyle fits Compatibility Perceived ease of use influence necessary 80 *** Perceived s ubstitutability Intention to use television intend

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127 Motivations Behind Video Content Consumption RQ1a asked what motivations consumers have behind video content consumption. To answer this question, an exploratory factor analysis was first carried out. Specifically, a principal component exploratory factor analysis with varimax rotation was performed. To investigate the adequacy of the data for the factor analysis, Kaiser -Meyer Olkin (KMO) measure of sampli ng adequacy and Bartletts test of sphericity were examined. The KMO measure of sampling adequacy was .840 ( p >.50) and the Chi -square value for the Bartletts test of sphericity was 5582.357 (df = 300, p <.05). The two test results indicated that there is an appropriate factor relationship among the 25 items that measure motivations behind video content consumption. Table 5 10 shows the results of the exploratory factor analysis. The exploratory factor analysis yielded six motives for watching video conte nt. The six motives explained 70.68% of the variance in the motivations for watching video content. The first factor contains 7 items that describe updating latest event information in a timely manner and learning new things. The first factor represents le arning updated event information as a motivation for watching video content. The first factor explained 27.29% of the variance in the motives for watching video content. The second factor illustrates relaxation motive. The second factor contains six items that describe entertainment and relaxation as the reasons why people watch video content. The relaxation motive explained 16.76% of the variance in the motives for watching video content. The third factor reflects pass time motive. The pass time motive has five items that describe boredom relief as the reason for watching video content. The forth factor contains three items that represent companionship. The fifth factor contains two items that illustrate escape motive. Lastly, the sixth factor contains two items that represent the social interaction motive. The social interaction motive explained 4.330 % of the total variance in the motivations for watching video content.

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128 Table 5 10. Exploratory factor analysis for motives behind video content consumption Factor 1 Factor 2 Factor 3 Factor4 Factor 5 Factor 6 Because I am interested in current events .873 .025 .000 .020 .093 .028 To find constantly updated event information .863 .094 .034 .129 .055 .094 To find breaking news events .835 .004 .075 .02 8 .093 .028 Because I am interested in the immediacy with which information can be obtained .813 .133 .091 .090 .040 .189 Because it opens me up to new ideas .770 .175 .020 .051 .153 .047 Because it extends my mind .764 .224 .070 .049 .198 .039 B ecause it lets me explore new things .604 .244 .073 .031 .190 .102 Because it entertains me .227 .850 .100 .028 .083 .095 Because its enjoyable .247 .829 .069 .015 .047 .148 Because it amuses me .130 .728 .136 .045 .100 .193 Because it relaxes me .051 .628 .062 .059 .460 .107 Because it allows me to unwind .084 .611 .147 .067 .461 .053 Because its pleasant rest .l03 .608 .137 .121 .336 .053 When I have nothing better to do .094 .112 .835 .031 .161 .035 Because it passes time when I am b ored .005 .174 .826 .138 .07 4 .054 Because it gives me something to do to occupy my time .050 .172 .711 .226 .186 .170 Just because its there .024 .011 .709 .243 .016 .026 Because its a habit, just something I do .182 .068 .679 .201 .113 .039 Because it makes me feel less lonely .074 .061 .189 .895 .159 .057 So I wont have to be alone .050 .024 .164 .850 .204 .009 When theres no one else to talk to or be with .003 .138 .344 .766 .123 .024 To forget my problems .064 .059 .285 .354 .726 .155 To escape my worries .020 .060 .258 .278 .759 .164 So I can be with other family or friends .045 .080 .061 .026 .150 .907 Because its something to do with my family or friends .049 .193 .100 .038 .034 .897 Eigen value 6.823 4.190 2.354 1.650 1.569 1.083 % of variance explained 27.294% 16.761% 9.416% 6.601% 6.278% 4.330%

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129 The items that have high loadings on each of the factors were averaged to create composite motivation variables. After the six motivation variables were created, the means and standard deviation were calculated to identify the motives behind why consumers watch video content most, and the motives behind why they watch video content least. Table 5 11. Descriptive statistics of motives behind video content consumption Mean SD Learning updated event information 5.338 1.156 Relaxation 5.298 1.058 Pass time 3.710 1.475 Companionship 2.558 1.568 Escape 2.692 1.650 Social interaction 3.855 1.645 As seen in Table 5 11, people watch video content most for learning updated even t information. The learning updated event information motive ( M = 5.338, SD = 1.156) showed the highest mean score followed by relaxation ( M = 5.298, SD = 1.058) and social interaction ( M = 3.855, SD = 1.645) motivations. People are least likely to watch v ideo content for companionship ( M = 2.558, SD = 1.568) and escapism ( M = 2.692, SD = 1.650). Perceived Substitutability RQ1b addressed what specific motivations behind video content consumption affect the degree to how consumers perceive substitutabilit y between online video platforms and television. To investigate the research question, Ordinary Least Squares (OLS) regression was performed. There was no multicollinearity as VIF ranged from 1.132 to 1.509. The suggested model was statistically significan t in predicting the perceived substitutability between online video platforms and television (F (6, 376) = 5.361, p < .01). The suggested model explained 6.4% of the variance in the perceived substitutability. The results of the regression indicated that both the learning updated event information motive and the relaxation motive are statistically significant in predicting the perceived

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130 substitutability. Table 5 12 shows the results of the regression. Learning updated event information and relaxation motiv ations behind video content consumption are negatively related to the level of the perceived substitutability between online video platforms and television. The more consumers watch video content for learning updated event information, the less likely they are to think that online video platforms and television are substitutable -.181, p < .01). The more consumers watch video content for relaxation, the less likely they are to think that online 140., p <.05). The results indicated that there are discrepancies between the Internet and television as video platforms when it comes to satisfying the learning updated event information and relaxation gratifications. The other motives (i.e., pass time, companionship, escape, and social interaction) for watching video content did not have statistically significant effects on the perceived substitutability. Table 5 12. Regression for predictors of the perceived substitutability of online video platforms and television B SE Beta t p value Learning updated event information 171 .051 .181 3.323 .001** Relaxation .144 .060 .140 2.390 .017* Pass time .047 .045 .064 1.062 .289 Companionship .047 .042 .068 1.121 .263 Escape .005 .040 .008 .127 .899 Social interaction .003 .035 .005 .095 .924 Constant 4.602 .32 0 14.380 .000*** F (6, 376) 5.361 .000*** R 2 .079 Adjusted R 2 .064 p < .05, ** p < .01, p < .001 (twotailed) Relative Advantage RQ2a asked what specific content, technology, and cost related attributes of online video platforms are perce ived as better or worse than those of television. Research question 2a also addressed what specific content, technology, and cost -related attributes of online video platforms affect establishing the perceived overall relative advantage of online video plat forms.

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131 To identify the attributes of online video platforms that are perceived as better than those of television, an array of one -way repeated measures Analysis of Variance (ANOVA) was performed. Perceptions of the 14 attributes of the Internet and telev ision as video platforms, which reflect content, technology, and cost dimensions, were identified as repeated factors. The 14 attributes are 1) video content variety, 2) video content quality, 3) financial benefit, 4) effort efficiency in search, 5) time e fficiency in search, 6) interactivity, 7) personalization, 8) timeliness, 9) usefulness of reviews and ratings, 10) time shifting, 11) cumbersomeness of advertising during viewing, 12) storage capability, 13) instant replay, and 14) reliability. Table 5 13 shows the results of the repeated measures ANOVAs. The results indicate that consumers perceive online video platforms better than television in terms of effort efficiency in search (F (1,350) = 6.669, p 2 =.019), time efficiency in search (F (1 352) = 38.499, p < 2 = .953), interactivity (F (1, 350), = 64.606, p 2 =.056), personalization (F (1, 349) = 90.893, p 2 = .207), timeliness (F = (1, 350) = 20.258, p 2 = .057), usefulness of reviews and ratings (F (1, 351) = 23.366, p 2 = .062), time shift functions (F (1, 348) = 32.617, p 2 = .086), pleasure of advertisements (F (1, 347) = 99.017, p < 2 = .222), storage capability (F (1, 343) = 19.566, p 2 = .054), and instant replay (F (1, 348) = 45.468, p 2 = .116). It was also found that consumers perceive television to be better than online video platforms in terms of the variety of video content (F (1,352) = 8.086, p 2 = .962), quality of video content (F (1, 346) = 175.047, p 2 = .336), and reliability (F (1, 353) = 40.539, p 2 = .103). When it comes to financial benefit, there was no statistically significant difference between online video platforms and television.

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132 Table 5 13. Repeated measures of ANOVA for perceptions of online video platforms and television with respect to content, technology, and cost related attributes Attribute Type Mean SD F p value Content Variety Online 5.16 1.797 8.086 .005** Television 5.53 1.424 Quality Online 4.40 1.614 175.047 .000*** Television 5.86 1.290 Cost Financial benefit Online 3.69 1.608 3.080 .080 Television 3.86 1.649 Effort efficiency in search Online 5.04 1.711 6.669 .010** Television 4.66 1.745 Time eff iciency in search Online 4.99 1.612 38.499 .000*** Television 4.12 1.697 Technology Interactivity Online 4.63 1.651 64.606 .000*** Television 3.58 1.619 Personalization Online 4.86 1.728 90.893 .000*** Television 3.68 1.596 Timeliness Online 5.19 1.513 21.258 .000*** Television 4.61 1.635 Usefulness of reviews and ratings Online 4.84 1.562 23.366 .000*** Television 4.24 1.573 Time shifting Online 4.99 1.603 32.617 .000*** Television 5.29 1.552 Cumbersomeness of advertisements Online 4.21 1.660 99.017 .000*** Television 5.41 1.470 Storage Online 4.69 1.567 19.566 .000*** Television 4.10 1.723 Instant replay Online 5.30 1.540 45.468 .000*** Television 4.38 1.924 Reli ability Online 4.66 1.592 40.539 .000*** Television 5.35 1.426 p < .05, ** p < .01, p < .001 (twotailed) The second part of RQ 2b asked what specific content, technology, and cost related attributes affect the overall perception of the relative advantage of online video platforms over television. To answer the second part of RQ 2b, OLS regression with a backward elimination technique was performed. Backward elimination method starts with all independent variables in the model and eliminates the o nes that do not make a significant contribution to the prediction (Hair et al., 1998). Because there is no prior study that investigated specific relationships between all of the attributes and the overall relative advantage of online video platforms, this

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133 study chose the backward elimination method. In the elimination process, the variables that do not make a contribution to the prediction were eliminated one at a time (Sutter & Kalivas, 1993). The relative advantage of online video platforms was regressed on 14 specific attributes of online video platforms. There was no multicollinearity among the variables included to the model with VIF from 1.072 to 3.167. Table 5 14 illustrates the results of the regression. Table 5 14. Regression for the perceived att ributes of online video platforms that affect the overall relative advantage of online video platforms B SE Beta t p value Quality of video content .230 .061 .230 3.742 .000** Interactivity .144 .058 .152 2.492 .013* Storage capability .153 .062 .1 54 2.481 .014* Constant .081 .281 .289 .773 F (3, 329) 26.953 .000*** R 2 .197 Adjusted R 2 .190 p < .05, ** p < .01, p < .001 (twotailed) The results of the regression revealed that three perceived attributes of online video platforms po sitively predict the perceived overall relative advantage of online video platforms. p p p < .05). Quality of video cont ent has the strongest effect on the perceived relative advantage of online video platforms followed by storage capability. The final regression model with the three independent variables (i.e., quality of video content, interactivity, and storage capabilit y) was statistically significant, predicting 19.0% the total variance in the perceived relative advantage of online video platforms (F (3,329) = 26.953, p < .001). Users Versus Non -Users of Online Video Platforms Some of the research questions sought to de termine the differences between users and non users of online video platforms. To address the research questions, this study divided the participants of this survey into users and nonusers of online video platforms. Of the 388

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134 participants in this survey, 57.0% (n = 221) said that they use the Internet to watch video content. Another 43.0 % (n = 167) said that they do not use the Internet to watch online video content. Motivations behind Video Content Consumption RQ1c asked whether there are any differen ces between users and non users of online video platforms with respect to motivations for watching video content. To find the answer, independent -samples t tests were carried out. Table 5 15 represents the results of the independent samples t -tests. Table 5 15. Comparison of users and nonusers of online videos in motivations behind video content consumption Users Non users t p Mean SD Mean SD Learning updated event information 5.490 .982 5.135 1.318 2.921 .004** Relaxation 5.359 .902 5.211 1.228 1.307 .192 Pass time 3.705 1.427 3.699 1.556 .039 .969 Companionship 2.420 1.502 2.709 1.633 1.783 .075 Escape 2.620 1.647 2.784 1.641 .976 .330 Social interaction 3.862 1.561 3.849 1.746 .079 .937 p < .05, ** p < .01 (twotailed) The result o f the t tests discovered that users of online video platforms ( M = 5.490, SD = .982) are more likely than the nonusers ( M = 5.135, SD = 1.318) to watch video content for learning updated event information (t = 2.921, df = 386, p < .01). With respect to th e remainder of the motivations, there were no statistically significant differences between users and nonusers of online video platforms. Fourteen Attributes of Online Video Platforms and Television RQ2b asked whether users and nonusers of online vide o platforms perceive online video platforms differently with respect to content, technology, and cost related attributes. To investigate the research question, independent -samples t tests were performed. Table 5 16 illustrates the results of the t tests.

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135 Table 5 16. T -tests for differences between users and non users of online video platforms with respect to the perceived attributes of the online video platforms Attribute Users Non users t p Mean SD Mean SD Content Variety 5.79 1.370 4.21 1.452 8.337 .000*** Quality 4.77 1.452 3.84 1.649 5.637 .000*** Cost Financial benefit 3.97 1.484 3.32 1.696 3.712 .000*** Effort efficiency in search 5.44 1.474 4.41 1.824 5.618 .000*** Time efficiency in search 5.41 1.357 4.33 1.716 6.666 .000*** Technology Interactivity 4.98 1.484 4.10 1.729 6.155 .000*** Personalization 5.30 1.468 4.21 1.851 6.199 .000*** Timeliness 5.62 1.164 4.54 1.713 6.578 .000*** Usefulness of reviews and ratings 5.23 1.326 4.24 1.679 6.170 .000*** Time shifting 5.40 1.387 4.35 1.668 6.511 .000*** Cumbersomeness of advertisements 4.40 1.694 3.91 1.550 2.803 .000** Storage 5.03 1.384 4.21 1.669 5.031 .000*** Instant replay 5.68 1.213 4.68 1.779 6.330 .000*** Reliability 4.90 1.487 4.28 1.649 3.771 .00 0** p < .05, ** p < .01, *** p < .001 (two-tailed) Overall, users of online video platforms perceive the specific attributes of online video platforms more positively than do non users across all of the attributes of online video platforms (i.e., video content variety, video content quality, financial benefit, time and effort efficiency in search, interactivity, personalization, timeliness, useful reviews and ratings, time shift functions, storage capability, instant reply, and reliability). The only exc eption is the perception of advertisements during viewing. Users of online video platforms ( M = 4.40, SD =1.694) are more likely than non users ( M = 3.91, SD = 1.550) to consider advertisements online to be cumbersome (t = 2.803, df = 352, p < .001). Desc riptive statistics showed that users of online video platforms view the variety of video content ( M = 5.80, SD = 1.365) as the most favorable attribute of online video platforms. In contrast, they were least likely to think that online video platforms provide them with financial

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136 benefit ( M = 3.97, SD = 1.484). Among nonusers of online video platforms, instant replay function is perceived to be the most favorable feature ( M = 4.68, SD = 1.779) of online video platforms. RQ2c addressed whether users and n onusers of online video platforms perceive television differently in terms of content, technology, and cost -related attributes. To investigate the research question, independent samples t tests were performed. Table 5 17 represents the results. Table 5 1 7. T -tests for differences between users and non users of online video platforms with respect to the perceived attributes of television Attribute Users Non users t p Mean SD Mean SD Content Variety 5.35 1.461 5.83 1.325 3.247 .001** Quality 5. 88 1.283 5.82 1.305 .399 .690 Cost Financial benefit 3.76 1.490 4.01 1.898 1.386 .167 Effort efficiency in search 4.57 1.751 4.89 1.723 1.783 .075 Time efficiency in search 3.98 1.635 4.40 1.750 2.354 .019* Technology Interactivity 3.42 1.52 3 3.85 1.733 2.559 .011* Personalization 3.51 1.531 3.96 1.681 2.674 .008** Timeliness 4.44 1.561 4.91 1.713 2.710 .007** Usefulness of reviews and ratings 4.11 1.493 4.51 1.684 2.345 .017* Time shifting 4.26 1.543 4.43 1.614 1.065 .287 Cumbersomeness of advertisements 5.43 1.489 5.28 1.501 .923 .356 Storage 4.04 1.721 4.28 1.711 1.336 .182 Instant replay 4.28 1.980 4.56 1.804 1.406 .161 Reliability 5.35 1.409 5.34 1.463 .428 .669 p < .05, ** p < .01, *** p < .001 (two-tailed ) The t -test results revealed that users of online video platforms are more skeptical than non users of online video platforms about televisions video content variety (t = 3.247, df = 373, p < .001), time efficiency in search (t = 2.354, df = 372, p < .05), interactivity (t = 2.559, df = 368, p < .05), personalization (t = 2.674, df = 367, p < .01), timeliness (t = 2.710, df = 371, p < .01), and usefulness of reviews and ratings (t = 2.345, df = 371, p < .05). There were no statistically

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137 significan t differences between users and nonusers of online video platforms in terms of the rest of the attributes. Interestingly, users of online video platforms did not perceive television to be better than online video platforms in any of the 14 attributes. RQ2d questioned how users and nonusers of online video platforms perceive online video platforms differently from television with respect to content, technology, and cost related attributes. Two sets of separate repeated measures of ANOVA were performed to examine how users and nonusers of online video platforms perceive online video platforms and television. Table 5 18. Repeated measures of ANOVA for the perceived attributes of online video platforms and television among users of online video platforms Attribute Type Mean SD F p value Content Variety Online 5.81 1.367 11.001 .001** Television 5.33 1.462 Quality Online 4.78 1.463 66.575 .000*** Television 5.89 1.276 Cost Financial benefit Online 3.96 1.493 4.147 .043* Televis ion 3.74 1.484 Effort efficiency in search Online 5.45 1.477 26.999 .000*** Television 4.57 1.747 Time efficiency in search Online 5.43 1.359 76.680 .000*** Television 4.00 1.628 Technology Interactivity Online 4.99 1.492 108.834 .000*** Television 3.43 1.521 Personalization Online 5.30 1.471 157.063 .000*** Television 3.53 1.525 Timeliness Online 5.63 1.161 66.298 .000*** Television 4.46 1.553 Usefulness of reviews and ratings Online 5.24 1.327 59.21 7 .000*** Television 4.13 1.485 Time shifting Online 5.42 1.393 63.767 .000*** Television 4.28 1.529 Cumbersomeness of advertisements Online 4.41 1.703 40.988 .000*** Television 5.43 1.460 Storage Online 5.03 1.386 36.887 .000* ** Television 4.06 1.723 Instant replay Online 5.70 1.212 71.792 .000*** Television 4.31 1.968 Reliability Online 4.92 1.493 12.184 .001** Television 5.37 1.380 n = 221, p < .05, ** p < .01, *** p < .001 (two -tailed)

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138 Table 5 18 shows how users of online video platforms perceive online video platforms and television. Users of online video platforms perceive online video platforms more favorable than television in regard to video content variety (F (1, 211) = 11.001, p 2 = .050), financial benefit (F (1, 207) = 4.147, p 2 = .020) effort efficiency in search (F (1, 209) = 26.999, p 2 = .114), time efficiency in search (F (1, 211) = 11.001, p 2 = .050), interactivity (F (1, 209) = 108.834, p <.001, 2 = .342), personalization (F (1, 210) = 157.063, p 2 = .428), timeliness (F (1, 210) = 66.298, p 2 = .240), usefulness of reviews and ratings (F (1, 211) = 59.217, p 2 = .218), pleasure of advertisements (F (1, 212) = 40.988, p <.001, 2 = .962), storage, and instant replay (F (1, 210) = 36.887, p 2 = .233). In contrast, users of online video platforms perceived online video platforms less favorable than television in terms of quality of video content (F (1, 208) = 66.575, p 2 = .242) and reliability (F (1, 211) = 12.184, p 2 = .055). There was no significant statistical difference in terms of financial benefit between online video platforms and television. Non users of online video platforms perceive the Internet and television as video platforms differently from users of online video platforms. Table 5 19 shows how nonusers of online video platforms perceive online video platforms and television. While users of online video platforms perceive online video platforms to provide more various video content than television, non users of online video platforms view television as a medium that offers more video content variety than online video platforms (F (1, 140) = 67.705, p < .001, 2 = .326). With respect to the quality of video content, non users of online video platforms think that television provides better quality of video content (F (1, 137) = 126.040, p 2 = 479) as do the users of online video platforms.

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139 Table 5 19. Repeated measures of ANOVA for the perceived attributes of online video platforms and television among non users of online video platforms Attribute Type Mean SD F p value Content Variety Online 4.19 1.927 67.705 .000*** Television 5.83 1.315 Quality Online 3.81 1.659 126.040 .000*** Television 5.81 1.316 Cost Financial benefit Online 3.28 1.688 19.309 .000*** Television 4.04 1.860 Effort efficiency in search Online 4.42 1.848 2.604 .109 Television 4.81 1.740 Ti me efficiency in search Online 4.32 1.733 .008 .927 Television 4.30 1.788 Technology Interactivity Online 4.09 1.736 1.708 .193 Television 3.80 1.737 Personalization Online 4.19 1.876 2.043 .155 Television 3.90 1.678 Timelines s Online 4.54 1.732 2.005 .159 Television 4.84 1.733 Usefulness of reviews and ratings Online 4.23 1.695 .736 .392 Television 4.41 1.689 Time shifting Online 4.34 1.685 .030 .863 Television 4.30 1.592 Cumbersomeness of adverti sements Online 3.91 1.548 66.845 .000*** Television 5.36 1.490 Storage Online 4.16 1.683 .001 .973 Television 4.16 1.729 Instant replay Online 4.70 1.778 .817 .368 Television 4.49 1.857 Reliability Online 4.29 1.666 32.371 000*** Television 5.31 1.498 n = 167, p < .05, ** p < .01, *** p < .001 (two -tailed) While users of online video platforms think that they can financially benefit from using the Internet to watch video content, non users of online video platforms believe that television is more likely to provide financial benefit than online video platforms (F (1, 139) = 19.309, p < 2 = .122). Both users and nonusers of online video platforms agree that television is more reliable than online video platform s (F (1, 141) = 32.371, p 2 = .187). With respect to cumbersomeness of advertisements, both users and nonusers of online video platforms think that advertisements on television during viewing is more cumbersome than advertisements on the

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140 Internet during viewing (F (1, 136) = 66.845, p 2 = .330). Nonusers of online video platforms think that there are no differences between television and online video platforms with respect to effort efficiency in search, time efficiency in search, intera ctivity, personalization, timeliness, usefulness of reviews and ratings, time shift function, storage, and instant replay. Table 5 20. Summary of the perceived attributes of online video platforms and television Users Non users Better attributes of onl ine video platforms Video content variety Financial benefit Effort efficiency in search Time efficiency in search Interactivity Personalization Timeliness Usefulness of reviews and ratings Less cumbersome advertisements during viewing Instant repl ay Less cumbersome advertisements during viewing Better attributes of Television Video content quality Reliability Video content variety Video content quality Financial benefit Reliability Table 5 20 summarizes how users and nonusers of online video platforms perceive the attributes of online video platforms and television. Users of online video platforms think that online video platforms are better than television in terms of video content variety, financial benefit, effort efficiency in searc h, time efficiency in search, interactivity, personalization, timeliness, usefulness of reviews and ratings, cumbersomeness of advertisements during viewing, and instant replay. By contrast, non users of online video platforms perceive online video platfor ms to be better than television with respect to only cumbersomeness of advertisements during viewing. When it comes to television, both users and non users of online video platforms agree that television is better than online video platforms with respect t o video content quality and overall reliability. In addition to those, nonusers of online video platforms

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141 think that television is also better than online video platforms in terms of video content variety and financial benefit. Perceived Characteristics of Online Video Platforms Another array of research questions addressed how users and nonusers of online video platforms see online video platforms differently. Specifically, the research questions concern the perceived substitutability, relative advanta ge, perceived ease of use, and compatibility of online video platforms. To answer the research questions, independent samples t tests were carried out. Table 5 21 shows the results of the t tests. Table 5 21. T -tests for differences between users and non u sers of online video platforms with respect to the perceived characteristics of online video platforms Users Non users t p ` Mean SD Mean SD Perceived substitutability 2.979 .898 3.306 1.189 6.447 .000*** Relative advantage 2.953 1.609 1.853 1.271 7.426 .000*** Perceived ease of use 5.446 1.440 3.625 1.813 10.671 .000*** Compatibility 3.718 1.691 1.982 1.298 11.383 .000*** p < .05, ** p < .01, *** p < .001 (two-tailed) RQ 1d asked whether there are differences between users and non users of o nline video platforms with respect to the perceived substitutability between online video platforms and television. The ttest result indicated that there are statistically significant differences between users and nonusers of online video platforms with respect the perceived substitutability. Nonusers of online video platforms ( M = 3.306, SD = 1.189) are more likely than the users ( M = 2.979, SD = .898) to think that online video platforms and television are substitutable (t = 6.447, df = 383, p < .001) RQ 2e addressed whether there are differences between users and nonusers of online video platforms with respect to the overall relative advantage of online video platforms. Users of online video platforms ( M = 2.953, SD = 1.609) are more likely than no n users of online video

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142 platforms ( M = 1.853, SD = 1.271) to think that online video platforms have relative advantage (t = 7.426, df = 384, p < .001). RQ 3 asked whether there are any differences between users and nonusers of online video platforms with respect to the perceived ease of use of online video platforms. The results of t tests showed that users of online video platforms ( M = 5.446, SD = 1.440) are more likely than the nonusers ( M = 3.625, SD = 1.813) to view online video platforms as easy to use (t = 10.671, df = 385, p < .001). RQ 4 addressed whether there are any differences between users and non users of online video platforms in terms of compatibility of online video platforms. Users of online videos ( M = 3.718, SD = 1.691) are more like ly than non users ( M = 1.982, SD = 1.298) to believe that online video platforms are compatible with their lifestyles, values, and past experiences (t = 11.383, df = 382, p < .001). Consumer Characteristics Some of the research questions mentioned previo usly investigated the differences between users and nonusers of online video platforms with respect to the perceived characteristics of online video platforms (i.e., perceived substitutability, relative advantage, compatibility, and perceived ease of use) On the other hand, RQ5 through RQ8 focus on how different users and non users of online video platforms are with respect to consumer characteristics -subjective norm, perceived behavioral control, orientation for watching video content, and flow experi ence online. To answer the research questions, independent samples t -tests were conducted. Table 5 22 shows the results of the t tests. Specifically, RQ 5 asked whether there are any differences between users and non users of online video platforms with re spect to flow experience online. Users of online video platforms

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143 (M = 3.288, SD = 1.691) tend to have more flow experience online than the nonusers of online video platforms ( M = 2.808, SD = 1.600, t = 2.810, df = 381, p < .01). Table 5 22. T -tests for d ifferences between users and non users with respect to consumer characteristics Users Non users t p Mean SD Mean SD Flow experience online 3.288 1.691 2.808 1.600 2.810 .005** Instrumental orientation 5.414 1.143 5.019 1.451 2.903 .004** Ritual istic orientation 3.705 1.427 3.700 1.556 .039 .969 Subjective norm 3.411 1.340 2.410 1.444 6.957 .000*** Perceived behavioral control 6.100 1.132 5.550 1.740 3.544 .000*** Subjective norm 3.411 1.340 2.410 1.444 6.957 .000*** p < .05, ** p < .0 1, *** p < .001 (twotailed) RQ 6 addressed whether there are differences between users and nonusers of online video platforms with respect to orientation behind video content consumption. The t test results indicated that users of online video platforms ( M = 5.414, SD = 1.143) tend to watch video content more for instrumental orientation than the non users ( M = 5.019, SD = 1.451, t = 2.903, df = 386, p < .01). There was no statistically significant difference between users and nonusers of online video p latforms with respect to ritualistic orientation behind video content consumption. RQ 7 asked whether there are any differences between users and nonusers of online video platforms with respect to subjective norm. Users of online video platforms ( M = 3.4 11, SD = 1.340) are more likely than the nonusers ( M = 2.410, SD = 1.444) to believe that people who are important to them would support their use of online video platforms (t = 6.957, df = 384, p < .001). RQ 8 asked whether there are any differences bet ween users and nonusers of online video platforms in terms of perceived behavioral control. Although both users ( M = 6.100, SD = 1.132) and non users of online video platforms ( M = 5.550, SD = 1.740) believe that they have very

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144 high control of using onlin e video platforms, users of online video platforms are more likely than non users to believe so (t = 3.544, df = 384, p < .001). Displacement Effect of Online Video Platforms on Television RQ 9a through RQ 10b addressed whether the emergence of the Inter net as a video platform affects the usage of television. Specifically, RQ 9a asked how the time spent using online video platforms affect the time spent using television. The descriptive statistics illustrate that 45.1% (n = 175) of the Internet users for watching video content think that the amount of time they spent watching television has neither decreased nor increased since they started to use the Internet to watch video content. Another 11.6 % (n = 45), 3.9 % (n = 15), and 2.3% (n = 9) of the Internet users to watch video content said that the time spent watching television has decreased slightly, decreased moderately, and decreased a lot since they started using the Internet to watch video content. In comparison, a relatively smaller percentage of peo ple think that the time spent watching television has increased slightly (4.6%, n = 18), increased moderately (.5%, n = 2), and increased a lot (1.0%, n = 4). The descriptive statistics simply showed the breakdown of the displacement effect among users of online video platforms, regardless of the level of video content consumption using the Internet. RQ 9a specifically focused on how the amount of time people spent using the Internet to watch video content affected the time spent watching television. To answer RQ 9a, a correlation analysis was first conducted. As seen in Table 5 23, there was a negative correlation between the amount of time people spent with online video platforms and the amount of time they spent with television as a consequence (r = -.2 12, p < .01). A further analysis was conducted with a simple regression. The amount of time using online video platforms was identified as an independent variable. The amount of time change watching television once people started using the Internet to watc h video

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145 content was specified as the dependent variable. The result of the simple regression found that the more time people spend with online video platforms, the less time they spend watching television -.039, p < .01). Table 5 23. Correlation matrix for the amount of time change with television TCTV UGV BV CLIP ENTIRE VSS TVNS TCTV --UGV .222*** --BV .032 .279*** --CLIP .087 .777*** .391*** --ENTIRE .053 .439*** .195*** .559*** --VSS .196** .618*** .225*** .438*** .332*** --TVNS .025 .517*** .297*** .686*** .483*** .303*** -* p < .05, ** p < .01, *** p < .001 (two-tailed) Note: TCTV: The amount of time change watching TV; UGV: Time spent watch ing user generated video content; BV: Time spent watching branded -video content; CLIP: Time spent watching clips of television programs online; ENTIRE: Time spent watching an entire episode of television programs online; VSS: Time spent on video sharing si tes; TVNS: Time spent on television network sites to watch video content RQ 9b asked how the time spent using different types of online video venues (i.e., television network websites and video sharing websites) affect the amount of time watching televi sion. The c orrelation showed that there is a negative correlation between the amount of time spent on video sharing sites and the amount of time watching television (r = .196, p < .001). In comparison, the amount of time spent using television network sit es has no statistically significant association with the amount of time watching television as a result (see Table 5 23). Multiple regression was further performed using the amount of time spent on video sharing sites and television network websites as in dependent variables. The change in amount of time watching television since the use of online video platforms was specified as the dependent variable. The regression results indicated that the time spent on video sharing sites statistically significantly r .079, p < .001 ). The time spent on television network web sites did not have a statistically significant relationship with the

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146 change in the amount of time watching television. T he suggested model explained 4.6% of the variance in the displacement effect of online video platforms on television. RQ 10a asked how the time spent on branded-video content and user -generated video content affects the amount of time change watching television since the use of onlin e video platforms. The correlation analysis showed that there is a negative association between the time spent watching user generated videos online and the change in the amount of time watching television (r = .222, p < .001). Further, the amount of time change watching television was regressed on the amount of time spent watching branded videos and user -generated videos online. It was found that the amount of time spent watching user -generated videos online significantly reduced the time spent on televis ion viewing .231, p < .001). The time spent watching branded videos through the Internet did not affect the amount of time change watching television. The proposed model explained 4.5% of the variance in the displacement effect of online video platforms on television. RQ 10b addressed how the amount of time spent viewing an entire episode of television programs and a clip of television programs through the Internet is related to the amount of time change watching television since the use of online video platf orms. Both correlation and regression analyses showed that none of the consumption patterns is associated with the displacement effect. Finally, another multiple regression was performed to examine how the consumption of different types of video content, online video venues, and content overlap affects the amount of time change watching television after controlling for each other. The amount of time change watching television since the use of online video platforms was regressed on the amount of time spent using the Internet for 1) user -generated videos, 2) branded videos, 3) video sharing sites, 4)

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147 television network sites, 5) clips of television programs, and 6) an entire episode of television programs. The backward elimination method was used to remove the variables that do not contribute to the prediction. There was no multicollinearity problem with the model. VIF in the state of the full model ranged from 1.241 to 1.788. Table 5 24. Multiple regression for the amount of time change with television B SE Beta t p value Model 1 User generated videos .068 .031 .176 2.211 .028* Branded videos .012 .014 .055 .823 .411 Video sharing sites .044 .029 .123 1.531 .127 Television network sites .012 .022 .039 .527 .599 Clips .048 .044 .084 1.091 .2 76 Entire episode .000 .030 .000 .007 .995 Constant 3.828 .071 53.584 .000*** Model 2 User generated videos .068 .031 .176 2.215 .028* Branded videos .012 .014 .055 .826 .410 Video sharing sites .044 .028 .123 1.552 .122 Television netw ork sites .012 .021 .039 .556 .579 Clips .048 .042 .084 1.151 .251 Constant 3.828 .071 53.713 .000*** Model 3 User generated videos .069 .031 .178 2.252 .025* Branded videos .014 .014 .064 .985 .326 Video sharing sites .045 .028 .126 1.59 0 .113 Clips .057 .038 .100 1.506 .133 Constant 3.838 .069 55.732 .000*** Model 4 User generated videos .067 .030 .173 2.187 .030* Video sharing sites .043 .028 .122 1.538 .125 Clips .069 .038 .120 1.890 .060 Constant 3.851 .068 57.0 18 .000*** Model 5 User generated videos .095 .024 .247 3.921 .000*** Clips .060 .036 .106 1.681 .094 Constant 3.835 .067 57.331 .000*** F (2, 260) 7.854 .000*** R 2 .057 Adjusted R2 .050 p < .05, ** p < .01, *** p < .001 (two-tailed) Table 5 24 presents the results of the regression. The final model with two significant predictors was statistically significant in predicting the change in amount of time watching television (F (2, 260) = 7.854, p < .001, R2 = .050). The final mod el indicated that the am ount of

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148 time spent watching user -generated videos online significantly reduced the time spent on -.095, p < .001). By contrast, the amount of time spent using the Internet to watch clips of television programs significantly increase d the time spent in television viewing p < .05, one tailed). Viewership Overlap Examining viewership overlap between online video platforms and television is another way to see how two types of video platforms are interrelated in terms of consu mer demand. RQ 11a asked if the Internet and television as video platforms reach mutually exclusive viewers. This study found that 57% (n = 221) of the survey participants use the Internet to watch video content whereas 43% (n = 167) of them do not use the Internet to watch video content. Viewership overlap between online video platforms and television was examined in three ways: 1) how much the users of online video platforms and television overlap when the population consists of general consumers (i.e., I nternet users in this study), 2) how much the users of online video platforms and television overlap when the population consists users of online video platforms, and 3) how much the users of online video platforms and television overlap when the populatio n consists of television users. First, viewership overlap was examined among all of the participants in this survey (see figure 5 3). The user overlap between online video platforms and television is 55.4% (n = 215). That is, 55.4% of the respondents in this survey were using both television and the Internet to watch video content. With respect to television, 97.7% of the respondents (n = 379) said that they use television to watch video content. The other 2.3% (n = 9) of the respondents said that they do not use television to watch video content. Also, 42.3% of the respondents (n = 164) use television, but they do not use the Internet to watch video content. Meanwhile, 1.5 % of the respondents (n = 6) said they use the Internet only to watch video conten t in lieu of television.

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149 About .8 % of the respondents (n = 3) use neither television nor the Internet to watch video content. Figure 5 3. Viewership overlap between Internet and television as video platforms among all of the respondents S econd, users of online video platforms were defined as a population. Of online video platform users (n =221), nearly 97.3% (n = 215) are also using television to watch video content. Only 2.7 % (n = 6) of online video platform users rely solely on the Inte rnet to watch video content. There is a large portion of the viewership overlap between online video platforms and television when the population is restricted to the users of online video platforms (see Figure 5 4). 42.3% (n = 164) Internet only 1.5% (n=6) 55.4% (n = 215) Television Internet Neither television nor Internet 0.8% (n= 3)

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150 Figure 5 4. Viewership overlap between Internet and television as video platforms among users of online videos Third, television viewers were defined as the population (n = 379). Of this population, 56.7% (n = 215) also use the Internet to watch video content. Another 43.3% ( n = 164) of television users do not use the Internet to watch video content. Therefore, they use television exclusively for watching video content (see Figure 5 5). Figure 5 5. Viewership overlap between Internet and television as video platforms a mong television users RQ 11b addressed whether the viewership overlap differs according to the types of television service subscription (i.e., over the air only, cable subscription, and satellite 56.7% (n=215) Television and the Internet 56.7% (n = 215) Telev ision only 43. 3% (n = 164) Population : Television users (n =379) 97.3% (n=215) Television and the Internet 97.3% (n = 215) Internet only 2. 7% (n = 6) Population : Users of online video platform (n =221)

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151 subscription). Descriptive statistics showed that there indeed exist differences in viewership overlap among different types of television service subscribers. Table 5 25. Differences of viewership overlap by television subscription type Over the air only Cable TV subscribers Satellite TV subscribers Other T V service TV and Internet 50.0% (15) 58.7% (142) 50.5% (54) 66.7% (4) TV only 36.7% (11) 30.9 (99) 46.7% (50) 33.3% (2) Internet only 13.3% (4) 0.0% (0) 1.9% (2) 0.0% (0) Neither TV nor Internet 0.0% (0) 0.4 % (1) .9% (1) 0.0% (0) Total 100% (30) 100.0% (242) 100.0% (107) 100.0% (6) Specifically, the results indicated that people who have over the air broadcasting and do not have any fee -based television subscriptions are more likely than the other fee -based television service subscri bers (i.e., cable, satellite, and other type) to solely use the Internet to watch video content (see Table 5 25). They are also less likely than subscribers of pay television services to utilize both television and the Internet to watch video content. Cabl e television subscribers (58.7%) and other type of pay television service subscribers (66.7%) are more active than people who receive broadcast networks over the air (50.0%) and satellite television subscribers (50.5%) in using both television and the Inte rnet to watch video content. Cable television subscribers are also less likely to use one medium (i.e., television only or Internet only) to watch video content than are satellite television subscribers and people who have over -the air broadcasting signals Satellite television subscribers are more likely than the other groups to use television only to watch video content Satellite television subscribers (46.7%) are more likely than over the air (36.7%) cable (30.9%) and other service subscribers (33.3%) to rely solely on television. This study has many research questions and hypotheses. Therefore, a summary of the findings is presented in Tables 5 26 and 5 27. Table 5 26 summarizes the results of hypothesis

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152 testing. Table 5 27 presents the results of the research questions. Figures 5 6 5 7 and 5 8 present the results of hypothesis testing, RQ 1b and RQ 2a. Table 5 26. Result summary for hypotheses Online Television H 1a. Perceived substitutability between online video platforms and television w ill be positively related to the intention to use online video platforms. (Supported) H 1b. Perceived substitutability between online video platforms and television will be negatively related to the intention to use television. (Not supported) H 2a. Rela tive advantage of online video platforms will be positively related to the intention to use online video platforms. (Supported) H 2b. Relative advantage of online video platforms will be negatively related to the intention to use television. (Supported) H 3a. Perceived ease of use of online video platforms will be negatively related to the intention to use online video platforms. (Supported) H 3b. Perceived ease of use of online video platforms will be negatively related to the intention to use telev ision. (Not supported) H 4a. Compatibility of online video platforms will be positively related to the intention to use online video platforms. (Supported) H 4b. Compatibility of online video platforms will be negatively related to the intention to us e television. (Supported) H 5a. Flow experience online will be positively related to the intention to use online video platforms. (Not supported) H 5b. Flow experience online will be negatively related to the intention to use television. (Not supporte d) H 6a. Instrumental viewing orientation will be positively related to the intention to use online video platforms. (Not supported) H 6c. Instrumental viewing orientation will be negatively related to the intention to use television. (Not supported) H 6b. Ritualistic viewing orientation will be negatively related to the intention to use online video platforms. (Not supported) H 6d. Ritualistic viewing orientation will be positively related to the intention to use television. (Supported) H 7a Subjective norm of using online video platforms will be positively related to the intention to use online video platforms. (Supported) H 7b. Subjective norm of using online video platforms will be negatively related to the intention to use television. (Not supported) H 8a. Perceived behavioral control of using online video platforms will be positively related to the intention to use online video platforms. (Supported) H 8b. Perceived behavioral control of using online video platforms will be negativ ely related to the intention to use television. (Not supported)

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153 Figure 5 6. Visual depiction of the results of hypothesis testing Flow experience online Perceived characteristics of online video platforms Relative advantage Perceived ease of use Compatibility Instrumental orientation Ritualistic orientation Subjective norm Perceived behavioral control Consumer characteristics Perceived substitutability ( ) (+) (+) (+) (+) (+) (+) (+) (+) ( ) ( ) Intention to use television Intention to use online video platforms

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154 Table 5 27. Result summary for research questions No. Rese arch question Result RQ 1a What motivations do consumers have for watching video content? Consumers watch video content for 1) learning updat ed event information, 2) relaxation, 3) passing time, 4) companionship, 5) escape, and 6) social interaction. The learning updated event information motive is the primary reason behind video content consumption Consumers are least likely to watch video content for companionship. RQ 1b What specific motivations for watching video content affect consumers perc eived substitutability between online video platforms and television? Two motivations for watching video content negatively affect the degree of the perceived substitutability between online video platforms and television. The motivations are 1) learning u pdated event information and 2) relaxation. RQ 1c Are there differences between users and non users of online video platforms with respect to motivations for watching video content? Users of online video platforms are more likely to watch video content for learning updated event information than are non users of online video platforms. RQ 1d Do users and non users of online video platforms differ in how they perceive the substitutability between online video platforms and television? Users of online video platforms are less lik ely to perceive online video platforms and television to be substitutable than are non users of online video platforms. RQ 2a How do consumers perceive online video platforms differently from television with respect to specif ic content, technology, and cost attributes? What specific content, technology, and cost attributes of online video platforms affect the overall relative advantage of online video platforms? Consumers perceive online video platforms to be better than tele vision in terms of effort efficiency in search, time efficiency in search, interactivity, personalization, timeliness, usefulness of reviews and ratings, time shift functions, cumbersomeness of advertisements during viewing, storage capability, and instant replay. Consumers perceive television to be better than online video platforms with respect to video content variety, video content quality, and overall reliability. Quality of video content, interactivity, and storage capability positively affect the overall relative advantage of online video platforms.

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155 Table 5 27. Continued RQ 2b Do users and non users of online video platforms differ in how they perceive online video platforms with respect to the content, technology, and cost attributes? Use rs of online video platforms view online video platforms more favorably than do non users of online video platforms in all of the attributes of online video platforms this study focused on. The only exception is cumbersomeness of advertisements d uring view ing. Users of online video platforms are more likely to think that advertisements during online viewing are cumbersome than are non users of online video platforms. RQ 2c Do users and non users of online video platforms differ in how they perceive telev ision with respect to the content, technology, and cost attributes? Users of online video platforms are more skeptical about televisions video content variety, time efficiency in search, interactivity, personalization, timeliness, and usefulness of ratin gs and reviews than non users of online video platform users. RQ 2d Do users and non users of online video platforms differ in how they perceive online video platforms compared with television with respect to content, technology, and cost attributes? Us ers of online video platforms think that online video platforms are better than television in terms of video content variety, financial benefit, effort efficiency in search, time efficiency in search, interactivity, personalization, timeliness, usefulness of reviews and ratings, cumbersomeness of advertisements, and instant replay. On the other hand, non users of online video platforms think that online video platforms are better than television only with respect to cumbersomeness of advertisements during viewing Both users and non users of online video platforms think that television is better than online video platforms in terms of video content quality and reliability. In addition, non users of online video platforms think that television is better th an online video platforms in terms of video content variety and financial benefit.

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156 Table 5 27. Continued RQ 2e Do users and non users of online video platforms differ in how they perceive the relative advantage of online video platforms? Use rs of online video platforms are more likely to perceive online video platforms as having a relative advantage than are non users of online video platforms. RQ 3 Do users and non users of online video platforms differ in how they perceive ease of use of online video platforms? Users of online video platforms are more likely to perceive online video platforms to be easy to use than are non users of online video platforms. RQ 4 Do users and non users of online video platforms differ in how they perceive compatibility of online video platforms? Users of online video platforms are more likely to perceive online video platforms to be compatible than are non users of online video platforms. RQ 5 Are there differences between users and non users of online video platforms with respect to flow experience online? Users of online video platforms are more likely to have flow experience online than are non users of online video platforms. RQ 6 Are there any differences between users and non users of online vi deo platforms with respect to viewing orientation? Users of online video platforms are more likely to watch video content with instrumental viewing orientation than are non users of online video platforms. With respect to ritualistic orientation, there i s no statistically significant difference between users and non users of online video platforms. RQ 7 Are there any differences between users and non users of online video platforms with respect to subjective norm of using online video platforms? Users of online video platforms are more likely to think that people surrounding them support the use of online video platforms than are non users of online video platforms. RQ 8 Are there any differences between users and non users of online video platforms w ith respect to perceived behavioral control of using online video platforms? Users of online video platforms are more likely to think that they have control over using online video platforms than are non users of online video platforms. RQ 9a How does th e amount of time using online video platforms affect the amount of time watching television? The more people spend time using online video platforms, the less they spend time watching television. RQ 9b How does the amount of time using different types of online video venues (i.e., video sharing sites and television network websites) affect the amount of time watching television? The more people spend time using video sharing sites, the less they spend time watching television. There is no statistic ally significant influence of the use of television websites on the time spent watching television.

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157 Table 5 27. Continued RQ 10a How does the amount of time watching different types of video content (i.e., branded video content and user generated vid eo content) affect the amount of time watching television? The more people spend time with user generated video content, the less they spend time watching television. There is no statistically significant influence of watching branded-video content onl ine on the time spent watching television. RQ 10b How does the amount of time watching an entire episode of television programs and clips of television programs online affect the amount of time watching television? The more people spend time watching c lips of television programs online, the more they spend time watching television. There is no statistically significant influence of watching an entire episode of television programs on the time spent watching television. RQ 11a Do online video platfor ms and television reach mutually exclusive viewers? 55.4% of U.S. Internet users employ both television and online video platforms to watch video content. 97.3% of online video platforms users employ television along with online video platforms. 56.7% of television users employ online video platforms along with television. RQ 11b How does the viewership overlap between online video platforms and television differ according to television subscription types? Cable television subscribers are more likel y to employ both online video platforms and television than the people who subscribe to other types of television services. Satellite television subscribers are more likely to rely on television alone without utilizing online video platforms than are the people who subscribe to other types of television services.

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158 Figure 5 7 Visual depiction of the results of RQ 1b: how motivations behind video content consumption affect the perceived substitutabili ty between online video platforms and television Learning updated event information Relaxation Pass time Companionship Escape Social interaction Perceived substitutability between online video platforms and television ( ) ( )

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159 Figure 5 8 Visual depiction of the results of RQ 2a: How the perceived specific attributes of online video platforms affect the overall relative advantage of online video platforms Video content v ariety Video content quality Financial benefit Effort efficiency in search Time efficiency in search Interactivity Personalization Timeliness Usefulness of reviews and ratings Time shifting Cumbersomeness of advertisements Storage Instant replay Reliability Relative advantage of onl ine video platforms Content Cost Technology (+) (+) (+)

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160 CHAPTER 6 DISCUSSION AND CONCL USION Broadly speaking, the research questions and hypotheses in the current study can be grouped into four topics: a) predictions of the intention to use the Internet and televi sion to watch video content, b) a comparison of users and non users of the Internet for watching video content, c) the time displacement effect of online video platforms on television viewing, and d) viewership overlap between online video platforms and te levision. In this chapter, the findings involving each topic are discussed first, followed by the theoretical and practical implications of the overarching research questions and hypotheses. Lastly, limitations of the study and future research prospects ar e discussed. Summary of Findings for Intention to Use and Actual Use of Video Platforms This dissertation identified the determinants of consumers intention to use online video platforms and further compared the predictors of using online video platforms with the predictors of television use. To identify the predictors, this study integrated innovation diffusion theory, the technology acceptance model, the theory of planned behavior, flow theory, and uses and gratification. To compare the predictors of the two different types of video platforms (i.e., the Internet and television), this study took a fresh approach. Instead of applying the perceived characteristics of television, the current study applied the perceived characteristics of the Internet as a vi deo platform to predict consumers intention to use television. The central aim of this approach is to investigate how the two different types of video platforms, which coexist as consumers video viewing options, influence consumers use of them. The mod el employed in this study explained 70.4% of the variance in the intention to use the Internet to watch video content. Six predictors were found to be statistically significant in predicting the intention to use the Internet to watch video content. The six predictors are:

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161 perceived substitutability, relative advantage, compatibility, perceived ease of use, subjective norm, and perceived behavioral control. The model suggested by this study explained 36.9% of the variance in the intention to use televisio n With respect to the intention to use television, the following five predictors were determined to be important: the relative advantage, compatibility, perceived ease of use of online video platforms, ritualistic viewing orientation, and instrumental viewin g orientation. The findings show that the perceived characteristics of online video platforms (i.e., relative advantage, perceived ease of use, and compatibility) commonly influence both the intention to use online video platforms and the intention to use television. In terms of the video platforms, the biggest difference between the two sets of predictors is that subjective norm and the perceived behavioral control of using the Internet as a video platform are important in the intention to use that platfor m, but did not influence the intention to use television. Perceptions of the Internet as a Video Platform To predict the intentions to use the different types of video platforms and to examine the differences between users and nonusers of online video p latforms, this study categorizes the constructs that were used into two groups: 1) perceived characteristics of online video platforms and 2) consumer characteristics. In this section, the findings regarding the perceived characteristics are summarized. Th e findings regarding consumer characteristics are summarized in the following section. This study revealed some unexpected findings regarding consumers intention to use the Internet and television to view video content. The perceived substitutability bet ween online video platforms and television was originally hypothesized to boost the likelihood of consumers use of the Internet to watch video content. Given the fact that television has attracted more video content viewers than any other medium since its advent, it seemed legitimate to expect a positive relationship between its substitutability and the intention to use online video platforms. This

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162 study discovered that the perceived substitutability between online video platforms and television has a stat istically significant relationship with the intention to use the Internet to watch video content. However, it turns out that the degree to which people perceived the substitutability between online video platforms and television reduces consumers intentio n to use online video platforms. That is, the less consumers think that online video platforms and television are substitutable, the more likely it is that they intend to use the Internet to watch video content. This consistent pattern was discovered fro m the comparison between users and non users of online video platforms. The comparison indicates that actual users of online video platforms are less likely than non users to consider the online video platforms as a substitute for television. The findings highlight that consumers who have different expectations of online video platforms than of television are more likely to use the Internet to watch video content. By contrast, consumers who have the same expectations for both television and the Internet are less likely to choose the Internet to watch video content. Given that television has existed in the market for a long time, the current study suggests that consumers are more likely to perceive the Internet as a different video platform that satisfies dif ferent gratifications than the traditional video platform. This study further investigated the antecedents of the perceived substitutability by attempting to identify the specific gratifications that boost or reduce the perceived substitutability between online video platforms and television. Motivations that both online video platforms and television commonly satisfy increase the perceived substitutability, whereas motivations that are not satisfied commonly by both video platforms reduce the perceived su bstitutability. The results indicate that the learning updated event information motive behind video content consumption decreases the perceived substitutability between online video platforms and television. Likewise, the relaxation motive also reduces th e perceived substitutability. However,

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163 the learning updated event information .181, p < .001) has a stronger effect on the .140, p < .05). Other critical predictors of the intention to use the Internet to watch video content include the compatibility, relative advantage, and perceived ease of use. All three constructs are rooted in innovation diffusion theory, but the relative advantage and perceived ease of use also stem from p < .001) has the strongest effect on the intention to use online video platforms. Although this study indicates that consumers have the necessary means and resources to use the Internet to watch video content and a high level of control over online vide o viewing ( M = 5.864, SD = 1.450), consumers perceived the compatibility of using the Internet for watching video content as low (M = 2.981, SD = 1.759). Given that using the Internet to watch video content is relatively new compared with television, the k ey to consumers decision to use the Internet to watch video content highly depends upon whether the idea of doing so is compatible with their lifestyle, values, and past viewing experiences. The second most powerful predictor of the intention to use the Internet to watch video content is its relative advantage, followed by the compatibility predictor. General Internet users identified search efficiency in terms of time and effort, interactivity, personalization, timeliness, usefulness of reviews and ratin gs, pleasure of advertisements, storage, and instant replay as the specific attributes of online video platforms that are statistically significantly better than television. In contrast, variety of video content, quality of video content, and reliability a re singled out as the attributes of television that are perceived to be better than those of online video platforms.

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164 Although consumers view many attributes of online video platforms as better than television, this study discovered that only three of the attributes actually contribute to improving = .230, p p p < .01). Lin (2001) suggested that the relative advantage of a new technology or medium is conceptually reflected in the aspects of content, cost, and technology. Yet the current empirical study did not find cost to be a factor that affects the relative advantage of online vide o platforms. The quality of video content is the strongest attribute that contributes to improving consumers perceived relative advantage of online video platforms over television. The important role of video content quality on consumers intention to c hoose video platforms is consistent with prior research (LaRose & Atkin, 1991; Vlachos, Vrechopoulos, & Doukidis, 2003). The significant contribution of product quality to improving the perceived relative advantage can also be found in online shopping lite rature (Ahn, Ryu, & Han, 2004). However, the present study found that consumers perceive the quality of video content online less favorably than the video content quality on television. Along with compatibility, consumers do not perceive the relative advan tage of online platforms to be high ( M = 2.497, SD = 1.575). Another important predictor of Internet use for video viewing is the perceived ease of use. Paralleling the meta analysis of empirical studies that used the technology acceptance model (Ma & Li p < .05) was weaker than the p < .05). The three perceived characteristics of online video platforms (i.e., relative advantage, compatibility, and pe rceived ease of use) indeed serve as pivotal predictors of the intention to use television. Moreover, the findings suggest that the use of online video platforms and the use of

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165 television interact with each other. One example of this phenomenon that this s tudy revealed is that the relative advantage and compatibility of using online video platforms negative ly affect the intention to use television. As expected, the more consumers think that online video platforms have relative advantage and compatibility, t he less likely they are to use television. While the perceived compatibility with the Internet as a video platform has the most powerful effect on the intention to use online video platforms, the strongest predictor of the intention to use television is t .401, p < .01). That is, the perceived = -.242, p < .05), significantly leads consumers to turn away from television. Nonetheless, consumers who have a strong tendency to stick to television without adopting online video platforms exhibit skepticism about the relative advantage and compatibility of online video platforms. The perceived ease of use of online video platforms was origi nally hypothesized to have a negative relationship with the intention to use television. However, this study found that the perceived ease of use has a positive relationship with the intention to use television. The results imply that those consumers who p erceive online video platforms as easy to use are more likely to use both television and the Internet to watch video content instead of using only one of the video platforms. The findings also suggest that the perceived ease of use is not a strong or uni que factor that makes consumers choose online video platforms over television or vice versa. Consumer Characteristics Constructs that reflect consumer characteristics were added to the prediction model along with the perceived characteristics of online v ideo platforms. The model contains five constructs that reflect consumer characteristics. The inclusion of those constructs was guided by the theory of planned behavior, flow theory, and uses and gratification. The specific constructs are

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166 subjective norm, perceived behavioral control, online flow experience, ritualistic viewing orientation, and instrumental viewing orientation. In light of t he theory of planned behavior this study found that both perceived behavioral control and subjective norm positive ly affect the intention to use the Internet to watch video content. The more people feel they have external control over the use of the Internet to watch video content, the more likely they are to use online video platforms. As mentioned previously, this stu dy showed that the perceived behavioral control of using online video platforms is very high ( M = 5.864, SD = 1.450). The most probable explanation for this is that the population of this study consists of Internet users; they may believe that they have the necessary resources and knowledge to use online video platforms. Moreover, this study found that the more consumers believe that people who are important to them support the idea of using the Internet for video content viewing, the more likely they are t o use the Internet as a video platform. This study found that flow experience online has no relationship with the intention to use the Internet for viewing video content. Neither instrumental nor ritualistic viewing orientation had a statistically significant relationship with the intention to use online video platforms. Although instrumental viewing orientation has no statistically significant relationship with the intention to use online video platforms, this study found that actual users of online video platforms tend to watch video content more for instrumental orientation ( M = 5.414, SD = 1.143) than for ritualistic orientation ( M = 3.705, SD = 1.427). Users of online video platforms ( M = 5.414, SD = 1.143) are more likely than the nonusers of online video platforms (M = 5.019, SD = 1.451) to watch video content with an instrumental orientation. In terms of the intention to use television to watch video content, there were no effects related to the subjective norm, perceived behavioral control, and onl ine flow experience.

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167 However, both instrumental viewing orientation and ritualistic viewing orientation positively affect the intention to use television. In contrast to the proposed hypothesis, instrumental orientation is positively related to the intention to use television. Moreover, the effect of p .094, p < .01) on the intention to use television. The descriptive statistics also showed that consumers are mo re likely to watch video content in general for instrumental orientation ( M = 5.244, SD = 1.297) than for ritualistic orientation ( M = 3.702, SD = 1.481), regardless of video platform types. Summary of Findings for Displacement Effect Since the emergence of the Internet, the displacement effect of the Internet on traditional media has been a hotly debated issue. The current study examined the relationship between time spent using online video platforms and the change in the amount of time watching televis ion once consumers started to use online video platforms. Furthermore this study investigated the predictors behind the amount of time change. The descriptive statistics from the present study illustrated that 45.1% of online video users feel that the amo unt of time watching television has neither decreased nor increased since using the Internet to watch video content. Another 17.8 % of users said that the amount of time watching television has decreased. By contrast, 10.6 % of online video users think tha t the amount of time spent watching the television has increased. The patterns pertaining to the displacement effect from the current study are similar to an existing industry report except for the proportion of the users who believe that the amount of time spent watching television has increased. The industry report, conducted by Time Warner AOL and the Associated Press, indicated that 52% of the respondents who watched video content using the Internet said that their amount of time watching television remained about the same as before. Another 15% of the users said that the amount of time watching television has

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168 decreased. The same report showed that 32% of the users of online video platforms argued that the amount of time watching television has increa sed (Holahan, 2006). The difference between the current study and the industry report is the proportion of people who thought that their amount of time watching television had increased (32% in the industry report versus 10.6% in the current study). The d iscrepancy might be attributable to the time gap between the two studies. The industry report was undertaken in 2006, whereas the current study was conducted in 2009. As more people become familiarized with online video platforms, some people are likely to spend more time using online video platforms to watch video content, which may lead to a decline in the percentage of people who claim that the amount of time spent watching television has increased. The industry report simply focused on whether or not consumers use the Internet to watch video content without delving into how the level of video consumption online influences television consumption. The current study, in contrast, investigated how the amount of time spent using the Internet to watch vi deo content affects the amount of time spent watching television. It was discovered that the more time consumers spend using the Internet to watch video content, the less they spend watching television as a result. This study also broke down the amount of time spent using the Internet to watch video content according to types of online video venues, content type, and content overlap. The current study found that the more people spend time watching clips of television episodes online, the more they spend ti me watching television as a result. By contrast, the time spent viewing user generated videos and using video sharing sites decreases the time spent watching television. Summary of Findings for Viewership Overlap Examining viewership overlap is another wa y to investigate the interactions between online video platforms and television with respect to consumer demands. The descriptive

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169 statistics showed that 55.4% of consumers use both television and the Internet to watch video content. However, 42.3% of consu mers use television without using the Internet to watch video content. Only 1.5% of consumers use the Internet to watch video content without using television at all. When the population is narrowed down to users of online video platforms, the descriptive statistics show that 97.3% of online video platform users employ television along with the Internet to watch video content. When television users are the primary focus, then 56.7% of them use both television and the Internet to watch video content, and 43. 3% rely solely on television to watch video content. This study demonstrates that the Internet is still not strong enough to be considered an independent video platform. For example, 97.3% of online video platform users utilize television along with the I nternet to view video content. Even though television has been the predominant medium for video content viewing, it seems inevitable that television would share its role as a video platform with the Internet. It is noteworthy that the proportion of people who use both television and the Internet to watch video content (55.4%) is larger than the proportion of people who solely rely on television (42.3%). The reliance on the Internet and television as video platforms depends on the type of television subscri ption that consumers have. People who have over the air receptions of broadcast networks are more likely than pay television service subscribers to only use the Internet to watch video content (13.3%), abandoning television. Cable television subscribers (58.7%) are more likely to use both television and online video platforms than people who have over -the air broadcasting (50.0%) and people who subscribe to satellite television (50.5%). Meanwhile, cable television subscribers (30.9%) are less likely to rel y solely on television to watch video content, compared with people who receive over the air broadcasting (only 36.7%)

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170 and people who subscribe to satellite television (46.7%). In comparison, the trend of relying heavily on television is clear among satell ite television subscribers. While 36.7% of over the air television users and 30.9% of cable subscribers use only television to watch video content, 46.7% of satellite television subscribers exclusively use television to watch video content. Theoretical Im plications Benefits of the Integrated Model and Comparative-Study Approach To achieve a more comprehensive explanation of why consumers use the Internet or television to watch video content, this study integrated different theories: the theory of planned behavior, technology acceptance model, innovation diffusion theory, flow theory, and the theory of uses and gratifications. Although each theory has different constructs, they have all been employed to explain consumers media choice and use. The results of this study show that the integration of the theories provides a greater understanding, not only of the adoption of the two different types of video platforms, but also of the relationship between the two platforms in terms of consumers demands. The mo del suggested by this study explained more variance in the intention to use online video platforms than the models proposed by previous studies with similar topics. More specifically, the model designed by this study explained 70.4% of the variance in the intention to use online video platforms, whereas the models suggested by Lin (2004) and Lin (2008) explained 17% and 30% of the variance in webcasting adoption interest, respectively. Furthermore, the current study provides evidence that the integrated model in this study explains more variance in behavioral intention than do the models that are based on just one theory. Armitage and Conner (2001) meta analyzed 185 independent empirical studies in light of the theory of planned behavior. Their analysis indi cated that the theory of planned behavior explained 39% of the variance in intention (Armitage & Conner, 2001). King and He (2006)

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171 analyzed 88 published studies that used the technology acceptance model as a theoretical foundation and found that the percei ved usefulness and the perceived ease of use in the technology acceptance model account for 50% of the variance in intention. Similarly, the model based on innovation diffusion theory explained 45% of the variance in intention (Plouffe, Hulland, & Vandenbosch, 2001). Not only did this study incorporate different theories, but it also took a unique comparative-study approach. Instead of isolating the study of online video platforms from the predominant video platform of television, the model of this study f ocuses on both the Internet as a video platform and on television, because of their coexistence in the market. Unlike the majority of the previous studies, this study also employed the perceived characteristics of online video platforms to predict both the intention to use television and to use online video platforms. Because of the substantial diffusion of television in the U.S., this study attempted to predict the intention to use television not with the perceived characteristics of television, but with the perceived characteristics of online video platforms As a result, this study used identical sets of the constructs to predict both the intention to use online video platforms and the intention to use television. This unique approach study allows resea rchers to directly compare the predictors of online video platforms and television and to see how the emergence of online video platforms affects television use. The results indicated that the model in this study explained more variance in the intention to use online video platforms than the intention to use television. Whereas the model explained 70.4% of the variance in the intention to use online video platforms, it only explained 36.9% of the variance in the intention to use television. The reason mig ht be attributed to the fact that this study used the perceived characteristics of online video platforms to predict the intention to use

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172 television Interestingly, this study revealed that the perceived characteristics of online video platforms (i.e., the perceived relative advantage, ease of use, and compatibility of online video platforms) are significantly related to the intention to use television The unique comparative model of this study revealed that the more consumers perceived the relative advant age and compatibility of a new video platform (i.e., online video platforms), the less likely they are to use a traditional video platform (i.e., television). Fundamental Functional Similarity and Functional Uniqueness When focusing on each of the constr ucts in the model, this study revealed unexpected findings. One is that the perceived substitutability between online video platforms and television negatively affects the intention to use online video platforms. The less consumers view online video platforms as a substitute for television, they are more likely to use online video platforms a finding that is the exact opposite of the hypothesized relationship. D espite the belief that the functional similarity between new and old media increases the use of t he new media, there is, in fact, little empirical research proving this relationship. The majority of the previous studies focused on how a new medium that is functionally similar to a traditional medium replaces the traditional one ( Himmelweit, Oppenheim & Vince, 1958; Kaplan, 1978; Lee & Leung, 2008). Consistent with the findings of previous studies, this study found that when demographic information and media usage variables are controlled, the more consumers think that online video platforms and television are substitutable, the less likely they are to use television. With respect to the relationship b etween the perceived substitutability and the intention to use a new medium, the relationship appears to be more complex. This study revealed that consum ers tend not to approach a new video platform with the same expectations they have of television in terms of fulfilling their purposes. Thus, when consumers perceive the

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173 new video platform to be different from television in satisfying their needs, the like lihood of using the new video platform increases. The findings of the present study are somewhat similar to the results from Lin (2004) and Lin (2008) studies which that investigated how the perceived substitutability of offline content by webcasting content influences webcasting adoption interest Lin (2004) found that the perceived substitutability of television content by webcasting content has a negative relationship with an interest in accessing webcasting, but it did not have a statistical signi ficance. Similarly, another study conducted by Lin (2008) pointed out that the perceived substitutability of television content by webcasting content has a negative correlation with webcasting use interests. The findings from the current study show that c onsumers who do not have identical expectations from both television and online video platforms in terms of satisfying their needs are more likely to use online video platforms. The negative relationship between the perceived substitutability and the inten tion to use online video platforms can be explained by two concepts fundamental functional similarity and functional uniqueness Although this study suggests that fundamental functional similarity between a new video platform and an old video platform is a necessary condition to boost the likelihood to use the new video platform, this condition alone is not sufficient. Online video platforms have fundamental functional similarity with television in that both deliver video content. However, the fundamental functional similarity of online video platforms alone would not increase the likelihood of using online video platforms. Likewise, this study suggests that functional uniqueness of online video platforms compared with television contributes to increasing the likelihood of using the online video platforms. Functional uniqueness can be understood as features that are better or simply different from television of the online video platforms in terms of satisfying consumer needs.

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174 Unlike functional desirabi lity or functional alternative which emphasize better or more desirable features of a new medium compared with the old one ( Schramm, Lyle, & Parker, 1961; DeFleur & Ball Rokeach, 1982) functional uniqueness does not necessarily indicate the existence of a better or more desirable manner with which the new medium gratifies consumers needs, because the new video platform can gratify consumers needs in ways that completely different from the old mediums ways and because the new medium can gratify new ne eds that the old medium could not satisfy before. The negative relationship between the perceived substitutability between online video platforms and television and the intention to use online video platforms implies that consumers who are more likely to use online video platforms expect functional uniqueness from online video platforms. The concepts fundamental functional similarity and functional uniqueness in this study can be viewed as similar to the concepts of points of parity (POPs) and points of dif ference (PODs) in strategic management literature, even though the contexts are different. These two concepts are used when a new brand is launched in the market. Points of parity are those associations that are not necessarily unique to the brand but may in fact be shared with other brands (Keller, 2003, p. 133). Points of difference are attributes or benefits that consumers strongly associated with a brand, positively evaluate, and believe that they could not find to the same extent with a competitive brand (Keller, 2003, p. 131). The management literature stresses that when a new brand is introduced to the market, it needs to establish points of parity with the strong brand. However, the condition of points of parity is not sufficient for guaranteeing the success of the new brand. To compete with the strong brand, the new brand also needs to build points of differen ce to position itself as distinct from the strong, longer -existing brand. In the video content industry, the Internet as a newer video plat form should first establish fundamental

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175 functional similarity with television to educate consumers about its role as a video platform. Following that, in order to attract more and more consumers, online video platforms should develop further their function al uniqueness Different Gratifications between Online Video Platforms and Television Most of the previous studies in the field of mass communication attempted to learn the degree to which an emerging medium substitutes for traditional media by examining the displacement effect (e.g., Kaye & Johnson, 2003) or to investigate the relationship between the perceived substitutability and the intention to use the new medium (e.g., Lin, 2004). The current study attempted to identify further the motivations that affect the degree of the perceived substitutability between online video platforms and television. The motivations that that commonly drive consumers to use both online video platforms and television positively affect the perceived substitutability. However, t he differences between the two types of video platforms with respect to satisfying consumers needs negatively affect the level of the perceived substitutability (Althaus & Tewksbury, 2000) The findings of the present study indicate that the learning updated event information motive and the relaxation motive represent part of the discrepancy between online video platforms and television in terms of satisfying consumers gratifications. Because consumers tend to use the Internet for specific goals (Pa pacharissi & Rubin, 2000), online video platforms would be better than television for fulfilling the learning updated event information purpose Focusing on the perceived substitutability between traditional newspapers and online newspapers, Flavian and Gu rrea (2007) also found that the learning updated event information motive negatively affects the level of the perceived substitutability between offline newspapers and online newspapers. Prior studies also found that the Internet does, like television, satisfy entertainment needs, along with escape and social interaction needs

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176 (DAmbra & Rice, 2001; Kaye, 1998; Lin, 2001b). Nevertheless, the findings of the current study show that the Internet, as a video platform, does not yet satisfy the relaxation needs as much as television does. Importance of the Perceived Characteristics as Predictors of the Intention to Use Online Video Platforms and the Intention to Use Television In predicting the intention to use online video platforms, this study found tha t the relative advantage, compatibility, and perceived ease of use of online video platforms are statistically significant predictors. The three constructs stem from the technology acceptance model and innovation diffusion theory. While all of these three constructs are important, compatibility is the strongest and relative advantage is the second strongest predictor of the intention to use online video platforms. This provides evidence that the three constructs are robust in explaining the adoption of new communication technologies. The effect of the most powerful predictor, compatibility, on the intention to use online video platforms is noteworthy. Previous studies that combined innovation diffusion theory and the technology acceptance model usually found that the relative advantage has a stronger effect on the adoption intention than either compatibility or the perceived ease of use (Busselle, Reagan, Pinkleton, & Jackson, 1999; Leung, 2000; Leung & Wei, 1998; 1999; Lin, 1998; Li, 2004). There are, however, exceptions. Wu and Wang (2005) found that compatibility is the strongest predictor of the intention to use mobile commerce, compared with perceived usefulness, perceived ease of use, cost, and perceived risk. Chen, Gillenson, and Sherrell (2002) also discovered that in comparison with the perceived ease of use and perceived usefulness, compatibility has the greatest effect on attitudes toward using virtual stores. Both studies tested the adoption interests of alternative platforms (i.e., mobile commerc e and virtual stores) in

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177 situations where consumers had become accustomed to and were very familiar with traditional platforms. By contrast, the current study suggests that if consumers have had a lengthy experience with an existing medium that has fundam ental functional similarity with a new medium, the compatibility of the new medium might be more important than the relative advantage and the perceived ease of use. In other words, the compatibility of a new medium might have the strongest effect on the i ntention to use it in a situation where consumers have become, over a significant period of time, accustomed to using the existing medium to gratify their needs, and when the new medium is at an early stage of development (beginning to serve merely as an a lternative rather than a complete replacement for the existing medium). In short, the degree of the importance that consumers attach to the compatibility of online video platforms in relation to their intention to use the online video platforms may depend on the diffusion rates of the platforms and the rates of regular use. The initial adoption of an innovation requires more behavioral modification than simply continued future use of an innovation (Rogers, 1983). During the initial use phase of a new techn ology, consumers have to change their behavior from the old way of doing something to the new way (Agawal & Prasad, 1997). Therefore, until the majority of individuals in society use the new online video platforms on a regular basis, the perceived compatibility of online video platforms will be important in predicting the intention to adopt these platforms. Vishwanath and Goldhaber (2003) combined the technology acceptance model and innovation diffusion theory to predict attitudes toward and the intention to adopt cell phones among people who had not adopted cell phones at a time when even the late majority had already adopted cell phones. Their findings empirically showed that there is no direct effect of the

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178 compatibility on the attitude toward adopting c ell phones, nor was there an indirect effect of compatibility on the intention to adopt cell phones. Overall, the findings of the current study and previous studies demonstrate that the effect of the perceived compatibility on intention is dependent upon t he diffusion stage at what a new medium is studied. Another reason why compatibility is more important than the relative advantage and perceived ease of use in predicting the intention to use online video platforms is attributed to the inherent difference s between Internet -based media and traditional media. Lin (2001a) also suggested that compatibility can play an important role in consumers decision to adopt an Internet based service or system because her study found that the adoption of online services is not compatible with non Internet based adoption rates. While the technology acceptance model theorizes that the perceived usefulness and the perceived ease of use are the key determinants of the intention to accept a new system, the present study sugges ts that the addition of compatibility to the technology acceptance model is necessary particularly at the early stage of the technologys diffusion to predict the intention to adopt an Internet -based system. The significance of perceived ease of use in predicting the intention to use online video platforms implies, according to this studys findings, that the diffusion of online video platforms have not yet reached a critical mass. Some of the previous studies suggested that the perceived ease of use, as an antecedent of the perceived usefulness, instead has an indirect, not direct, influence on the intention to adopt a particular technology (Venkatesh & Morris, 2000; Davis, 1989; Davis, Bagozzi, & Warshaw, 1989). When a critical mass has already adopt ed a new technology, it is not uncommon to find that the effect of the perceived ease of use on adoption intention disappears. Li (2004) empirically found that there is no effect of the perceived ease of use on the adoption of cable television once cable p enetration had reached 80% of the

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179 population. The significant direct effect of the perceived ease of use on the intention to use online video platforms in this study illustrates that some consumers have difficulties using the Internet to watch video conten t and that the perceived difficulty can act as a deterrent to using online video platforms. Even though the press and industry reports illustrate the explosive growth of online videos (e.g., CNN Money, 2008), there seems to be a difference between one time online video watching and the consistent and regular use of online video platforms. When it comes to the perceived ease of use, the unique nature of online video platforms also need to be considered. While television allows people to watch the provided te levision shows, movies, or commercials simply by turning it on, online video platforms require more effort and overall know how on the part of viewers. This often results in viewers having to engage in activities such as searching, storing, or uploading th e video content they want. Such activities can be considered laborious by inexperienced users. Jrvelinen (2007) suggested that the effect of the perceived ease of use is direct and central in accepting a technology that requires more time and labor. That might be one of the reasons why the perceived ease of use has a direct effect on the intention to use online video platforms at this stage of its diffusion. In alignment with many previous studies, this study provides evidence in support of the idea that the perceived characteristics of online video platforms influence the intention to use online video platforms. Moreover, this study provides additional support to the discovery that relative advantage, compatibility, and perceived ease of use of online vi deo platforms are the determinants of the intention to use television. Specifically, the relative advantage and compatibility of online video platforms reduce the likelihood of using television. The findings imply that newer video platforms compete for con sumer demands with old video platforms, especially with respect to the relative advantage and compatibility. The relative advantage of

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180 online video platforms has the strongest effect; it negatively affects the intention to use television. The finding is co nsistent with some studies that proposed that a new medium will displace an existing medium when it serves the function of the existing medium in a better and more effective manner (Himmelweit, Oppenheim, & Vince, 1958; Schramm, Lyle, & Parker, 1961; DeFle ur & Ball Rokeach, 1982). The Influence of Subjective Norm and Perceived Behavioral Control on the Intention to Use Online Video Platforms In predicting the intention to use online video platforms, the perceived characteristics are not the only determinants. The perceived behavioral control and subjective norm the two constructs in the theory of planned behavior positively affect the intention to use the Internet to watch video content. By combining the technology acceptance model with the theory of pl anned behavior, a richer understanding of the intention to adopt a new technology emerges, and corroborates the findings of the studies conducted by Riemenschneider, Harrison, and Mykytyn (2003) and Yi, Jackson, Park, and Probst (2006). The current study s uggests that the integration of the technology acceptance model, innovation diffusion theory, and theory of planned behavior is reasonable for explaining the adoption of a new medium. The prediction model, which solely focuses on the perceived characterist ics of a new medium, disregards the impact of social influence and non -volitional control on the adoption of the new medium. The integration of the technology acceptance model and innovation diffusion theory with the theory of planned behavior offered a greater understanding of the intention to use online video platforms. However, it did not work well for predicting the intention to use television. In the end, the perceived behavioral control and the subjective norm of using online video platforms did not serve as the determinants of the intention to use television. A plausible explanation for this is that the effect of social norm on behavioral intention diminishes as users gain more direct

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181 experience (e.g., Karahanna, Straub, & Chervany, 1999; Venkates h, Morris, Davis, & Davis 2003). Given that over 98 % of U.S. households are already using television and thus have direct experience with television, social influence regarding the use of online video platforms, and the degree to which consumers have the necessary resources and knowledge of using online video platforms, neither reduce nor increase the likelihood to use television. Skepticism of the Influence of Flow Experience on Intention With respect to online flow experience, this study revealed that users of online video platforms have more online flow experience than do non users. However, the findings of this study illustrate that the level of flow experience online is not a significant predictor either of the intention to use the Internet or t he intention to use television to watch video content. Why is there is no effect of flow experience online on the intention to use online video platforms, even though users of online video platforms have more flow experience than do nonusers? It might be that flow experience is more likely to influence the actual use of, rather than intention to use a particular video platform. The previous research yielded mixed results regarding the influence of flow on behavioral intention. In the context of a game, Hs u and Lu (2004) found that flow experience positively affects the intention to play online games, but Lee and LaRose (2007) failed to detect the direct effect of flow experience on the intention to play video games. Lee and LaRose (2007) argued that flow i s so fleeting that the experience does not seem to influence behavioral intention or even the overall amount of time spent using video games. Instead, they suggested that flow experience might affect the duration of an actual behavior. In addition, Hoffman and Novak (1997) empirically showed that both frequency and duration of website visits increased according to the degree to which websites facilitate the flow experience. Therefore, the findings

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182 of the present study and previous studies suggest that futur e research can examine how online flow experience influences the duration or frequency of using online video platforms. Another possible reason why the current study did not detect a significant effect of flow experience on the intention to use online video platforms might be because consumers are more likely to use online video platforms for information -seeking purposes, rather than recreational purposes. Hsu and Lu (2004) suggested that the addition of online flow experience to the technology acceptance model is needed for a medium that is designed for entertainment, such as online gaming websites. Furthermore, Novak, Hoffman, and Yung (2000) also proposed that online flow experience is more likely to have an association with recreational activities than with task oriented activities. However, there is still little empirical research available that investigated how flow experience influences different types of media activities. Therefore, future research can examine this issue more deeply in order to provi de a valuable contribution to the field in general. Overall, the present study supports the argument of Siekpes (2005) that the flow construct is complex and multidimentional. Instrumental Viewing Orientation as a Critical Determinant of Television Use In light of uses and gratifications, this study examined how ritualistic and instrumental viewing orientations predict the intentions to use different types of video platforms. The findings indicated that both ritualistic and instrumental viewing orienta tions predict the intention to use television, but neither affects the intention to use online video platforms. On the surface, the finding is surprising for two reasons. First, instrumental viewing orientation positively predicts the intention to use tele vision, which is the opposite of the proposed hypothesis. Second, instrumental viewing orientation exerts a more dominant influence than ritualistic viewing orientation on the intention to use television.

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183 This study partly, but not fully, supports the pre vious studies that found that ritualistic viewing orientation positively influences television use (Greenberg, 1974; Hawkins, Reynolds, & Pingree, 1991; Stone & Stone, 1990; Metzger & Flanagin, 2002). Like the previous studies, this study also indicates th at ritualistic viewing orientation is linked to a greater intention to use television. At the same time, this study discovered that instrumental viewing orientation positively, rather than negatively, predicts the intention to use television and has a larg er influence on the intention to use television than does ritualistic viewing orientation. Indeed, the instrumental viewing orientation has the second largest effect on the intention to use television, followed by the relative advantage of online video pl atforms. These findings contradict prior studies that emphasized the importance of ritualistic orientation regarding the use of an old medium that consumers have been using for a long time. For example, Triandis (1971) suggested that repeated previous beha vior dictates current behavior, independent of rational assessments. Gefen (2003) also found that habit explains up to 40% of the variance in the intention to use the technologies with which consumers are already familiar. Nevertheless, the greater effect of instrumental viewing over ritualistic viewing orientations on the intention to use television represents the recent changes in the television viewing environment and consumer behaviors. The conception of television 40 years ago is not the same as that of television in the twenty -first century. In the broadcast era, most television viewing did not stem from a deliberate selection of programs, but rather was determined by convenience, availability of spare time, and the decision to spend that time in front of the television set (Prior, 2005). The theory of least objectionable program (Klein, 1972), which emerged in the broadcast television era, suggested that audiences went through two stages in the process of watching video content. People first

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184 decide to watch television and then pick the available program they like best Today, however, consumers have more choices in terms of channels, types of video platforms, and content. Thus, consumers goal -oriented orientation and preferences for, or interests in, a particular video content type or content characteristics presumably play a larger role in watching video content. Prior (2005) found that people who have a relative entertainment preference are less likely to learn about news when they have access to bot h cable and the Internet. Addressing the modern multichannel television environment with cable television and remote control, Perse (1990a, 1998) also found that instrumental television-viewing orientation is positively related to consumers program select ivity in watching television (Perse, 1990a, 1998). The descriptive statistics from the current study also showed that consumers are more likely to have an instrumental viewing orientation than a ritualistic orientation when watching video content, regardl ess of video platform types. Over the past decades, the emphasis of the ritualistic viewing orientation underlying television use may have caused researchers to overlook the importance of the instrumental viewing orientation factor. As a matter of fact, si nce the 1990s, instrumental orientation for television use has actually been higher than ritualistic orientation (Perse, 1990a; 1998). Considering the more than 100 television channels that average American households have access to today, the findings of the present study stress that instrumental viewing orientation which has been a more important orientation for watching video content than ritualistic viewing orientation since the 1990s has finally begun to determine the intention to use television in this multichannel video environment. Although the findings of this study indicated that there is no effect of instrumental orientation on the intention to use online video platforms, this study nonetheless shows that actual users of online video platforms have a greater instrumental viewing orientation than

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185 ritualistic viewing orientation. In a comparison between users and non users of online video platforms, the users of online platforms demonstrated that they also have a greater instrumental viewing orie ntation than do non users of online video platforms. This finding supports the fact that the Internet is more oriented to instrumental use than to ritualistic use (Papacharissi & Rubin, 2000). Why is there is no effect of instrumental viewing orientation o n the intention to use online video platforms? One reason might be that the perceived characteristics of a new medium have a greater influence than does media use orientation on consumers decision to adopt the new medium. In other words, the perceived rel ative advantage and compatibility of online video platforms are strong and critical predictors of the intention to use online video platforms but the poor perceptions of the relative advantage and compatibility seem to mitigate the effect of instrumental viewing orientation on the intention to use online video platforms. Displacement and Complementary Effects of Online Video Platforms on Television While the discussion on the theoretical implications so far have greatly focused on the predictors of the intentions to use the two different types of video platforms, another important investigation of this study was whether the Internet, as a video platform, has a time displacement effect on television. Prior studies tended to either focus on how the time sp ent with television has changed since audiences began to use the Internet, or addressed how the time spent on the Internet and on the television is related but without examining the displacement effect. The latter approach is insufficient to make claims about the displacement effect of the Internet on television, because there is a possibility that people who spend more time on the Internet have spent less time watching television even before they started to use the Internet, and in general have spent les s time watching television than average video content consumers.

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186 The current study showed that, overall, there is a time displacement effect of online video platforms on television. This study found that the more people spend time using online video platf orms, the less they spend time using television as a result. That is, there is a competition between the two modalities with respect to consumers time spent viewing video content. More importantly, this study revealed that the existence of the time displa cement effect depends on: 1) what type of online video venues consumers use, 2) how much video content overlaps between online video platforms and television in general, and 3) what type of video content consumers watch. The time spent using the Internet t o watch user -generated videos decreases the amount of time spent watching television as a result. However, the time spent using the Internet to watch branded videos did not alter the time spent watching television as a result. These findings may counter t he study conducted by Simon and Kadiyali (2007). They found that the cannibalization effect of the online versions of paper -based magazines becomes larger as there is a great amount of content overlap between the two modalities. The current study found the consumption of user -generated videos, which have little content overlap with branded-video content available on television, reduces the time spent watching television. The difference in findings may be partly attributed to the different research focuses. This study focused on how the overall content overlap between online video platforms and television influence the time spent watching television. On the other hand, Simon and Kadiyali (2007) concentrated on how the content overlap between online magazines and their corresponding print versions influences the viewership of each version. Whenever a displacement effect of a new medium on traditional media is debated, the discussion on a zero -sum game, or the principal of relative constancy, ensues. The zero-sum game suggests that the emergence of a new medium decreases the time spent with traditional

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187 media, because spending of consumers and advertisers is relatively constant; what changes with the introduction of new media is simply the way the consumers res ources are distributed (Picard, 2002; McCombs & Eyal, 1980). Owen and Wildman (1992) argued that attention to a particular channel is a zero -sum game in that one channels viewers come at the expense of anothers (p. 165). However, the results of the cur rent study suggest that the zero-sum game and the principle of relative constancy might oversimplify the time displacement effect of a new medium on traditional media. Although this study found that the time spent using online video platforms reduces the time spent watching television, it also discovered that the degree to which consumers spend time watching branded -video content online and on television network websites does not decrease or increase the time spent on television. If the scenario of the sim ple zero -sum game is true, the time spent watching branded video content through the Internet, and the time spent on television network sites, should be expected to reduce the time spent watching television. Yet this study also found that the time spent wa tching clips of television programs online actually increases the time spent watching television. As mentioned, the time spent watching branded video content online and the time spent on television network sites to watch video content did not have statis tically significant effects on the amount of time spent watching television as a result. How can this be? The heavy consumption of branded-video content online, especially on television network sites, might mean that these viewers do not generally have negative attitudes toward existing content on television. Therefore, people who consume branded -video content online and on television network sites seem also to spend a similar amount of time watching television regardless. By contrast, the people who spend a lot of time on engaging in user -generated videos and video sharing sites may not be

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188 attracted to content on television or a particular function of television. These people want functional uniqueness from online video platforms. Therefore, they ended up r educing their time spent watching television once they started to use online video platforms. Practical Implications In order to examine how the use of online video platforms and television are interrelated, this study applied the same sets of factors ( i.e., the perceived characteristics of online video platform and consumer characteristics) to predict the intention to use online video platforms and the intention to use television. Furthermore, from an applied perspective, this study aims to provide mana gerial implications not only for the online video industry but also for the television industry. Managerial Implications for the Online Video Industry This study found that some of the significant predictors overlap between the intention to use online video platforms and the intention to use television. Specifically, the perceived relative advantage, compatibility, and ease of use of online video platforms predict both the intention to use online video platforms and the intention to use television. The more consumers think that online video platforms have relative advantage over television, the more likely they are to use the Internet to watch video content. The more consumers think that online video platforms have relative advantage, the less likely the y are to use television. The perceived compatibility of online video platforms boosts the likelihood of using the Internet to watch video content, but it reduces the likelihood to use television. In other words, the relative advantage and compatibility of online video platforms can reduce consumer demand of television use. From the perspective of the online video industry, it is important for the online video firms to improve and promote the features that reflect the relative advantage and compatibility o f online video platforms. An important question is how to improve the perceived relative

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189 advantage of online video platforms. General Internet users agree that online video platforms are better than television in terms of many specific attributes namely the effort efficiency of search, time efficiency of search, interactivity, personalization, timeliness, usefulness of reviews and ratings, cumbersomeness of advertisements during viewing, instant replay, storage, and reliability. Although general Interne t users admit that online video platforms are better than television in those specific attributes, that does not preclude the fact that all of those attributes combine to contribute to attracting more consumers to their venues. However, the attributes cont ribute only partially to improving the overall perception of the relative advantage, which directly affects the likelihood to use online video platforms. The quality of video content, interactivity, and storage capability are the three key attributes that boost the perceived relative advantage of using online video platforms. Video content quality is more important than both interactivity and storage capability in making consumers believe that an online video platform is overall better than television. Unf ortunately, general Internet users, including both users and nonusers of online video platforms, do not perceive the video content quality of online video platforms to be better than that of television. Therefore, it is imperative for the online video ind ustry to acquire high -profile video content to draw more audiences to their venues. To improve the perception of video content quality, this study suggests that video sharing sites would benefit from establishing alliances with traditional media companies This study shows that general Internet users perceive video content quality on television to be much better than video content quality online. Likewise, non users of online video platforms are also more likely, with respect to the quality of the video co ntent, to think that television is better than online video platforms. Therefore, the introduction of the video content produced by traditional

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190 media firms and posted to their online venues can boost the perception of video content quality on video sharing sites. However, the acquisition of branded videos through alliances with traditional media firms cannot provide a complete solution for video sharing sites. This study found that both the current users of online video platforms and the people who are mor e likely to use these types of video platforms in the future want different things from television than from online video venues. Therefore, it is pivotal for the operators of video sharing sites to develop reliable and efficient systems to identify, support, and monetize the individual users who regularly upload video content and who attract large audiences. It is not uncommon for a very popular individual user of video sharing sites to attract mass audiences on a regular basis. Therefore, the operators of video sharing sites should create innovative business models that support high quality user -generated video content. This would help video sharing sites not only reinforce functional uniqueness but also secure the video content quality. Although the p ress illustrates the popularity of online videos, this study indicated that there is a discrepancy between one time watching of online video and the use of online video platforms. The statistics of this study revealed that 43% of U.S. Internet users do not employ the Internet to watch video content. To reinforce the position of the Internet as a video platform, it is important for the online video industry to convert nonusers of online video platforms to users. The non users of online video platforms perce ive online video platforms to be very poor in many specific attributes. The only attribute of online video platforms that the non -users of online video platforms consider better than television is that advertisements during viewing are less cumbersome. Non users of online video platforms think that television is better than online video platforms with respect to content variety, content quality, and reliability. They do not think that

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191 there are differences between online video platforms and television with respect to financial benefit, effort efficiency in search, time efficiency in search, interactivity, personalization, timeliness, usefulness of reviews and ratings, time shifting functions, and instant replay. Inexperience with online video platforms might be a reason why nonusers of online video platforms do not perceive online video platforms to be better than television in these many attributes. Therefore, online video venues need to offer opportunities that allow consumers to try and experience online video platforms. In converting non users into users of online video platforms, it is also essential for video sharing sites to improve the perceived compatibility of online video platform. The perceived compatibility is the strongest predictor of the inte ntion to use online video platforms, and Internet users overall view online video platforms as incompatible. Some people are reluctant to try to use online video platforms because they strongly believe that online video platforms conflict with their lifest yles, values, and past experiences. As a result, they do not consider the Internet to be an option for watching video content. To co mbat this, the operators of online video venues should make the viewing environment more encouraging so that non users and i nexperienced users can easily try online video platforms. To that end, the operators of online video venues need to simplify the interface of online video venues, allow viewing processes without cumbersome downloading of any software, and simplify the regi stration process. Another way to attract inexperienced users of online video platforms is to establish and stress the strong relative advantage of online venues, instead of only focusi ng on improving the compatibility. Given that consumers tend to think that offline media and Internet based media are incompatible, it might take time to change the perceptions of compatibility. However, the online video industry could develop and promote the relative advantage of online video

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192 platforms more easily, and can benefit from it within a short period time. Relative advantage of online video platforms is the second strongest determinant that increases the likelihood to use online video platforms and the strongest determinant that decreases the likelihood to use te levision. Considering that content quality, interactivity, and storage capabilities contribute to the overall perception of the relative advantage, the operators of online video sites could make exclusive deals with traditional media to deliver upto -date clips of appealing branded videos, reinforce the functions that help the users communicate easily with each other, and provide reliable and convenient virtual archive features where users can save their favorite clips of video content. The online video industry should bear in mind that the less consumers think that online video platforms and television are substitut able the more likely they are to use online video platforms. This negative relationship implies that online video venues need to promote how their venues are functionally unique compared with television. Of course, they should let consumers know that online video platforms and television have fundamental functional similarity in delivering video content at the nascent stage of the diffusion. However, the strategies that focus only on the complete functional substitutability between online video platforms and television may not increase the diffusion of online video platforms. Consumers who are more likely to use online video platforms want dif ferent things from online video platforms than from television. To reinforce functional uniqueness of online video platforms compared with television, this study suggests that the online video industry needs to focus on their platforms ability to satisfy consumers needs of learning updated event information efficiently. In other words, online video venues need to focus on acquiring content that provides consumers with opportunities to learn something new and with the latest news events.

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193 Managerial Implic ations for the Television Industry From the perspective of the television industry, the results of this study bring both good and bad news. The bad news is that the relative advantage and compatibility of online video platforms actually lessen consumer de mand for television. The more people spend time using online video platforms, the less time they spend time watching television as a result. The good news is that consumers perceive the relative advantage and compatibility of online video platforms to be poor. The perceived compatibility and the relative advantage of online video platforms are even lower among non users of online video platforms. Another piece of good news is that whether consumers spend more or less time watching television as a result of using online video platforms depends on: 1) what type of content they watch, 2) how much the content between online video platforms and television overlaps, and 3) what type of online video venues they use. It is noteworthy that overall, Internet users th ink that they have much control over using online video platforms with the necessary knowledge and resources, but they do not see much of the relative advantage and compatibility of online video platforms at the current stage. However, television firms sho uld be cautious about the users of online video platforms because these people are the television audiences who end up spending less time watching television as a consequence of online video use. This study found that the more a user of online video platfo rms spends time using the Internet to watch video content, the less he or she spends time watching television. Users of online video platforms have more positive evaluations regarding the relative advantage and compatibility of online video platforms compared with non users of online video platforms. Users of online video platforms also evaluated online video platforms as better than television in terms of content variety, financial benefit, effort efficiency in search, time

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194 efficiency in search, interacti vity, personalization, timeliness, usefulness of reviews and ratings, time shifting, cumbersomeness of advertising, storage, and instant replay. Note that users of online video platforms perceive interactivity and storage capabilities of online video platf orms, which contribute to improving the overall relative advantage of online video platforms, to be better than television even though they perceive video content quality of television to be better than that of online video platforms. Given that general Internet users and users of online video platforms perceive interactivity and storage capability of online video platforms to be better than those of television, the television industry should emphasize the quality of their video content through developing, producing, and programming. Conventional television is limited to offering interactivity and storage capabilities. Therefore, television firms can utilize their affiliated websites to overcome these limits. During or after the broadcasting of a show on television, the network can remind consumers that they have interaction opportunities with other viewers of the show on its website. Television networks can develop a feature that allows audiences to archive their favorite clips of the television programs on their affiliated television network websites. The era in which the role of television websites consists merely of delivering information about their shows and uploading their programs is over. Television network websites need to boost social interactio n opportunities between viewers of their shows and between viewers of their shows and cast and crews of the shows. Also, television network websites need to provide more personalized services by reinventing archive features. The television industry has be en concern ed about the displacement effect of online video platforms on television T h e findings of this study indicate that the time spent watching video content through the Internet indeed reduces the time spent watching television. That does not

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195 necessarily mean that the television industry should avoid utilizing the Internet as a distribution channel. Results from the basic model of game theoretic spatial differentiated model emphasize that it is important for firms to have hybrid operation (operating a cross different types of channels), which decreases the competitive intensities of the two channels (Viswanathan, 2005). The key to the success of the television industry with the rising use of online video platforms lies in balancing the different types o f video platforms. Specifically, the television industry needs to retain television as the primary distribution platform to maximize revenue. However, the role of online distribution platforms should not be disregarded. In this multichannel and multiplatf orm environment, the online distribution platform that television firms utilize should play an important ancillary role to attract more consumers and contribute to the profit of the television networks. To that end, this study suggests two ways that help t elevision networks fully exploit online distribution channels. First, television firms should utilize their own websites instead of video sharing sites in putting their video content online This study found that the more time consumers spend on video sha ring sites, the less time they spend watching television as a result. Therefore television networks need to be careful about providing video sharing sites with their video content, because there is a possibility that video sharing sites could end up erodi ng consumers time spent watching television, which is vital for generating revenues. The video sharing sites might be used for promotional purposes. Second, television firms can post clips of their television programs on line to increase audiences time s pent on television. The results of this study indicate that the time spent watching clips of television episodes online increases the time spent watching television. Also, the time spent using television network websites to watch video content does not red uce the

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196 amount of time watching television. Therefore, television firms can benefit from putting clips of their television programs on their own websites to attract more audiences to their programs on television. Or, it might use the video sharing sites to improve the visibility of its programs. If television firms want to upload an entire episode of a television program, doing so on their own websites instead of video sharing sites is a safer choice since the time spent watching branded videos online and the time spent on television websites do not reduce the time spent watching television. On the other hand, this study found that the time spent with video sharing sites decreases the time spent watching television. Despite the surge in the popularity of online video platforms, the Internet has a long way to go before it can stand as a more independent video platform. Only 1.5% of Internet users employ online video platforms alone to watch video content. Of online video platform users, 97.3% employ telev ision along with online video platforms. Although the time spent using online video platforms overall erodes the time spent watching television, from a viewership standpoint online video platforms still serve as an complementary alternative to television a t this stage, because the majority of online video platform users employs television along with online video platforms and the people who use online video platforms exclusively still account for a small portion of Internet users. It is also noteworthy tha t consumers spend far less time on online video platforms than on television, even though this study found that the time spent using online video platforms decreases the time spent watching television. On average, U.S. Internet users employ the Internet to watch video content 1.70 days during a typical week. They use the Internet to watch video content 2.05 hours during a typical week. In contrast, U.S. Internet users use television to watch

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197 video content 6.01 days per week. The time spent using television to watch video content is 19.89 hours per week on average. With respect to the relationship between television subscription types and the use of online video platforms, 13.5% of the Internet users who receive broadcast television networks over the air use online video platforms exclusively, abandoning television. They also spend slightly more time using the Internet to watch video content than do general Internet users. They spend 2.81 hours using online video platforms during a typical week. These finding s imply that online video platforms might replace television for those who have fewer channel choices. The cannibalistic effect of online video platforms on television might critically affect broadcast networks and affiliated stations. Television is still a powerful medium that mass audiences use 6 days a week. However, practitioners also need to pay attention to the changes of consumers viewing orientation regarding television use. Ritualistic viewing orientations, such as habit and pass time, were a str ong predictor of television use in the past. That is, consumers tended to stick to television as a medium to watch video content regardless of the content they wanted. Today, instrumental viewing orientations such as learning updated event information have a more powerful influence than ritualistic viewing orientation on consumers intention to use television. The greater influence of instrumental viewing orientation on the intention to use television in this study implies that todays consumers are more a ttached to content than to the medium per se. Th at is, they will utilize any type of video platform and will switch across different types to acquire the content they want. Given that other alternative video platforms such as the Internet and mobile device s are readily available, the results suggest that todays consumers are less likely to stick with television and more likely to employ any other alternative platforms that

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198 serve the specific content and gratifications they want, where and when they want it Therefore, the television industry needs to utilize multi -platforms. The key for the success of the television industry is how to repurpose and leverage its content to meet the functional uniqueness of different types of video platforms. Limitations of the Study This study highlights some valuable and unexpected findings involving the use of online video platforms and television, but it has some limitations. The findings of this dissertation depict what is currently going on with consumers video cont ent consumption and provides insights into how researchers and practitioners in the media industry can prepare for the future. Given that there is little research that employed a probability sample with Internet users as a population, the use of the probability sample in this study enables the researcher to move one step closer to generalizing the results of this study to general Internet users in the U.S. Nevertheless, it should be noted that the collected responses might be less representative of people between the ages of late teens to late 20s. The underrepresentation of young people in the responses might be because this study used mail surveys. If a researcher wants to learn more about video content consumption among young people, another research tar geting those people can be further conducted. In addition, it should be noted that the focus of this study was limited to how consumers perceive online video platforms and to what consumer characteristics determine consumers use of different types of v ideo platforms. Therefore, the theoretical and practical implications of this study also focused on those aspects. However, there would be other external factors, such as economic and regulatory issues, that might influence consumers use of different type s of video platforms. To fully examine how consumers demands of online video platforms and television interact with one another, it will be necessary to take these external factors into account.

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199 Another limitation of this study is the fact that the su rvey questionnaire was somewhat long since it examined the uses of two different types of video platforms instead of focusing on one platform. As a result, some of the participants might have felt tired while they filled out the questionnaire. There are al so some missing data. To overcome the problem involving missing data, this study utilized the full information approach over listwise deletion for testing hypotheses. The full information approach uses all of the available data and has fewer assumptions. With respect to the differences of viewership overlap between different types of television subscriptions, descriptive statistics were utilized without further investigation using inferential statistics, because there were less than five observations in s ome of the cells. Therefore, the results of the viewership overlap might slightly differ by sample. The generalization of the results regarding viewership overlap should call for caution. In predicting the intention to use online video platforms and the intention to use television, this study tested both a simple model and a complex model. The results of the complex model should be interpreted with caution for two reasons. First, the sa mple size is relatively small compared with the number of the variable s. Second, the inclusion of the control variables to the structural model is not theoretical but rather intuitive, so the complex model can be used for a more exploratory purpose. Lastly, the results regarding the ritualistic and instrumental viewing or ientation should be interpreted carefully when compared with previous studies. As mentioned in the method and discussion sections, researchers operationalized the two constructs differently. For instance, some of the researchers considered entertainment an d relaxation motives to be part of the instrumental viewing orientation. Others saw those motives as part of the ritualistic viewing

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200 orientation. To compare the results of this study with previous studies, it is recommended to pay attention to how each stu dy operationalized the ritualistic and instrumental viewing orientation. Future Research This study suggests several future research directions from theoretical and practical standpoints. The results of this study indicate that the compatibility of online video platforms is a positive predictor of the intention to use online video platforms and a negative predictor of the intention to use television. Yet many consumers still consider online video platforms to be incompatible with their lifestyles, values, and past experiences. At the initial stage of diffusion, the role of the perceived compatibility of a new technology is relatively more important than the later stage of diffusion. Nevertheless, there is little research that investigated how to improve th e perceived compatibility of a new medium. In order to advance an understanding of the compatibility construct in general, and to aid online video venues to improve the compatibility of online video platforms, future studies can examine the antecedents of the perceived compatibility of a new technology. Future studies can also focus on how the effect of the perceived compatibility is moderated by 1) the role of the new medium in relationship to the existing media and 2) the adoption stages of the new medi um and the existing media. It took 13 years and 10 years for broadcast television and cable television to reach 50 million viewers. On the other hand, it took the Internet less than 5 years to reach that number (Katz, 2006). It is still unclear how long it will take for a critical mass to use the Internet regularly to watch video content. Another valuable approach would be to examine how the compatibility, relative advantage, and perceived ease of use of online video platforms impact the consumers intentio n to use it differently as the online video platforms go through the different diffusion stages, and how the existing media will enter into a mature or declined stage.

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201 As a starting point, this study compares regarding the general use of online video plat forms to the general use of television without delving into content types consumed through each video platform. Dutta Burgman (2004) argued that the consumption of a medium should be understood from the aspect of content along with both context and consume r characteristics, because content can make a difference on the consumption of the medium (Duguid, 1996; Nunberg, 1996). Theories of program choice postulate that audiences have preferences for specific program characteristics (Bowman 1975; Lehmann 1971) o r program types (Youn 1994). The selection is derived from individuals interests in a specific subject area (Petty & Cacioppo, 1986). One fruitful approach would be for future studies to address how video content genre preference is related to the consum ption of favorite genres through the use of the Internet and television. Plausible research questions include whether individuals who watch a particular genre of video content online are more likely to watch that genre on television than are people who do not watch that genre online. Another useful research question would be whether there are any differences between users and nonusers of online video platforms with respect to video genre preferences. With the popularity of user -generated videos online, a possible research direction is also to examine how user -generated videos influence consumers intention to use online video platforms and television. A contrary argument to the relationship between genre preference and media consumption is that people may decide upon the video content type they want to use based on the characteristics of the video platform type. Or, consumers may choose video platforms according to the program type they want to watch. In other words, the content type that individuals want may depend on the platform characteristics or vice versa. Niche theory posits that different media forms serve different gratifications (Dimmick, Kline, & Stafford, 2000). People might

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202 prefer to watch the news rather than a series of dramas or comedies via the Internet because of their belief that the Internet facilitates searching for specific information, or because they might believe that the Internet is still not a good medium for relaxation -motivated video consumption. Even though there are a few indu stry reports that indicate which genres of video content are popular online, there is little theoretical and empirical research on why particular video genres are more popular than other types of video genres online. Future studies can explore whether the popularity of a specific video genre online is based on the genre popularity in general, regardless of video platforms or on consumers perceived fit between video genres and online video platforms. The general popularity of particular video genres over ot hers without a constraint of video platforms would moderately change over time. External factors also influence the preference for, or popularity, of the genres. From the managerial perspectives of both television networks and online video venues, knowing whether different types of video platforms have influences on audiences selections of specific genres of video content is crucial. In that vein, future research can examine the perceived fit between a video platform and video content genres. This study also suggests that there is a need to further explore how demographic variables are related to the intention to use and the actual use of different types of video platforms. The evaluation of the complex model in this study revealed that some of the demo graphic variables affect the intention to use online video platforms and intention to use television. Specifically, this study found that younger people are more likely to use online video platforms. Females are less likely than males to use online video platforms. Hispanics are less likely to use online video platforms than are nonHispanic Caucasians. On the other hand, there were no statistically significant relationships between other ethnic minority groups and the likelihood of using online

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203 video platf orms. The small sample size for the complex model calls for caution and the careful interpretation of the results from the complex model. There would be possibilities that different age and ethnic groups perceive and use online video platforms and televisi on differently. The literature focusing on diffusion of the Internet also supports the additional analysis of that matter by suggesting that the early adopters of the Internet are more likely to be male, of the ethnic majority, younger, better educated, an d more affluent than the general population (Bonfadelli, 2002; Chen & Wellman, 2004). Another future research direction is related to online flow experience. This study found that actual users of online video platforms have more online flow experience th an do nonusers of online video platforms. However, this study did not detect a direct effect of flow experience online on the intention to use online video platforms. Likewise, a previous study addressed the notion that online flow experience might not influence intention, but may instead have an impact on the duration of each session spent using a medium. Therefore, future studies can investigate whether the degree of online flow experience increases the amount of time watching video content online during each session. In the same vein, future research can also explore whether flow experience influences the displacement effect of online video platforms on television.

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204 APPENDIX QUESTIONNAIRE For the purpose of this study, the term video content is defined a s any type of content that is based on the combination of audio and video. Examples include television programs, music videos, movies, YouTube clips. PART I. Motives Q1.In your daily life, you watch video content for a variety of purposes. The follo wing statements describe reasons for watching video content. Please indicate how much you agree with each of the following statements. I watch video content ___________________________. Strongly disagree Moderately disagree Slightly disa gree Neutral Slightly agree Moderately agree Strongly agree Because it relaxes me 1 2 3 4 5 6 7 So I wont have to be alone 1 2 3 4 5 6 7 Because it allows me to unwind 1 2 3 4 5 6 7 When theres no one else to talk to or be with 1 2 3 4 5 6 7 Because it makes me feel less lonely 1 2 3 4 5 6 7 Just because its there 1 2 3 4 5 6 7 Because it lets me explore new things 1 2 3 4 5 6 7 Because its a habit, just something I do 1 2 3 4 5 6 7 To escape my worries 1 2 3 4 5 6 7 When I have nothing better to do 1 2 3 4 5 6 7 Because it passes time when I am bored 1 2 3 4 5 6 7 To forget my problems 1 2 3 4 5 6 7 To find breaking news events 1 2 3 4 5 6 7 Strongly disagree Moderately disagree Slightly disagree Neutral Slightly agree Moderately agree Strongly agree Because it opens me up to new ideas 1 2 3 4 5 6 7 Because it gives me something to do to occupy my time 1 2 3 4 5 6 7 Because I am interested in current events 1 2 3 4 5 6 7 Because it entertains me 1 2 3 4 5 6 7 Because its enjoyable 1 2 3 4 5 6 7 So I can be with other family or friends 1 2 3 4 5 6 7 Because it amuses me 1 2 3 4 5 6 7 Because its something to do with my family or friends 1 2 3 4 5 6 7 Because I am interested in the immediacy with which information can b e obtained 1 2 3 4 5 6 7 Because its pleasant rest 1 2 3 4 5 6 7 To find constantly updated event information 1 2 3 4 5 6 7 Because it extends my mind 1 2 3 4 5 6 7

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205 PART II. Online Video Viewing Television and the Internet are two of various means you can use to watch video content. For the purpose of this study, watching video content through the Internet is limited to the case of viewing video content on the computer through the Internet in real time The questions below do not include the fo llowing two cases: using the Internet to download video content or watching video content on a mobile phone. Q2. Which of the following media is your primary means to watch video content? Please choose one. Television [ ] Internet [ ] Q3. Do you use the Internet to watch video content? Yes [ ] No [ ] Q4. If you use the Internet to watch video content, please tell us how this has changed the amount of time you have spent watching television since you started using the Internet to watch video content. Decreased a lot Moderately decreased Slightly decreased Neither decreased nor increased Slightly increased Mo derately increased Increased a lot 1 2 3 4 5 6 7 Q5. How many days during a typical week do you use the Internet to watch video content? ( ) days during a typical week Q6. How often do you use the Internet to watch video content? Never Very rarely Rarely Sometimes Often Very often All the time 1 2 3 4 5 6 7 Q7. How many hours during a typical week do you use the Internet to watch video content? ( ) hours during a typical week Q8. There are two types of video content available online. One is user generated videos which are completely produced by individual Internet users The other type is branded videos that are originally produced by media companies such as television net works and film studios. How many days during a typical week do you use the Internet to watch each type of video content? User generated videos ( ) days during a typical week Branded videos ( ) days dur ing a typical week Q8 1. How many hours during a typical week do you use the Internet to watch each type of video content? User generated videos ( ) hours during a typical week Branded videos ( ) hours during a typi cal week Q9. How many days during a typical week do you use the Internet to watch each form of television programming? Clips of television programs ( ) days during a typical week An entire episode of television programs ( ) days during a typical week Q9 1. How many hours during a typical week do you use the Internet to watch each form of television programming? Clips of television programs ( ) hours during a typical week An ent ire episode of television programs ( ) hours during a typical week

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206 Q10. There are two types of online venues where you can watch video content. One type of venue is video sharing websites or video aggregation sites (e.g., YouTube, Veoh, Yahoo Video). The other type is websites that are affiliated with television networks (e.g., ABC.com, CNN.com). How many days during a typical week do you use each type of venue to watch video content? Video sharing si tes ( ) days during a typical week Television network websites ( ) days during a typical week Q10 1. How many hours during a typical week do you use each type of venue to watch video content? Video sharing sites ( ) hours during a typical week Television network websites ( ) hours during a typical week Q11. How often do you use both the Internet and the television for viewing a television program that you are inter ested in? Never Very rarely Rarely Sometimes Often Very often All the time 1 2 3 4 5 6 7 Q11 1. If you use the Internet to watch a certain television program, please tell us how this has changed the amount of time you have spent the televi sion program on television since you started using the Internet to watch the program. Decreased a lot Moderately decreased Slightly decreased Neither decrease nor increased Slightly increased Moderately increased Increased a lot 1 2 3 4 5 6 7 Q12. Of the two media below, which medium do you plan to use in the future to watch video content ? Television [ ] Internet [ ] The following statements describe the percei ved characteristics of using the Internet to watch video content. Q13. Please indicate how much you agree with each of the following statements. Q14. Please indicate how much you agree with each of the following statements. Strongly disagree Moderately disagree Slightly disagree Neutral Slightly agree Moderately agree Strongly a gree Using the Internet to watch video content is compatible with most aspects of my video content viewing. 1 2 3 4 5 6 7 Using the Internet to watch video content fits my lifestyle. 1 2 3 4 5 6 7 Using the Internet to watch video content fits well with the way I like to engage in video content viewing. 1 2 3 4 5 6 7 Strongly disagree Moderately disagree Slightly disagree Neutral Slightly agree Moderately agree Strongly ag ree Using the Internet to watch video content is better than television. 1 2 3 4 5 6 7 Using the Internet to watch video content fulfills my needs for video content consumption better than television. 1 2 3 4 5 6 7 Using the Internet to watch video content improves my lifestyle. 1 2 3 4 5 6 7

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207 Q15 17. Please indicate how much you agree with each of the following statements. Strongly disagree Moderately disagree Slightly disagree Neutral Slightly agree Moderately agree Strongly agree Learning to use the Internet to watch video content is easy for me. 1 2 3 4 5 6 7 It is easy for me to become skilled at using the Internet to watch video content. 1 2 3 4 5 6 7 It is easy to use the Internet for watching video content. 1 2 3 4 5 6 7 Strongly disa gree Moderately disagree Slightly disagree Neutral Slightly agree Moderately agree Strongly agree People important to me support my use of the Internet to watch video content. 1 2 3 4 5 6 7 People who influence my behavior want me to use the Internet t o watch video content. 1 2 3 4 5 6 7 People whose opinions I value prefer that I use the Internet to watch video content. 1 2 3 4 5 6 7 I feel free to use the Internet to watch what I want to watch. 1 2 3 4 5 6 7 Whether I use the Internet to watch vi deo content or not is completely within my control. 1 2 3 4 5 6 7 Whether or not I use the Internet to watch video content is entirely up to me. 1 2 3 4 5 6 7 I have the necessary means and resources to use the Internet to watch video content. 1 2 3 4 5 6 7 Strongly disagree Moderately disagree Slightly disagree Neutral Slightly agree Moderately agree Strongly agree The Internet and television offer different services for watching video content. 1 2 3 4 5 6 7 The Internet and television offer cont ent in the same way for watching video content. 1 2 3 4 5 6 7 The Internet and television satisfy different needs for watching video content. 1 2 3 4 5 6 7 Audiences consult the Internet and television in different situations for watching video content 1 2 3 4 5 6 7 The Internet and television can be considered different media for watching video content. 1 2 3 4 5 6 7

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208 Q18. I intend to use each of the following media to watch video content. Q19. I predict that I will use each of the following media to watch video content in the future. Q20. Please rate the following characteristics of using the Internet and television to watch video content. Using the following scale, circle the corresponding number in ALL boxes. PART III. Media use and demographics Please read the description of the term, flow, below and answer Questions 21 and 22. Strongly disagree Moderately disagree Slightly disagree Neutral Slightly agree Moderately agree Strongly agree Internet 1 2 3 4 5 6 7 Television 1 2 3 4 5 6 7 Strongly disagree Moderately disagree Slightly d isagree Neutral Slightly agree Moderately agree Strongly agree Internet 1 2 3 4 5 6 7 Television 1 2 3 4 5 6 7 Strongly disagree Moderately disagree Slightly disagree Neutral Slightly agree Moderately agree Strongly agree 1 2 3 4 5 6 7 Using the Internet to watch video content would Using television t o watch video content would Provide a variety of video content 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Provide high quality of video content 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Provide financial benefit 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Make it easy to search the video content I want to watch 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Provide sufficie nt interaction opportunities 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Be highly personalized 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Provide video content in a timely manner 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Provide helpful reviews and ratings of video content 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Provide convenient time shift functions 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Be cumbersome due to advertising during viewing 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Provide efficient storage capability 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Provide useful instant replay functions 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Allow me to save time searching for the video content I want to watch 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Be reliable 1 2 3 4 5 6 7 1 2 3 4 5 6 7 The word flow is used to describe a state of mind sometimes experienced by people who are totally invol ved in some activity. One example of flow is the case where a user is playing extremely well and achieves a state of mind where nothing else matter but the Internet; you engages in the Internet with total involvement, concentration and enjoyment. You are c ompletely and deeply immersed in it. The experience is not exclusive to the Internet: many people report this state of mind when web pages browsing, on line chatting and word processing.

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209 Q21. In general, how frequently would you say you have experienced flow when you use the Internet? Never Very rarely Rarely Sometimes Often Very often All the time 1 2 3 4 5 6 7 Q22. How much do you agree with the foll owing statement? Most of the time I use the Internet I feel that I am in flow. Strongly disagree Moderately disagree Slightly disagree Neutral Slightly agree Moderately agree Strongly agree 1 2 3 4 5 6 7 Q23. How many TV sets do you have in your household? ( ) Q24. Do you watch television? Yes [ ] No [ ] Q25. How many days during a typical week do you watch television? ( ) days during a typical week Q26. How often do you watch the television? Never Very rarely Rarely Sometimes Often Very often All the time 1 2 3 4 5 6 7 Q27. How many hours during a typical week do you watch the television? ( ) hours during a typical week Q28. Please check the subscription type(s) of your television service. Over the air only [ ] Basic and expanded basic cable [ ] Premium cable [ ] Satellite [ ] Others [ ] Q29. Number of TV channels you receive? ( ) Q30. Do you use the Internet? Yes [ ] No [ ] Q31. Do you have Internet connection in your house? Yes [ ] No [ ] Q32. Please indicate the subscription type of your Internet connection in your house. Dial up [ ] Highspeed [ ] Q33. Do you own a digital video recorder (DVR)? Yes [ ] No [ ] Q34. Gender: Male [ ] Female [ ] Q35. Age: ( ) years old Q36. Highest education level completed? Less than high school [ ] High school [ ] College [ ] Graduate school [ ] Q37. Current marital status? Single [ ] Married [ ] Other [ ] Q38. Household income? Less than $20,000 [ ] $20,000 $39,999 [ ] $40,000 $59,999 [ ] $60,000 $79,999 [ ] $80,000 $99,999 [ ] $100,000 or more [ ] Q39. Ethnic background? African American [ ] Asian [ ] Caucasian [ ] Hispanic [ ] Others [ ]

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231 BIOGRAPHICAL SKETCH Jiyoung Cha worked as an editing assistant, assistant director, and marketing director in the Korean film industry before she resumed her graduate studies. She earned her masters degree in Television, Radio, and Film at the S.I. Newhouse School of Communications at Syracuse University. Her research interests include new media, with emphasis on managerial perspectives; the interrelationship between new communication technologies and tradi tional media; and media brand management.