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Exploring Factors Influencing Personal Digital Assistant (PDA) Adoption


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EXPLORING FACTORS INFLUENCING PERSONAL DIGITAL ASSISTANT (PDA) ADOPTION By SUNGWOO KIM A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS IN MASS COMMUNICATION UNIVERSITY OF FLORIDA 2003

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This document is dedicated to my loving parents, Jin-Gil Kim and Soon-Ae You.

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iii ACKNOWLEDGMENTS I would like to express my deepest gratitude to my advisor, Dr. David Ostroff. Without his instructions, advice, patience, and generous support, this thesis would never have been possible. Dr. Ostroff’s guidance made the completion of this project a more enjoyable and enriching experience. Additionally, I would like to thank the other members on my committee: Dr. Sylvia Chan-Olmsted and Dr. Chang-Hoan Cho. Both gave me their willingness to serve on my committee and their insightful feedback throughout the process. I would like to thank Jaewon Kang, Hanjun Ko, and Seungeun Lee for inspiring me to get this thesis off the ground and their statistical know-how and support. I am also indebted to my Korean friends for supporting me whenever I faced problems. Finally, I would like to say thank you to my family: my parents, my sister, brother-in-law, and my lovely niece. Their love and support have encouraged me to overcome all difficulties while I have studied here. I dedicate this thesis to them.

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iv TABLE OF CONTENTS page ACKNOWLEDGMENTSCKNOWLEDGMENTS .................................................................................................iii LIST OF TABLES ............................................................................................................. vi LIST OF FIGURES ...........................................................................................................ix ABSTRACT .........................................................................................................................x CHAPTER 1 INTRODUCTION ............................................................................................................1 Personal Digital Assistant (PDA) ................................................................................... 2 PDA Definition....................................................................................................... 2 Operation Systems of PDA..................................................................................... 2 A History of PDA ................................................................................................... 3 Applications of PDA............................................................................................... 5 The Competitive Products of PDA: Smartpho ne and Pocket PC .... ............. .......... 6 Objective of the Study .................................................................................................... 7 2 LITERATURE REVIEW .................................................................................................9 Diffusion of Innovations................................................................................................. 9 The Innovatio n................. ................ ................ ................ ................ ............. ........ 10 Communication Channels..................................................................................... 13 Time...................................................................................................................... 13 A Social System.................................................................................................... 17 Diffusion and Adoption of Other New Technologies................................................... 18 Internet Adoption........ ................ ................ ................ ................ ................ .......... 19 Online Shopping Adoption ........................... ........................................................ 21 Personal Computers Adoption.............................................................................. 24 Information Systems (Audiotex, Videotex, and Electronic Bulletin Board) Adoption ....................................................................................................... 25 Other New Technologies Adoption ...................................................................... 27 3 RESEARCH MODEL ...................................................................................................29 Independent Variables ................................................................................................. .29 Perceived Characteristics of the Innovation ......................................................... 29

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v Ownership of New Technology Products........... ................ ............. ............. ........ 30 Personal Innovativeness........................................................................................ 32 Intervening Variables.................................................................................................... 33 Attitude toward PDA ............................................................................................ 33 Perceived Uncertainty........................................................................................... 34 Dependent Variable. ...................................................................................................... 36 Additional Variable....................................................................................................... 36 Hypothesized Model..................................................................................................... 36 4 HYPOTHESES AND RESEARCH QUESTIONS .......................................................38 Hypotheses.................................................................................................................... 38 The Persuasion Stage............................................................................................ 38 The Decision Stage ............................................................................................... 42 Research Questions....................................................................................................... 44 5 RESEARCH ME THODOLOGY ..................................................................................46 Sample......................................................................................................................... .. 46 Measurements ............................................................................................................... 46 Statistical Analysis.53 6 RESULTS ..................................................................................................................... .56 Descriptive Statistics..................................................................................................... 56 Sample Characteristics.......................................................................................... 56 Normality of Items................................................................................................ 58 Reliability.............................................................................................................. 59 Ownership and Familiarity of New Technologies................................................ 62 The Results of Hypotheses and Research Question s.................................................... 62 Factors Influencing Attitude toward Personal Digital Assistant (PDA)............... 63 Factors Influencing Perceived Uncertainty toward Personal Digital Assistant (PDA)............................................................................................................ 67 Relationship between the Intervening Variables (Attitude and Perceived Uncertainty toward PDA) and Purchase In tention........................................ 69 Factors Discri minating Two Purchasing Intention Group (High/Low)................ 70 Relation between Functi ons of PDA and Atti tude and Purchase Intention.......... 73 Demographic Characteristics of Purchase In tention Group.................................. 76 Final Parsimonious Model............................................................................................ 77 7 DISCUSSION................................................................................................................78 Review of the Present Study......................................................................................... 78 Summary of Results of Research Questions and Hypotheses ...................................... 79 Hypotheses............................................................................................................ 79 Research Questions............................................................................................... 84

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vi Implications................................................................................................................... 86 Theoretical Implications ....................................................................................... 86 Practical Implications............................................................................................ 8 9 Future Research .................................................................................................... 91 Limitations.................................................................................................................... 95 Conclusion .................................................................................................................... 97 LIST OF REFERENCES...................................................................................................98 BIOGRAPHICAL SKETCH ...........................................................................................113

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vii LIST OF TABLES Table page 2-1. Summary of the generalization of the adopter s.................. ................ ............. .........16 5-1. Measured variables...................................................................................................51 5-2. Observed variables ...................................................................................................52 5-3. Statistical methods....................................................................................................55 6 -1. Sample Characteristice.............................................................................................57 6-2. Descriptive profile of each variable .........................................................................59 6 -3. Ownership of new technologies. ..............................................................................62 6 -4. Results of hypotheses and research questions..........................................................63 6-5. Multiple regression analysis of attitude toward PDA on relative advantage, compatibility, complexity, trialabi lity, ownership of new technology, and personal innovativeness ..........................................................................................................65 6-6. Collinearity diagnostic s of multiple regression anal ysis of attitude and perceived uncertainty toward PDA on relative advant age, compatibil ity, complexi ty, trialability, ownership of ne w technology, and personal innovativeness.................66 6 -7. Stepwise multiple regression of attitude toward PDA on rela tive advantage, compatibility, trialability, and personal inno vativeness...........................................66 6-8. Multiple regression analysis of perceived uncertainty toward PDA on relative advantage, compatibility, complexity, trialabi lity, ownership of new technology, and personal innovativenss......... ................ ................ ................ ................ ..............68 6 -9. Stepwise multiple regression of perceived uncertainty toward PDA on relative advantage and complexity........................................................................................69 6 -10. Multiple regression analysis of purchase intention on attitude and perceived uncertainty toward PDA...........................................................................................70 6-11. Regression of purchase intention on attitu de toward PDA ......................................70

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viii 6 -12. Discrimina nt analysis between high-purchase intention group and low-purchase intention group .........................................................................................................72 6 -13. Classification results of discriminant analysis .........................................................73 6-14. Descriptive profile of perceived importance of functions of PDA ..........................74 6-15. Pearson co rrelation between perceived importance of PDA functions and attitude and purchase intention..............................................................................................75 6 -16. Factor analysis of perceived importance of PDA functions.....................................76 6 -17. t-test results comparing between purchase intention group .....................................76

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ix LIST OF FIGURES Figure page 3-1. Hypothesized Model. ...............................................................................................37 5-1. Personal Digital Assistant. .......................................................................................47 6-1. Final Parimonious Model.........................................................................................77

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x Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Arts in Mass Communication EXPLORING FACTORS INFLUENCING PERSONAL DIGITAL ASSISTANT (PDA) ADOPTION By Sungwoo Kim August 2003 Chair: David H. Ostroff Cochair: Sylvia Chan-Olmsted Major Department: Journalism and Communications This present study explored factors influencing the adoption of a Personal Digital Assistant (PDA). Under rapidly changing new media environment, it is important to know what affects the adoption of innovations. With this purpose, this study examined a few relationships and showed the results of data analysis. The theoretical background of this current study is diffusion of innovations. In particular, this study focused on the persuasion stage and the decision stage of the innovation-decision process. This research presents eight hypotheses and three research questions: (1) Which factors influence perceived uncertainty toward a Personal Digital Assistant (PDA) at the persuasion stage of the innovation-decision process and which variables have the relatively strong or weak influence on attitude and perceived uncertainty? (2) Will factors affecting attitude and perceived uncertainty toward a PDA be useful in discriminating two purchase intention

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xi groups (high or low)? and (3) Which functions of a PDA are related to attitude toward PDA and purchase intention? To investigate hypotheses and research questions, a survey was conducted. This study recruited 191 students from a large southeastern university. This current study employed multiple regression, stepwise re gression, t-test, and Pearson correlation analysis. Consistent with past studies, some hypotheses were supported, while other hypotheses were not supported. This study partially supported diffusion theory. As expected, most perceived attributes of PDA were significant determinant in this study. Relative advantage, compatibility, triala bility, and personal innovativeness were positively and significantly related to attitude toward PDA. This study found that relative advantage and complexity were significantly correlated to perceived uncertainty. In addition, only attitude toward PDA was a significant determinant to predict purchase intention. The lack of support might be from limitations of this study. This study dealt with only a few parts of diffusion theory. There remain many topics to be dealt with in future PDA adoption research.

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1 CHAPTER 1 INTRODUCTION Currently, the keyword of telecommunication industry is convergence. Good examples are Interactive TV (ITV) and a Personal Digital Assistant (PDA). ITV has several functions such as Electronic Programming Guide, Internet, Video On Demand (VOD), online shopping, Personal Video Recorder (PVR), and online game. In case of the PDA, most PDAs are used just as a digital organizer presently and only a few of models have digital organizer as well as other functions e-mail, Web browsing, mobile phone, MP3 player, playing games, digital ca mera, word processing/spreadsheets, and Global Positioning System (GPS). Some communication industry analysts contended that a PDA will become the most popular personal communication device and the necessity for businessmen in the near future. Computer Industry Almanac predicts that the PDA market will keep on growing and phone/PDA combos will become prevalent in 2003. In addition, by 2007, the common PDA will become a multifunctional communication device equipped with mobile phone, GPS, digital camera, etc., and the unit sales of PDA will reach more than sixty one billion units in the worldwide. eTForecasts (2002) also predicts that Pen-PDA, which is the leader in the PDA market, will be the market leader by 2008 among three kinds of PDAs (Pen-PDA, Keyboard-PDA, and Phone-PDA). Presently, total PDA sales are only about fifteen percent of total PC sales, but in 2008, PDA sales will reach about thirty three percent of total PC sales (eTForecasts, 2002). Based on data, it is evident that a PDA is the next generation of mobile computing.

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2 Personal Digital Assistant (PDA) PDA Definition A Personal Digital Assistant (PDA) is lightweight, hand-held computer designed for use as a personal organizer with communications capabilities (Infoplease, 2003, 1st paragraph). It can be held in persons hand, so it is sometimes called a Handheld PC. Many PDAs use a pen-like stylus to input information. Some PDAs work on a keyboard-based input system. Most PDAs adopt Window CE, EPOC, or PalmOS as an operating system. Currently, a PDA is used as not only an organizer or scheduler but also a cell-phone, fax-sender, and so on. According to International Data Corporation, the size of the PDA market will increase more than $26 billion and 63.4 million units will be sold by 2004. Operation Systems of PDA The operation system is one of the most important factors to be considered when consumers purchase a PDA. It is likely that consumers should choose either a Macintosh or an IBM PC when they purchase a personal computer (Freudenrich, 2003). There are three operation systems: (1) Palm OS (3Co m), (2) EPOC (Psion), and (3) Pocket PC (formerly Windows CE). Currently, Palm OS dominates the operation system market. It accounts for more than 70 percent of market share. Pocket PC, however, is encroaching on the territory of Palm OS (Freudenrich, 2003). Relatively, EPOC accounts for only a small portion of the market. Pocket PC supports color displays, graphics, standard packages software (e.g., MS Word, Excel), MP3, MPEG movie files and etc. Palm OS takes up less memory, so it runs faster. On the other hand, Pocket PC takes up more memory and runs slower.

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3 Generally, Palm OS is easier to use than Pocket PC (Freudenrich, 2003). However, the greatest advantage of Pocket PC is familiarity because most consumers are accustomed to use Windows as an operating system on their PC. According to eTForecasts (2002), Microsoft's Pocket PC, Compaq, HP, Toshiba and several other leading PC companies have introduced Pocket PC-based products that are now growing faster than the overall market. In addition, Linux-based PDAs are developing and appearing. By 2008, Pocket PC-based products and PalmOS-based products will account for almost the same portion of PDA operation system market share in the U.S. (eTForecasts, 2002). A History of PDA The origin of PDA is in the UK technology company Psion in 1984. The first model, the Psion I, was a bit thicker, longer, and narrower than a large pack of cigarettes. It had 10K of non-volatile character storage in cartridges, a search function, an LCD display, calendar etc. The Psion II superseded the Psion I in the mid-1980s. The highest version of the Psion II had 64K ROM, 32K RAM and a 4 X 20 character display. The next model, the Series 3a, opened the new generation in Psions evolution. It had the function to transfer, convert, and synchronize data between the different places. In addition, it had a 40 characters X 8 line mono LCD and 58-key keyboard in the base. These models enabled Psion to dominate the PDA market over more powerful models; Series 3c and Series 5 followed (PCTechGuide, 2003). One of the notable commercial PDA models is Apple Computers Newton Message Pad, a milestone of the information age. Soon after, other companies HewlettPackard Co., Motorola Inc., Sharp Electronics Corp. and Sony Electronics Inc.

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4 attempted to make a handheld communication device. The Newton model was not successful because it was too big and expensive. In particular, its handwriting recognition system was too complicated for users to understand. Even if the Newtons handwriting recognition technology was improved outstandingly compared with the first model, it never would be appealing to consumers. So, Apple gave up continued development of the Newton operating system in 1998 (Freudenrich, 2003; PCTechGuide, 2003). In 1996, Palm Computing, Inc. introduced Pilot products. This PDA had distinctive characters such as a palm-sized form, remarkably developed graphic interface, and the synchronization between the PDA and other computers. It had a different data input system with Apples Newton handwriting recognition technology. It enabled consumers to manage their personal and business information, schedules, and other matters anywhere and anytime. Pilots data input device was either a stylus or a touchsensitive screen. Pilot was small and light, us ed AAA batteries as an electronic power supply, and was easy to put to use. Thats why a Pilot was popular among consumers. In 1999, Palm Pilot devices were upgraded with excellent Personal Information Management (PIM) software and 160 X 160 pixel backlit screen. PIM software included personal address/phone book, diary, scheduler, calculator, personal account software, watch with alarm function, to-do list and so on. At that time some models were equipped with Graffiti power writing software, an enhanced version of Palm Computing, which enabled consumer to input data with ease. Si nce the advent of Pilot, Palm Computing has dominated the PDA market. In 2001 the sales of Palm rose to about 13 million (Freudenrich, 2003; PCTechGuide, 2003).

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5 Applications of PDA A PDA is one of the convergence communication devices. A PDA has several applications: Personal Information Management System (PIMS), mobile phone, Internet, E-mail, wordprocessing/spreadsheet, MP3/movie file player, video games, digital camera, global positioning system, and so on. Like other new technologies, a PDA can be used for several motivations and needs. PDA functions can be categorized into three types: information, entertainment, and communication. PIMS, internet, global positioning system, wordprocessing/ spreadsheet and digital camera can be classified as information function. Video games and MP3/movie file player are categorized into entertainment. Finally, as communication functions, mobile phone, and e-mail are classified. A PDA as an information device Personal information management system (PIMS). A PDA was originally invented as a personal organizer that allows consumer to access, store, and organizer personal information. Most models have these functions such as personal book (addresses, phone numbers, e-mail addresses), diary, scheduler, take notes/write memos, calculator, personal account software, watch (alarm function), and to-do list. E-Mail and Web browsing : The new communication technology allows consumers to send or receive e-mail and surf the Internet through a PDA without connecting with a desktop or notebook. Even though consumers should pay some fees for wireless service, they can use a PDA as mobile computer. Without wireless service, consumers should be able to download e-mails and Internet content from a desktop or notebook by connecting with some PDAs. Some models allow consumers to write e-mails, but not to send them.

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6 In order to send them, consumers must later send them through their computer. Some PDAs need some software for accessing e-mail accounts and Internet. Word processor/spreadsheet Keyboard-based PDA can provide attributes such as word processor, spreadsheet, and related softwares. Getting information. Because most PDAs have application for accessing the Internet, they allow consumers to get information like news, stock quotes, or something from the Internet. Digital camera and Global Positioning System (GPS). Some PDA models can be used as digital camera and GPS receiver. A PDA as an entertainment device MP3 and movie file player Some PDAs have entertainment functions such as playing mp3 music files and mpeg movie files. Video games. Some PDA models provide consumers with video games that can be played by oneself or with other people through wireless network. A PDA as a communication device Mobile phone. The latest PDA model includes mobile Phone. This model does everything a mobile phone does. The Competitive Products of PDA: Smartphone and Pocket PC Smartphone is defined as a mobile, digital telephone that has features not associated with traditional home or mobile phone and Pocket PC is defined as an upgraded version of Windows CE that offers greater stability and a new interface. Features include mobile Internet capabilities, an e-book reader, and handwriting recognition (Yahoo!, 2003). Smartphone is a new technology in mobile phone that

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7 combines voice and data communication. Like a PDA, Smartphone has functions such as personal information management, sending or receiving e-mails, Internet surfing, playing mp3 and movie files etc. A PDA is based on PIMS, whereas Smartphone is based on a mobile phone with other functions added. These days, however, it is difficult to differentiate among a PDA, a Pocket PC, and a Smartphone because they have almost the same applications. Some specialists suggest that the classification of PDA, Pocket PC, and Smartphone is meaningless. Objective of the Study In Korea, which is one of the leading countries for information technology industry, the sales of PDA were decreased in 2002 compare to the previous year. Generally, it was predicted that the sales of PDA would increase every year. A decrease in sales of PDA was not expected by a number of specialists, professor, and analysts of electronic and communication industry. As re asons for this situation, some analysts proposed that the price of PDA is still too high or a PDA does not give consumers better benefits compared to other new communication devices. Considering this present situation of PDA, this study wants to identify which variables affect consumers adoption of PDA. In addition, even though PDA will become an important communication device in the near future, there is little research about PDAs. This study expects that research about the adoption of PDA will be helpful for marketers or developers related to a PDA at this time. This study focuses on only two stages of adoption process because of the novelty of PDA (Eastlick, 1996). This research intends to investigate eight hypotheses and three research questions: (1) Which factors influence perceived uncertainty toward a Personal

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8 Digital Assistant (PDA) at the persuasion stage of the Innovation-Decision process and which variables have the relatively strong or weak influence on attitude and perceived uncertainty? (2) Will factors affecting atti tude and perceived uncertainty toward a PDA be useful in discriminating two purchase intention groups (high or low)? and (3) Which functions of PDA are related to attitude toward PDA and purchase intention? Through research hypotheses and questions, this present study will explore the relative influence of perceived characteristics of innovation, ownership of new technologies, personal innovativeness, and attributes of a PDA in exploring attitude toward a PDA, perceived uncertainty for a PDA and adoption of a PDA, based on a model of the Innovation-Decision Process of Diffusion theory.

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9 CHAPTER 2 LITERATURE REVIEW This present study intends to examine the relative influence of perceived characteristics of innovation, personal innovativeness, attributes of PDA in exploring attitude toward PDA, perceived uncertainty toward PDA and adoption of PDA. First, a literature review focuses on Rogers diffusion of innovation theory. The diffusion of innovations perspective will be helpful in explaining key concepts of the adoption of PDA. Second, previous adoption studies will be introduced and reviewed. Variables that have been investigated to explain adoption of other new technologies will be examined. Diffusion of Innovations Rogers (1995) defined diffusion as the process by which (1) an innovation is (2) communicated through certain channels over (3) time among the members of (4) a social system (p. 5). Rogers investigated more than 2,000 empirical diffusion research studies and 3,000 publications (Severin and Tankard, Jr., 1992). Among voluminous diffusion studies, one of the most influential is The Iowa Hybrid Seed Corn Study (Ryan and Gross, 1943) (cited in Severin and Tankard, Jr., 1992). The investigation of the diffusion of hybrid-seed corn in Iowa affected the methodology, theoretical framework, implications, and interpretations for later diffusion studies and established the classical diffusion paradigm (Rogers, 1995; Severin and Tankard, Jr., 1992). The Iowa hybrid corn study interviewed 259 farmers to investigate when and how they adopted hybrid seed corn and to get information about them and their farm operation. The Iowa study found that the rate of adoption was S-shaped and various communication channels played

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10 important and different roles during the diffusion process. In addition, the Iowa study found the four main component of diffusion: (1) an innovation, (2) the communication channels, (3) time, and (4) a social system. The Innovation An innovation is an idea, practice, or object that is perceived as new by an individual or other unit of adoption (Rogers, 1995, p .11). According to Rogers (1995), an individuals reaction to innovation depends on the perceived newness of the idea and whether an individual thinks the idea novel, so it could be innovation. An individual exhibits newness of an innovation as knowledge, persuasion, or a decision to adopt. Most of the new ideas are related to technological innovations, so sometimes technology (p. 12) is used as a synonym of innovation (Rogers, 1995). Thomson (1967) and Eveland (1986) proposed that a technology is an instrumental design which can decrease the uncertainty in cause-effect relationships in order to fulfill a desired goal. A technology is generally composed of two elements: hardware and software (Rogers, 1995). Rogers (1995) asserted that hardware consists of the tool that embodies the technology as a material or physical object (p. 12) and software consists of the information base for the tool (p. 12). New technology usually has both a hardware aspect and a software aspect. According to Bayus (1987), a company intends to sell the hardware at a relatively low price in order to capture market share, and then sell the software at a relatively high price in order to improve profits. For example, Xbox and Playstation 2 are currently sold at $199, a relative low price, whereas game softwares for Xbox and Playstation 2 are sold at prices ranging from $40 to $50, a relative high price.

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11 A technological innovation usually provides not only a sort of uncertainty about expected consequences, but also an opportunity to reduce uncertainty (Rogers, 1995). In order to reduce uncertainty about an innovation, an individual seeks information. There are two kinds of information in terms of a technological innovation: software information which is embodied in a technology and serves to reduce uncertainty about the causeeffect relationships in achieving a desired outcome (p. 14) and innovation-evaluation information which is the reduction in uncertainty about an innovations expected consequences (p. 14). Perceived characteristics of innovations What perceived attributes of innovations influence the rate of adoption? This research question is important in diffusion research studies. Many previous diffusion studies focused on the characteristics of adopters. Little effort, however, has been made to investigate what properties affect rate of adoption (Rogers, 1995). Rate of innovation is the relative speed with which an innovation is adopted by members of a social system and a numerical indicator of the steepness of the adoption curve for an innovation (Rogers, 1995, p. 22). The five perceived attributes of innovations may explain rate of adoption from 49 to 87 percent of the variance (Rogers, 1995). The five attributes are (1) relative advantage, (2) compatibility, (3) complexity, (4) trialability, and (5) observability (p. 206). In addition, there are other variables: (1) the type of innovation-decision, (2) the nature of communication channels diffusing the innovation at various stages in the innovation-decision process, (3) the nature of the social system in which the innovation is diffusing, and (4) the extent of change agents promotion efforts in diffusing the innovation (p. 206).

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12 Rogers (1995) defined the five characteristics as the followings; (1) relative advantage is the degree to which an innovation is perceived as better than the idea it supersedes (p. 212), (2) compatibility is the degree to which an innovation is perceived as consistent with the existing values, past experiences, and needs of potential adopters (p. 224), (3) complexity is the degree to wh ich an innovation is perceived as relatively difficult to understand and to use (p. 242), (4) trialability is the degree to which an innovation may be experimented with on a limited basis (p. 243), and (5) observability is the degree to which the results of an innovation are visible to others (p. 244). The perceived relative advantage, compatibility, trialability, and observability are positively associated with the rate of adoption, but complexity is negatively related to the rate of adoption (Rogers, 1995). There have been several researches intended to explain adoption of innovations by the perceived attributes of innovations as predictors of adoption (LaRose and Atkin, 1992; Eastlick, 1993 & 1996; Lin, 1998; Parthasarathy et al., 1998; Du, 1999). Eastlick (1993) examined whether relative advantage, compatibility, complexity, and trialability would affect the adoption of videotex more than other properties of the innovation. Relative advantage and compatibility properties were more significant predictors than other properties (Eastlick, 1993). Lin (1998) proposed that in adopting a personal computer, consumers considered relative advantage of personal computer, but complexity was not a real apprehension. LaRose and Atkin (1992) found that compatibility was not a good predictor in explaining the adoption of Audiotext. Du (1999) contended that the complexity and relative advantage of internet was significantly related to adoption of

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13 Internet in China. Based on previous studies, this study expects that perceived attributes of the innovation should be the important variables affecting adoption of PDA. Communication Channels Communications is a process in which pa rticipants create and share information with one another in order to reach a mutual understanding (p. 35), and a communication channel is the means by which messages get from one individual to another (p. 36) (Rogers, 1995). According to Rogers (1995), usually, communication channels includes two kinds of channels: mass media channels and interpersonal channels. Mass media channels include any mass medium such as television, internet, radio, and so on, which transmit messages to one or more individuals, so mass media channels are more rapid and efficient channels to send potential adopters information about innovations than interpersonal channels. Interpersonal channels are more effective in encouraging an individual to adopt an innovation, because interpersonal channels involve a face-to face exchange between two or more individuals who are in similar in socioeconomic status (Rogers, 1995). Time Time is one element in the diffusion process (Rogers, 1995). Rogers (1995) showed several processes relative to the time dimension in adoption of innovations: (1) the innovation-decision process, (2) the innovativeness of an individual or other unit of adoption compared with other members of a system, and (3) an innovations rate of adoption in a system (p. 20).

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14 The innovation-decision process The innovation-decision process is the process through which an individual (or other decision-making unit) passes from first knowledge of an innovation to forming an attitude toward the innovation, to a decision to adopt or reject, to implementation and use of the new idea, and to confirmation of this decision (Rogers, 1995, p. 20). Rogers (1995) operationalized five steps in this process: (1) knowledge, (2) persuasion, (3) decision, (4) implementation, and (5) confirmation. This process is an informationseeking and information-processing activity (p. 20), by which an individual intends to get information in order to reduce uncertainty about innovations (Rogers, 1995). Rajagopal (2002) conceptualized the six steps into (1) initiation, (2) adoption, (3) adaptation, (4) acceptance, (5) routinization, and (6) infusion. Grover and Goslar (1993) suggested three steps of adoption: (1) initiation, (2) adoption, and (3) implementation. The knowledge stage occurs when an individual is exposed to an innovations existence and obtains some information about how it functions (Rogers, 1995). At the persuasion stage, an individual develops a favorable or unfavorable attitude toward the innovation. At the decision stage, an individual takes part in activities to choose adoption or rejection of an innovation. The implementation stage occurs when an individual puts an innovation into use and at this stage, re-invention is likely to occur. And finally, at the confirmation stage, an individual intends to discover reinforcement of the innovationdecision already made or reverses a previous decision to adopt or reject the innovation if exposed to conflicting messages about the innovation (Rogers, 1995, p. 181). In this study, the persuasion stage and the decision stage will be focused on.

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15 Innovativeness and adopter categories Innovativeness is the degree to which an individual or other unit of adoption is relatively earlier in adopting new ideas than the other members of a system (Rogers, 1995, p. 22). Innovativeness is the criterion for adopter categorization, so Rogers (1995) categorized adopters based on the relative time at which an innovation is adopted: (1) innovators: venturesome, (2) early adopters: respect, (3) early majority: deliberate, (4) late majority: skeptical, and (5) laggards: traditional. Generally, the adopter distribution is closely bell-shaped. It means that the classification of adopters is almost the normal frequency distribution. Rogers (1995) found that the innovators accounts for about 2.5 percent, early adopters accounts for about 13.5 percent, early majority accounts for about 34 percent, late majority accounts for about 34 percent, and laggards accounts for the last 16 percent. Numerous researches have investigated which variables are related to innovativeness and the classification of adopters. Rogers (1995) summarized variables into three categories: (1) socioeconomic status, (2) personality values, and (3) communications behavior (p. 268). Rogers (1995, p. 269-274) asserted the following generalizations based on three categories (see table 1). According to him, earlier adopters are younger, better-educated, and higher-status than later adopters. In terms of personal variables, earlier adopters have greater empathy, ability to deal with abstraction, rationality, and intelligence than later adopter s. In addition, earlier adopters are have more highly interconnected through interpersonal networks in their social system, more change agent contact, greater exposure to mass media communication channels and interpersonal communication channels, greater knowledge of innovations than later adopters (Rogers, 1995).

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16 Table 2-1: Summary of the generalization of the adopters Socioeconomic Characteristics Earlier adopters are not different from later adopters in age. Earlier adopters have more years of formal education than later adopter s. Earlier adopters are more likely to be literate than are later adopters. Earlier adopters have higher social status than later adopters. Earlier adopters have a greater degree of upward social mobility. Earlier adopters have larger units (farms, schools, companies, and so o n) than later adopters. Personality Variables Earlier adopters have greater empathy than later adopters. Earlier adopters may be less dogmatic than later adopters. Earlier adopters have a greater ability to deal with abstractions than do later adopters. Earlier adopters have greater rationality than later adopters. Earlier adopters have greater intelligence than later adopters. Earlier adopters have a more favorable attitude toward change than late r adopters. Earlier adopters are better able to cope with uncertainty and risk than la ter adopters. Earlier adopters have a more favorable attitude toward science than late r adopters. Earlier adopters are less fatalistic than later adopters. Earlier adopters have higher aspirations (for formal education, occupati ons, and so on) than later adopters. Communication Behavior Earlier adopters have more social participation than later adopters. Earlier adopters are more highly interconnected through interpersonal n etworks in their social system than later adopters. Connectedness is the degree to which an individual is linked to others. Earlier adopters are more cosmopolite than later adopters. Earlier adopters have more change ag ent contact than later adopters. Earlier adopters have greater exposure to mass media communication c hannels than later adopters. Earlier adopters have greater exposure to interpersonal communication channels than later adopters. Earlier adopters seek information about innovations more actively than later adopters. Earlier adopters have greater knowledge of innovations than later adopt ers. Earlier adopters have a higher degree of opinion leadership than later a dopters. Rate of adoption The rate of adoption is the relative speed with which an innovation is adopted by members of a social system (Rogers, 1995, p. 206). Generally, most innovations have an S-shaped curve and the stiffness of curve depends on the rate of innovations (Rogers,

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17 1995). Rogers (1995) contended that five variables affect the rate of adoption of innovations: (1) perceived attributes of innovations, (2) type of innovation-decision, (3) communication channels, (4) nature of the social system, and (5) extent of change agents promotion efforts (p. 207). Perceived attributes of innovations relative advantage, compatibility, complexity, trialability, and observability explain from 49 to 87 percent of the variance of the rate of adoption (Rogers, 1995). Rogers (1995) suggested that Innovation-decision falls into three types: optional, collective, and authority (p. 372) and defined the three types of innovation-decisions as the followings (p. 372); Optional innovation-decisions : choices to adopt or reject an innovation that are made by an individual independent of the decisions by other members of a system. Collective innovation-decisions : choices to adopt or reject an innovation that are made by consensus among the members of a system. Authority innovation-decisions : choices to adopt or reject an innovation that are made by a relatively few individuals in a system who possess power, status, or technical expertise. In general, optional innovation-decisions by individual are more rapidly adopted than collective innovation-decisions by an organization, because the number of persons who take part in making a decision influences negatively the rate of adoption (Rogers, 1995). Additionally, communication channels, the nature of the social system, and the extent of change agents promotion efforts affect an innovations rate of adoption. A Social System Rogers (1995) defined a social system as a set of interrelated units that are engaged in joint problem-solving to accomplish a common goal (p. 23). Individuals, informal groups, organizations, or subsystems may be the units of a social system

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18 (Rogers, 1995). A social system is some place for diffusion to occur. The effect of system norms, opinion leaders and change agents in a social system, types of innovationdecisions, and consequences of innovation are affected by the systems social structure (Rogers, 1995). There are individuals to provide information and advice about innovations to other members in the social system. Opinion leaders influence members of the social system (Rogers, 1995). Rogers (1995) defined opinion leadership as the degree to which an individual is able to influence other indi viduals attitudes or overt behavior informally in a desired way with relative frequency (p. 27). Compared with the followers, opinion leaders are (1) more exposed to all forms of external communication, and thus are more cosmopolite, (2) have somewhat higher social status, and (3) are more innovative (Rogers, 1995, p. 27). In a social system, an opinion leader may support or oppose change. A change agent is a professional who represents change agencies external to the system (Rogers, 1995). A change agent is an individual who influences clients innovation-decisions in a direction deemed desirable by a change agency and generally attempts to obtain the adoption of innovations or prevent the adoption (Rogers, 1995). Sometimes, change agents use opinion leaders in a social system as a means of diffusion campaigns (Rogers, 1995). Diffusion and Adoption of Other New Technologies Television, radio, Video Cassette Record er (VCR), videotex, audiotex, cable, personal computer, digital TV, HDTV, internet, mobile phone, Satellite TV, Personal Digital Assistant (PDA) during the past decades, we have met various new

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19 communication technologies. It is no wonder that there are many previous researches about adoption of new technologies. Diffusion of innovation theory is the dominant paradigm for explaining innovation adoption in communication studies. This has been applied to a wide range of new technologies adoption: Audiotext (LaRose and Atkin, 1992; Neuendorf and Atkin, 1998), computer (Danko and MacLachlan, 1983; Dickerson and Gentry, 1983; Dutton, Rogers, and Jun, 1987; Lin, 1998), videotex (Heikkinen and Reese, 1986; Eastlick, 1993), cable (LaRose and Atkin, 1988), internet (Atkin, Jeffres, and Neuendorf, 1998; Du, 1999; Ferguson and Perse, 2000; Papacharissi and Rubin, 2000; La Ferle, Edwards, and Mizuno, 2002), HDTV (Dupagne, 1999), ISDN (Jeffres and Atkin, 1996), electronic bulletin board (James and Wotring, 1995), personal social services (Martinez-Brawley, 1995), comput er-mediated political communication systems (Garramone, Harris, and Pizante, 1986), and online shopping (Zellweger, 1997; Jarvenpaa and Tractinsky, 1999; Li, Kuo, and Russell, 1999; Lohse and Spiller, 1999; Swanminathan, Lepkowska-White, and Rao, 1999; Tan, 1999; Wolfinbarger and Gilly, 1999; Vellido, Lisboa, and Meehan, 2000; Miyazaki and Fernandez, 2001; Fenech and OCass, 2001; Limayem et al., 2001: Koufaris, et al., 2002). From television to Internet, various innovations have been researched as to their adoption. A PDA has several new technologies as functions (e.g., Internet, e-mail, mobile phone, wordprocessing/spreadsheet, etc.). Therefore, this study believes that previous adoption studies may be helpful in examining which factors influence adoption of PDA. Internet Adoption Internet is the most rapidly growing new mass media. Internet has already become one of the important necessities in daily life. Internet has grown significantly

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20 during the past decade, particularly with resp ect to its use as a tool for communication, entertainment, and online shopping. The new PDA models allow consumers to access wireless Internet. So, it may be argued that a PDA has a close relation to Internet. Many researchers have studied the Internet. Papacharissi and Rubin (2000) examined predictors of Internet use in terms of a uses-and-gratifications perspective and how (1) social and psychological antecedents; contextual age and unwillingness to communicate, (2) perceptions of media attributes; social presence, and (3) internet motives influence behavioral (patterns of internet exposure) and attitudinal (internet affinity and satisfaction) outcomes of internet use (p. 182). They found some interesting results that the relationship between interper sonal utility and passing time was the highest correlation among Internet motives and the relationships between internet motives and the social and psychological antecedents support the use of the internet as a functional alternative for internet users for whom other channels were not as available or rewarding (p. 191). Ferguson and Perse (2000) intended to explore the World Wide Web (WWW) as a functional alternative to television. Thei r research investigated the similarity of television viewing and internet surfing, and showed that the most significant motive for visiting WWW is entertainment and that, as a way to pass time (p. 169), Internet can compete with television. In addition, they proposed that television viewing and the WWW are functionally different and the Web may not become a functional alternative to television viewing for relaxation (p. 170). Atkin et al., (1998) explained adoption of internet by several variables: social locators, media use, new media adoption, and communication needs. According to Atkin et al., (1998), among independent variables, social locators (age, education, and income) and technology compatibility were

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21 significantly associated with adoption of internet, while communication needs, activities, and orientations were not related to Internet access. Du (1999) and Carrie et al. (2002) examined Internet diffusion in China and Japan respectively. Du (1999) purposed to find out which factors affect Internet adoption and Chinese usage patterns. It was found that in China, early adopters of internet were prominently male, young, well-educated, higher-income, and single (Du, 1999). In addition, Du (1999) contended that relative advantage, complexity, innovativeness, Internet content, resources, speed, and ISP se rvice quality affected Internet adoption in China. Carrie et al. (2002) considered cultural difference in Internet diffusion and suggested individualism, uncertainty avoidance, the power distance, and masculinity as factors which contribute to differences in adoption of the Internet and computers. Online Shopping Adoption The Internet has grown significantly during the past decade. In particular, online shopping is a rapidly growing area in Internet business. Forrester Research forecasted that online retail trade will be about $ 217.8 billion by 2007 and account for 8% of total retail revenue. Researches for online shopping or electronic exchange usually have examined the factors that influence adoption of online shopping. Perceived risk (security/privacy) (Donthu and Garcia, 1999; Jarvenpaa et al., 1999; Vellido et al., 2000; Fenech and OCass, 2001; Miyazaki and Fernandez, 2001), trust (Donthu and Garcia, 1999; Swaminathan et al., 1999; Jarvenpaa et al., 1999; McKnight and Chervany, 2001), demographics (Donthu and Garcia, 1999; Li et al., 1999), shopping orientation (Li et al., 1999; Swaminathan et al., 1999; Wolfinbarger and Gilly, 1999; Fenech and OCass, 2001; Fenech and OCass, 2001; Koufaris et al., 2001), innovativeness (Donthu and

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22 Garcia, 1999; Citrin, 2000) and other factors (retailers reputation, internet usage, channel utilities, etc.) were used as independent variab les in order to explore the factors that affect online shopping behavior, attitude, and adoption. A PDA has a function of Internet. It means that consumers can do online shopping through a PDA. Therefore, the previous studies about the adoption of online shopping will be helpful for this present study to select the factors influencing the adoption of PDA. Demographic has been an important factor in diffusion, adoption, and uses & gratifications studies. Li et al. (1999) posited that consumers who are better educated, have a higher income, and are male will purchase online more frequently than these who are not. In addition, they suggested a proposition that age is not a significant factor. Donthu and Garcia (1999) also posited that Internet shopper differ from non-shoppers in age, education, income, and gender (p. 53). Especially, it was expected that age might be a factor that affects internet shopping (Donthu and Garcia, 1999). Both researches found that income is significantly related to online shopping behavior. Interestingly, however, the results of the other factors (age, education, and gender) were different each other. Li et al. (1999) asserted that gender and education were significant variables and age was not significant, while Donthu and Garcia (1999) contended that age was a significant variable, and education and gender were not significant. Perceived risk is one of the most important concerns for internet shopping. Dowling and Staelin (1994) defined risk as a consumers perceptions of the uncertainty and adverse consequences of engaging in an activity. Generally, consumers perceived risks on the Internet are associated with privacy and security of consumer records (Swaminathan et al., 1999; Fenech and OCass, 2001; Miyazaki and Fernandez, 2001).

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23 According to Zellweger (1997), perceived unsatisfactory security is one of the obstructions to online purchasing. Miyazaki and Fernandez (2001) asserted that higher levels of Internet experience may lead to lower risk perceptions regarding online shopping and fewer specific concerns regarding system security and online retailer fraud yet more concerns regarding online privacy (p. 41) and perceived risk as least partially mediates the impact of Internet experience on online purchase behavior (p. 41). According to Fenech and OCass (2001), perceived Web security strongly affects Internet users attitude toward Web retailing. Donthu and Garcia (1999) found that nonshoppers show more adversity to risk than internet shoppers. In addition, Vellido et al. (2000) contended that consumer risk affects attitude toward shopping, but not intention toward shopping. However, differently from other studies, Swaminathan et al. (1999) showed that the security of electronic exchanges and privacy issues are not a concern to average consumers when they use the Internet for shopping, because Internet security and payment systems have developed more confidentiality every year. Consumer shopping orientation is one of the important factors in online shopping behavior studies (Li et al., 1999; Swaminathan et al., 1999; Wolfinbarger and Gilly, 1999; Fenech and OCass, 2001; Koufaris et al., 2001). According to Li et al. (1999), there were differences in convenience and experience orientations between Web buyers and non-Web buyers, while there were not any differences in recreational and economic orientations. Swaminathan et al. (1999) found that convenience-oriented consumers are more likely to purchase online and those who value social interactions are less likely to purchase online and use the Internet less frequently for shopping. Donthu and Garcia (1999) also suggested that Internet shoppers are more convenience-oriented than

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24 nonshoppers. Fenech and OCass (2001) showed that consumers shopping (recreational rather than economic) orientation influe nces attitude toward Web-retailing. Citrin et al. (2000) contended that domain-specific innovativeness had a significant positive relation with the adoption of the Internet for shopping. According to Limayem et al. (2000), personal innovativeness has a significant affect on attitude and intention in relation to online shopping. In addition, Donthu and Garcia (1999) found that Internet shoppers were more innovative than nonshoppers. Personal Computers Adoption Some consumers think of a PDA as a kind of mini computer. Currently, Pocket PC, created for a smaller notebook, is referred as a different device from a PDA. However, in a few of years, Pocket PC and PDA will be referred to as the same communication device because both of them will have the same functions and applications. This study expects that previous researches about adoption of personal computers provide important implications for examining PDA adoption. The personal computer was used as an important innovation in previous diffusion studies (Dickerson and Gentry, 1983; Dutton, Rogers, and Jun, 1987; Lin, 1998). Dickerson and Gentry (1983) intended to investigate the nature of the adopter of one particular technological innovation, the home computer (p. 227) and suggested three predictors to provide profiles of innovation adopters: demographic characteristics, consumer creativity, and previous experiences. In addition, Dickerson and Gentry (1983) showed that adopters were more likely to be home owners, better educated, older, and higher income than non-adopters. Additionally, a consumers creativity and previous experiences were significantly related to adoption of the home computer. Dutton, Rogers,

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25 and Jun (1987) also researched adoption of personal computer and patterns of use. Their study took four categories of variables: (1) the independent factors shaping patterns of personal computing, the intervening variables of (2) adoption and (3) use, and (4) impacts (p. 220) and drew the eight general conclusions from meta-analysis (p. 243245): (1) Years of formal education is strong in explaining the adoption and use of home computing. (2) One of the important uses of home computers is to learn how to use a computer, in addition to accomplishing specific tasks such as word processing or playing video games. (3) Early research on home computing underemphasized the use for work at home, and overemphasized the computers use for education and entertainment. (4) The potential social impacts of home computing are illustrated by contemporary shifts in time use in adopting households. (5) The role of contextual factors in shaping the uses and impacts of personal computing needs to be examined more fully. (6) Different types of computer users should be more fully differentiated. (7) The negative impacts of home computing such as computer addiction, less sleep, social isolation, and family conflicts found in past research need further investigation. (8) Future research is needed concerning home computing as one part of communications technologies in the home. Lin (1998) explored the adoption of the personal computer as an interactive multimedia entertainment and information provider and posited that resources, innovativeness need, complexity, advantages, communication technology ownership, media use level, and demographics would affect adoption of personal computer. In addition, Lin (1998) found that education, ownership of communication technology, the perceived advantage, resources, and need for innovativeness are significant predictors. Information Systems (Audiotex, Videotex, and Electronic Bulletin Board) Adoption Information systems such as audiotex, videotex, electronic bulletin board, and so on, were perceived as innovations in the late 80s and early 90s. At that time, many

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26 researchers examined information systems diffusion and adoption (Heikkinen and Reese, 1986; LaRose and Atkin, 1992; Eastlick, 1993; James and Wotring, 1995; Jeffres and Atkin, 1996; Neuendorf, Atkin, and Jeffres, 1998). LaRose and Atkin (1992) posited that early adoption is typical of consumers who are (1) younger, better educated, and male and (2) heavier users of functionally similar technologies. That research found that household size, use of cellular phones, videotext, 800 numbers, automatic teller machines, speaker phones and auto-dialers were the most powerful predictors of adoption of audiotext. In addition, answering machine use, female, conference call use, ethnicity, and education level were positive predictors, while electronic mail use, personal use, and VCR ownership were negative predictors (LaRose and Atkin, 1992) and interestingly, result s were found that VCRs compatibility (the functional similarity with audiotext) was negatively related to adoption of audiotext and other technologies, which were not functionally similar with audiotext, were positively associated with adoption of audiotext (LaRose and Atkin, 1992). Neuendorf et al. (1998) examined adoption of two audio information services: audiotext (including 1-900 service) and fax. They posited the following research question: what are the relative influences of social indicators (including demographics), media use, communication needs, and, particularly, QOL (Quality of Life) assessments on peoples use of audio information services and fax (p. 86). In result, media use were more predictive of adoption for audio information service that social indicator and communication needs QOL might become the predictors of adoption (Neuendorf et al., 1998). Heikkinen and Reese (1986) researched newspaper readers adoption of videotext with individual characteristics (information need and channel orientation).

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27 Eastlick (1993) intended to investigate factors which affect the adoption of videotex shopping. Eastlick (1993) posited that perceptions of the properties of a videotex shopping system and the innovation properties (relative advantage, compatibility, trialability, and complexity) will be better predictors than any other variables. Eastlick (1993) asserted that perceptions of the advantages of videotex shopping and its consistency with shopping needs and experiences were important factors in determining either an adoption or nonadoption decision. (p. 73). James and Wotring (1995) investigated and characterized the users and uses of electronic bulletin board messages in terms of adopter characteristics and social impacts. That study found that education, income, gender, and occupation were related to adoption of electronic bulletin board, but age was not. In addition, James and Wotring (1995) showed that electronic bulletin board would not affect radio listening, large group communication, and small group communication. Other New Technologies Adoption Dupagne (1999) investigated the characteristics of potential High-Definition Television (HDTV) adopters. Additionally, th is researcher examined how demographics, mass media use, ownership of home entertainment products, and importance of television attributes affect HDTV awareness, interest, and purchase intention (Dupagne, 1999). According to Dupagne (1999), demographics, mass media use, and the number of home entertainment products were partially significant related to HDTV awareness and interest, and only the perceived importance of television attributes had a significant relation with purchase intention.

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28 Garramone, Harris, and Pizante (1986) examined predictors of motivation to use Computer-Mediated Political Communication Systems (CMPCS). Interestingly, among several variables (demographics, needs, tradi tional political participation, satisfactions obtained from traditional political participation, and satisfactions anticipated from CMPCS use), only needs and satisfactions expected from CMPCS use were significant predictors of motivation to use CMPCS (Garramone et al., 1986). Factors influencing the adoption of multimedia cable technology were examined by Lin and Jeffres (1998). Lin and Jeffres (1998) contended that existing media use patterns and media content satisfaction might be helpful in establishing the potential dynamics of functional substitutions between an existing and emerging medium. Parthasarathy and Bhattacherjee (1998) examined post-adoption behavior in the context of online services. Their study focused on investigating discontinuance in the online services industry by variables (communication channels, utilization level, perceived innovation attributes, network externalities, and reasons for discontinuance). Okolica and Stewart (1996) examined fa ctors affecting the use of voice mail. Perceived usefulness, individual innovativeness, and training had a positive relation with use of voice messaging (Okolica and Stewart, 1996).

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29 CHAPTER 3 RESEARCH MODEL This chapter will present the hypothesi zed model. This model is based on the innovation-decision process. Additionally, in th e basis of previous researches, this current study selected several variables to explore the adoption of PDA. This chapter will explain each of variables. In the last part, this curre nt study will show the hypothesized model. In the next chapter, the relations among variables will be suggested. Independent Variables Perceived Characteristics of the Innovation In many researches, perceived characteristics of the innovation explained individuals perceptions about innovations as important predictors of adoption behavior (Agarwal and Prasad, 1997). According to Ro gers (1995), the making-decision unit forms attitude toward the innovation at the persuasion stage. After making-decision unit knows about the innovation, it can begin to form an attitude toward the innovation. Rogers (1995) contended that at the persuasion stage, perceived characteristics of an innovation play in an important role to form an attitude. Perceived characteristics of the innovation consist of (1) relative advantag e, (2) compatibility, (3) comple xity, (4) trialability, and (5) observability. All attributes except complexity are positively related to adoption of innovations (Rogers, 1995). Ostlund (1974) found that perceived characteristics of innovation are significant predictors of new product purchase. Eastlick (1993), in videotex adoption study, expected that relative advantage, compatibility, trialability, and complexity would be better predictors of adoption of videotex than other characteristics

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30 and found that relative advantage and compatibility were better properties. Du (1999) proposed that relative advantage, compatibility, and complexity were factors influencing Internet adoption in China. Sund et al., (2001) found that relative advantage, complexity, and compatibility are relevant to adoption of ERP systems. According to Lin (1998), relative advantage was a significant predictor of adoption, while complexity was not significantly associated with adoption of personal computers. Most researches, which had perceived characteristics of an innovation as independent variables, exclude trialability and observability. In addition, in most researches, these two variables didnt have a significant effect. On the other hand, several studies found that trialability is a significant predictor for adoption of innovations (Moore and Benbasat, 1991; Agarwal and Prasad, 1997). Based on previous assertions, it is clear that perceived characteristics of innovations play an important role in predicting innovation adoption. This present study will leave relative advantage, compatibility, complexity, and trialability as independent variables and exclude observ ability in this study. Ownership of New Technology Products Rogers (1995) contended that an individuals experience with one innovation influences individuals adoption of the next innovation. In addition, Rogers (1995) suggested one concept: technology cluster (p. 15). A technology cluster is defined as one or more distinguishable elements of technology that are perceived as being interrelated (Rogers, 1995, p. 15). Rogers (1995) asserted that any innovation doesnt have a clear-cut boundary with other innovation and potential adopters often perceive one innovation as closely related to another new innovation.

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31 In terms of the notion of technology cluster, previous several studies examined the relation between adoption of innovation and other technology experience (Dickerson and Gentry, 1983; Jeffres and David, 1996; Lin, 1998; Dupagne, 1999). Ettema (1984) found that the adoption of other innovations affected the adoption of text services. On the basis of Rogers concept, Lin (1998) contended that communication media sharing certain functional similarities may create synergies insofar as adoption rates are concerned assuming that other circumstantial factors such as pricing are held constant (p. 99) and found that communication technology ownership (e.g., satellite dish, VCR, video camera, compact disc player, laser disc player, video game player, electronic personal organizer, electronic pager, answering machine, cellular telephone, fax machine, word processor, cable TV subscription, premium cable TV subscription, DBS subscription, and voice mail subscription) is an important predictor of the personal computer adoption rate. According to Dupagne (1999), adoption of HDTV is positively related to the number of home entertainment products. Dickerson and Gentry (1983) found that adopters of home computers have had more experience with a variety of technical products and services than nonowners (p. 234). Jeffres and David (1996) proposed that media use pattern is a significant predictor of the degree to use the new technologies. Danko and MacLachlan (1983) found that the early adopters of personal computers owned other high technology products (e.g. microwave oven, tape-deck equipment, and video games). On the basis of previous adoption studies, this current study expects that ownership of technology products can be one of predictors for adoption of PDA.

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32 Personal Innovativeness According to diffusion theory, adoption of innovations is a function of personal innovativeness, or willingness to try the innovations (Jeffres and Atkin, 1996). Many studies employed personal innovativeness as a predictor in order to explain the adoption of innovations (Venkatraman, 1991; Manning et al., 1995; Lin 1998; Lin and Jeffres, 1998; Donthu and Garcia, 1999; Du, 1999; Citrin et al., 2000; Im et al., 2003). Rogers (1995) defined innovativeness as the degree to which an individual or other unit of adoption is relatively earlier in adopting new ideas than the other members of a system (p. 37) Additionally, Rogers (1995) asserted that innovativeness affects the rate of adoption. Lin (1998) posited that need for innovativeness (p. 97) is a positive factor in showing an interest in and involvement with innovations. Pope et al. (1999) proposed that personal innovativeness is positively associated with purchase intention for sports products via the Internet. Donthu and Garcia (1999) also suggested that Internet shoppers have more innovativeness than nonshoppers. Im et al. (2003) found that new product adoption behavior is positively affected by innate innovativeness and personal characteristics dont influence innovative potentiality. Citrin et al. (2000) classified consumer innovativeness into two types: open-processing innovativeness and domainspecific innovativeness (p. 294). Open-processing innovativeness focuses on a cognitive style and domain-specific innovativeness focuses on product category or domain specific (Citrin et al., 2000). Citrin et al. (2000) also found that open-processing innovativeness doesnt have a significant relation with the adoption of Internet shopping, while domainspecific innovativeness has a positive relation with the adoption of Internet shopping. Venkatraman (1991) also sorted innovators into two types: cognitive innovators and

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33 sensory innovators (p. 52). According to Venkatraman (1991), cognitive innovators are defined as people who have a strong preference for new mental experiences (p. 52) and sensory innovators are defined as people who have a strong preference for both new cognitive and sensory experiences (p. 52). In addition, Venkatraman (1991) found that the interaction between innovativeness tendencies and product type determines the demographic profile of the adopter within each segment of cognitive and sensory innovators (p. 64). Manning et al. (1995) showed two scales of consumer innovativeness: Consumer Independent Judgment Making (CIJM), which is defined as the degree to which an individual makes innovation decisions independently of the communicated experience of others (p. 329) and Consumer Novelty Seeking (CNS), which is defined as the desire to seek out new product information (p. 329) and examined measurements of the two scales and the relationship between them and the adoption process. Findings were that CIJM was significantly related to only the later trial stage and CNS was positively associated with the initial stages of the adoption process (Manning et al., 1995). Based on the previous studies, personal innovativeness may be a significant determinant to predict the adoption of PDA. Intervening Variables Attitude toward PDA The persuasion stage is the step involved in forming favorable or unfavorable attitude toward the innovation. Rogers (1995) suggested that the main outcome of the persuasion stage in the innovation-decision process is either a favorable or unfavorable attitude toward the innovation (p. 169). At this stage, individuals consider mentally

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34 various situations, and then develop an attitude toward the innovation. Finally, they form a favorable or unfavorable attitude. Therefore, attitude toward PDA will be examined in this study. Perceived Uncertainty Rogers (1995) defined uncertainty as the degree to which a number of alternatives are perceived with respect to the occurrence of an event and the relative probability of these alternatives (p. 6). Rogers (1995) also asserted that during innovation-decision process, the making-decision unit tries to reduce uncertainty about the expected consequences of an innovation and information is a significant means of reducing uncertainty. Especially, at the persuasion and decision stage, an individual seek innovation-evaluation information (p. 168) to reduce uncertainty. Eastlick (1996) defined perceived uncertainty as a variety of risks associated with adopting an innovation. Consumer behavior is motivated to reduce ri sk and reduction of uncertainty is related to information acquisition, transmission, and processing (Taylor, 1974). Grnhaug (1972) also contended that consumer behavior is related to a problem-solving process. Dowling and Staelin (1994) said that the concept of perceived risk most often used by consumer researchers defines risk in terms of the consumers perceptions of the uncertainty and adverse consequences of buying a product or service (p. 119). Cox and Rich (1964) referred perceived risk as the nature and amount of risk perceived by a consumer in contemplating a particular purchase intention (p. 33). Miller and Friesen (1982) operationalized uncertainty into the com ponents of hostility, heterogeneity, and dynamism. Sheth and Parvatiyar (1995) contended that the uncertainty is associated with perceived risk and consumers intend to develop various ways to reduce perceived risk.

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35 Eastlick (1996) defined perceived uncertainty as a variety of risks associated with adopting an innovation. On the other hand, Knight (1965) defined the concepts of risk and uncertainty separately. Knight (1965) contended that when it is lack of knowledge of a precise probability, uncertainty exists and ri sk exists in cases where there is a known probability. Usually, however, researchers have accepted the two concepts to be used synonymously (Mitchell, 1998). On the basis of previous suggestions, the present study will conceptualize perceived uncertainty as perceived risk, so this study assumes that risk will be exchangeable with uncertainty. Garner (1996) suggested six types of risks: social, financial, physical, performance, time, and psychological. Dholakia (1997) contended that the six dimensions of perceived risk are helpful in explaining importance for a product classes. Ho and Ng (1994) explained customers risk perception of electronic payment systems with five risks (physical, performance, psychological, financial and time risks). Ko (2001) found that perceived risk is related to online auctions. The perceived risk was used as a variable in several studies for online shopping (Donthu and Garcia, 1999; Jarvenpaa et al., 1999; Swaminathan et al., 1999; Fenech and OCass, 2001). Sometimes risk or uncertainty is viewed as an expectation of loss (Stone and Winter, 1987). Mitchell and Greatorex (1993) addressed four types of loss: financial loss, time loss, physical loss, and psychosocial loss. Finally, the reduction of perceived uncertainty is one of the consequences in the persuasion stage.

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36 Dependent Variable Purchase Intention The next stage of the persuasion stage is the decision stage. According to Rogers (1995), the decision stage occurs when an individual (or other decision-making unit) engages in activities that lead to a choice to adopt or reject an innovation (p. 171). He defined adoption as a decision to make full use of an innovation as the best course of action available (p. 171). In this study, adoption of PDA will be operationalized as purchase intention for PDA. Additional Variable Functions of PDA As a convergence communication device, a PDA has several functions: PIMS, internet/e-mail, global positioning system, wordprocessing/ spreadsheet, digital camera, video games, MP3/movie file player, mobile phone, and so on. This study expects that the perceived importance of each function wi ll be related to attitude toward PDA. Therefore, for exploratory attempt, this pres ent study will examine which functions will be related to attitude and purchase intention. Hypothesized Model This study presents the following hypothesized model with variables to be selected for this current study (see Figure 3-1). The relations among the variables will be explained in the next chapter.

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37 RQ Functions Of PDA Perceived Characteristics of the Innovation (Relative advantage, Compatibility, Complexity, and Trialability) Personal Innovativeness Ownership of New Technolo g ies Products Perceived Uncertaint y Purchase Intention Attitude Toward PDA Persuasion Stage Decision Stage H4 H4 RQ H1 H2 RQ H3 RQ RQ Figure 3.1: Hypothesized Model

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38 CHAPTER 4 HYPOTHESES AND RESEARCH QUESTIONS This research intends to examine the relative influence of perceived characteristics of innovation, personal innovativeness, attributes of PDA in exploring attitude toward PDA, perceived uncertainty, and purchase intention, and the relationships among the variables, based on a model of the Innovation-Decision Process of Diffusion theory. In order to investigate relations among variables, hypotheses and research questions were created. Hypotheses The first hypotheses deals with the persuasion stage of the innovation-decision process of diffusion theory. The second part is derived from the decision stage. The Persuasion Stage In the persuasion stage, an individual (or making decision unit) forms favorable or unfavorable attitude toward innovations and attempts to reduce uncertainty about expected consequences of an innovation and get information, which is a significant means of reducing uncertainty (Rogers, 1995). This study posits that perceived characteristics of innovations, ownership of new technology products, and personal innovativeness influence attitude toward PDA at the persuasion stage. With respect to perceived uncertainty, this study created a research question about the relationship between variables and perceived uncertainty.

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39 Relation between perceived characteristics of innovations and attitude toward PDA Hypothesis 1 expresses the relation between perceived characteristics of innovations and attitude toward PDA. Rogers (1995) asserted that perceived attributes of innovations (relative advantage, complexity, co mpatibility, trialability, and observability) play an important role in the persuasion stage and only complexity influences negatively the adoption of innovations. Du (1999) showed that relative advantage, compatibility, and complexity affected significantly the adoption of the Internet in China. Lin (1998) found that relative advantage was a significant predictor of adoption of personal computer. Ostlund (1974) showed that perceived innovation attributes were strongly correlated to new product purchase. In addition, according to Eastlick (1993), relative advantage and compatibility were positively related to videotex adoption. Holak (1988) found that product trial was positively correlated to purchase intention. Sund et al., (2001) also contended that three innovation attributes (relative advantage, complexity, and compatibility) are related to the adoption of ERP systems. According to Parthasarathy and Bhattacherjee (1998), relative advantage (usefulness) and compatibility was significant factors of post-adoption behavior, but complexity was not significant. Based on previous studies, the following hypotheses are designed. H1.1: Relative advantage will be positively related to attitude toward PDA H1.2: Compatibility will be positivel y related to attitu de toward PDA H1.3: Complexity will be negatively related to attitude toward PDA H1.4: Trialability will be positively related to attitude toward PDA

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40 Relation between ownership of new technology products and attitude toward PDA Hypothesis 2 is designed to investigat e the relationship between ownership of new technology products and attitude toward PDA. Atkin (1993) suggested the notion of functional similarity/need compatibility explanation (p. 52) in his study. It means that the adoption of innovations is associated with the adoption of functionally similar tec hnology (Lin and Jeffres, 1998). Perse and Dunn (1998) asserted that in terms of uses and gratifications perspective, perceptions about the different communication channels have an important meaning for two reasons: (1) people turn to different communication channels, because they believe that they will derive something from that use, and (2) few communication channels are uniquely able to fill communication needs. Most are functional alternates to other channels, or able to fill similar communication needs (p. 436). Atkin (1993) found that cable subscribership is related to functionally similar media and cable subscribers tended to adopt VCRs, camcorders, and cordless phones more than nonsubscribers. In addition, he found that pay viewers were more likely to adopt cellular phone, computers, walkman, and video games. Perse and Courtright (1993) contended that the adoption of new technologies is related to the adoption of functionally similar product. Danko and MacLachlan (1983) found that the early adopters of personal computers owned other high technology products. According to Reagan et al. (1995), people have a tendency to select technologies to fit a function and several technologies can provide a similar function to other technologies. They supported the concept of functional similarity and found that functional similarity seems to vary depending on the innovations. Dickerson and Gentry (1983) posited that adopters of home computers will have had more experience than

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41 non-adopters with other technical consumer products (p. 228). Their study found that 17 of 19 technical products and services were more likely to be used by home computer adopters. Lin (1998) focused on investigating the relation between the adoption of personal computer and communication technology ownership and found that the relation was significant. Dupagne (1999) examined how the number of home entertainment products influences HDTV awareness, interest, and purchase intention. LaRose and Atkin (1992) found that ownership of several technology products is a positive or negative predictor. Atkin et al. (1998) asserted that the adoption of new product is associated with the adoption of other innovations. Neuendorf et al. (1998) investigated the adoption of an audio information service and fax in terms of two kinds of functionally similar media: entertainment media (e.g., television, movi es) and utilitarian media (e.g., personal computers). On the basis of these previous researches, ownership of other technologies is expected as an important predictor of PDA adoption. In order to investigate ownership of new technologies, seven products were selected: (1) mobile phone, (2) video game player, (3) DVD player, (4) digital camera, (5) digital Cable/satellite TV, (6) broadband, and (7) Personal Video Recorder. H2: The number of ownership of new technologies (Mobile Phone, Video game player, DVD player, Digital camera, Digital Cable/Satellite TV, Broadband, and Personal Video Recorder) will be positively related to attitude toward PDA. Relation between personal innovativeness and attitude toward PDA Hypothesis 3 is designed to examine how personal innovativeness influences attitude toward PDA. Citrin et al. (2000) in vestigated the relation between two types of innovativeness (open-processing innovativeness and domain-specific innovativeness) and

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42 adoption of Internet shopping. Manning et al. (1995) conceptualized innovativeness as consumer independent judgment making and consumer novelty seeking (p. 329) and examined how the two types of innovativeness affect adoption at each stage of the adoption process. Im et al. (2003) explored the relation between innate consumer innovativeness and new product adoption behavior. Venkatraman (1991) developed different innovativeness segments (cognitive innovativeness and sensory innovativeness) and explored their influence on the adoption of an innovation. Donthu and Garcia (1999) also examined the relation between innovativeness and adoption of Internet shopping. Du (1999) found that innovativeness was a significant predictor of Internet adoption. Lin and Jeffres (1998) focused on the relation between innovativeness traits and the adoption of multimedia cable technology. Limayem et al. (2000) posited that personal innovativeness affects directly and indirectly both attitude and intention of online shopping. In addition, Pope et al. (1999) hypothesized that individuals innovativeness would have a positive relation with intention to purchase sport product through the Internet. Considering previous studies, the following hypothesis below were formulated for this study H3: Personal innovativeness will be positively related to attitude toward PDA. The Decision Stage At the decision stage, an individual takes part in activities to choose adoption or rejection of an innovation (Rogers, 1995). Rogers (1995) asserted that in this stage, attitude toward PDA and perceived uncertainty affect purchase intention.

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43 Relation between attitude toward PDA and perceived uncertainty, and purchase intention Hypothesis 4 is derived from the decision stage of the innovation-decision process of diffusion theory. At the decision stage, an individual decides on adoption or rejection of an innovation. Rogers (1995) asserted that attitude toward an innovation does not lead to adoption or rejection under many other circumstances, but there is a positive relation between attitude and behavior. Brown and Stayman (1992) examined antecedents and consequences of attitude toward the ad with a meta-analysis of 47 researches about attitude toward the ad and focused on the re lations between ad attitude, brand attitude, and purchase intention. Ko (2002) found that attitude toward the brand positively affect purchase intention. Lutz et al. (1983) contended that attitude toward the ad is positively related to brand attitude and purchase intention. Limayem et al. (2000) also found that an individuals attitude had a strong correlation with intent toward online shopping. According to the Theory of Reasoned Action (Fishbein and Ajzen, 1975) and the Theory of Planned Behavior (Ajzen, 1985), beliefs affect persons attitudes and attitudes, in turn, influence behavioral intention, which is a good forecaster of actual behavior. Therefore, it is expected that attitude is one of the factors influencing purchase intention of PDA. H4.1: Attitude toward PDA will be positively related to purchase intention. Rogers (1995) contended that an individual seeks information to reduce perceived uncertainty for the innovation and this may affect his/her adoption/rejection. Reduction of perceived risk positively affects consumers shopping activities (Dowling and Staelin, 1994). Cox and Rich (1964) contended that perceived risk by the consumer is a function of the amount at stake in the purchase intention and perceived risk is one determinant of telephone shopping. Mitchell (1998) found that consumer risk perceptions had an important role in grocery retailing. Perceived risk was applied to

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44 examine online purchase of sport products (Pope, 1999). Perceived risk affects overall evaluation of the deal and purchase intention (Wood and Scheer, 1996). In addition, Ko (2001) found that perceived risks influence the adoption of online auction participation. On the basis of other researches, the following hypothesis is provided: H4.2: Perceived uncertainty will be negatively related to purchase intention. Research Questions This study suggests three research questions about the adoption of PDA: RQ1: Which factors influence attitude toward Personal Digital Assistant (PDA) and perceived uncertainty at the persuasion stage of the Innovation-Decision process and which variables have the relatively strong or weak influence on attitude and perceived uncertainty? The first research question is designed to investigate the relationships between the independent variables (relative advantage, compatibility, complexity, trialability, ownership of new technologies, and personal innovativeness) and perceived uncertainty. In addition, this study will examine whether there is a significant difference among the significant variables. RQ2: Which factors will be useful in discriminating between two purchase intention groups (high or low)? The second research question addresses which variables can be used as significant factors to discriminant between a high purchase intention group and a low purchase intention group. Relation between the Functions of PDA and Purchase Intention According to previous studies, from the perspective of uses and gratifications, one innovation can have several functions to satisfy motivations and needs. December (1996) contended that the Internet is used mainly for communication, interaction, and

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45 information. According to the 9th WWW Us er Survey conducted by Georgia Tech (GVU's 9th WWW user survey, 1998), entert ainment, education, time wasting and personal information are main purposes for the youngest user to use the web. Hunter (1996) suggested that the Internet involves five categories of needs: cognitive needs, affective needs, personal integrative needs, social integrative needs, and escapist needs. Rubin (1981) showed nine motivations to view television: to pass time, for companionship, arousal, content, relaxation, information, escape, entertainment, and social interaction. According to Sherry et al. (2001), challenge, arousal, and diversion were the most frequently reported reason for using video games. In HDTV adoption research, Dupagne (1999) found that HDTV purchase intention was related only to the perceived importance of HDTV attributes. Even though a PDA has many attributes, few models have these functions currently and most of them will be added in the near future. So, this present study expects to know how respondents perceive each function of PDA and how each function is related to attitude toward PDA. Finally, the third research question is the following: RQ3: which functions of PDA are related to attitude toward PDA and purchase intention?

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46 CHAPTER 5 RESEARCH METHODOLOGY Babbie (2001) proposed that survey research is probably the best method available to the social researcher who is interested in collecting original data for describing a population too large to observe directly and surveys are excellent vehicles for measuring attitudes and orientations in a large population (p. 238). Most adoption researches have used a survey as a research technique. In addition, previous studies provide a number of reliable scales for measuring variables regarding the adoption of the innovation. So, this current study expected that a survey would be adequate for exploring the adoption of PDA and this study used a survey. Sample The sample used for the present study was 218 students from a large southeastern university. This study recruited respondents from several classes at the university. The researcher visited these classes and administered a survey. In other words, this study used a convenience sampling. The survey was conducted from March 18 to April 17, 2003; 191 samples were valid, and 27 samples were invalid. Among the subjects, 62.3 percent were female and 37.7 percent were male. Most respondents were undergraduate students (76.4%). Measurements The survey for this study provided all subjects basic information about a PDA with a picture (see Figure 5-1) and the following information:

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47 Figure 5-1: Personal Digital Assistant Information: This is the picture of Personal Digital Assistant (PDA). The newest model can provide you several functions: It enables you to access Internet/E-mail and Global Positioning System (GPS) through satellite. As a communication device, it has a mobile phone. It also allows entertainment: You can play MP3, movie files and video games In addition, it has functions of digital camera digital organizer and wordprocessing/Spreadsheet After the subjects read the information, they were asked to answer questionnaires. First, respondents were asked to address their awareness, expected cost, and ownership of PDA. Some of the questions we re: (1) Have you ever heard of a Personal Digital Assistant (PDA)? (2) How much do you think the newest PDA would cost? (3) Do you own a PDA? Second, subjects were asked to express their levels of agreement with 12 statements, in order to measure perceived characteristics of PDA. Relative advantage Parthasarathy and Bhattacherjee (1998) conceptualized relative advantage as usefulness (p. 336). That study proposed that earlier adopters think of online services as being more useful than later adopters do. Okolica and Stewart (1996) also employed perceived usefulness to examine the extent of use of voice messaging. The

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48 present study adapted a usefulness measuring scale from Parthasarathy and Bhattacherjee (1998). Usefulness was measured using a seven-point scale ranging from strongly agree (7) to strongly disagree (1). This part of questionnaire consisted of three items: (1) I feel that a PDA will save me time/effort over other means of performing the same tasks, (2) I feel that a PDA will enable me to perform many tasks better than through other means, and (3) I feel that a PDA will provide a greater value than other ways of performing the same task. Compatibility. A compatibility measuring scale was also adapted from Parthasarathy and Bhattacherjee (1998) and used a seven-point scale. This scale had three items: (1) I feel that a PDA will be easy for me to adjust to, (2) I feel that a PDA will fit my lifestyle very well, and (3) I feel that a PDA will fit the way I perform my daily tasks well. Complexity. Parthasarathy and Bhattacherjee (1998) operationalized complexity as ease of use (p. 337). Ease of use has an invers e meaning to complexity. For measurement of complexity this study also used Parthasarathy and Bhattacherjee (1998) scale. This scale ranges from strongly agree (7) to strongly disagree (1) and involves questions like the followings: (1) I feel that a PDA will be hard to learn, (2) I feel that a PDA will be quite complicated to master, (3) I feel that a PDA will be difficult to use, and (4) I feel that a PDA will have a complex, hard-to-learn system. Trialability. Agarwal and Prasad (1997) measured trialability as the extent to which potential adopters perceive that they have an opportunity to experiment with the innovation prior to committing to its usage (p. 562). Several previous researches indicated the relation between only the first three attributes (relative advantage,

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49 compatibility, and complexity) and their dependent variables (Eastlick, 1993; Lin, 1998; Parthasarathy and Bhattacherjee, 1998; Du, 1999). However, some studies employed trialability as a factor to measure the perceptions of adopting innovations (Moore and Benbasat, 1991; Agarwal and Prasad, 1997). The present study uses trialability as a predictor that affects attitude toward PDA and perceived risk. In this study, previous study (Agarwal and Prasad, 1997) scale was adapted. The following two questions were used: (1) I might try out a PDA long enough to see what I could use it for, and (2) Before deciding to use the PDA, I would like to be able to try one out. Third, in order to investigate ownership of new technology products, subjects were asked to address their ownership of seven new technologies. In previous studies, experiences with other technologies played an important role in exploring new technology adoption (Dickerson and Gentry, 1983; Jeffres and David, 1996; Lin, 1998; Dupagne, 1999). In particular, Dupagne (1999) examined the relation between the number of home entertainment and adoption of HDTV. So, this present study adapted the Dupagne questionnaire (1999) to measure the number of new technology products: Do you personally own the following product/service? (1) Mobile Phone, (2) Video game player (Xbox, Playstation 2, etc.), (3) DVD, (4) Digital Camera, (5) Digital Cable/Satellite TV, (6) Broadband (High-Speed Internet), (7) Digital Video Recorder (TiVo or Replay TV). These seven items were coded as dummy variables (0 = no, 1 = yes). Results were added together. In other words, if a respondent owns all products/services, he or she gets point. The total number of devices owned indicates the degree of ownership of new technology products.

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50 Fourth, personal innovativeness was exam ined. Several scales for measurement of innovativeness were used in a number of studies (Hurt et al, 1977; Oliver and William, 1985; Venkatraman, 1991; Manning et al., 1995; Lin and Jeffres, 1998: Du, 1999; Donthu and Garcia, 1999; Citrin et al., 2000; Limayem et al., 2000; Im et al., 2003). This present study adapted a scale from Oliver and William (1985). This scale had three items: (1) I like to buy new and different things, (2) I am usually among the first to try new products, and (3) I dont like to take chances. A seven-point scale ranging from strongly agree (7) to strongly disagree (1) was used. Fifth, in order to examine RQ3, this study asked the respondents the following questions: How important is each of the following PDA functions to you? (1) Mobile Phone, (2) Internet/E-mail, (3) Video game s, (4) MP 3 and movie file player, (5) Wordprocessing/Spreadsheet, (6) Digital camera, (7) Global Positioning System. This item has a seven-point scale ranging from not important at all (1) to extremely important (7). Sixth, perceived risks were measured. In this study, perceived uncertainty is interchangeable with perceived risk. There are numerous scales to measure perceived risk. Among them, this present study used Garner (1986) scale in order to measure the perceived risk. This scale can measure six kinds of risks. The six dimensions of perceived risk can explain a significant portion of the overall risk (Stone and Grenhaug, 1993; Dholakia, 1997). This study removed the health risk and psychological risk items because they were expected to be unrelated to adoption of PDA. Respondents were asked to indicate their answer with the following four statements: (1) The product might fail to perform to my satisfaction (performance risk ), (2) My friends or relatives will judge

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51 51 my purchase (social risk),” (3) “I might lose my money (financial risk),” and (4) “I might waste my time or effort getting the product repaired or replaced (time risk).” Each statement used a seven-point scale (from stro ngly agree (7) to strongly disagree (1)). Seventh, subjects were asked to express their agreement about attitude toward PDA. Attitude toward PDA was measured with a three-item scale from Sujan and Bettman (1989). Originally, this scale was used to measure brand evaluation. Three items (positive/negative, good/bad, and favorable/unfavorable) used a seven-point scale. Eighth, as the dependent variable, subjects were asked to address their purchase intention. In order to investigate purchase intention, a three-item index was adapted from a previous study (MacKenzie et al., 1986). A seven-point scale was used ranging from 7 (strongly agree) to 1 (strongly disagree). Respondents stated their agreement with three statements about purchase intention. This scale included: likely/unlikely, probable/improbable, and possible/impossible. Finally, subjects were asked to write down their demographic information. Table 5-1 presents detailed information on the types of scale adapted and Table 52 shows all questionnaires used in this current study. Table 5-1: Measured variables Variables Adapted measure and source Scale Type Relative advantage Adapted from Parthasarathy and Bhattacherjee (1998) Seven-point scale: strongly disagree (1) to strongly agree (7) Compatibility Adapted from Parthasarathy and Bhattacherjee (1998) Seven-point scale: strongly disagree (1) to strongly agree (7) Complexity Adapted from Parthasarathy and Bhattacherjee (1998) Seven-point scale: strongly disagree (1) to strongly agree (7) Trialability Adapted from Agarwal and Prasad (1997) Seven-point scale: strongly disagree (1) to strongly agree (7)

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52 Table 5-1. Continued Variables Adapted measure and source Scale Type Ownership of new technology products Adapted from Dupagne (1999) Nominal Yes: 1, No: 0 Personal innovativeness Adapted from Oliver and William (1985) Seven-point scale: strongly disagree (1) to strongly agree (7) Perceived uncertainty Adapted from Garnet (1986) Seven-point scale: strongly disagree (1) to strongly agree (7) Attitude toward PDA Adapted from Sujan and Bettman (1989) Seven-point scale Purchase intention Adapted from MacKenzie et al. (1986) Seven-point scale Table 5-2: Observed variables Variables Questionnaires Awareness Have you ever heard of a Personal Digital Assistant (PDA)? Ownership of PDA Do you own a PDA? Expected cost How much do think the newest PDA would cost? Relative advantage I feel that a PDA will save me time/effort over other means of performing the same tasks. I feel that a PDA will enable me to perfor m many tasks better than through other means. I feel that a PDA will provide a greater value than other ways of performing the same task. Compatibility I feel that a PDA will be easy for me to adjust to. I feel that a PDA will fit my lifestyle very well. I feel that a PDA will fit the way I perform my daily tasks well. Complexity I feel that a PDA will be hard to learn. I feel that a PDA will be quite complicated to master. I feel that a PDA will be difficult to use. I feel that a PDA will have a complex, hard-to-learn system. Trialability I might try out a PDA long enough to see what I could use it for. Before deciding to use the PDA, I would like to be able to try one out. Ownership of new technology products Do you personally own the following product/service and how familiar are you with each of them? (1) Mobile Phone, (2) Video game player (Xbox, Pl aystation 2, etc.), (3) DVD, (4) Digital Camera, (5) Digital Cable/Satellite TV, (6) Broadband (HighSpeed Internet), (7) Digital Video Recorder (Ti Vo or Replay TV)* Personal innovativeness I like to buy new and different things. I am usually among the first to try new products. I dont like to take chances.(reverse scale) Perceived importance of functions How important is each of the following PDA functions to you? (1) Mobile Phone, (2) Internet/E-mail, (3) Vide o games, (4) MP 3 and movie file player, (5) Wordprocessing/Spreadsheet, (6) Digital camera, (7) Global Positioning System

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53 Table 5-2. Continued Variables Questionnaires Perceived uncertainty The PDA might fail to perform to my satisfaction. My friends or relatives will judge my purchase.* I might waste my money. I might waste my time or effort getting the product repaired or replaced. Attitude toward PDA Unfavorable/Favorable Bad/Good Negative/Positive Purchase intention Unlikely/Likely Improbable/Probable Impossible/Possible Variables which were deleted to improve reliability. Statistical Analysis This present study used the following statistical methods: multiple regression analysis, stepwise regression analysis, t-test, and discriminant analysis. All hypotheses were developed to examine the relation among variables. According to Garson (2003), the ratio of the relative predictive power of the independent variables is indicated by the standardized b coefficients and the ratio of the beta coefficients. So, multiple regression analysis was employed to test all hypotheses. In order to make an equation model with only significant variables, this study used stepwise regression analysis. Stepwise regression is one of the ways to compute Ordinary Least Squares (OLS) (Garson, 2003). In stage one, the independent best correlated with the dependent is included in the equation. In the second stage, the remaining independent with the highest partial correlation with the dependent, controlling for the first independent, is entered (Garson, 2003). This process is repeated until R2 is not significantly increased by the addition of a remaining independent or all variables are

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54 added (Garson, 2003). Therefore, stepwise regression can help identify which factor has a relatively strong effect or weak effect. The first research question was suggested to investigate the relationship among predictors (perceived characteristics of innovations, ownership of new technology products, and personal innovativeness) and attitude and perceived uncertainty toward PDA. In order to examine the relation, multiple regression and stepwise regression were be preformed (Eastlick, 1996). The second research question suggested which factors are useful for discriminating two purchase intention groups (high/low). This study categorized two groups: high purchase intention and low purchase intention. Respondents, who score higher than mean scores on purchase intention, were identified as the high purchase intention group and respondents, who got a lower score than the mean of purchase intention, were identified as the low purchase intention group. Eastlick (1996) used multiple discriminant analysis to investigate wh ether factors of attitude toward interactive teleshopping differentiate subjects on intent to adoption. According to Parthasarathy and Bhattacherjee (1998), when independent variables are continuous and dependent variables are categorical, multiple discriminant analysis is an appropriate statistical method. Since there are only two groups by the dependent variable in this study, instead of multiple discriminant analysis, discriminant analysis was performed. Finally, the third research question addressed which functions of PDA are related to attitude toward PDA and purchase intention. In order to investigate the relation between each function of PDA and attitude to ward PDA, and the relation between each

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55 function and purchase intention, Pearson correlation analysis was adapted for this research question. Statistical methods employed in this study are shown in Table 5-3. Table 5-3: Statistical methods Hypotheses & Research Questions Statistical Methods H1.1: Relative advantage will be positively related to attitude toward PDA. H1.2: Compatibility will be positively related to attitude toward PDA. H1.3: Complexity will be negatively related attitude toward PDA. H1.4: Trialability will be positively related to attitude toward PDA. H2: The number of ownership of new technologies will be positively related to attitude toward PDA. H3: Personal innovativeness will be positively related to attitude toward PDA. Multiple Regression Stepwise Regression H4.1: Attitude toward PDA will be positively related to purchase intention. H4.2: Perceived uncertainty will be negatively related to purchase intention. Multiple Regression Stepwise Regression RQ1: Which factors influence attitude and perceived uncertainty toward PDA at the persuasion stage of the Innovation-Decision process? Multiple Regression Stepwise Regression RQ2: Which factors will be useful in discriminating two purchase intention group (High/Low)? Discriminant Analysis RQ3: Which functions of PDA are related to attitude toward PDA? Pearson Correlation

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56 CHAPTER 6 RESULTS This chapter comprises two parts. The first part discusses the descriptive statistics about the study subjects. The second part presents the results of several statistics methods that were used to examine hypotheses and research questions. Finally, final parsimonious model will be presented in the third part. Descriptive Statistics Sample Characteristics This study recruited a total of 218 respondents (see Table 6-1). Most respondents for this survey were students from a large southeastern university. Completed questionnaires were received from 191 of 218 respondents. 27 questionnaires were excluded because some questionnaires were uncompleted and some respondents own a PDA. Among the respondents, 119 were female (62.3%) and 72 were male (37.7%). In terms of education demographics analysis of the sample, only three respondents were first-year college students (1.6%), six respondents were second-year college students (3.1%), 55 respondents were third-year college students (28.8%), 82 respondents were fourth-year college students (42.9%), 43 respondents were graduate students (22.5%), and two respondent were others (1.0%). Respondents age ranged from 18 to 61. The mean age was 22.62 years and the median age was 21 years. 167 respondents (87.4%) were 18-24 age group, 21 respondents (11.0%) were 25-34 age group, and three respondents (1.6%) were over the age of 35.

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57 In order to examine respondents awaren ess of PDA, respondents were asked the following question: Have you ever heard of a Personal Digital Assistant (PDA)? 175 respondents (91.6%) answered yes and 16 respondents (8.4%) answered no. Among respondents who were not aware of a PDA, nine respondents were female and seven respondents were male. On the basis of this data, it is clear that the PDA already passed the knowledge stage. In addition, respondents were asked about their ownership of PDA. 22 respondents (10.1%) owned a PDA and 191 respondents (89.9%) didnt own one. Respondents who currently own a PDA were excluded from this research because this study investigates the relationship between purchase intention and predictors. With respect to expected cost, the survey for this research included the following question: how much do you think the newest PDA would cost? Many respondents (19.3%) answered that they think the newest PDA would cost in a range from $301 to $400; 18.5 percent of respondents answered that it would cost between $401 and $500; seven respondents answered that it would cost more than $1,000. Table 6-1: Sample Characteristics Items Number % (cumulative) Total 218 Invalid data 27 12.4% Valid data 191 87.6% Adopter Adopter 22 10.1% Non-Adopter 191 89.9% Awareness Awareness 175 91.6% Non-awareness 16 8.4%

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58 Table 6-1. Continued Items Number % (cumulative) Price Under $100 16 8.4% (8.4%) $101 $200 35 18.3% (26.7%) $201 $300 24 12.6% (39.3%) $301 $400 37 19.3% (58.6%) $401 $500 40 18.5% (77.0 %) $501 $600 7 3.6% (80.6 %) $601 $700 15 7.8% (88.5 %) $701 $800 7 3.7% (92.1 %) $801 $900 2 1.0% (93.2 %) $901 $1000 6 3.1% (96.3 %) More than $1000 7 3.5% (100 %) Mean $444.88 Median $400.00 Gender Male. 72 37.7% Female 119 62.3% Age Range 18-61 Mean 22.62 Median 21 18 24 167 87.4% (87.4%) 25 34 21 11.0% (98.4%) More than 35 3 1.6% (100%) Education 1st year college student 3 1.6% (1.6%) 2nd year college student 6 3.1% (4.7%) 3rd year college student 55 28.8% (33.5%) 4th year college student 82 42.9% (76.4%) Graduate Students 43 22.5% (98.9%) Others 2 1.0% (100%) Normality of Items Generally, if calculated values of skewness and kurtosis dont exceed 2.58 at .01 probability level, the null hypothesis about the normality of the distribution is rejected (Hair et al., 1998). Table 6-2 shows the descriptive statistics of each observed item and variable in terms of mean, standard deviation, skewness, and kurtosis. As seen from the

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59 table, most observed values of skewness and kurtosis didnt exceed 2.58, so the null hypothesis about the normality was rejected However, both skewness and kurtosis of observed value about ownership of Digital Video Recorder (DVR) were over 2.58. The reason for this may be that only two respondents own a DVR. Therefore, the item about ownership of DVR was deleted in this study. Observed value about ownership of new technologies was calculated excluding ownership of a DVR. Reliability Cronbach's alpha ranges from 0.0 to 1.0. Generally, an alpha coefficient of .70 or greater indicates that a scale is appropriate for use in research. A scale with an alpha coefficient over .60, however, can be used for a research (Garson, 2003). Table 6-2 presents that Cronbachs alpha of all scales exceeded .60. This means that all scales can be used statistically in this research. In the case of perceived risks scale, a Cronbach alpha of four original items didnt exceed .60, but if the 2nd questionnaire (My friends or relatives will judge my purchase.) is deleted, the Cronbach alpha becomes over .60 ( =.6706). For valid reliability, this study eliminated 2nd item of the perceived risks scale. Table 6-2: Descriptive profile of each variable Mean SD Skewness Kurtosis Relative Advantage A PDA will save me time/effort over other means of performing the same tasks. 4.325 1.314 -.169 .002 A PDA will enable me to perform many tasks better than through other means. 4.126 1.348 -.192 -.221 A PDA will provide a greater value than other ways of performing the same task. 3.966 1.330 .104 .189 Cronbach Alpha .8599 Compatibility A PDA will be easy for me to adjust to. 4.322 1.584 -.118 -.688 A PDA will fit my lifestyle very well. 4.141 1.442 -.069 -.304

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60 Table 6-2. Continued Mean SD Skewness Kurtosis A PDA will fit the way I perform my daily tasks well. 4.126 1.471 -.139 -.307 Cronbach Alpha .8352 Complexity A PDA will be hard to learn. 3.319 1.657 .441 -.546 A PDA will be quite complicated to master. 3.361 1.683 .434 -.653 A PDA will be difficult to use. 3.162 1.580 .458 -.625 A PDA will have a complex, hard-to-learn system. 3.136 1.570 .489 -.519 Cronbach Alpha .9508 Trialability A PDA will I might try out a PDA long enough to see what I could use it for. 4.924 1.613 -.622 -.066 A PDA will Before deciding to use the PDA, I would like to be able to try one out. 5.510 1.450 -1.036 .968 Cronbach Alpha .6025 Ownership of new technology Mobile Phone .804 .397 -1.541 .392 Video game player (Xbox, Playstation, etc.) .332 .466 .717 -1.470 DVD .717 .446 -.972 -1.032 Digital Camera .306 .458 .847 -1.273 Digital Cable/Satellite TV .398 .485 .420 -1.819 Broadband (High-Speed Internet) .673 .467 -.742 -1.446 Digital Video Recorder (TiVo or Replay TV) .094 .293 2.800 5.900 Familiarity with new technology Mobile Phone 6.270 1.198 -1.827 3.098 Video game player (Xbox, Playstation, etc.) 4.869 1.956 -.570 -.828 DVD 5.929 1.372 -1.530 2.314 Digital Camera 4.856 1.713 -.538 -.476 Digital Cable/Satellite TV 4.759 1.838 -.498 -.736 Broadband (High-Speed Internet) 5.772 1.547 -1.437 1.575 Digital Video Recorder (TiVo or Replay TV) 3.029 1.995 .610 -.723 Cronbach Alpha .7405

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61 Table 6-2. Continued Mean SD Skewness Kurtosis Personal Innovativeness I like to buy new and different things. 5.293 1.454 -.606 -.105 I am usually among the first to try new products. 3.749 1.539 .088 -.627 I dont like to take chances. 4.885 1.548 -.373 -.562 Cronbach Alpha .7779 Importance of PDA functions Mobile Phone 5.157 1.994 -.839 -.550 Internet/E-mail 5.942 1.295 -1.422 2.220 Video games 2.670 1.570 .733 -.207 MP 3 and movie file player 4.016 1.761 -.124 -.967 Wordprocessing/Spreadsheet 5.162 1.606 -.807 -.080 Digital camera 4.340 1.749 -.270 -.892 Global Positioning System 3.497 1.954 .122 -1.206 Perceived Risks The PDA might fail to perform to my satisfaction. 4.026 1.308 -.220 -.199 My friends or relatives will judge my purchase. 2.812 1.578 .404 -.890 I might waste my money. 4.356 1.602 -.256 -.744 I might waste my time or effort getting the product repaired or replaced. 4.042 1.486 -.082 -.425 Cronbach Alpha .5310 (.6706 if 2nd item deleted) Attitude Unfavorable/Favorable 4.733 1.264 -.386 .263 Bad/Good 4.777 1.222 -.423 .271 Negative/Positive 4.712 1.267 -.368 .274 Cronbach Alpha .9472 Purchase Intention Unlikely/Likely 4.073 1.687 -.163 -.955 Probable / Improbable 4.099 1.551 -.245 -.608 Possible / Impossible 4.743 1.473 -.396 -.137 Cronbach Alpha .9091

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62 Ownership and Familiarity of New Technologies In terms of ownership of new technologies (Table 6-3), a mobile phone is owned by the largest respondents, among the new technologies measured in this study. 153 respondents personally own a mobile phone. Secondly, 135 respondents have a DVD player. A digital camera is owned by only 57 respondents (29.8%). A Digital Video Recorder (DVR), which was deleted from the results of analysis because of normality, is owned by just two respondents. Table 6-3: Ownership of new technologies Mobile Phone Video Game DVD Digital Camera Digital Cable Broadband DVR Owner 153 61 135 57 74 127 2 No owner 37 125 52 131 113 61 189 % owner 80.5% 32.6% 72.2% 30.3% 39.6% 67.6% 1.1% With respect to familiarity with new technologies, regardless of ownership, respondents feel familiar with new technologies except that of a DVR. Table 6-2 presents that all means relative to familiarity with new technologies are more than 4.8. Because 4 means neutral and means very familiar, it can be verified that a mobile phone, a video game console, a DVD player, a digital camera, digital cable/satellite TV, and broadband are communication devices with which respondents feel familiar. The Results of Hypotheses and Research Questions Some hypotheses were supported and other hypotheses were not supported. Table 6-4 presents the results of hypotheses and research questions.

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63 Table 6-4: Results of Hypotheses and Research Questions Hypotheses Results H1.1: Relative advantage will be positively related to attitude toward PDA. Supported H1.2: Compatibility will be positively related to attitude toward PDA. Supported H1.3: Complexity will be negatively related attitude toward PDA. Not supported H1.4: Trialability will be positively related to attitude toward PDA. Supported H2: The number of ownership of new technologies will be positively related to attitude toward PDA. Not supported H3: Personal innovativeness will be positively related to attitude toward PDA. Supported H4.1: Attitude toward PDA will be positively related to purchase intention. Supported H4.2: Perceived uncertainty will be negatively related to purchase intention. Not supported Research Questions Results RQ1: Which factors influence attitude and perceived uncertainty toward PDA at the persuasion stage of the Innovation-Decision process and is there the relative influence among variables? Attitude: compatibility > personal innovativeness > trialability > relative advantage Perceived uncertainty: relative advantage > complexity RQ2: Which factors will be useful in discriminating two purchase intention group (High/Low)? attitude, relative advantage, compatibility, trialability, and personal innovativeness RQ3: Which functions of PDA are related to attitude toward PDA and purchase intention? Attitude: mobile phone, video games, digital camera, and global positioning system Purchase intention: MP3 player, digital camera, and global positioning system Factors Influencing Attitude toward Personal Digital Assistant (PDA) The first research question and six hypotheses were developed to investigate the relationship between independent variables (relative advantage, compatibility, complexity, trialability, ownership of new technologies, and personal innovativeness) and attitude toward PDA. Multiple regression and stepwise regression were performed. Stepwise regression is one of the ways to compute Ordinary Least Squares (OLS) (Garson, 2003). The results of multiple regression analysis of attitude toward PDA on relative advantage, compatibility, complexity, triala bility, ownership of new technology, and

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64 personal innovativeness were presented in Table 6-5, with tolerance and VarianceInflation Factor (VIF) provided as supporting information. Multicollinearity means the linear relation of independent variables, so high multicollinearity may make assessment of the unique role of independent variable difficult. In order to measure mulitcollinearity, tolerance or VIF is used (Garson, 2003). According to Garson (2003), generally, if tolerance is less than .20, a multicollinearity problem is indicated. In other words, the closer to 0 tolerance is, the higher multico llinearity is. In case of VIF, which is the reciprocal of tolerance, a value above 5 indicates a problem with multicollinearity. SPSS suggests another method, called condition indices, to assess if there is too much multicollinearity in the model (Garson, 2003). If a condition index is over 30, there is a serious collinearity problem. A condition index over 15 suggests possible collinearity problems (Garson, 2003). Table 6-5 shows that all independent variables tolerance levels fall between .590 and .878 and VIF scores range from 1.138 to 1.694. In case of a condition index (see Table 6-6), there is no factor that exceeds 30. On the basis of this data, multicollinearity was not found in this analysis. The results of multiple regression analysis in Table 6-5 present that this regression equation is significant (F=20.553, p < .001). For this equation, 40.1 percent of the variance is statistically explained by the independents variables. Among the independent variables, relative advantage ( = .153), compatibility ( = .364), trialability ( = .147), and personal innovativeness ( = .217) were significantly (p < .05) and positively related to attitude toward PDA. Complexity and ownership of new technologies had no significant impact on attitude toward PDA. Based on the results of multiple regression analysis, hypotheses were examined.

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65 H1.1, H1.2, H1.3, and H1.4 stated that perceived attributes of PDA are significantly related to attitude toward PDA. The results of regression revealed that relative advantage, compatibility, and trialability had a signif icant positive relationship with attitude. Therefore, H1.1, H1.2, and H1.4 are supported, while H1.3 is not supported (see Table 64). H2 and H3 predicted that ownership of new technologies and personal innovativeness would be positively related to attitude toward PDA, respectively. The results from multiple regression analysis supported H3 with a significant positive relation, but H2 was not supported (see Table 6-5). Table 6-5: Multiple regression analysis of attitude toward PDA on relative advantage, compatibility, complexity, trialability, ownership of new technology, and personal innovativeness. Dependent variable: Attitude toward PDA Unstandarized Coefficients Standardized Coefficients Variables B SD Beta t Sig. Tolerance VIF Relative Advantage .154 .069 .153 2.232 .027 .694 1.440 Compatibility .331 .068 .364 4.897 .000 .590 1.694 Complexity -.011 .036 -.019 -.313 .755 .878 1.138 Trialability .201 .088 .147 2.293 .023 .795 1.258 Ownership of new technologies -.041 .147 -.017 -.280 .780 .838 1.193 Innovativeness .204 .060 .217 3.386 .001 .794 1.259 R: .633 R2: .401 Adjusted R2: .382 F-ratio: 20.553 (p< .001)

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66 Table 6-6: Collinearity diagnostics of multiple regression analysis of attitude and perceived uncertainty toward PDA on relative advantage, compatibility, complexity, trialability, ownership of new technology, and personal innovativeness Dimension Eigenvalue Condition Index 1 6.506 1.000 2 .197 5.753 3 .144 6.726 4 .05524 10.852 5 .04237 12.391 6 .03436 13.760 7 .02175 17.295 Stepwise regression was performed to examine a model with variables (relative advantage, compatibility, trialability, and personal innovativeness) that contributed significantly to attitude toward PDA from the previous multiple regression and to investigate their relative influence on attitude. Table 6-7 presents the results of stepwise regression. They showed unique and substantial contribution in this study. The model obtained from this stepwise regression proc edure has four variables that statistically significantly explained 40.1 percent of the variance in attitude toward PDA. Compatibility had the strongest influence in this equation, while relative advantage, trialability, and personal innovativeness had weak influence on attitude toward PDA. Since all tolerance values are not below .20 and all VIF are not above 5, there may be no serious multicollinearity problem in this model. Table 6-7: Stepwise multiple regression of attitude toward PDA on relative advantage, compatibility, trialability, and personal innovativeness Model 1 Variables R R2 Adj. R2R2 changeF changeB Beta t ToleranceVIF .565 .319 .315 .319 88.492 Compatibility .514.565 10.76 1.00 1.00Model 2 Variables R R2 Adj. R2R2 changeF changeB Beta t ToleranceVIF .600 .360 .354 .042 12.213

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67 Table 6-7. Continued Variables R R2 Adj. R2R2 changeF changeB Beta t ToleranceVIF Compatibility .446.490 7.875 .880 1.136 Innovativeness .204.058 3.495 .880 1.136 Model 3 Variables R R2 Adj. R2R2 changeF changeB Beta t ToleranceVIF .620 .384 .374 .024 7.232 Compatibility .402.441 6.929 .811 1.233 Innovativeness .200.213 3.483 .880 1.137 Trialability .222.162 2.689 .906 1.104 Model 4 Variables R R2 Adj. R2R2 changeF changeB Beta t ToleranceVIF .633 .401 .388 .016 5.103 Compatibility .337.370 5.260 .650 1.540 Innovativeness .199.212 3.496 .880 1.137 Trialability .190.138 2.287 .879 1.138Relative advantage .153.152 2.259 .710 1.408 all p < .05 Factors Influencing Perceived Uncertainty toward Personal Digital Assistant (PDA) In order to examine the relationship between perceived uncertainty and the independent variables (relative advantage, compatibility, complexity, trialability, ownership of new technologies, and personal innovativeness), multiple regression and stepwise regression were used. Table 6-8 shows the results of multiple regression analysis of perceived uncertainty toward PDA on the independent variables. Results revealed that this equation is significant (F=5.633, p < .001), but only 15.5 percent of the variation in perceived uncertainty toward PDA is explained. Contrary to the previous equation for attitude, there are only two variables which are significantly (p < .05) related to perceived uncertainty toward PDA: relative advantage ( = -.317) and complexity ( = .153). Relative advantage is negatively related to perceived uncertainty and complexity

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68 is positively related to perceived uncertainty. Because tolerance values ranged from .590 to .878 and VIF values ranged from 1.138 to 1.694, no serious multicollinearity problem was found. In addition, a condition index didnt exceed 30 (see table 6-5). Table 6-8: Multiple regression analysis of perceived uncertainty toward PDA on relative advantage, compatibility, complexity, triala bility, ownership of new technology, and personal innovativeness Dependent variable: Perceived uncertainty toward PDA Unstandarized Coefficients Standardized Coefficients Variables B SD Beta t Sig. Tolerance VIF Relative Advantage -.308 .079 -.317 -3.903 .000 .694 1.440 Compatibility -.047 .077 -.053 -.602 .548 .590 1.694 Complexity .08669 .041 .153 2.122 .035 .878 1.138 Trialability .116 .100 .088 1.155 .250 .795 1.258 Ownership of new technologies .07093 .169 .031 .420 .675 .838 1.193 Innovativeness -.058 .069 -.064 -.841 .402 .794 1.259 R: .394 R2: .155 Adjusted R2: .128 F-ratio: 5.633 (p< .001) Stepwise regression was performed to examine a model with variables (relative advantage and complexity) that significantly contributed to perceived uncertainty toward PDA from the previous multiple regression and to investigate the relative influence of two variables (see Table 6-9). The obtained model from stepwise regression explains 14.3 percent of the variation in perceived uncertainty. Relative advantage had the strongest influence on perceived uncertainty. In addition, there was no serious multicollinearity problem in this model.

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69 Table 6-9: Stepwise multiple regression of perceived uncertainty toward PDA on relative advantage and complexity Model 1 Variables R R2 Adj. R2R2 changeF changeB Beta t ToleranceVIF .330 .109 .104 .109 23.113 Relative Advantage .-.32-.330-4.808 1.00 1.00 Model 2 Variables R R2 Adj. R2R2 changeF changeB Beta t ToleranceVIF .378 .143 .134 .034 7.486 Relative Advantage .-.32-.330-4.887 1.00 1.00 Complexity .104.185 2.736 1.00 1.00 all p < .05 Relationship between the Intervening Variables (Attitude and Perceived Uncertainty toward PDA) and Purchase Intention The set of fourth hypotheses addressed whether there are significant relationships between intervening variables (attitude and perceived uncertainty) and purchase intention. Multiple regression was also employed to measure the relationship. The prediction equation for purchase intention explains 43.0 percent of the variance in purchase intention (see Table 6-10). Purchase intention is significantly predicted by only attitude toward PDA. H4.1 and H4.2 predicted that attitude toward PDA would be positively related to purchase intention and perceived uncertainty would be negatively related to purchase intention. The results of multiple regression analysis show that attitude has a significant effect for predicting purchase intention, while perceived uncertainty is not a significant predictor. So, H4.1 was supported, H4.2 was not supported (see Table 6-4).

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70 Table 6-10: Multiple regression analysis of purchase intention on attitude and perceived uncertainty toward PDA Dependent variable: Purchase intention Unstandarized Coefficients Standardized Coefficients t Variables B SD Beta Sig. Variables VIF Attitude .774 .071.634 10.941 .000 .904 1.106 Perceived risks -.0790 .073-.062 -1.076.283 .904 1.106 R: .655 R2: .430 Adjusted R2: .424 F-ratio: 70.886 (significant: .000) Stepwise regression results are presented in Table 6-11, with only attitude as an independent variable. This regression model predicting purchase intention results in 42.6 percent of variance explained. Table 6-11: Regression of purchase intention on attitude toward PDA Model 1 Variables R R2 Adj. R2R2 changeF changeB Beta t ToleranceVIF .653 .426 .423 .426 140.457 Attitude .797.653 11.85 1.00 1.00 all p < .05 Factors Discriminating Two Purchasing Intention Group (High/Low) The second research question addressed which factors are useful in discriminating among purchase intention groups. In order to examine this research question, discriminant analysis was employed. All variables (relative advantage, compatibility, complexity, trialability, ownership of ne w technologies, personal innovativeness, perceived uncertainty, and attitude toward PDA) served as the independent variables, and

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71 two purchase intention groups (high and low) were used as the dependent variables in discriminant analysis. Questionnaires about purchase intention were composed of three items, which adapt a seven-point scale. In other words, a score of 21 indicates the highest purchase intention and a score of 3 presents the lowest purchase intention. The mean of purchase intention scores was 12.9, so this study classified respondents whose scores were over 12.9, into the high purchase intention group (n = 88), and respondents whose scores were under 12.9, into the low purchase intention group (n = 103). Table 6-12 reveals the result of Wilkss lambda of each variable in discriminant analysis. The larger the Wilkss lambda, the less important the independent variables to the discriminant analysis (Garson, 2003). Wilkss lambdas of relative advantage, compatibility, trialability, innovativeness, and attitude are significant by the F test. This study dropped complexity, ownership of ne w technologies, and perceived uncertainty because they were not significant. Homogeneity of covariance matrices between groups is the assumption of discriminant analysis. This assumption is measured by Boxs M test. On the basis of Boxs M test, a null hypothesis of equal population covariance matrices was rejected (see Table 6-12). This study has only one discriminant function because there are only two categories in the dependent variable. The eigenvalue of discriminant function is statistically significant (p < .001). Table 6-11 also presents canonical discriminant function coefficients. Standard canonical discriminant function coefficients indicate the relative importance of the independent variables in explaining the dependent variable (Garson, 2003). Attitude is the strongest discriminating factor among the independent variables. Compared to

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72 attitude, the importance of other variables is very weak. In terms of the correlations of each variable with discriminant function, attitude has the strongest correlation with discriminant function. Table 6-12: Discriminant analysis between high-purchase intention group and lowpurchase intention group Tests of equability of group means Means Low intention High intention Wilks Lambda F Sig. Relative advantage 11.7136 13.2386 .953 9.255 .003 Compatibility 11.1893 14.2273 .849 33.704 .000 Complexity 13.3689 12.5227 .995 .925 .337 Trialability 9.9709 10.9773 .962 7.380 .007 Ownership of new technology 3.2233 3.4432 .995 1.017 .314 Innovativeness 13.0680 14.9318 .939 12.218 .001 Perceived uncertainty 12.9029 11.8636 .977 4.449 .036 Attitude 12.3204 16.4545 .662 96.396 .000 Boxs M test F Boxs M Approx. df1 df2 sig. 75.926 2.014 36 114249.6 .000 Eigenvaules Function eigenvalue % of variance Cumulative %Canonical correlation 1 .536 100.0 100.0 .591 Wilks Lambda Function Wilks Lambda Chi-square df Sig. 1 .651 79.454 8 .000 Canonical discriminant function coefficients Standardized Canonical Discriminant Function Coefficients Unstandarized Canonical Discriminant Function Coefficients Structure Matrix Relative advantage -.161 -.047 .302 Compatibility .244 .068 .577 Complexity .036 .006 -.096 Trialability .007 .003 .270

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73 Table 6-12. Continued Standardized Canonical Discriminant Function Coefficients Unstandarized Canonical Discriminant Function Coefficients Structure Matrix Ownership of new technology -.057 -.038 .100 Innovativeness .040 .011 .347 Perceived risks .047 .014 -.209 Attitude .935 .322 .975 Classification results of discriminant analysis are presented in Table 6-13. 75.9 percent of original grouped cases are correctly classified by discriminant function. In order to measure the significance of hit rate, t-ratio test was employed. to was calculated by the equation, and it was 5.40. Therefore, the hit rate is statistically significant (p < .01). Table 6-13: Classification results of discriminant analysis Predicted Group Membership Purchase Intention Low High Total Low 74 29 103 Count High 17 71 88 Low 71.8 28.2 100.0 % High 19.3 80.7 100.0 Relation between Functions of PDA and Attitude and Purchase Intention The third research question was developed to explore the relationships among each function of PDA, attitude, and purchase intention. Pearson correlation was performed. Questions about the perceived importance of functions of PDA used a sevenpoint scale, with meaning that respondents consider that function extremely important. Respondents answered that Internet/Email (mean=5.517) and mobile phone (mean=5.942) are important functions in PDA, while Global Positioning System (GPS)

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74 (mean=3.497) and video games (mean=2.670) we re not perceived as important functions (see Table 6-14). Table 6-14: Descriptive profile of perceived importance of functions of PDA Mean Median SD Skewness Kurtosis Mobile Phone 5.517 6.00 1.994 -.839 -.550 Internet/Email 5.942 6.00 1.294 -1.422 2.220 Video games 2.670 2.00 1.569 .733 -.207 MP3 player 4.016 4.00 1.761 -.124 -.967 Wordprocessing 5.162 6.00 1.616 -.807 -.080 Digital Camera 4.340 4.00 1.749 -.270 -.892 GPS 3.497 4.00 1.954 .122 -1.206 The correlation matrix is presented in Table 6-15. In terms of relationship between each function of PDA and attitude toward PDA, mobile phone, video games, digital camera, and global positioning system are significantly correlated to attitude. Interestingly, Internet/Email, which is perceived as a relatively important function, didnt have a significant relation with attitude, and video games, which was perceived to be the least important function, is significantly and positively related to attitude toward PDA. With respect to the relationship between each function and purchase intention, MP3 player, digital camera, and global positioning system are significantly correlated to purchase intention, while mobile phone, Internet/Email, video games, and wordprocessing/spreadsheet are not significantly related to purchase intention. As in the result of the relationship to attitude, Internet/Email was not significantly related to purchase intention.

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75 Table 6-15: Pearson correlation between perceived importance of PDA functions and attitude and purchase intention Variables Func1 Func2 Func3 Func4 Func5Func6 Func7 Attitude Purchase intention Funcition1: Mobile Phone 1.000 Function 2: Internet/Email .354** 1.000 Function 3: Video games .064 .022 1.000 Function 4: MP3 player .029 .067 .451**1.000 Function 5: Wordprocessing .179* .394** -.002 .131 1.000 Function 6: Digital Camera .329** .141 .179* .359**.065 1.000 Function 7: GPS -.036 -.072 .289**.276**-.113 .357**1.000 Attitude .187** .071 .180* .096 .030 .246**.178* 1.000 Purchase intention .120 -.001 .123 .180* .059 .343**.260* .653** 1.000 *: p < .05, **: p < .01 This study classified the dimensions of functions with factor analysis. With eigenvalues of 1.00 or higher as the criteria, two factors were yielded by explaining 52.721 percent of the variance (see Table 6-16). Factor 1 consists of video games, MP3 player, digital camera, and global positioning system. Factor 2 is composed of mobile phone, Internet/Email, and wordprocessing/spreadsheet. Factor 1 has more entertainment aspects than factor 2. On the other hand, factor 2 has more information aspects than factor 1. So, this study refers factor 1 as an entertainment factor and factor 2 as an information factor. In addition, entertainm ent has more significant functions than information, in terms of the relationship with attitude and purchase intention, even though respondents thought that functions related to entertainment are less important than those related to information.

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76 Table 6-16: Factor analysis of perceived importance of PDA functions Items Mean SD Factor 1 Factor 2 Importance of PDA functions Mobile Phone 5.157 1.994 .127 .681 Internet/E-mail 5.942 1.295 -.007 .797 Wordprocessing/Spreadsheet 5.162 1.606 -.044 .690 Table 6-16. Continued Items Mean SD Factor 1 Factor 2 Importance of PDA functions Video games 2.670 1.570 .692 -.023 MP 3 and movie file player 4.016 1.761 .745 .108 Digital camera 4.340 1.749 .648 .314 Global Positioning System 3.497 1.954 .700 -.204 Table 6-17. Continued Items Mean SD Factor 1 Factor 2 Eigenvalue 1.962 1.728 Percent of variance explained 28.034% 24.687% Cumulative percent 28.034% 52.721% Cronbach Alpha .6490 .5391 Demographic Characteristic of Purchase Intention Group Differences in demographic characteristics between the high purchase intention group and the low intention group were investigated to reveal if there were distinct characteristics for each group. t -test results are shown in Table 6-17. Results indicated that there was little demographic difference between the high purchase group and the low purchase intention group. In the case of age, tvalue was -.846 (p > .05) and the t-value of gender was -.948 (p > .05). Table 6-17: t-test Results Comparing between Purchase Intention Group Low purchase intention group High purchase intention group Demographics Mean SD Mean SD t-Value Age 22.388 4.6510 22.909 3.7035 -.846 (p = .399) Gender 1.592 .4938 1.659 .4767 -.948 (p = .345) Gender: Male = 1; Female = 2. Purchase intention: Low = 0; High = 1.

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77 Final Parsimonious Model This study examined the relationships among variables. Relative advantage, compatibility, trialability, and personal innovativeness were the significant factors that affected attitude toward PDA. In addition, attitude was the significant predictor as to purchase intention. Based on these results, th is current study created a final parsimonious model like the following: .653** .212** .138* .152* .370** Trialability Relative Advantage Personal Innovativenes Compatibility Purchase Intention Attitude Toward PDA Figure 6-1: Final Parsimonious Model

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78 CHAPTER 7 DISCUSSION This chapter consists of four parts. The first part will present a brief review of the theoretical model of this current study. The second part will summarize the findings from the statistics analysis and the results of hypo theses and research questions. The third part will discuss the implications of this study. In the fourth part, limitations of this study will be presented. Finally, conclusion will be addressed in the last part. Review of the Present Study This current study had a goal. This study wanted to examine the impact and the relative influence of perceived characteristics of innovation, ownership of new technologies, personal innovativeness, and functions of PDA in exploring attitude, perceived uncertainty, and adoption of PDA. Toward this goal, diffusion theory was used as the conceptual framework and theoretical paradigm for understanding the adoption of PDA. Rogers (1995) argued that during the process of the adoption of new technology, an individual (or other decision-making unit), who passed the knowledge stage of an innovation, forms his/her attitude toward the innovation, decides to adopt or reject, implements, uses the new idea, and confirms his/her decision. Following from this focus, this present study focused on two stages (persuasion stage and decision stage) of the Innovation-Decision Process of Diffusion theory to examine which variables affect attitude and perceived uncertainty toward PDA, and whether PDA adoption can be explained by variables.

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79 According to Rogers (1995), an individual forms a favorable or unfavorable attitude toward the innovation and seeks information to decrease a perceived uncertainty about the innovation at the persuasion stage, and decides to adopt or reject the innovation at the decision stage. This study applied Rogerss theory to predict and examine the factors influencing adoption of PDA. The independent variables were selected on the basis of previous adoption researches: rela tive advantage, compatibility, complexity, trialability, ownership of new technologies, and personal innovativeness. There are three research questions and eight hypotheses in the present study. Hypotheses and research questions presented the relationship among the variables during the adoption process. A total of 218 undergraduate and graduate students from a large southeastern university participated in the survey for this current study and 191 sets of valid data were acquired. Summary of Results of Research Questions and Hypotheses This section will summarize findings and results of each of eight hypotheses and four research questions. Hypotheses Hypotheses 1 predicted the relationship between perceived attributes of PDA and attitude toward PDA. H1.1: Relative advantage will be positively related to attitude toward PDA. H1.2: Compatibility will be positivel y related to attitu de toward PDA. H1.3: Complexity will be negatively related to attitude toward PDA. H1.4: Trialability will be positively related to attitude toward PDA.

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80 These hypotheses were created based on diffusion theory. Rogers (1995) asserted that perceived attributes of innovations are significantly associated with the rate of adoption and are significant determinants to influence forming an attitude toward the innovation. Perceived attributes of the innovation have been used as a predictor to explain adoption of innovations (LaRose and Atkin, 1992; Eastlick, 1993 & 1996; Lin, 1998; Parthasarathy et al., 1998; Du, 1999). The findings of previous studies indicated that these variables (relative advantage, compatibility, complexity, trialability, and observability) are significant factors in predicting the adoption of innovations. Their findings were consistent with Rogerss assertion that perceived attributes of innovations play an important role in forming an attitude (Rogers, 1995). Some results of H1 were consistent with the findings of previous researches and others were not consistent. In most previous researches, relative advantage was a significant predictor of adoption of innovations (Ostlund, 1974; Eastlick, 1993 & 1996; Parthasarathy and Bhattacherjee, 1998; Du, 1999; Sund et al., 2001). As this study expected, relative advantage was a significant predictor of attitude toward PDA in the current study. This study also found that co mpatibility and trialability are significantly and positively related to attitude. Compatibility (Ostlund, 1974; Eastlick, 1993 & 1996; Rogers, 1995; Lin, 1998; Parthasarathy and Bhattacherjee, 1998; Du, 1999; Sund et al., 2001) and trialability (Ostlund, 1974; Holak, 1988; Eastlick, 1996; Rogers, 1995; Sund et al., 2001) were factors influencing adoption of innovations in other researches. In terms of complexity, this study didnt support that complexity is significantly associated with attitude toward PDA. Ostlund (1974), Du (1999), and Sund et al. contended that complexity is one of the factors predicting an adoption, while Eastlick (1993) and Lin

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81 (1998) found that there was no significant relationship between complexity and adoption. As long as an individuals technology apprehension is outweighed by the perceived advantage of innovations, complexity is not a serious concern for consumers (Lin, 1998). Results of this study mean that relative advantage and compatibility may be a strong predictor of adoption, while complexity may not be as strong a predictor as it appears to be in diffusion theory. H2: The number of ownership of new technologies (Mobile Phone, Video game player, DVD player, Digital camera, Digital Cable/Satellite TV, Broadband, and Personal Video Recorder) will be positively related to attitude toward PDA. H2 expressed that ownership of new technologies would be significantly and positively related to attitude toward PDA. In order to measure ownership of new technologies, this study asked respondents about ownership of seven technologies. Lin (1998) found that communication technology ownership is an important factor in predicting the personal computer adoption rate and ownership of other communication technology devices predicted PC adoption. Dupagne (1999) also contended that the number of home entertainment products was positively related to adoption of HDTV. The early adopters of personal computers owned other high technology products (Danko and MacLachlan, 1983). Therefore, it was expected that the more new technologies are owned, the more favorable attitude the subject would have. However, there was no significant effect of ownership of new technolo gies on attitude toward PDA. This result was not consistent with some past researches (Lin, 1998; Dupagne, 1999). Next paragraph will explain the reason why the result of this study were not consistent with previous studies.

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82 The notion of functional similarity and need compatibility was suggested by several researchers. Atkin (1993) contended that the adoption of innovations is related to functionally similar media or technologies. Perse and Courtright (1993) asserted that the adoption of a functionally similar product has an impact on the adoption of new technologies. According to Henke and Donohue (1989), new technological advancement tends to affect the way consumers reorganize their view about the established media. In order to research functional similarity and functional displacement, the number of new technologies owned as well as exposure time to media, familiarity of established media, and other variables have been used as predictors. Even though ownership of new technologies was a significant predictor in pr evious researches, this study didnt support that ownership of new technologies would affect attitude toward PDA. Ownership of new technologies alone may not explain adoption of innovations and functional substitution. H3: Personal innovativeness will be positively related to attitude toward PDA. Hypothesis 3 presented that personal innovativeness would be a significant predictor of attitude toward PDA. In adoption studies, innovativeness has been an important variable to examine adoption. As in previous studies that suggested that personal innovativeness would lead to a more positive attitude (Venkatraman, 1991; Manning et al., 1995; Lin, 1998; Lin and Jeffres, 1998; Donthu and Garcia, 1999; Du, 1999; Citrin et al., 2000; Im et al., 2003), it was shown that personal innovativeness significantly influenced attitude toward PDA. In other words, consumers who have a higher level of innovativeness with innovations are more likely to have positive attitude toward PDA.

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83 H4.1: Attitude toward PDA will be positively related to purchase intention. H4.2: Perceived uncertainty will be negatively related to purchase intention. Hypothesis 4 predicted that attitude would positively affect purchase intention and perceived uncertainty would negatively influence purchase intention at the decision stage. A persons attitude affects behavioral intention, which is a good forecaster of actual behavior (Fishbein and Ajzen, 1975; Ajzen, 1985). Previous researches found that attitude has a strong correlation with intent to purchase (Lutz et al., 1983; Brown and Stayman, 1992; Rogers, 1995; Ko, 2002). Based on previous studies, this present study expected that attitude would have a positively significant relation with purchase intention. As expected, there was significant relationship between attitude and purchase intention. Respondents were asked to rate their attitude (unfavorable/favorable, bad/good, and negative/positive) toward PDA, and the mean of attitude score was 4.74. This means that subjects of this study, by and large, have favorable attitude toward PDA and their attitudes were significantly related to purchase intention. Contrary to attitude, perceived risks negatively affect adoption of innovations (Cox and Rich, 1964; Dowling and Staelin, 1994; Wood and Scheer, 1996; Mitchell, 1998; Pope, 1998; Ko, 2001). According to Cox and Rich (1964), perceived risk is a function of the amount at stake in the purchase intention. Pope (1998) contended that perceived risk was related to online purchase of sport products. Therefore, this current study expected that there would be a significant relationship between perceived risks and purchase intention. Contrary to the expectation, perceived uncertainty had no significant relation with purchase intention (p > .05).

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84 Research Questions RQ1: Which factors influence attitude and perceived uncertainty toward Personal Digital Assistant (PDA) at the persuasion stage of the Innovation-Decision process and which variables have the relatively strong or weak influence on attitude and perceived uncertainty? Following from hypotheses 1, hypotheses 2, and hypotheses 3, research question 1 investigated which variables affect attitude and perceived uncertainty toward PDA and whether there are the relative weights of the significant variables on attitude and perceived uncertainty. The results present that relative advantage, compatibility, trialability, and personal innovativeness positively affect attitude, and perceived uncertainty is affected by relative advantage and complexity. The strongest variable in predicting attitude toward PDA among significant independent variables was compatibility, which explained 31.9 percent of the variation in attitude toward PDA, while relative advantage, which statistically explained 1.6 percent of the variation, was the weakest variable. Contrary to the result of attitude, among the variables which are significantly related to perceived uncertainty, relative advantage was the strongest variable, but it could explain only 10.9 percent of the variation in perceived uncertainty toward PDA. Only relative advantage had a significant impact on both attitude and perceived uncertainty, while ownership of new technologies was not significantly related to both attitude and perceived uncertainty. RQ2: Which factors will be useful for discriminating two purchase intention groups (high/low)? Research question 2 examined which factors would be useful in discriminating between the high purchase intention group and the low intention group. According to the results of discriminant analysis, discriminant function classified correctly 75.9 percent of the original grouped cases. As expected, variables (relative advantage, compatibility,

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85 trialability, innovativeness, and attitude), which were found as significant predictors in hypotheses, were significant discriminant factors. Among the significant factors, attitude was the strongest discriminating factor, and trialability was the weakest discriminating factor. In addition, relative advantage, compatibility, and personal innovativeness significantly differentiated the high purchase intention group from the low purchase intention group. RQ3: Which functions of PDA are related with attitude toward PDA and purchase intention? Research question 3 intended to examine if there was any significant relationship between each function of PDA, and attitude and purchase intention. This study expected that each function of PDA would be related to attitude toward PDA and purchase intention. The results of Pear son correlation analysis reveal ed that attitude toward PDA was significantly correlated to mobile phone, video games, digital camera, and global positioning system. In addition, MP3 player, digital camera, and global positioning system had a significant correlation with purchase intention. Functions of PDA were classified with two factors by factor analysis. Factor 1 is composed of video games, MP3 player, digital camera, and global positioning system. Factor 2 consists mobile phone, Internet/Email, and wordprocessing/spreadsheet. Subjects perceived functions of factor 2 as more important function of PDA than thos e of factor 1(see Table 6-14). A PDA is still likely to be perceived as a communicatio n device for information rather than an entertainment. Digital camera and global positioning system, which are factor 1, were significantly correlated with both attitude a nd purchase intention, even though they were not perceived as an important function by respondents. Except two functions, mobile phone and video games were significantly correlated with attitude and MP3 player was

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86 significantly correlated with purchase intention. Among functions of factor 2, only mobile phone had a significant relationship with attitude. It means that perceived importance for functions of PDA is not linked with attitude and purchase intention. Regardless of perceived importance, more significant correlations were found from variables of factor 1. This study provides an exploratory attempt to figure out the difference among each function of PDA. This study examined only the relationship between attitude and purchase intention, and functions of PDA. Only perceived importance for each function of PDA is not sufficient to explain attitude and purchase intention. In order to effectively examine the relations between functions of PDA and attitude or purchase intention, other factors such as motivations or gratification linked with ea ch function of PDA should be investigated. Implications This section consists of three parts: theo retical implication, practical implication and future research. Theoretical Implications The theoretical background of this present study is diffusion of innovations. In particular, this study focuses on the innovation-decision process of diffusion of innovations. Rogers (1995) proposed that such persuasion will lead to a subsequent change in overt behavior (that is, adoption or rejection) consistent with the attitude held, but in many cases attitudes and actions are quite disparate (p. 169). In other words, attitude is a significant predictor for adoption, but the relationship between attitude and adoption is not strong. From this theoretical perspective, this study investigated the effect of attitude at the decision stage. The result was little different from Rogerss (1995)

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87 assertation. In this study, attitude was a strong significant predictor for purchase intention (R2=.426). Based on this result, this present study may offer th e notion that attitude has a strong impact on adoption of innovations. Additionally, this study partially supports the effect of perceived attributes of innovations in the persuasion stage. According to Rogers (1995), perceived attributes of an innovation, such as its relative advantage, compatibility, complexity, trialability, and observability, play an important role at the persuasion stage. This study examined whether perceived attributes of PDA have a si gnificant relationship with attitude toward PDA. Consistent with previous findings of adoption researches, this study presents that perceived attributes, except complexity, are significantly related to attitude. Several previous researches also found that complexity was not a significant predictor of adoption (Eastlick, 1993; Lin, 1998; Parthasarathy and Bhattacherjee, 1998). The rapidly changing media environment provides consumers much opportunity to experience a variety of new technologies (e.g., Interactive Television, Satellite Radio, etc.). Under this circumstance, complexity may not be a significant predictor of adoption any longer. Lin (1998) asserted that it is apparent that, as long as the perceived advantage of adopting outweighs ones technology apprehension, perceived complexity is of no real concern (p. 108). Therefore, this study may present the notion that complexity is not a significant factor in predicting adoption of innovations. Rogers (1995) said that at the persuasion stage, and especially at the decision stage, an individual is motivated to seek innovation-evaluation information, the reduction in uncertainty about an innovations expected consequences (p. 168). From this perspective, this study intended to examine which factors are significantly related to

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88 perceived uncertainty. According to Rogers (1995), in order to reduce uncertainty, an individual wants to know the innovations consequences and advantage/disadvantage. Consistent with Rogerss assertation, the result of this current study shows that among the independent variables, relative advantage was the strongest factor in predicting perceived uncertainty. This study supports that an individual has some degree of uncertainty for innovations, and knowledge about an i nnovations expected consequences and advantages can reduce individuals perceived uncertainty. Finally, this study used trialability as an independent variable. A number of adoption studies excluded trialability from perceived attributes of innovations for particular reasons (e.g., Parthasarathy and Bh attacherjee, 1998), or trialability was not a significant predictor of adoption in previous studies (e.g., Eastlick, 1993). Agarwal and Prasad (1997), however, contended that trialability had a significant and important impact on the acceptance of information technologies. Rogers (1995) asserted that trialability will speed up the rate of adoption, and most individuals will not adopt an innovation without trialability in order to know its relative advantage in their own situation. Even though this study didnt examine if trialability affects adoption (purchase intention) of PDA, this study shows that trialability has a significant affect in predicting attitude, which is the strongest predictor of adoption of PDA in the current study. This current study expects that under the rapidly changing media environment, consumer want to get more opportunity to try out new technologies before they decide to purchase them. This study may offer the notion that triability plays an important role in adoption research.

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89 Practical Implications The current study contributes to the practical field of marketing for a PDA. First, this study examined whether at titude toward PDA leads to a purchase intention. According to the results pertaining to causal relationship, there was a strong relationship between attitude and purchase intention. In the basis of this result, marketers should have the ability to influence consumers attitude toward PDA, providing information on the benefits associated with a PDA. Marketers should identify important factors influencing attitude toward PDA, because attitude toward PDA has been found as the foremost predictor of purchase intention. Second, this study contributes to the practical field of development of PDA. In this study, relative advantage, compatibility, and trialability appeared to be influential in forming attitude toward PDA. In addition, perceived uncertainty was influenced by relative advantage and complexity. Lin (2001) asserted that unless an innovation can provide better content, superior technical benefits, and cost efficiency to consumers, an innovation can hardly displace the traditional technologies. The main attributes of PDA should present dimensions of relative advantage over other technologies (Atkin et al., 1998). Developers and researchers should focus on identifying which aspects of PDA lead to better benefits, compared to other technologies. Third, several types of PDAs should be offered. This current study found that Internet, video games, digital camera, and GPS were significantly correlated to attitude toward PDA, and purchase intention is significantly related to MP3 player, digital camera, and GPS. Functions related to entertainment have stronger impact on attitude and purchase intention than functions related to information or others. Consumers are likely

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90 to purchase a PDA with different motivations and objectives. Some consumers focus on entertainment aspects of PDA rather than others of PDA. Some consumers will purchase a PDA as a mobile communication device. As a rule of thumbs, the number of functions affects the price. In other words, the more functions, the higher price. For example, hedonic innovators will be little interested in functions related to interaction or information. Word processor and GPS are useless functions to hedonic innovators. They just increase the production cost. Marketers should provide diverse consumers various PDAs such as a PDA, which focuses on entertainment aspects, or a PDA, which focuses on information aspects. It also can decrease the price of PDA. Fourth, the result of this present study showed that trialability had a significant impact on attitude toward PDA. In other words, it seems that consumers want to try a PDA out long enough to see what they can use it for before deciding to use a PDA. The more consumers try it out, the less risk about a PDA they will have. Therefore, marketers should give consumers more opportunity to try it out. For example, companies should provide several ways to try it out such as 30-day free trial or booth for trial in big stores (e.g. Best Buy, Circuit City). Finally, personal innovativeness has been an important predictor of adoption of innovations. This study also shows that personal innovativeness is a significant factor in predicting attitude toward PDA, which is the strongest determinant of purchase intention. Personal innovativeness can help marketers identify early adopters of PDA (Citrin et al., 2000). According to Citrin et al. (2000), early adopters can provide important interpersonal communication about an innovation to later adopters. If a marketer can identify potential early adopters by personal innovativeness, he/she is able to provide

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91 early adopters a positive image of PDA, and then encourages them to engage in subsequent positive interpersonal communicatio n to the later adopters. Even though this present study didnt examine which demographic and socioeconomic factors are related to personal innovativeness, it is generally known that earlier adopters are younger, better educated, richer, and more literate than later adopters (see table 2-1). This present study expects that a sound understanding of the predictors of the adoption of PDA lays a useful foundation to approach for future opportunities in marketing strategy. Future Research This current study will present several ideas for future research. First, future research will need to examine the effect of demographic and socioeconomic factors on the adoption of PDA. Rogers (1995) contended that earlier adopters are younger, better-educated, upscale, and more literate than later adopters. A number of adoption researches also indicate that demographic and socioeconomic factors are significantly related to the adoption of innovations (Dickerson and Gentry, 1983; Dutton, Rogers, and Jun, 1987; LaRose and Atkin, 1988 & 1992; Atkin et al., 1998; Lin, 1998). On the other hand, several researches found that demography is not a significant determinant of adoption of innovations (Jeffres and Atkin, 1996; Lin and Jeffres, 1998). Since little work has directly addressed PDA adoption, it may be useful to examine demographic and socioeconomic factors (Atkin et al., 1998). Second, considering the functions of PDA, a PDA is definitely one of convergence communication devices. Future PDA will have functions such as Internet, Email, video games, mobile phone, global positioning system, digital camera, and so on. Rogers (1995) presented that one or more distinguishable elements of technology can be

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92 interrelated. Lin and Jeffres (1998) said that the introduction of a new medium encourages a restructuring in the way consumers view established media (p. 342). Even though functional substitution assumptions have been widely studied for the impact of television on radio and that of video on television (Atkin et al., 1998), there is no literature on PDA adoption research providing the indication of the impact of PDA on other established communication technologies, such as mobile phone, Internet, etc. This current study investigated only the relationship between ownership of new technologies, and attitude and perceived uncertainty. This study, however, didnt support other hypothesis. In the future research, ownership of new technologies as well as related media use level, satisfactions, familiarity, and other variable should be investigated for functional displacement research. Third, a PDA has a variety of functions. Among them, some functions are related to escape/interaction, some functions are about information and learning, and some functions are related to entertainment. In other words, one PDA has several functions, which can satisfy several gratification-seeking motivations. This present study investigated whether functions of PDA are significantly associated with attitude and purchase intention. The result showed that functions related with entertainment were significantly associated with attitude and purchase intention. According to Ko (2002), motivations for using the Internet have a significant effect on Internet usage. From a uses and gratifications perspective, this study be lieves that the motivations for using a PDA will be different with consumers and motivation will be a significant determinant in predicting the adoption of PDA. For example, future research should examine the difference in attitude and purchase intention among several PDAs such as a PDA that has

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93 only functions related to entertainment, a PDA that has only functions related to information, and a PDA that has both entertainment and information functions, and then researchers can discover which motivation is significantly correlated with the adoption of PDA. Fourth, during the past decades, the relationship between consumer innovativeness and adoption behavior has been examined (Venkatraman and Price, 1990). A few of researchers developed conceptualizations of innovativeness and its relation with the adoption of innovations (Manning et al., 1995). Innovativeness is not homogenous but can be differentiated as different types of new experiences consumers prefer and seek (Venkatraman and Price, 1990). Some researchers classified innovativeness into several types for measuring innovativeness or exploring the adoption of innovations (Midgley and Dowling, 1978; Hirschman, 1980 and 198 4; Foxall, 1988; Venkatraman and Price, 1990; Goldsmith and Hofacker, 1991; Goldsmith and Flynn, 1992; Venkatraman, 1992; Manning et al., 1995; Citrin et al., 2000). Venkatraman and Price (1990) focused on differentiating between cognitive and sensory innovativeness by concepts, measurement, and implications. Sensory innovators and cognitive innovators are similar to each other in that both of them like novelty and consider the newness of innovations in a purchase decision. Generally, however, cognitive innovators have different adoption behaviors from sensory innovators in that cognitive consumers are likely to focus on functional and practical aspects of innovations, whereas sensory innovators focus on the sensory qualities of the innovations (Venkatraman, 1991). Based on this predisposition, Venkatraman and Price (1990) posited that cognitive and sensory innovativeness have significant positive relationship with the purchase of functional and hedonic products,

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94 respectively (p. 303). The newest PDA has a variety of applications. Considering the functions, one PDA has both characteristics: functional and hedonic. Sensory innovators prefer hedonic products to functional products and cognitive innovators, in contrast, prefer functional products to hedonic products (Hirschman, 1984). In addition, Okolica and Stewart (1996) found that individual innovativeness is positively related to extent of use of the innovation. Since a PDA has both functional and hedonic aspects, it may be useful in investigating whether both cognitive and sensory innovators have a significant positive relation with attitude toward PDA and purchase intention. Fifth, this study examined the relationships based on a model of the innovationdecision process of diffusion theory. This current study investigated whether the independent variables (relative advantage, compatibility, complexity, trialability, ownership of new technologies, and personal innovativeness) had a significant impact on attitude toward PDA. Several adoption researches examined the relationship between perceived attributes of the innovation, innovativeness, and other variables and adoption (purchase intention). This study expected that the independent variables would affect attitude, and then attitude would influence purchase intention. However, it may be argued that the independent variables would have a significant direct effect on purchase intention. Therefore, future research should use other statistical methods such as path analysis, in order to identify the relations among all variables. Finally, future research is needed to provide insight into the exact nature of relationships among variable. This current study used multiple regression, discriminant analysis, and Pearson correlation. However, most first generation regr ession models such as linear regression can analyze only one layer of linkages between independent and

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95 dependent variables at a time (Gefen et al., 2000). Contrary to first generation statistical tools such as regression, a Structural Equation Modeling (SEM) enables researchers to answer a set of interrelated research questions in a single, systematic, and comprehensive analysis by modeling the relationships among multiple independent and dependent constructs simultaneously (Gefen et al., 2000, p. 6-7). Therefore, future study will need to use further analysis such as a SEM to i nvestigate insight into the exact nature of relations among variables. Limitations Like most researches, this current study is not without limitations. First, this current study used students as sample. As one of the major weakness in academic research, a homogeneous student sample might have a different result from a general population sample (Ko, 2002). According to Brown and Stayman (1990), past studies which used students as subjects had a biasing effect in the causal relationship. The sample composition of this study was not repr esentative of the U.S. population. Therefore, this study has a limitation on the generality of the results. The young age group is more comfortable with computer-based media (Ferguson and Perse, 2000). Even though a student sample for this study limits the generalization of findings, this study may offer an initial look at the adoption of PDA. Second, it may be argued that several variables relative advantage, compatibility, and complexity were not effectively measured without exposure to reallife demonstrations of PDA. Most subjects answered questionnaires without having experienced a PDA. Instead of real experience for a PDA, this study provided the same instruction and information of PDA to all subjects. Dupagne (1999) contended that

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96 respondents interest and purchase intention in relation to innovation could not really be measured without exposure in real life. Therefore, this may be the limitation of this study. Third, two scales were less reliable than expected: trialability and perceived uncertainty. The scales for trialability and perceived uncertainty were adapted from a previous study. Although they were considered to be reliable measures of trialability and perceived uncertainty, their reliability were not as high as in the previous studies in this study. In addition, this study measured reliability of scales, while validity was not measured. Improved reliability and validity may be achieved in future research. Fourth, this current study used cross-sectional data. According to Rogers (1995), there are two types of researches needed to answer the question whether stages exist in the innovation-decision process: process research and variance process (p. 188). Process research is defined as a type of data gathering and analysis that seeks to determine the time-ordered sequence of a set of events, and variance process is defined as a type of data gathering and analysis th at consists of determ ining the co-variances among a set of variables, but not their time-order (p. 188). Most diffusion studies have used variance process (Rogers, 1995). Because such studies involve gathering data with cross-sectional methods, such as a survey, researchers cannot investigate backward in time to examine what happened across several stages, and which variables would affect the next stage (Rogers, 1995). In other words, variance process can not explore the cause and results of a series of events over time in terms of the nature of the innovationdecision process. This present study assume d that a PDA exists between the knowledge stage and the persuasion stage. This study didnt examine at which stage a PDA exists

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97 now. Since this study is based on this assumption, it may be a limitation of this current study. Conclusion This present study explored factors influencing the adoption of Personal Digital Assistant. Under rapidly changing new media environment, it is important to know what affects the adoption of innovations. With this purpose, this study examined a few of relationships and showed the results of data analysis. Consistent with past studies, some hypotheses were supported, while other hypotheses were not supported. This study partially supported diffusion theory. As expected, most perceived attributes of PDA were significant determinant in this study. The lack of support might be from limitations of this study. This study dealt with only a few part of diffusion theory. There remain many topics to be dealt with in future PDA adoption research. It is hoped that this study will provide PDA markets improved information for targeting and identifying potential adopters and other researchers useful information and implications in understanding what affects adoption.

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113 BIOGRAPHICAL SKETCH Sungwoo Kim was born and raised in Seoul, Korea. He graduated from Seoul National University in 1998 with a Bachelor of Arts in fiber and polymer science. He worked as a trader at Samsung Corporation for 2 years. He completed his Master of Arts in Mass Communication in 2003, at the University of Florida, specializing in telecommunication. After gaining experience in the telecommunication industry, he has a plan to pursue a Ph.D. program.


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EXPLORING FACTORS INFLUENCING
PERSONAL DIGITAL ASSISTANT (PDA) ADOPTION

















By

SUNGWOO KIM


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF ARTS IN MASS COMMUNICATION

UNIVERSITY OF FLORIDA


2003





























This document is dedicated to my loving parents, Jin-Gil Kim and Soon-Ae You.


















ACKNOWLEDGMENTS

I would like to express my deepest gratitude to my advisor, Dr. David Ostroff

Without his instructions, advice, patience, and generous support, this thesis would never

have been possible. Dr. Ostroff's guidance made the completion of this project a more

enjoyable and enriching experience.

Additionally, I would like to thank the other members on my committee: Dr.

Sylvia Chan-Olmsted and Dr. Chang-Hoan Cho. Both gave me their willingness to serve

on my committee and their insightful feedback throughout the process. I would like to

thank Jaewon Kang, Hanjun Ko, and Seungeun Lee for inspiring me to get this thesis off

the ground and their statistical know-how and support. I am also indebted to my Korean

friends for supporting me whenever I faced problems.

Finally, I would like to say thank you to my family: my parents, my sister,

brother-in-law, and my lovely niece. Their love and support have encouraged me to

overcome all difficulties while I have studied here. I dedicate this thesis to them.




















TABLE OF CONTENTS
page

A C K N O W L E D G M E N T S ................................................................................................. iii

L IS T O F T A B L E S ................................................................... ............... .... ........ ... v i

L IST O F F IG U R E S ......... ..................................................................... .............. ix

A B S T R A C T ...................................... .................................... ................ .. x

CHAPTER

1 IN TR O D U C TIO N ........................................................... .... .. ............. 1

Personal D igital A assistant (PD A) .......................... ....... .................................. 2
PD A D definition ........................................................................................ 2
O operation System s of PD A ........................................... .............................. 2
A H history of PD A ................ ..................... ........................ .............. 3
A pp locations of P D A ....................... ..... ...... .................................. ............ 5
The Competitive Products of PDA: Smartphone and Pocket PC........................... 6
O objective of the Study ................... ..................... ....................... ............... 7

2 LITERA TU R E REV IEW ............................................................. ........................... 9

D iffusion of Innovations .............. ................... ................... .................... 9
The Innovation .............. ........................... .................. ..... 10
Com m unication Channels........................ ............. ................ .............. 13
T im e ........................................ ............................... ............................. 13
A Social System .................. .. ........................................ .............................. 17
Diffusion and Adoption of Other New Technologies...... ......... .............. 18
Internet A adoption ............. ....... ....... ...... ...... ... .......... .......... ... 19
Online Shopping Adoption .............................. .................................. 21
Personal Computers Adoption ......... ............ ............ 24
Information Systems (Audiotex, Videotex, and Electronic Bulletin Board)
Adoption ..................................................................... 25
Other New Technologies Adoption .............................................. ............. 27

3 R E SE A R C H M O D E L ....................................................................... ...................... 29

Independent Variables ............................................................. ............ 29
Perceived Characteristics of the Innovation ............................... 29














Ownership of New Technology Products........................................................... 30
P personal Innovativ eness.......................................................................... ....... 32
Intervening V ariables...................................... ..................... ..... ....... ...... 33
A attitude tow ard PD A ...................................... ............. ....... .... ......... ... 33
P erceiv ed U uncertainty .................................................................................. 34
D dependent V variable ............................................. .......... ............. ................ 36
A additional V variable ............................................................ ..... ............ 36
H ypothesized M odel ................................... ............. ........ ............. 36

4 HYPOTHESES AND RESEARCH QUESTIONS ....................... .......................... 38

H y p o th eses .................. ................................................................. 3 8
T he P ersuasion Stage ............................................ .......... ..... .... ......... ... 38
The D decision Stage ............... .................... .................. .................... 42
R research Q questions ................................... ............. .......... ......... .... 44

5 RESEA RCH M ETH OD OLO GY ....................................................... .....................46

S am p le ................... .................................. 4 6
M ea su rem en ts ................................................................. ..................................... 4 6
Statistical Analysis................... ..................... .................. .. .......... 53

6 R E S U L T S ................................................................................5 6

D escriptiv e Statistics.......................................................... ............... ........ ...... 56
Sample Characteristics................................ ......... 56
N orm ality of Item s ............... ................... ................ ........... ............. 58
R liability ...................... .............. ... .... ........................... 59
Ownership and Familiarity of New Technologies.............................................. 62
The Results of Hypotheses and Research Questions................................................ 62
Factors Influencing Attitude toward Personal Digital Assistant (PDA) ........... 63
Factors Influencing Perceived Uncertainty toward Personal Digital Assistant
(PD A ) ..................................... .......................... .. .. .......... .............. 67
Relationship between the Intervening Variables (Attitude and Perceived
Uncertainty toward PDA) and Purchase Intention.................................. 69
Factors Discriminating Two Purchasing Intention Group (High/Low) ............ 70
Relation between Functions of PDA and Attitude and Purchase Intention.......... 73
Demographic Characteristics of Purchase Intention Group.............................. 76
F inal P arsim onious M odel ................................................................................... 77

7 D ISCU SSIO N ................................................................................ ......... 78

R review of the Present Study ................................................................................... 78
Summary of Results of Research Questions and Hypotheses .................................. 79
H ypotheses ............................................................... ... ..... ......... 79
R research Q question s .................................... ....... ............................. .......... ... .. 84













Im plications.......................................... 86
Theoretical Im plications .......................... .................................. .......................... 86
Practical Implications......................................... .............. 89
F future R research ........................................................ .............. 9 1
L im stations ......... ............................................................................................ 95
C on clu sio n ......... ... .................................................................................. 9 7

LIST OF REFERENCES ...................... ................ ...............98

BIOGRAPHICAL SKETCH ........................ .............. ...................... ............... 113















































vi



















LIST OF TABLES


Table page

2-1. Summary of the generalization of the adopters............................................16

5-1. M measured variables ........................................................... .............. .51

5-2. O observed variables ......................................... ............ ...... ...... ........... 52

5-3. Statistical m ethods..................... ............ ....................................... .. .......55

6-1. Sam ple C haracteristice ............................................................................ ........ 57

6-2. D escriptive profile of each variable ..................................................................... 59

6-3. Ownership of new technologies. ........................................ ......................... 62

6-4. Results of hypotheses and research questions.................... ... .........................63

6-5. Multiple regression analysis of attitude toward PDA on relative advantage,
compatibility, complexity, trialability, ownership of new technology, and personal
innovativeness ................................................................... ... ....... ........ 65

6-6. Collinearity diagnostics of multiple regression analysis of attitude and perceived
uncertainty toward PDA on relative advantage, compatibility, complexity,
trialability, ownership of new technology, and personal innovativeness.................66

6-7. Stepwise multiple regression of attitude toward PDA on relative advantage,
compatibility, trialability, and personal innovativeness.........................................66

6-8. Multiple regression analysis of perceived uncertainty toward PDA on relative
advantage, compatibility, complexity, trialability, ownership of new technology,
and personal innovativenss...................... ........ .............................. 68

6-9. Stepwise multiple regression of perceived uncertainty toward PDA on relative
advantage and com plexity ......................................................................... ... ...... 69

6-10. Multiple regression analysis of purchase intention on attitude and perceived
uncertainty tow ard PD A ........................................................................... ..... .. 70

6-11. Regression of purchase intention on attitude toward PDA ....................................70












6-12. Discriminant analysis between high-purchase intention group and low-purchase
inten tion g ro u p ..................................................... ................ 72

6-13. Classification results of discrim inant analysis ............... .. ... ... ................... 73

6-14. Descriptive profile of perceived importance of functions of PDA ..........................74

6-15. Pearson correlation between perceived importance of PDA functions and attitude
and purchase intention............... .........................................75

6-16. Factor analysis of perceived importance of PDA functions................................76

6-17. t-test results comparing between purchase intention group ....................................76



















LIST OF FIGURES

Figure page

3-1. H v othesized M odel. ........................................................................... .... 37

5-1. Personal Digital Assistant. ............................................. .............................. 47

6-1. Final Parimonious Model. ............................................... ............................. 77


















Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Arts in Mass Communication

EXPLORING FACTORS INFLUENCING PERSONAL DIGITAL
ASSISTANT (PDA) ADOPTION

By

Sungwoo Kim

August 2003



Chair: David H. Ostroff
Cochair: Sylvia Chan-Olmsted
Major Department: Journalism and Communications

This present study explored factors influencing the adoption of a Personal Digital

Assistant (PDA). Under rapidly changing new media environment, it is important to

know what affects the adoption of innovations. With this purpose, this study examined a

few relationships and showed the results of data analysis. The theoretical background of

this current study is diffusion of innovations. In particular, this study focused on the

persuasion stage and the decision stage of the innovation-decision process. This research

presents eight hypotheses and three research questions: (1) Which factors influence

perceived uncertainty toward a Personal Digital Assistant (PDA) at the persuasion stage

of the innovation-decision process and which variables have the relatively strong or weak

influence on attitude and perceived uncertainty? (2) Will factors affecting attitude and

perceived uncertainty toward a PDA be useful in discriminating two purchase intention












groups (high or low)? and (3) Which functions of a PDA are related to attitude toward

PDA and purchase intention?

To investigate hypotheses and research questions, a survey was conducted. This

study recruited 191 students from a large southeastern university. This current study

employed multiple regression, stepwise regression, t-test, and Pearson correlation

analysis.

Consistent with past studies, some hypotheses were supported, while other

hypotheses were not supported. This study partially supported diffusion theory. As

expected, most perceived attributes of PDA were significant determinant in this study.

Relative advantage, compatibility, trialability, and personal innovativeness were

positively and significantly related to attitude toward PDA. This study found that relative

advantage and complexity were significantly correlated to perceived uncertainty. In

addition, only attitude toward PDA was a significant determinant to predict purchase

intention.

The lack of support might be from limitations of this study. This study dealt with

only a few parts of diffusion theory. There remain many topics to be dealt with in future

PDA adoption research.

















CHAPTER 1
INTRODUCTION

Currently, the keyword of telecommunication industry is "convergence." Good

examples are Interactive TV (ITV) and a Personal Digital Assistant (PDA). ITV has

several functions such as Electronic Programming Guide, Internet, Video On Demand

(VOD), online shopping, Personal Video Recorder (PVR), and online game. In case of

the PDA, most PDAs are used just as a digital organizer presently and only a few of

models have digital organizer as well as other functions e-mail, Web browsing, mobile

phone, MP3 player, playing games, digital camera, word processing/spreadsheets, and

Global Positioning System (GPS). Some communication industry analysts contended that

a PDA will become the most popular personal communication device and the necessity

for businessmen in the near future.

Computer Industry Almanac predicts that the PDA market will keep on growing

and phone/PDA combos will become prevalent in 2003. In addition, by 2007, the

common PDA will become a multifunctional communication device equipped with

mobile phone, GPS, digital camera, etc., and the unit sales of PDA will reach more than

sixty one billion units in the worldwide. eTForecasts (2002) also predicts that Pen-PDA,

which is the leader in the PDA market, will be the market leader by 2008 among three

kinds of PDAs (Pen-PDA, Keyboard-PDA, and Phone-PDA). Presently, total PDA sales

are only about fifteen percent of total PC sales, but in 2008, PDA sales will reach about

thirty three percent of total PC sales (eTForecasts, 2002). Based on data, it is evident that

a PDA is the next generation of mobile computing.












Personal Digital Assistant (PDA)

PDA Definition

A Personal Digital Assistant (PDA) is "lightweight, hand-held computer

designed for use as a personal organizer with communications capabilities" (Infoplease,

2003, 1st paragraph). It can be held in person's hand, so it is sometimes called a

"Handheld PC." Many PDAs use a pen-like stylus to input information. Some PDAs

work on a keyboard-based input system. Most PDAs adopt Window CE, EPOC, or

PalmOS as an operating system. Currently, a PDA is used as not only an organizer or

scheduler but also a cell-phone, fax-sender, and so on. According to International Data

Corporation, the size of the PDA market will increase more than $26 billion and 63.4

million units will be sold by 2004.

Operation Systems of PDA

The operation system is one of the most important factors to be considered when

consumers purchase a PDA. It is likely that consumers should choose either a Macintosh

or an IBM PC when they purchase a personal computer (Freudenrich, 2003). There are

three operation systems: (1) Palm OS (3Com), (2) EPOC (Psion), and (3) Pocket PC

(formerly Windows CE).

Currently, Palm OS dominates the operation system market. It accounts for more

than 70 percent of market share. Pocket PC, however, is encroaching on the territory of

Palm OS (Freudenrich, 2003). Relatively, EPOC accounts for only a small portion of the

market. Pocket PC supports color displays, graphics, standard packages software (e.g.,

MS Word, Excel), MP3, MPEG movie files and etc. Palm OS takes up less memory, so it

runs faster. On the other hand, Pocket PC takes up more memory and runs slower.












Generally, Palm OS is easier to use than Pocket PC (Freudenrich, 2003). However, the

greatest advantage of Pocket PC is familiarity because most consumers are accustomed to

use Windows as an operating system on their PC.

According to eTForecasts (2002), Microsoft's Pocket PC, Compaq, HP, Toshiba

and several other leading PC companies have introduced Pocket PC-based products that

are now growing faster than the overall market. In addition, Linux-based PDAs are

developing and appearing. By 2008, Pocket PC-based products and PalmOS-based

products will account for almost the same portion of PDA operation system market share

in the U.S. (eTForecasts, 2002).

A History of PDA

The origin of PDA is in the UK technology company Psion in 1984. The first

model, the Psion I, was a bit thicker, longer, and narrower than a large pack of cigarettes.

It had 10K of non-volatile character storage in cartridges, a search function, an LCD

display, calendar etc. The Psion II superseded the Psion I in the mid-1980s. The highest

version of the Psion II had 64K ROM, 32K RAM and a 4 X 20 character display. The

next model, the Series 3a, opened the new generation in Psion's evolution. It had the

function to transfer, convert, and synchronize data between the different places. In

addition, it had a 40 characters X 8 line mono LCD and 58-key keyboard in the base.

These models enabled Psion to dominate the PDA market over more powerful models;

Series 3c and Series 5 followed (PCTechGuide, 2003).

One of the notable commercial PDA models is Apple Computer's Newton

Message Pad, a milestone of the information age. Soon after, other companies Hewlett-

Packard Co., Motorola Inc., Sharp Electronics Corp. and Sony Electronics Inc. -












attempted to make a handheld communication device. The Newton model was not

successful because it was too big and expensive. In particular, its handwriting recognition

system was too complicated for users to understand. Even if the Newton's handwriting

recognition technology was improved outstandingly compared with the first model, it

never would be appealing to consumers. So, Apple gave up continued development of the

Newton operating system in 1998 (Freudenrich, 2003; PCTechGuide, 2003).

In 1996, Palm Computing, Inc. introduced Pilot products. This PDA had

distinctive characters such as a palm-sized form, remarkably developed graphic interface,

and the synchronization between the PDA and other computers. It had a different data

input system with Apple's Newton handwriting recognition technology. It enabled

consumers to manage their personal and business information, schedules, and other

matters anywhere and anytime. Pilot's data input device was either a stylus or a touch-

sensitive screen. Pilot was small and light, used AAA batteries as an electronic power

supply, and was easy to put to use. That's why a Pilot was popular among consumers. In

1999, Palm Pilot devices were upgraded with excellent Personal Information

Management (PIM) software and 160 X 160 pixel backlit screen. PIM software included

personal address/phone book, diary, scheduler, calculator, personal account software,

watch with alarm function, to-do list and so on. At that time some models were equipped

with Graffiti power writing software, an enhanced version of Palm Computing, which

enabled consumer to input data with ease. Since the advent of Pilot, Palm Computing has

dominated the PDA market. In 2001 the sales of Palm rose to about 13 million

(Freudenrich, 2003; PCTechGuide, 2003).












Applications of PDA

A PDA is one of the "convergence" communication devices. A PDA has several

applications: Personal Information Management System (PIMS), mobile phone, Internet,

E-mail, wordprocessing/spreadsheet, MP3/movie file player, video games, digital camera,

global positioning system, and so on.

Like other new technologies, a PDA can be used for several motivations and

needs. PDA functions can be categorized into three types: information, entertainment,

and communication. PIMS, internet, global positioning system, wordprocessing/

spreadsheet and digital camera can be classified as information function. Video games

and MP3/movie file player are categorized into entertainment. Finally, as communication

functions, mobile phone, and e-mail are classified.

A PDA as an information device

Personal information management system (PIMS). A PDA was originally

invented as a personal organizer that allows consumer to access, store, and organizer

personal information. Most models have these functions such as personal book (addresses,

phone numbers, e-mail addresses), diary, scheduler, take notes/write memos, calculator,

personal account software, watch (alarm function), and to-do list.

E-Mail and Web browsing: The new communication technology allows consumers to

send or receive e-mail and surf the Internet through a PDA without connecting with a

desktop or notebook. Even though consumers should pay some fees for wireless service,

they can use a PDA as mobile computer. Without wireless service, consumers should be

able to download e-mails and Internet content from a desktop or notebook by connecting

with some PDAs. Some models allow consumers to write e-mails, but not to send them.












In order to send them, consumers must later send them through their computer. Some

PDAs need some software for accessing e-mail accounts and Internet.

Word processor/spreadsheet. Keyboard-based PDA can provide attributes such

as word processor, spreadsheet, and related software.

Getting information. Because most PDAs have application for accessing the

Internet, they allow consumers to get information like news, stock quotes, or something

from the Internet.

Digital camera and Global Positioning System (GPS). Some PDA models can

be used as digital camera and GPS receiver.

A PDA as an entertainment device

MP3 and movie file player. Some PDAs have entertainment functions such as

playing mp3 music files and mpeg movie files.

Video games. Some PDA models provide consumers with video games that can

be played by oneself or with other people through wireless network.

A PDA as a communication device

Mobile phone. The latest PDA model includes mobile Phone. This model does

everything a mobile phone does.

The Competitive Products of PDA: Smartphone and Pocket PC

Smartphone is defined as "a mobile, digital telephone that has features not

associated with traditional home or mobile phone" and Pocket PC is defined as "an

upgraded version of Windows CE that offers greater stability and a new interface.

Features include mobile Internet capabilities, an e-book reader, and handwriting

recognition" (Yahoo!, 2003). Smartphone is a new technology in mobile phone that












combines voice and data communication. Like a PDA, Smartphone has functions such as

personal information management, sending or receiving e-mails, Internet surfing, playing

mp3 and movie files etc. A PDA is based on PIMS, whereas Smartphone is based on a

mobile phone with other functions added. These days, however, it is difficult to

differentiate among a PDA, a Pocket PC, and a Smartphone because they have almost the

same applications. Some specialists suggest that the classification of PDA, Pocket PC,

and Smartphone is meaningless.

Objective of the Study

In Korea, which is one of the leading countries for information technology

industry, the sales of PDA were decreased in 2002 compare to the previous year.

Generally, it was predicted that the sales of PDA would increase every year. A decrease

in sales of PDA was not expected by a number of specialists, professor, and analysts of

electronic and communication industry. As reasons for this situation, some analysts

proposed that the price of PDA is still too high or a PDA does not give consumers better

benefits compared to other new communication devices. Considering this present

situation of PDA, this study wants to identify which variables affect consumers' adoption

of PDA. In addition, even though PDA will become an important communication device

in the near future, there is little research about PDAs. This study expects that research

about the adoption of PDA will be helpful for marketers or developers related to a PDA

at this time.

This study focuses on only two stages of adoption process because of the novelty

of PDA (Eastlick, 1996). This research intends to investigate eight hypotheses and three

research questions: (1) "Which factors influence perceived uncertainty toward a Personal












Digital Assistant (PDA) at the persuasion stage of the Innovation-Decision process and

which variables have the relatively strong or weak influence on attitude and perceived

uncertainty?" (2) "Will factors affecting attitude and perceived uncertainty toward a PDA

be useful in discriminating two purchase intention groups (high or low)?" and (3) "Which

functions of PDA are related to attitude toward PDA and purchase intention?"

Through research hypotheses and questions, this present study will explore the

relative influence of perceived characteristics of innovation, ownership of new

technologies, personal innovativeness, and attributes of a PDA in exploring attitude

toward a PDA, perceived uncertainty for a PDA and adoption of a PDA, based on a

model of the Innovation-Decision Process of Diffusion theory.

















CHAPTER 2
LITERATURE REVIEW

This present study intends to examine the relative influence of perceived

characteristics of innovation, personal innovativeness, attributes of PDA in exploring

attitude toward PDA, perceived uncertainty toward PDA and adoption of PDA. First, a

literature review focuses on Roger's diffusion of innovation theory. The diffusion of

innovations perspective will be helpful in explaining key concepts of the adoption of

PDA. Second, previous adoption studies will be introduced and reviewed. Variables that

have been investigated to explain adoption of other new technologies will be examined.

Diffusion of Innovations

Rogers (1995) defined diffusion as "the process by which (1) an innovation is (2)

communicated through certain channels over (3) time among the members of (4) a social

system" (p. 5). Rogers investigated more than 2,000 empirical diffusion research studies

and 3,000 publications (Severin and Tankard, Jr., 1992). Among voluminous diffusion

studies, "one of the most influential is 'The Iowa Hybrid Seed Corn Study' (Ryan and

Gross, 1943) (cited in Severin and Tankard, Jr., 1992)." The investigation of the diffusion

of hybrid-seed corn in Iowa affected the methodology, theoretical framework,

implications, and interpretations for later diffusion studies and established the classical

diffusion paradigm (Rogers, 1995; Severin and Tankard, Jr., 1992). The Iowa hybrid corn

study interviewed 259 farmers to investigate when and how they adopted hybrid seed

corn and to get information about them and their farm operation. The Iowa study found

that the rate of adoption was S-shaped and various communication channels played












important and different roles during the diffusion process. In addition, the Iowa study

found the four main component of diffusion: (1) an innovation, (2) the communication

channels, (3) time, and (4) a social system.

The Innovation

An innovation is "an idea, practice, or object that is perceived as new by an

individual or other unit of adoption" (Rogers, 1995, p .11). According to Rogers (1995),

an individual's reaction to innovation depends on the perceived newness of the idea and

whether an individual thinks the idea novel, so it could be innovation. An individual

exhibits "newness" of an innovation as knowledge, persuasion, or a decision to adopt.

Most of the new ideas are related to technological innovations, so sometimes

"technology" (p. 12) is used as a synonym of "innovation" (Rogers, 1995). Thomson

(1967) and Eveland (1986) proposed that a technology is an instrumental design which

can decrease the uncertainty in cause-effect relationships in order to fulfill a desired goal.

A technology is generally composed of two elements: hardware and software (Rogers,

1995). Rogers (1995) asserted that hardware consists of "the tool that embodies the

technology as a material or physical object" (p. 12) and software consists of "the

information base for the tool" (p. 12). New technology usually has both a hardware

aspect and a software aspect. According to Bayus (1987), a company intends to sell the

hardware at a relatively low price in order to capture market share, and then sell the

software at a relatively high price in order to improve profits. For example, Xbox and

Playstation 2 are currently sold at $199, a relative low price, whereas game software for

Xbox and Playstation 2 are sold at prices ranging from $40 to $50, a relative high price.












A technological innovation usually provides not only a sort of uncertainty about

expected consequences, but also an opportunity to reduce uncertainty (Rogers, 1995). In

order to reduce uncertainty about an innovation, an individual seeks information. There

are two kinds of information in terms of a technological innovation: software information,

"which is embodied in a technology and serves to reduce uncertainty about the cause-

effect relationships in achieving a desired outcome" (p. 14) and innovation-evaluation

information, "which is the reduction in uncertainty about an innovation's expected

consequences" (p. 14).

Perceived characteristics of innovations

"What perceived attributes of innovations influence the rate of adoption?" This

research question is important in diffusion research studies. Many previous diffusion

studies focused on the characteristics of adopters. Little effort, however, has been made

to investigate what properties affect rate of adoption (Rogers, 1995). Rate of innovation

is "the relative speed with which an innovation is adopted by members of a social system

and a numerical indicator of the steepness of the adoption curve for an innovation"

(Rogers, 1995, p. 22). The five perceived attributes of innovations may explain rate of

adoption from 49 to 87 percent of the variance (Rogers, 1995). The five attributes are (1)

"relative advantage", (2) "compatibility", (3) "complexity", (4) "trialability", and (5)

observabilityy" (p. 206). In addition, there are other variables: (1) "the type of

innovation-decision", (2) "the nature of communication channels diffusing the innovation

at various stages in the innovation-decision process", (3) "the nature of the social system

in which the innovation is diffusing", and (4) "the extent of change agents' promotion

efforts in diffusing the innovation" (p. 206).












Rogers (1995) defined the five characteristics as the followings; (1) relative

advantage is "the degree to which an innovation is perceived as better than the idea it

supersedes" (p. 212), (2) compatibility is "the degree to which an innovation is perceived

as consistent with the existing values, past experiences, and needs of potential adopters"

(p. 224), (3) complexity is "the degree to which an innovation is perceived as relatively

difficult to understand and to use" (p. 242), (4) trialability is "the degree to which an

innovation may be experimented with on a limited basis" (p. 243), and (5) observability

is "the degree to which the results of an innovation are visible to others" (p. 244). The

perceived relative advantage, compatibility, trialability, and observability are positively

associated with the rate of adoption, but complexity is negatively related to the rate of

adoption (Rogers, 1995).

There have been several researches intended to explain adoption of innovations

by the perceived attributes of innovations as predictors of adoption (LaRose and Atkin,

1992; Eastlick, 1993 & 1996; Lin, 1998; Parthasarathy et al., 1998; Du, 1999). Eastlick

(1993) examined whether relative advantage, compatibility, complexity, and trialability

would affect the adoption of videotex more than other properties of the innovation.

Relative advantage and compatibility properties were more significant predictors than

other properties (Eastlick, 1993). Lin (1998) proposed that in adopting a personal

computer, consumers considered relative advantage of personal computer, but complexity

was not a real apprehension. LaRose and Atkin (1992) found that compatibility was not a

good predictor in explaining the adoption of Audiotext. Du (1999) contended that the

complexity and relative advantage of internet was significantly related to adoption of












Internet in China. Based on previous studies, this study expects that perceived attributes

of the innovation should be the important variables affecting adoption of PDA.

Communication Channels

Communications is "a process in which participants create and share information

with one another in order to reach a mutual understanding" (p. 35), and a communication

channel is "the means by which messages get from one individual to another" (p. 36)

(Rogers, 1995). According to Rogers (1995), usually, communication channels includes

two kinds of channels: mass media channels and interpersonal channels. Mass media

channels include any mass medium such as television, internet, radio, and so on, which

transmit messages to one or more individuals, so mass media channels are more rapid and

efficient channels to send potential adopters information about innovations than

interpersonal channels. Interpersonal channels are more effective in encouraging an

individual to adopt an innovation, because interpersonal channels involve a face-to face

exchange between two or more individuals who are in similar in socioeconomic status

(Rogers, 1995).

Time

Time is one element in the diffusion process (Rogers, 1995). Rogers (1995)

showed several processes relative to the time dimension in adoption of innovations: (1)

"the innovation-decision process", (2) "the innovativeness of an individual or other unit

of adoption compared with other members of a system," and (3) "an innovation's rate of

adoption in a system" (p. 20).












The innovation-decision process

The innovation-decision process is "the process through which an individual (or

other decision-making unit) passes from first knowledge of an innovation to forming an

attitude toward the innovation, to a decision to adopt or reject, to implementation and use

of the new idea, and to confirmation of this decision" (Rogers, 1995, p. 20). Rogers

(1995) operationalized five steps in this process: (1) knowledge, (2) persuasion, (3)

decision, (4) implementation, and (5) confirmation. This process is "an information-

seeking" and "information-processing activity" (p. 20), by which an individual intends to

get information in order to reduce uncertainty about innovations (Rogers, 1995).

Rajagopal (2002) conceptualized the six steps into (1) initiation, (2) adoption, (3)

adaptation, (4) acceptance, (5) routinization, and (6) infusion. Grover and Goslar (1993)

suggested three steps of adoption: (1) initiation, (2) adoption, and (3) implementation.

The knowledge stage occurs when an individual is exposed to an innovation's

existence and obtains some information about how it functions (Rogers, 1995). At the

persuasion stage, an individual develops a favorable or unfavorable attitude toward the

innovation. At the decision stage, an individual takes part in activities to choose adoption

or rejection of an innovation. The implementation stage occurs when an individual puts

an innovation into use and at this stage, re-invention is likely to occur. And finally, at the

confirmation stage, an individual intends to discover "reinforcement of the innovation-

decision already made or reverses a previous decision to adopt or reject the innovation if

exposed to conflicting messages about the innovation" (Rogers, 1995, p. 181).

In this study, the persuasion stage and the decision stage will be focused on.












Innovativeness and adopter categories

Innovativeness is "the degree to which an individual or other unit of adoption is

relatively earlier in adopting new ideas than the other members of a system" (Rogers,

1995, p. 22). Innovativeness is the criterion for adopter categorization, so Rogers (1995)

categorized adopters based on the relative time at which an innovation is adopted: (1)

innovators: venturesome, (2) early adopters: respect, (3) early majority: deliberate, (4)

late majority: skeptical, and (5) laggards: traditional. Generally, the adopter distribution is

closely bell-shaped. It means that the classification of adopters is almost the normal

frequency distribution. Rogers (1995) found that the innovators accounts for about 2.5

percent, early adopters accounts for about 13.5 percent, early majority accounts for about

34 percent, late majority accounts for about 34 percent, and laggards accounts for the last

16 percent. Numerous researches have investigated which variables are related to

innovativeness and the classification of adopters. Rogers (1995) summarized variables

into three categories: (1) "socioeconomic status," (2) "personality values," and (3)

"communications behavior" (p. 268). Rogers (1995, p. 269-274) asserted the following

generalizations based on three categories (see table 1). According to him, earlier adopters

are younger, better-educated, and higher-status than later adopters. In terms of personal

variables, earlier adopters have greater empathy, ability to deal with abstraction,

rationality, and intelligence than later adopters. In addition, earlier adopters are have

more highly interconnected through interpersonal networks in their social system, more

change agent contact, greater exposure to mass media communication channels and

interpersonal communication channels, greater knowledge of innovations than later

adopters (Rogers, 1995).













Table 2-1: Summary of the generalization of the adopters
"Earlier adopters are not different from later adopters in age."
"Earlier adopters have more years of formal education than later adopter
Socioeconomic s.
Characteristics "Earlier adopters are more likely to be literate than are later adopters."
"Earlier adopters have higher social status than later adopters."
"Earlier adopters have a greater degree of upward social mobility."
"Earlier adopters have larger units (farms, schools, companies, and so o
n) than later adopters."
"Earlier adopters have greater empathy than later adopters."
"Earlier adopters may be less dogmatic than later adopters."
"Earlier adopters have a greater ability to deal with abstractions than do
later adopters."
"Earlier adopters have greater rationality than later adopters."
Perso "Earlier adopters have greater intelligence than later adopters."
variables "Earlier adopters have a more favorable attitude toward change than late
Variables ,,
r adopters."
"Earlier adopters are better able to cope with uncertainty and risk than la
ter adopters."
"Earlier adopters have a more favorable attitude toward science than late
r adopters."
"Earlier adopters are less fatalistic than later adopters."
"Earlier adopters have higher aspirations (for formal education, occupati
ons, and so on) than later adopters."
"Earlier adopters have more social participation than later adopters."
"Earlier adopters are more highly interconnected through interpersonal n
networks in their social system than later adopters. Connectedness is the
degree to which an individual is linked to others."
"Earlier adopters are more cosmopolite than later adopters."
"Earlier adopters have more change agent contact than later adopters."
Communication "Earlier adopters have greater exposure to mass media communication c
Behavior channels than later adopters."
"Earlier adopters have greater exposure to interpersonal communication
channels than later adopters."
"Earlier adopters seek information about innovations more actively than
later adopters."
"Earlier adopters have greater knowledge of innovations than later adopt
ers."
"Earlier adopters have a higher degree of opinion leadership than later a
dopters."



Rate of adoption

The rate of adoption is "the relative speed with which an innovation is adopted

by members of a social system" (Rogers, 1995, p. 206). Generally, most innovations have

an S-shaped curve and the stiffness of curve depends on the rate of innovations (Rogers,












1995). Rogers (1995) contended that five variables affect the rate of adoption of

innovations: (1) "perceived attributes of innovations", (2) "type of innovation-decision",

(3) "communication channels", (4) "nature of the social system", and (5) "extent of

change agents' promotion efforts" (p. 207).

Perceived attributes of innovations relative advantage, compatibility,

complexity, trialability, and observability explain from 49 to 87 percent of the variance

of the rate of adoption (Rogers, 1995). Rogers (1995) suggested that Innovation-decision

falls into three types: "optional", "collective", and "authority" (p. 372) and defined the

three types of innovation-decisions as the followings (p. 372);

Optional innovation-decisions: "choices to adopt or reject an innovation that are
made by an individual independent of the decisions by other members of a
system".
Collective innovation-decisions: "choices to adopt or reject an innovation that are
made by consensus among the members of a system".
Authority innovation-decisions: "choices to adopt or reject an innovation that are
made by a relatively few individuals in a system who possess power, status, or
technical expertise".

In general, optional innovation-decisions by individual are more rapidly adopted

than collective innovation-decisions by an organization, because the number of persons

who take part in making a decision influences negatively the rate of adoption (Rogers,

1995).

Additionally, communication channels, the nature of the social system, and the

extent of change agents' promotion efforts affect an innovation's rate of adoption.

A Social System

Rogers (1995) defined a social system as "a set of interrelated units that are

engaged in joint problem-solving to accomplish a common goal" (p. 23). Individuals,

informal groups, organizations, or subsystems may be the units of a social system












(Rogers, 1995). A social system is some place for diffusion to occur. The effect of system

norms, opinion leaders and change agents in a social system, types of innovation-

decisions, and consequences of innovation are affected by the system's social structure

(Rogers, 1995).

There are individuals to provide information and advice about innovations to

other members in the social system. Opinion leaders influence members of the social

system (Rogers, 1995). Rogers (1995) defined opinion leadership as "the degree to which

an individual is able to influence other individuals' attitudes or overt behavior informally

in a desired way with relative frequency" (p. 27). Compared with the followers, opinion

leaders are (1) "more exposed to all forms of external communication, and thus are more

cosmopolite," (2) "have somewhat higher social status," and (3) "are more innovative"

(Rogers, 1995, p. 27). In a social system, an opinion leader may support or oppose

change.

A change agent is a professional who represents change agencies external to the

system (Rogers, 1995). A change agent is "an individual who influences clients'

innovation-decisions in a direction deemed desirable by a change agency" and generally

attempts to obtain the adoption of innovations or prevent the adoption (Rogers, 1995).

Sometimes, change agents use opinion leaders in a social system as a means of diffusion

campaigns (Rogers, 1995).

Diffusion and Adoption of Other New Technologies

Television, radio, Video Cassette Recorder (VCR), videotex, audiotex, cable,

personal computer, digital TV, HDTV, internet, mobile phone, Satellite TV, Personal

DigitalAssistant (PDA)...... during the past decades, we have met various new












communication technologies. It is no wonder that there are many previous researches

about adoption of new technologies. Diffusion of innovation theory is the dominant

paradigm for explaining innovation adoption in communication studies. This has been

applied to a wide range of new technologies adoption: Audiotext (LaRose and Atkin,

1992; Neuendorf and Atkin, 1998), computer (Danko and MacLachlan, 1983; Dickerson

and Gentry, 1983; Dutton, Rogers, and Jun, 1987; Lin, 1998), videotex (Heikkinen and

Reese, 1986; Eastlick, 1993), cable (LaRose and Atkin, 1988), internet (Atkin, Jeffres,

and Neuendorf, 1998; Du, 1999; Ferguson and Perse, 2000; Papacharissi and Rubin,

2000; La Ferle, Edwards, and Mizuno, 2002), HDTV (Dupagne, 1999), ISDN (Jeffres

and Atkin, 1996), electronic bulletin board (James and Wotring, 1995), personal social

services (Martinez-Brawley, 1995), computer-mediated political communication systems

(Garramone, Harris, and Pizante, 1986), and online shopping (Zellweger, 1997;

Jarvenpaa and Tractinsky, 1999; Li, Kuo, and Russell, 1999; Lohse and Spiller, 1999;

Swanminathan, Lepkowska-White, and Rao, 1999; Tan, 1999; Wolfinbarger and Gilly,

1999; Vellido, Lisboa, and Meehan, 2000; Miyazaki and Fernandez, 2001; Fenech and

O'Cass, 2001; Limayem et al., 2001: Koufaris, et al., 2002).

From television to Internet, various innovations have been researched as to their

adoption. A PDA has several new technologies as functions (e.g., Internet, e-mail, mobile

phone, wordprocessing/spreadsheet, etc.). Therefore, this study believes that previous

adoption studies may be helpful in examining which factors influence adoption of PDA.

Internet Adoption

Internet is the most rapidly growing new mass media. Internet has already

become one of the important necessities in daily life. Internet has grown significantly












during the past decade, particularly with respect to its use as a tool for communication,

entertainment, and online shopping. The new PDA models allow consumers to access

wireless Internet. So, it may be argued that a PDA has a close relation to Internet.

Many researchers have studied the Internet. Papacharissi and Rubin (2000)

examined predictors of Internet use in terms of a uses-and-gratifications perspective and

how (1) "social and psychological antecedents; contextual age and unwillingness to

communicate, (2) perceptions of media attributes; social presence, and (3) internet

motives influence behavioral (patterns of internet exposure) and attitudinal (internet

affinity and satisfaction) outcomes of internet use" (p. 182). They found some interesting

results that the relationship between interpersonal utility and passing time was the highest

correlation among Internet motives and "the relationships between internet motives and

the social and psychological antecedents support the use of the internet as a functional

alternative for internet users for whom other channels were not as available or rewarding"

(p. 191). Ferguson and Perse (2000) intended to explore the World Wide Web (WWW)

as a functional alternative to television. Their research investigated the similarity of

television viewing and internet surfing, and showed that the most significant motive for

visiting WWW is entertainment and that, as a way "to pass time" (p. 169), Internet can

compete with television. In addition, they proposed that television viewing and the

WWW are functionally different and "the Web may not become a functional alternative

to television viewing for relaxation" (p. 170). Atkin et al., (1998) explained adoption of

internet by several variables: social locators, media use, new media adoption, and

communication needs. According to Atkin et al., (1998), among independent variables,

social locators (age, education, and income) and technology compatibility were












significantly associated with adoption of internet, while communication needs, activities,

and orientations were not related to Internet access.

Du (1999) and Carrie et al. (2002) examined Internet diffusion in China and

Japan respectively. Du (1999) purposed to find out which factors affect Internet adoption

and Chinese usage patterns. It was found that in China, early adopters of internet were

prominently male, young, well-educated, higher-income, and single (Du, 1999). In

addition, Du (1999) contended that relative advantage, complexity, innovativeness,

Internet content, resources, speed, and ISP service quality affected Internet adoption in

China. Carrie et al. (2002) considered cultural difference in Internet diffusion and

suggested individualism, uncertainty avoidance, the power distance, and masculinity as

factors which contribute to differences in adoption of the Internet and computers.

Online Shopping Adoption

The Internet has grown significantly during the past decade. In particular, online

shopping is a rapidly growing area in Internet business. Forrester Research forecasted

that online retail trade will be about $ 217.8 billion by 2007 and account for 8% of total

retail revenue. Researches for online shopping or electronic exchange usually have

examined the factors that influence adoption of online shopping. Perceived risk

(security/privacy) (Donthu and Garcia, 1999; Jarvenpaa et al., 1999; Vellido et al., 2000;

Fenech and O'Cass, 2001; Miyazaki and Fernandez, 2001), trust (Donthu and Garcia,

1999; Swaminathan et al., 1999; Jarvenpaa et al., 1999; McKnight and Chervany, 2001),

demographics (Donthu and Garcia, 1999; Li et al., 1999), shopping orientation (Li et al.,

1999; Swaminathan et al., 1999; Wolfinbarger and Gilly, 1999; Fenech and O'Cass,

2001; Fenech and O'Cass, 2001; Koufaris et al., 2001), innovativeness (Donthu and












Garcia, 1999; Citrin, 2000) and other factors (retailer's reputation, internet usage, channel

utilities, etc.) were used as independent variables in order to explore the factors that affect

online shopping behavior, attitude, and adoption. A PDA has a function of Internet. It

means that consumers can do online shopping through a PDA. Therefore, the previous

studies about the adoption of online shopping will be helpful for this present study to

select the factors influencing the adoption of PDA.

Demographic has been an important factor in diffusion, adoption, and uses &

gratifications studies. Li et al. (1999) posited that consumers who are better educated,

have a higher income, and are male will purchase online more frequently than these who

are not. In addition, they suggested a proposition that age is not a significant factor.

Donthu and Garcia (1999) also posited that "Internet shopper differ from non-shoppers in

age, education, income, and gender" (p. 53). Especially, it was expected that age might be

a factor that affects internet shopping (Donthu and Garcia, 1999). Both researches found

that income is significantly related to online shopping behavior. Interestingly, however,

the results of the other factors (age, education, and gender) were different each other. Li

et al. (1999) asserted that gender and education were significant variables and age was

not significant, while Donthu and Garcia (1999) contended that age was a significant

variable, and education and gender were not significant.

Perceived risk is one of the most important concerns for internet shopping.

Dowling and Staelin (1994) defined risk as a consumer's perceptions of the uncertainty

and adverse consequences of engaging in an activity. Generally, consumer's perceived

risks on the Internet are associated with privacy and security of consumer records

(Swaminathan et al., 1999; Fenech and O'Cass, 2001; Miyazaki and Fernandez, 2001).












According to Zellweger (1997), perceived unsatisfactory security is one of the

obstructions to online purchasing. Miyazaki and Fernandez (2001) asserted that "higher

levels of Internet experience may lead to lower risk perceptions regarding online

shopping and fewer specific concerns regarding system security and online retailer fraud

yet more concerns regarding online privacy" (p. 41) and "perceived risk as least partially

mediates the impact of Internet experience on online purchase behavior" (p. 41).

According to Fenech and O'Cass (2001), perceived Web security strongly affects Internet

users' attitude toward Web retailing. Donthu and Garcia (1999) found that nonshoppers

show more adversity to risk than internet shoppers. In addition, Vellido et al. (2000)

contended that consumer risk affects attitude toward shopping, but not intention toward

shopping. However, differently from other studies, Swaminathan et al. (1999) showed

that the security of electronic exchanges and privacy issues are not a concern to average

consumers when they use the Internet for shopping, because Internet security and

payment systems have developed more confidentiality every year.

Consumer shopping orientation is one of the important factors in online shopping

behavior studies (Li et al., 1999; Swaminathan et al., 1999; Wolfinbarger and Gilly,

1999; Fenech and O'Cass, 2001; Koufaris et al., 2001). According to Li et al. (1999),

there were differences in convenience and experience orientations between Web buyers

and non-Web buyers, while there were not any differences in recreational and economic

orientations. Swaminathan et al. (1999) found that convenience-oriented consumers are

more likely to purchase online and those who value social interactions are less likely to

purchase online and use the Internet less frequently for shopping. Donthu and Garcia

(1999) also suggested that Internet shoppers are more convenience-oriented than












nonshoppers. Fenech and O'Cass (2001) showed that consumer's shopping (recreational

rather than economic) orientation influences attitude toward Web-retailing.

Citrin et al. (2000) contended that domain-specific innovativeness had a

significant positive relation with the adoption of the Internet for shopping. According to

Limayem et al. (2000), personal innovativeness has a significant affect on attitude and

intention in relation to online shopping. In addition, Donthu and Garcia (1999) found that

Internet shoppers were more innovative than nonshoppers.

Personal Computers Adoption

Some consumers think of a PDA as a kind of mini computer. Currently, Pocket

PC, created for a smaller notebook, is referred as a different device from a PDA.

However, in a few of years, Pocket PC and PDA will be referred to as the same

communication device because both of them will have the same functions and

applications. This study expects that previous researches about adoption of personal

computers provide important implications for examining PDA adoption.

The personal computer was used as an important innovation in previous

diffusion studies (Dickerson and Gentry, 1983; Dutton, Rogers, and Jun, 1987; Lin,

1998). Dickerson and Gentry (1983) intended to investigate "the nature of the adopter of

one particular technological innovation, the home computer" (p. 227) and suggested three

predictors to provide profiles of innovation adopters: demographic characteristics,

consumer creativity, and previous experiences. In addition, Dickerson and Gentry (1983)

showed that adopters were more likely to be home owners, better educated, older, and

higher income than non-adopters. Additionally, a consumer's creativity and previous

experiences were significantly related to adoption of the home computer. Dutton, Rogers,












and Jun (1987) also researched adoption of personal computer and patterns of use. Their

study took four categories of variables: (1) "the independent factors shaping patterns of

personal computing, the intervening variables of (2) adoption and (3) use, and (4)

impacts" (p. 220) and drew the eight general conclusions from meta-analysis (p. 243-

245):

(1) Years of formal education is strong in explaining the adoption and use of
home computing.
(2) One of the important uses of home computers is to learn how to use a
computer, in addition to accomplishing specific tasks such as word processing or
playing video games.
(3) Early research on home computing underemphasized the use for work at home,
and overemphasized the computer's use for education and entertainment.
(4) The potential social impacts of home computing are illustrated by
contemporary shifts in time use in adopting households.
(5) The role of contextual factors in shaping the uses and impacts of personal
computing needs to be examined more fully.
(6) Different types of computer users should be more fully differentiated.
(7) The negative impacts of home computing such as computer addiction, less
sleep, social isolation, and family conflicts found in past research need further
investigation.
(8) Future research is needed concerning home computing as one part of
communications technologies in the home.

Lin (1998) explored the adoption of the personal computer as an interactive

multimedia entertainment and information provider and posited that resources,

innovativeness need, complexity, advantages, communication technology ownership,

media use level, and demographics would affect adoption of personal computer. In

addition, Lin (1998) found that education, ownership of communication technology, the

perceived advantage, resources, and need for innovativeness are significant predictors.

Information Systems (Audiotex, Videotex, and Electronic Bulletin Board) Adoption

Information systems such as audiotex, videotex, electronic bulletin board, and so

on, were perceived as innovations in the late 80's and early 90's. At that time, many












researchers examined information systems diffusion and adoption (Heikkinen and Reese,

1986; LaRose and Atkin, 1992; Eastlick, 1993; James and Wotring, 1995; Jeffres and

Atkin, 1996; Neuendorf, Atkin, and Jeffres, 1998).

LaRose and Atkin (1992) posited that early adoption is typical of consumers who

are (1) younger, better educated, and male and (2) heavier users of functionally similar

technologies. That research found that household size, use of cellular phones, videotext,

800 numbers, automatic teller machines, speaker phones and auto-dialers were the most

powerful predictors of adoption of audiotext. In addition, answering machine use, female,

conference call use, ethnicity, and education level were positive predictors, while

electronic mail use, personal use, and VCR ownership were negative predictors (LaRose

and Atkin, 1992) and interestingly, results were found that VCR's compatibility (the

functional similarity with audiotext) was negatively related to adoption of audiotext and

other technologies, which were not functionally similar with audiotext, were positively

associated with adoption of audiotext (LaRose and Atkin, 1992). Neuendorf et al. (1998)

examined adoption of two audio information services: audiotext (including 1-900 service)

and fax. They posited the following research question: "what are the relative influences

of social indicators (including demographics), media use, communication needs, and,

particularly, QOL (Quality of Life) assessments on people's use of audio information

services and fax" (p. 86). In result, media use were more predictive of adoption for audio

information service that social indicator and communication needs QOL might become

the predictors of adoption (Neuendorf et al., 1998).

Heikkinen and Reese (1986) researched newspaper readers' adoption of

videotext with individual characteristics (information need and channel orientation).












Eastlick (1993) intended to investigate factors which affect the adoption of videotex

shopping. Eastlick (1993) posited that perceptions of the properties of a videotex

shopping system and the innovation properties (relative advantage, compatibility,

trialability, and complexity) will be better predictors than any other variables. Eastlick

(1993) asserted that "perceptions of the advantages of videotex shopping and its

consistency with shopping needs and experiences were important factors in determining

either an adoption or nonadoption decision." (p. 73).

James and Wotring (1995) investigated and characterized the users and uses of

electronic bulletin board messages in terms of adopter characteristics and social impacts.

That study found that education, income, gender, and occupation were related to adoption

of electronic bulletin board, but age was not. In addition, James and Wotring (1995)

showed that electronic bulletin board would not affect radio listening, large group

communication, and small group communication.

Other New Technologies Adoption

Dupagne (1999) investigated the characteristics of potential High-Definition

Television (HDTV) adopters. Additionally, this researcher examined how demographics,

mass media use, ownership of home entertainment products, and importance of television

attributes affect HDTV awareness, interest, and purchase intention (Dupagne, 1999).

According to Dupagne (1999), demographics, mass media use, and the number of home

entertainment products were partially significant related to HDTV awareness and interest,

and only the perceived importance of television attributes had a significant relation with

purchase intention.












Garramone, Harris, and Pizante (1986) examined predictors of motivation to use

Computer-Mediated Political Communication Systems (CMPCS). Interestingly, among

several variables (demographics, needs, traditional political participation, satisfactions

obtained from traditional political participation, and satisfactions anticipated from

CMPCS use), only needs and satisfactions expected from CMPCS use were significant

predictors of motivation to use CMPCS (Garramone et al., 1986).

Factors influencing the adoption of multimedia cable technology were examined

by Lin and Jeffres (1998). Lin and Jeffres (1998) contended that existing media use

patterns and media content satisfaction might be helpful in establishing the potential

dynamics of functional substitutions between an existing and emerging medium.

Parthasarathy and Bhattacherj ee (1998) examined post-adoption behavior in the

context of online services. Their study focused on investigating discontinuance in the

online services industry by variables (communication channels, utilization level,

perceived innovation attributes, network externalities, and reasons for discontinuance).

Okolica and Stewart (1996) examined factors affecting the use of voice mail.

Perceived usefulness, individual innovativeness, and training had a positive relation with

use of voice messaging (Okolica and Stewart, 1996).

















CHAPTER 3
RESEARCH MODEL

This chapter will present the hypothesized model. This model is based on the

innovation-decision process. Additionally, in the basis of previous researches, this current

study selected several variables to explore the adoption of PDA. This chapter will explain

each of variables. In the last part, this current study will show the hypothesized model. In

the next chapter, the relations among variables will be suggested.

Independent Variables

Perceived Characteristics of the Innovation

In many researches, perceived characteristics of the innovation explained

individuals' perceptions about innovations as important predictors of adoption behavior

(Agarwal and Prasad, 1997). According to Rogers (1995), the making-decision unit forms

attitude toward the innovation at the persuasion stage. After making-decision unit knows

about the innovation, it can begin to form an attitude toward the innovation. Rogers

(1995) contended that at the persuasion stage, perceived characteristics of an innovation

play in an important role to form an attitude. Perceived characteristics of the innovation

consist of (1) relative advantage, (2) compatibility, (3) complexity, (4) trialability, and (5)

observability. All attributes except complexity are positively related to adoption of

innovations (Rogers, 1995). Ostlund (1974) found that perceived characteristics of

innovation are significant predictors of new product purchase. Eastlick (1993), in

videotex adoption study, expected that relative advantage, compatibility, trialability, and

complexity would be better predictors of adoption of videotex than other characteristics












and found that relative advantage and compatibility were better properties. Du (1999)

proposed that relative advantage, compatibility, and complexity were factors influencing

Internet adoption in China. Sund et al., (2001) found that relative advantage, complexity,

and compatibility are relevant to adoption of ERP systems. According to Lin (1998),

relative advantage was a significant predictor of adoption, while complexity was not

significantly associated with adoption of personal computers.

Most researches, which had perceived characteristics of an innovation as

independent variables, exclude trialability and observability. In addition, in most

researches, these two variables didn't have a significant effect. On the other hand, several

studies found that trialability is a significant predictor for adoption of innovations (Moore

and Benbasat, 1991; Agarwal and Prasad, 1997).

Based on previous assertions, it is clear that perceived characteristics of

innovations play an important role in predicting innovation adoption. This present study

will leave relative advantage, compatibility, complexity, and trialability as independent

variables and exclude observability in this study.

Ownership of New Technology Products

Rogers (1995) contended that an individual's experience with one innovation

influences individual's adoption of the next innovation. In addition, Rogers (1995)

suggested one concept: "technology cluster" (p. 15). A technology cluster is defined as

"one or more distinguishable elements of technology that are perceived as being

interrelated" (Rogers, 1995, p. 15). Rogers (1995) asserted that any innovation doesn't

have a clear-cut boundary with other innovation and potential adopters often perceive one

innovation as closely related to another new innovation.












In terms of the notion of technology cluster, previous several studies examined

the relation between adoption of innovation and other technology experience (Dickerson

and Gentry, 1983; Jeffres and David, 1996; Lin, 1998; Dupagne, 1999). Ettema (1984)

found that the adoption of other innovations affected the adoption of text services. On the

basis of Roger's concept, Lin (1998) contended that "communication media sharing

certain functional similarities may create synergies insofar as adoption rates are

concerned assuming that other circumstantial factors such as pricing are held constant"

(p. 99) and found that communication technology ownership (e.g., satellite dish, VCR,

video camera, compact disc player, laser disc player, video game player, electronic

personal organizer, electronic pager, answering machine, cellular telephone, fax machine,

word processor, cable TV subscription, premium cable TV subscription, DBS

subscription, and voice mail subscription) is an important predictor of the personal

computer adoption rate. According to Dupagne (1999), adoption of HDTV is positively

related to the number of home entertainment products. Dickerson and Gentry (1983)

found that "adopters of home computers have had more experience with a variety of

technical products and services than non-owners" (p. 234). Jeffres and David (1996)

proposed that media use pattern is a significant predictor of the degree to use the new

technologies. Danko and MacLachlan (1983) found that the early adopters of personal

computers owned other high technology products (e.g. microwave oven, tape-deck

equipment, and video games).

On the basis of previous adoption studies, this current study expects that

ownership of technology products can be one of predictors for adoption of PDA.












Personal Innovativeness

According to diffusion theory, adoption of innovations is a function of personal

innovativeness, or willingness to try the innovations (Jeffres and Atkin, 1996). Many

studies employed personal innovativeness as a predictor in order to explain the adoption

of innovations (Venkatraman, 1991; Manning et al., 1995; Lin 1998; Lin and Jeffres,

1998; Donthu and Garcia, 1999; Du, 1999; Citrin et al., 2000; Im et al., 2003). Rogers

(1995) defined innovativeness as "the degree to which an individual or other unit of

adoption is relatively earlier in adopting new ideas than the other members of a system"

(p. 37) Additionally, Rogers (1995) asserted that innovativeness affects the rate of

adoption. Lin (1998) posited that "need for innovativeness" (p. 97) is a positive factor in

showing an interest in and involvement with innovations. Pope et al. (1999) proposed that

personal innovativeness is positively associated with purchase intention for sports

products via the Internet. Donthu and Garcia (1999) also suggested that Internet shoppers

have more innovativeness than nonshoppers. Im et al. (2003) found that new product

adoption behavior is positively affected by innate innovativeness and personal

characteristics don't influence innovative potentiality. Citrin et al. (2000) classified

consumer innovativeness into two types: "open-processing innovativeness" and "domain-

specific innovativeness" (p. 294). Open-processing innovativeness focuses on a cognitive

style and domain-specific innovativeness focuses on product category or domain specific

(Citrin et al., 2000). Citrin et al. (2000) also found that open-processing innovativeness

doesn't have a significant relation with the adoption of Internet shopping, while domain-

specific innovativeness has a positive relation with the adoption of Internet shopping.

Venkatraman (1991) also sorted innovators into two types: "cognitive innovators" and












"sensory innovators" (p. 52). According to Venkatraman (1991), cognitive innovators are

defined as "people who have a strong preference for new mental experiences" (p. 52) and

sensory innovators are defined as "people who have a strong preference for both new

cognitive and sensory experiences" (p. 52). In addition, Venkatraman (1991) found that

"the interaction between innovativeness tendencies and product type determines the

demographic profile of the adopter within each segment of cognitive and sensory

innovators" (p. 64). Manning et al. (1995) showed two scales of consumer

innovativeness: "Consumer Independent Judgment Making (CIJM)", which is defined as

"the degree to which an individual makes innovation decisions independently of the

communicated experience of others" (p. 329) and "Consumer Novelty Seeking (CNS)",

which is defined as "the desire to seek out new product information" (p. 329) and

examined measurements of the two scales and the relationship between them and the

adoption process. Findings were that CIJM was significantly related to only the later trial

stage and CNS was positively associated with the initial stages of the adoption process

(Manning et al., 1995).

Based on the previous studies, personal innovativeness may be a significant

determinant to predict the adoption of PDA.

Intervening Variables

Attitude toward PDA.

The persuasion stage is the step involved in forming favorable or unfavorable

attitude toward the innovation. Rogers (1995) suggested that "the main outcome of the

persuasion stage in the innovation-decision process is either a favorable or unfavorable

attitude toward the innovation" (p. 169). At this stage, individuals consider mentally












various situations, and then develop an attitude toward the innovation. Finally, they form

a favorable or unfavorable attitude. Therefore, attitude toward PDA will be examined in

this study.

Perceived Uncertainty

Rogers (1995) defined "uncertainty" as "the degree to which a number of

alternatives are perceived with respect to the occurrence of an event and the relative

probability of these alternatives" (p. 6). Rogers (1995) also asserted that during

innovation-decision process, the making-decision unit tries to reduce uncertainty about

the expected consequences of an innovation and information is a significant means of

reducing uncertainty. Especially, at the persuasion and decision stage, an individual seek

"innovation-evaluation information" (p. 168) to reduce uncertainty. Eastlick (1996)

defined perceived uncertainty as a variety of risks associated with adopting an innovation.

Consumer behavior is motivated to reduce risk and reduction of uncertainty is related to

information acquisition, transmission, and processing (Taylor, 1974). Gronhaug (1972)

also contended that consumer behavior is related to a problem-solving process. Dowling

and Staelin (1994) said that "the concept of perceived risk most often used by consumer

researchers defines risk in terms of the consumer's perceptions of the uncertainty and

adverse consequences of buying a product or service" (p. 119). Cox and Rich (1964)

referred perceived risk as "the nature and amount of risk perceived by a consumer in

contemplating a particular purchase intention" (p. 33). Miller and Friesen (1982)

operationalized uncertainty into the components of hostility, heterogeneity, and

dynamism. Sheth and Parvatiyar (1995) contended that the uncertainty is associated with

perceived risk and consumers intend to develop various ways to reduce perceived risk.












Eastlick (1996) defined perceived uncertainty as a variety of risks associated with

adopting an innovation. On the other hand, Knight (1965) defined the concepts of risk

and uncertainty separately. Knight (1965) contended that when it is lack of knowledge of

a precise probability, uncertainty exists and risk exists in cases where there is a known

probability. Usually, however, researchers have accepted the two concepts to be used

synonymously (Mitchell, 1998). On the basis of previous suggestions, the present study

will conceptualize perceived uncertainty as perceived risk, so this study assumes that

"risk" will be exchangeable with "uncertainty."

Garner (1996) suggested six types of risks: social, financial, physical,

performance, time, and psychological. Dholakia (1997) contended that the six dimensions

of perceived risk are helpful in explaining importance for a product classes. Ho and Ng

(1994) explained customers' risk perception of electronic payment systems with five

risks (physical, performance, psychological, financial and time risks). Ko (2001) found

that perceived risk is related to online auctions. The perceived risk was used as a variable

in several studies for online shopping (Donthu and Garcia, 1999; Jarvenpaa et al., 1999;

Swaminathan et al., 1999; Fenech and O'Cass, 2001). Sometimes risk or uncertainty is

viewed as an expectation of loss (Stone and Winter, 1987). Mitchell and Greatorex

(1993) addressed four types of loss: financial loss, time loss, physical loss, and

psychosocial loss. Finally, the reduction of perceived uncertainty is one of the

consequences in the persuasion stage.












Dependent Variable

Purchase Intention

The next stage of the persuasion stage is the decision stage. According to Rogers

(1995), the decision stage occurs when "an individual (or other decision-making unit)

engages in activities that lead to a choice to adopt or reject an innovation" (p. 171). He

defined "adoption" as "a decision to make full use of an innovation as the best course of

action available" (p. 171). In this study, adoption of PDA will be operationalized as

purchase intention for PDA.

Additional Variable

Functions of PDA

As a "convergence" communication device, a PDA has several functions: PIMS,

internet/e-mail, global positioning system, wordprocessing/ spreadsheet, digital camera,

video games, MP3/movie file player, mobile phone, and so on. This study expects that

the perceived importance of each function will be related to attitude toward PDA.

Therefore, for exploratory attempt, this present study will examine which functions will

be related to attitude and purchase intention.

Hypothesized Model

This study presents the following hypothesized model with variables to be

selected for this current study (see Figure 3-1). The relations among the variables will be

explained in the next chapter.













Attitude
Toward PDA


0-
Perceived Characteristics 'R
of the Innovation
(Relative advantage, Compatibility,
Complexity, and Trialability) H2


0 ------- R,

Ownership of New
Technologies Products





Personal
Innovativeness


------- RQ


H4



H4


Perceived
Uncertainty


- Persuasion Stage -


- Decision Stage -


Figure 3.1: Hypothesized Model


Functions


Purchase
Intention

















CHAPTER 4
HYPOTHESES AND RESEARCH QUESTIONS

This research intends to examine the relative influence of perceived

characteristics of innovation, personal innovativeness, attributes of PDA in exploring

attitude toward PDA, perceived uncertainty, and purchase intention, and the relationships

among the variables, based on a model of the Innovation-Decision Process of Diffusion

theory. In order to investigate relations among variables, hypotheses and research

questions were created.

Hypotheses

The first hypotheses deals with the persuasion stage of the innovation-decision

process of diffusion theory. The second part is derived from the decision stage.

The Persuasion Stage

In the persuasion stage, an individual (or making decision unit) forms favorable

or unfavorable attitude toward innovations and attempts to reduce uncertainty about

expected consequences of an innovation and get information, which is a significant

means of reducing uncertainty (Rogers, 1995). This study posits that perceived

characteristics of innovations, ownership of new technology products, and personal

innovativeness influence attitude toward PDA at the persuasion stage. With respect to

perceived uncertainty, this study created a research question about the relationship

between variables and perceived uncertainty.












Relation between perceived characteristics of innovations and attitude toward PDA

Hypothesis 1 expresses the relation between perceived characteristics of

innovations and attitude toward PDA. Rogers (1995) asserted that perceived attributes of

innovations (relative advantage, complexity, compatibility, trialability, and observability)

play an important role in the persuasion stage and only complexity influences negatively

the adoption of innovations. Du (1999) showed that relative advantage, compatibility, and

complexity affected significantly the adoption of the Internet in China. Lin (1998) found

that relative advantage was a significant predictor of adoption of personal computer.

Ostlund (1974) showed that perceived innovation attributes were strongly correlated to

new product purchase. In addition, according to Eastlick (1993), relative advantage and

compatibility were positively related to videotex adoption. Holak (1988) found that

product trial was positively correlated to purchase intention. Sund et al., (2001) also

contended that three innovation attributes (relative advantage, complexity, and

compatibility) are related to the adoption of ERP systems. According to Parthasarathy

and Bhattacherjee (1998), relative advantage (usefulness) and compatibility was

significant factors of post-adoption behavior, but complexity was not significant.

Based on previous studies, the following hypotheses are designed.


HI. 1: Relative advantage will be positively related to attitude toward PDA

H1.2: Compatibility will be positively related to attitude toward PDA

HI. 3: Complexity will be negatively related to attitude toward PDA

HI. 4: Trialability will be positively related to attitude toward PDA












Relation between ownership of new technology products and attitude toward PDA

Hypothesis 2 is designed to investigate the relationship between ownership of

new technology products and attitude toward PDA.

Atkin (1993) suggested the notion of "functional similarity/need compatibility

explanation" (p. 52) in his study. It means that the adoption of innovations is associated

with the adoption of functionally similar technology (Lin and Jeffres, 1998). Perse and

Dunn (1998) asserted that in terms of uses and gratifications perspective, perceptions

about the different communication channels have an important meaning for two reasons:

(1) "people turn to different communication channels, because they believe that they will

derive something from that use," and (2) "few communication channels are uniquely able

to fill communication needs. Most are functional alternates to other channels, or able to

fill similar communication needs" (p. 436). Atkin (1993) found that cable subscribership

is related to functionally similar media and cable subscribers tended to adopt VCRs,

camcorders, and cordless phones more than nonsubscribers. In addition, he found that

pay viewers were more likely to adopt cellular phone, computers, walkman, and video

games. Perse and Courtright (1993) contended that the adoption of new technologies is

related to the adoption of functionally similar product. Danko and MacLachlan (1983)

found that the early adopters of personal computers owned other high technology

products. According to Reagan et al. (1995), people have a tendency to select

technologies to fit a function and several technologies can provide a similar function to

other technologies. They supported the concept of "functional similarity" and found that

functional similarity seems to vary depending on the innovations. Dickerson and Gentry

(1983) posited that "adopters of home computers will have had more experience than












non-adopters with other technical consumer products" (p. 228). Their study found that 17

of 19 technical products and services were more likely to be used by home computer

adopters. Lin (1998) focused on investigating the relation between the adoption of

personal computer and communication technology ownership and found that the relation

was significant. Dupagne (1999) examined how the number of home entertainment

products influences HDTV awareness, interest, and purchase intention. LaRose and Atkin

(1992) found that ownership of several technology products is a positive or negative

predictor. Atkin et al. (1998) asserted that the adoption of new product is associated with

the adoption of other innovations. Neuendorf et al. (1998) investigated the adoption of an

audio information service and fax in terms of two kinds of functionally similar media:

entertainment media (e.g., television, movies) and utilitarian media (e.g., personal

computers).

On the basis of these previous researches, ownership of other technologies is

expected as an important predictor of PDA adoption. In order to investigate ownership of

new technologies, seven products were selected: (1) mobile phone, (2) video game player,

(3) DVD player, (4) digital camera, (5) digital Cable/satellite TV, (6) broadband, and (7)

Personal Video Recorder.

H2: The number of ownership of new technologies (Mobile Phone, Video game
player, DVD player, Digital camera, Digital Cable/Satellite TV, Broadband, and
Personal Video Recorder) will be positively related to attitude toward PDA.
Relation between personal innovativeness and attitude toward PDA

Hypothesis 3 is designed to examine how personal innovativeness influences

attitude toward PDA. Citrin et al. (2000) investigated the relation between two types of

innovativeness (open-processing innovativeness and domain-specific innovativeness) and












adoption of Internet shopping. Manning et al. (1995) conceptualized innovativeness as

"consumer independent judgment making" and "consumer novelty seeking" (p. 329) and

examined how the two types of innovativeness affect adoption at each stage of the

adoption process. Im et al. (2003) explored the relation between innate consumer

innovativeness and new product adoption behavior. Venkatraman (1991) developed

different innovativeness segments (cognitive innovativeness and sensory innovativeness)

and explored their influence on the adoption of an innovation. Donthu and Garcia (1999)

also examined the relation between innovativeness and adoption of Internet shopping. Du

(1999) found that innovativeness was a significant predictor of Internet adoption. Lin and

Jeffres (1998) focused on the relation between innovativeness traits and the adoption of

multimedia cable technology. Limayem et al. (2000) posited that personal innovativeness

affects directly and indirectly both attitude and intention of online shopping. In addition,

Pope et al. (1999) hypothesized that individual's innovativeness would have a positive

relation with intention to purchase sport product through the Internet.

Considering previous studies, the following hypothesis below were formulated

for this study

H3: Personal innovativeness will be positively related to attitude toward PDA.
The Decision Stage

At the decision stage, an individual takes part in activities to choose adoption or

rejection of an innovation (Rogers, 1995). Rogers (1995) asserted that in this stage,

attitude toward PDA and perceived uncertainty affect purchase intention.












Relation between attitude toward PDA and perceived uncertainty, and purchase
intention
Hypothesis 4 is derived from the decision stage of the innovation-decision process of

diffusion theory. At the decision stage, an individual decides on adoption or rejection of

an innovation. Rogers (1995) asserted that attitude toward an innovation does not lead to

adoption or rejection under many other circumstances, but there is a positive relation

between attitude and behavior. Brown and Stayman (1992) examined antecedents and

consequences of attitude toward the ad with a meta-analysis of 47 researches about

attitude toward the ad and focused on the relations between ad attitude, brand attitude,

and purchase intention. Ko (2002) found that attitude toward the brand positively affect

purchase intention. Lutz et al. (1983) contended that attitude toward the ad is positively

related to brand attitude and purchase intention. Limayem et al. (2000) also found that an

individual's attitude had a strong correlation with intent toward online shopping.

According to the Theory of Reasoned Action (Fishbein and Ajzen, 1975) and the Theory

of Planned Behavior (Ajzen, 1985), beliefs affect person's attitudes and attitudes, in turn,

influence behavioral intention, which is a good forecaster of actual behavior. Therefore, it

is expected that attitude is one of the factors influencing purchase intention of PDA.

H4.1: Attitude toward PDA will be positively related to purchase intention.
Rogers (1995) contended that an individual seeks information to reduce

perceived uncertainty for the innovation and this may affect his/her adoption/rejection.

Reduction of perceived risk positively affects consumers' shopping activities (Dowling

and Staelin, 1994). Cox and Rich (1964) contended that perceived risk by the consumer is

a function of the amount at stake in the purchase intention and perceived risk is one

determinant of telephone shopping. Mitchell (1998) found that consumer risk

perceptions had an important role in grocery retailing. Perceived risk was applied to












examine online purchase of sport products (Pope, 1999). Perceived risk affects overall

evaluation of the deal and purchase intention (Wood and Scheer, 1996). In addition, Ko

(2001) found that perceived risks influence the adoption of online auction participation.

On the basis of other researches, the following hypothesis is provided:

H4.2: Perceived uncertainty will be negatively related to purchase intention.

Research Questions

This study suggests three research questions about the adoption of PDA:

RQ1: Which factors influence attitude toward Personal Digital Assistant (PDA)
and perceived uncertainty at the persuasion stage of the Innovation-Decision
process and which variables have the relatively strong or weak influence on
attitude and perceived uncertainty?
The first research question is designed to investigate the relationships between the

independent variables (relative advantage, compatibility, complexity, trialability,

ownership of new technologies, and personal innovativeness) and perceived uncertainty.

In addition, this study will examine whether there is a significant difference among the

significant variables.

RQ2: Which factors will be useful in discriminating between two purchase
intention groups (high or low)?
The second research question addresses which variables can be used as significant

factors to discriminant between a high purchase intention group and a low purchase

intention group.

Relation between the Functions of PDA and Purchase Intention

According to previous studies, from the perspective of uses and gratifications, one

innovation can have several functions to satisfy motivations and needs. December (1996)

contended that the Internet is used mainly for communication, interaction, and












information. According to the 9th WWW User Survey conducted by Georgia Tech

(GVU's 9th WWW user survey, 1998), entertainment, education, time wasting and

personal information are main purposes for the youngest user to use the web. Hunter

(1996) suggested that the Internet involves five categories of needs: cognitive needs,

affective needs, personal integrative needs, social integrative needs, and escapist needs.

Rubin (1981) showed nine motivations to view television: to pass time, for

companionship, arousal, content, relaxation, information, escape, entertainment, and

social interaction. According to Sherry et al. (2001), challenge, arousal, and diversion

were the most frequently reported reason for using video games.

In HDTV adoption research, Dupagne (1999) found that HDTV purchase

intention was related only to the perceived importance of HDTV attributes. Even though

a PDA has many attributes, few models have these functions currently and most of them

will be added in the near future. So, this present study expects to know how respondents

perceive each function of PDA and how each function is related to attitude toward PDA.

Finally, the third research question is the following:

RQ3: which functions ofPDA are related to attitude toward PDA and purchase
intention?

















CHAPTER 5
RESEARCH METHODOLOGY

Babbie (2001) proposed that "survey research is probably the best method

available to the social researcher who is interested in collecting original data for

describing a population too large to observe directly and surveys are excellent vehicles

for measuring attitudes and orientations in a large population" (p. 238). Most adoption

researches have used a survey as a research technique. In addition, previous studies

provide a number of reliable scales for measuring variables regarding the adoption of the

innovation. So, this current study expected that a survey would be adequate for exploring

the adoption of PDA and this study used a survey.

Sample

The sample used for the present study was 218 students from a large southeastern

university. This study recruited respondents from several classes at the university. The

researcher visited these classes and administered a survey. In other words, this study used

a convenience sampling. The survey was conducted from March 18 to April 17, 2003;

191 samples were valid, and 27 samples were invalid. Among the subjects, 62.3 percent

were female and 37.7 percent were male. Most respondents were undergraduate students

(76.4%).

Measurements

The survey for this study provided all subjects basic information about a PDA

with a picture (see Figure 5-1) and the following information:

























Figure 5-1: Personal Digital Assistant


Information:
This is the picture of Personal Digital Assistant (PDA).
The newest model can provide you several functions:
It enables you to access Internet/E-mail and Global Positioning System
(GPS) through satellite. As a communication device, it has a mobile phone. It
also allows entertainment: You can play MP3, movie files and video games.
In addition, it has functions of digital camera, digital organizer, and
wordprocessing/Spreadsheet.


After the subjects read the information, they were asked to answer questionnaires.

First, respondents were asked to address their awareness, expected cost, and

ownership of PDA. Some of the questions were: (1) "Have you ever heard of a Personal

Digital Assistant (PDA)?" (2) "How much do you think the newest PDA would cost?" (3)

"Do you own a PDA?"

Second, subjects were asked to express their levels of agreement with 12

statements, in order to measure perceived characteristics of PDA.

Relative advantage. Parthasarathy and Bhattacherjee (1998) conceptualized relative

advantage as "usefulness" (p. 336). That study proposed that earlier adopters think of

online services as being more useful than later adopters do. Okolica and Stewart (1996)

also employed perceived usefulness to examine the extent of use of voice messaging. The












present study adapted a usefulness measuring scale from Parthasarathy and Bhattacherjee

(1998). "Usefulness" was measured using a seven-point scale ranging from strongly

agree (7) to strongly disagree (1). This part of questionnaire consisted of three items: (1)

"I feel that a PDA will save me time/effort over other means of performing the same

tasks," (2) "I feel that a PDA will enable me to perform many tasks better than through

other means," and (3) "I feel that a PDA will provide a greater value than other ways of

performing the same task".

Compatibility. A compatibility measuring scale was also adapted from Parthasarathy

and Bhattacherjee (1998) and used a seven-point scale. This scale had three items: (1) "I

feel that a PDA will be easy for me to adjust to," (2) "I feel that a PDA will fit my

lifestyle very well," and (3) "I feel that a PDA will fit the way I perform my daily tasks

well."

Complexity. Parthasarathy and Bhattacherjee (1998) operationalized complexity as "ease

of use" (p. 337). Ease of use has an inverse meaning to complexity. For measurement of

complexity this study also used Parthasarathy and Bhattacherjee (1998) scale. This scale

ranges from strongly agree (7) to strongly disagree (1) and involves questions like the

followings: (1) "I feel that a PDA will be hard to learn," (2) "I feel that a PDA will be

quite complicated to master," (3) "I feel that a PDA will be difficult to use," and (4) "I

feel that a PDA will have a complex, hard-to-learn system".

Trialability. Agarwal and Prasad (1997) measured trialability as "the extent to which

potential adopters perceive that they have an opportunity to experiment with the

innovation prior to committing to its usage" (p. 562). Several previous researches

indicated the relation between only the first three attributes (relative advantage,












compatibility, and complexity) and their dependent variables (Eastlick, 1993; Lin, 1998;

Parthasarathy and Bhattacherjee, 1998; Du, 1999). However, some studies employed

trialability as a factor to measure the perceptions of adopting innovations (Moore and

Benbasat, 1991; Agarwal and Prasad, 1997). The present study uses trialability as a

predictor that affects attitude toward PDA and perceived risk. In this study, previous

study (Agarwal and Prasad, 1997) scale was adapted. The following two questions were

used: (1) "I might try out a PDA long enough to see what I could use it for," and (2)

"Before deciding to use the PDA, I would like to be able to try one out."

Third, in order to investigate ownership of new technology products, subjects

were asked to address their ownership of seven new technologies. In previous studies,

experiences with other technologies played an important role in exploring new

technology adoption (Dickerson and Gentry, 1983; Jeffres and David, 1996; Lin, 1998;

Dupagne, 1999). In particular, Dupagne (1999) examined the relation between the

number of home entertainment and adoption of HDTV. So, this present study adapted the

Dupagne questionnaire (1999) to measure the number of new technology products: "Do

you personally own the following product/service? (1) Mobile Phone, (2) Video game

player (Xbox, Playstation 2, etc.), (3) DVD, (4) Digital Camera, (5) Digital

Cable/Satellite TV, (6) Broadband (High-Speed Internet), (7) Digital Video Recorder

(TiVo or Replay TV)." These seven items were coded as dummy variables (0 = no, 1 =

yes). Results were added together. In other words, if a respondent owns all

products/services, he or she gets "7" point. The total number of devices owned indicates

the degree of ownership of new technology products.












Fourth, personal innovativeness was examined. Several scales for measurement of

innovativeness were used in a number of studies (Hurt et al, 1977; Oliver and William,

1985; Venkatraman, 1991; Manning et al., 1995; Lin and Jeffres, 1998: Du, 1999;

Donthu and Garcia, 1999; Citrin et al., 2000; Limayem et al., 2000; Im et al., 2003). This

present study adapted a scale from Oliver and William (1985). This scale had three items:

(1) "I like to buy new and different things," (2) "I am usually among the first to try new

products," and (3) "I don't like to take chances". A seven-point scale ranging from

strongly agree (7) to strongly disagree (1) was used.

Fifth, in order to examine RQ3, this study asked the respondents the following

questions: "How important is each of the following PDA functions to you? (1) Mobile

Phone, (2) Internet/E-mail, (3) Video games, (4) MP 3 and movie file player, (5)

Wordprocessing/Spreadsheet, (6) Digital camera, (7) Global Positioning System." This

item has a seven-point scale ranging from "not important at all" (1) to "extremely

important" (7).

Sixth, perceived risks were measured. In this study, perceived uncertainty is

interchangeable with perceived risk. There are numerous scales to measure perceived risk.

Among them, this present study used Garner (1986) scale in order to measure the

perceived risk. This scale can measure six kinds of risks. The six dimensions of perceived

risk can explain a significant portion of the overall risk (Stone and Groenhaug, 1993;

Dholakia, 1997). This study removed the health risk and psychological risk items because

they were expected to be unrelated to adoption of PDA. Respondents were asked to

indicate their answer with the following four statements: (1) "The product might fail to

perform to my satisfaction (performance risk)," (2) "My friends or relatives will judge













my purchase (social risk)," (3) "I might lose my money (financial risk)," and (4) "I might

waste my time or effort getting the product repaired or replaced (time risk)." Each

statement used a seven-point scale (from strongly agree (7) to strongly disagree (1)).

Seventh, subjects were asked to express their agreement about attitude toward

PDA. Attitude toward PDA was measured with a three-item scale from Sujan and

Bettman (1989). Originally, this scale was used to measure brand evaluation. Three items

(positive/negative, good/bad, and favorable/unfavorable) used a seven-point scale.

Eighth, as the dependent variable, subjects were asked to address their purchase

intention. In order to investigate purchase intention, a three-item index was adapted from

a previous study (MacKenzie et al., 1986). A seven-point scale was used ranging from 7

(strongly agree) to 1 (strongly disagree). Respondents stated their agreement with three

statements about purchase intention. This scale included: likely/unlikely,

probable/improbable, and possible/impossible.

Finally, subjects were asked to write down their demographic information.

Table 5-1 presents detailed information on the types of scale adapted and Table 5-

2 shows all questionnaires used in this current study.


Table 5-1: Measured variables

Variables Adapted measure and source

Rel e a e Adapted from Parthasarathy and
Relative advantage
Bhattacherjee (1998)

Adapted from Parthasarathy and
Compatibility
Co y Bhattacherjee (1998)

Adapted from Parthasarathy and
Complexity
Co y Bhattacherjee (1998)

Trialabiy Adapted from Agarwal and Prasad
Talability(1997)


Scale Type

Seven-point scale: strongly disagree
(1) to strongly agree (7)

Seven-point scale: strongly disagree
(1) to strongly agree (7)

Seven-point scale: strongly disagree
(1) to strongly agree (7)

Seven-point scale: strongly disagree
(1) to strongly agree (7)















Table 5-1. Continued


Variables


Ownership of new
technology products

Personal innovativeness


Perceived uncertainty


Attitude toward PDA

Purchase intention


Adapted measure and source


Adapted from Dupagne (1999)

Adapted from Oliver and William
(1985)

Adapted from Garnet (1986)

Adapted from Sujan and Bettman
(1989)

Adapted from MacKenzie et al. (1986)


Scale Type


Nominal
Yes: 1, No: 0

Seven-point scale: strongly disagree
(1) to strongly agree (7)

Seven-point scale: strongly disagree
(1) to strongly agree (7)

Seven-point scale

Seven-point scale


Table 5-2: Observed variables

Variables Questionnaires

Awareness Have you ever heard of a Personal Digital Assistant (PDA)?

Ownership of
Ownrspof Do you own a PDA?
PDA

Expected cost How much do think the newest PDA would cost?

I feel that a PDA will save me time/effort over other means of performing the same tasks.
Relative
adatae I feel that a PDA will enable me to perform many tasks better than through other means.
I feel that a PDA will provide a greater value than other ways of performing the same task.

I feel that a PDA will be easy for me to adjust to.
Compatibility I feel that a PDA will fit my lifestyle very well.
I feel that a PDA will fit the way I perform my daily tasks well.

I feel that a PDA will be hard to learn.
I feel that a PDA will be quite complicated to master.
Complexity I feel that a PDA will be difficult to use.
I feel that a PDA will have a complex, hard-to-learn system.

Trialability I might try out a PDA long enough to see what I could use it for.
Before deciding to use the PDA, I would like to be able to try one out.

Ownership of Do you personally own the following product/service and how familiar are you with each of them?
new (1) Mobile Phone, (2) Video game player (Xbox, Playstation 2, etc.), (3) DVD, (4) Digital Camera,
technology (5) Digital Cable/Satellite TV, (6) Broadband (High-Speed Internet), (7) Digital Video Recorder (Ti
products Vo or Replay TV)*

I like to buy new and different things.
nnoaene I am usually among the first to try new products.
innovativeness t like to take chances.(reverse scale)
I don t like to take chances.(reverse scale)


Perceived
importance of
functions


How important is each of the following PDA functions to you?
(1) Mobile Phone, (2) Intemet/E-mail, (3) Video games, (4) MP 3 and movie file player, (5)
Wordprocessing/Spreadsheet, (6) Digital camera, (7) Global Positioning System














Table 5-2. Continued

Variables Questionnaires

The PDA might fail to perform to my satisfaction.
Perceived My friends or relatives will judge my purchase.*
uncertainty I might waste my money.
I might waste my time or effort getting the product repaired or replaced.

Unfavorable/Favorable
Attitude
Bad/Good
toward PDA BdG
Negative/Positive

Pu e Unlikely/Likely
intPurcse Improbable/Probable
Impossible/Possible

* Variables which were deleted to improve reliability.



Statistical Analysis

This present study used the following statistical methods: multiple regression

analysis, stepwise regression analysis, t-test, and discriminant analysis.

All hypotheses were developed to examine the relation among variables.

According to Garson (2003), the ratio of the relative predictive power of the independent

variables is indicated by the standardized b coefficients and the ratio of the beta

coefficients. So, multiple regression analysis was employed to test all hypotheses.

In order to make an equation model with only significant variables, this study

used stepwise regression analysis. Stepwise regression is one of the ways to compute

Ordinary Least Squares (OLS) (Garson, 2003). In stage one, the independent best

correlated with the dependent is included in the equation. In the second stage, the

remaining independent with the highest partial correlation with the dependent, controlling

for the first independent, is entered (Garson, 2003). This process is repeated until R2 is

not significantly increased by the addition of a remaining independent or all variables are












added (Garson, 2003). Therefore, stepwise regression can help identify which factor has a

relatively strong effect or weak effect.

The first research question was suggested to investigate the relationship among

predictors (perceived characteristics of innovations, ownership of new technology

products, and personal innovativeness) and attitude and perceived uncertainty toward

PDA. In order to examine the relation, multiple regression and stepwise regression were

be preformed (Eastlick, 1996).

The second research question suggested which factors are useful for

discriminating two purchase intention groups (high/low). This study categorized two

groups: "high" purchase intention and "low" purchase intention. Respondents, who score

higher than mean scores on purchase intention, were identified as the "high" purchase

intention group and respondents, who got a lower score than the mean of purchase

intention, were identified as the "low" purchase intention group. Eastlick (1996) used

multiple discriminant analysis to investigate whether factors of attitude toward interactive

teleshopping differentiate subjects on intent to adoption. According to Parthasarathy and

Bhattacherjee (1998), when independent variables are continuous and dependent

variables are categorical, multiple discriminant analysis is an appropriate statistical

method. Since there are only two groups by the dependent variable in this study, instead

of multiple discriminant analysis, discriminant analysis was performed.

Finally, the third research question addressed which functions of PDA are related

to attitude toward PDA and purchase intention. In order to investigate the relation

between each function of PDA and attitude toward PDA, and the relation between each














function and purchase intention, Pearson correlation analysis was adapted for this

research question.

Statistical methods employed in this study are shown in Table 5-3.

Table 5-3: Statistical methods
Hypotheses & Research Questions Statistical Methods

H1.1: Relative advantage will be positively related to attitude toward PDA.
H1.2: Compatibility will be positively related to attitude toward PDA.
H1.3: Complexity will be negatively related attitude toward PDA. M e Rr
Multiple Regression
H1.4: Trialability will be positively related to attitude toward PDA. ie Regression
T *1Stepwise Regression
H2: The number of ownership of new technologies will be positively related to
attitude toward PDA.
H3: Personal innovativeness will be positively related to attitude toward PDA.


H4.1: Attitude toward PDA will be positively related to purchase intention. Multiple Regression
H4.2: Perceived uncertainty will be negatively related to purchase intention. Stepwise Regression

RQ1: Which factors influence attitude and perceived uncertainty toward PDA at Multiple Regression
the persuasion stage of the Innovation-Decision process? Stepwise Regression

RQ2: Which factors will be useful in discriminating two purchase intention group Discriminant Analysis
Discriminant Analysis
(High/Low)?

RQ3: Which functions of PDA are related to attitude toward PDA? Pearson Correlation

















CHAPTER 6
RESULTS

This chapter comprises two parts. The first part discusses the descriptive statistics about

the study subjects. The second part presents the results of several statistics methods that

were used to examine hypotheses and research questions. Finally, final parsimonious

model will be presented in the third part.

Descriptive Statistics

Sample Characteristics

This study recruited a total of 218 respondents (see Table 6-1). Most respondents for this

survey were students from a large southeastern university. Completed questionnaires

were received from 191 of 218 respondents. 27 questionnaires were excluded because

some questionnaires were uncompleted and some respondents own a PDA. Among the

respondents, 119 were female (62.3%) and 72 were male (37.7%). In terms of education

demographics analysis of the sample, only three respondents were first-year college

students (1.6%), six respondents were second-year college students (3.1%), 55

respondents were third-year college students (28.8%), 82 respondents were fourth-year

college students (42.9%), 43 respondents were graduate students (22.5%), and two

respondent were others (1.0%). Respondents' age ranged from 18 to 61. The mean age

was 22.62 years and the median age was 21 years. 167 respondents (87.4%) were 18-24

age group, 21 respondents (11.0%) were 25-34 age group, and three respondents (1.6%)

were over the age of 35.












In order to examine respondents' awareness of PDA, respondents were asked the

following question: "Have you ever heard of a Personal Digital Assistant (PDA)?" 175

respondents (91.6%) answered "yes" and 16 respondents (8.4%) answered "no." Among

respondents who were not aware of a PDA, nine respondents were female and seven

respondents were male. On the basis of this data, it is clear that the PDA already passed

the knowledge stage.

In addition, respondents were asked about their ownership of PDA. 22

respondents (10.1%) owned a PDA and 191 respondents (89.9%) didn't own one.

Respondents who currently own a PDA were excluded from this research because this

study investigates the relationship between purchase intention and predictors.

With respect to expected cost, the survey for this research included the following

question: "how much do you think the newest PDA would cost?" Many respondents

(19.3%) answered that they think the newest PDA would cost in a range from $301 to

$400; 18.5 percent of respondents answered that it would cost between $401 and $500;

seven respondents answered that it would cost more than $1,000.



Table 6-1: Sample Characteristics
Items Number % (cumulative)
Total 218
Invalid data 27 12.4%
Valid data 191 87.6%

Adopter
Adopter 22 10.1%
Non-Adopter 191 89.9%

Awareness
Awareness 175 91.6%
Non-awareness 16 8.4%













Table 6-1. Continued
Items
Price
Under $100
$101 $200
$201 $300
$301 $400
$401 $500
$501 $600
$601 $700
$701 $800
$801 $900
$901 $1000
More than $1000
Mean
Median

Gender
Male.
Female

Age
Range
Mean
Median
18 24
25 34
More than 35

Education
1st year college student
2nd year college student
3rd year college student
4th year college student
Graduate Students
Others


Number

16
35
24
37
40
7
15
7
2
6
7
$444.88
$400.00


72
119


18-61
22.62
21
167
21
3


% (cumulative)

8.4% (8.4%)
18.3% (26.7%)
12.6% (39.3%)
19.3% (58.6%)
18.5% (77.0 %)
3.6% (80.6%)
7.8% (88.5 %)
3.7% (92.1%)
1.0% (93.2%)
3.1% (96.3 %)
3.5% (100 %)





37.7%
62.3%






87.4% (87.4%)
11.0% (98.4%)
1.6% (100%)


1.6% (1.6%)
3.1% (4.7%)
28.8% (33.5%)
42.9% (76.4%)
22.5% (98.9%)
1.0% (100%)


Normality of Items

Generally, if calculated values of skewness and kurtosis don't exceed +2.58 at .01

probability level, the null hypothesis about the normality of the distribution is rejected

(Hair et al., 1998). Table 6-2 shows the descriptive statistics of each observed item and

variable in terms of mean, standard deviation, skewness, and kurtosis. As seen from the













table, most observed values of skewness and kurtosis didn't exceed +2.58, so the null

hypothesis about the normality was rejected. However, both skewness and kurtosis of

observed value about ownership of Digital Video Recorder (DVR) were over 2.58. The

reason for this may be that only two respondents own a DVR. Therefore, the item about

ownership of DVR was deleted in this study. Observed value about ownership of new

technologies was calculated excluding ownership of a DVR.

Reliability

Cronbach's alpha ranges from 0.0 to 1.0. Generally, an alpha coefficient of .70 or

greater indicates that a scale is appropriate for use in research. A scale with an alpha

coefficient over .60, however, can be used for a research (Garson, 2003). Table 6-2

presents that Cronbach's alpha of all scales exceeded .60. This means that all scales can

be used statistically in this research. In the case of perceived risks scale, a Cronbach

alpha of four original items didn't exceed .60, but if the 2nd questionnaire ("My friends or

relatives will judge my purchase.") is deleted, the Cronbach alpha becomes over .60

(a=.6706). For valid reliability, this study eliminated 2nd item of the perceived risks scale.

Table 6-2: Descriptive profile of each variable

Mean SD Skewness Kurtosis
Relative Advantage
A PDA will save me time/effort over other means of 4.325 1.314 .169 .002
performing the same tasks.
A PDA will enable me to perform many tasks better 4.126 1.348 -.192 -.221
than through other means.
A PDA will provide a greater value than other ways 3.966 1.330 .104 .189
of performing the same task.
Cronbach Alpha .8599

Compatibility
A PDA will be easy for me to adjust to. 4.322 1.584 -.118 -.688
A PDA will fit my lifestyle very well. 4.141 1.442 -.069 -.304














Table 6-2. Continued

Mean SD Skewness Kurtosis

A PDA will fit the way I perform my daily tasks well. 4.126 1.471 -.139 -.307
Cronbach Alpha .8352


Complexity
A PDA will be hard to learn. 3.319 1.657 .441 -.546
A PDA will be quite complicated to master. 3.361 1.683 .434 -.653
A PDA will be difficult to use. 3.162 1.580 .458 -.625
A PDA will have a complex, hard-to-learn system. 3.136 1.570 .489 -.519
Cronbach Alpha .9508


Trialability
A PDA will I might try out a PDA long enough to see 4.924 1.613 .622 -.066
what I could use it for.
A PDA will Before deciding to use the PDA, I would 5.510 1.450 -1.036 .968
like to be able to try one out.
Cronbach Alpha .6025


Ownership of new technology
Mobile Phone .804 .397 -1.541 .392
Video game player (Xbox, Playstation, etc.) .332 .466 .717 -1.470
DVD .717 .446 -.972 -1.032
Digital Camera .306 .458 .847 -1.273
Digital Cable/Satellite TV .398 .485 .420 -1.819
Broadband (High-Speed Internet) .673 .467 -.742 -1.446
Digital Video Recorder (TiVo or Replay TV) .094 .293 2.800 5.900


Familiarity with new technology
Mobile Phone 6.270 1.198 -1.827 3.098
Video game player (Xbox, Playstation, etc.) 4.869 1.956 -.570 -.828
DVD 5.929 1.372 -1.530 2.314
Digital Camera 4.856 1.713 -.538 -.476
Digital C .i.k' \,..iii'ic TV 4.759 1.838 -.498 -.736
Broadband (High-Speed Internet) 5.772 1.547 -1.437 1.575
Digital Video Recorder (TiVo or Replay TV) 3.029 1.995 .610 -.723
Cronbach Alpha .7405














Table 6-2. Continued

Mean SD Skewness Kurtosis

Personal Innovativeness
I like to buy new and different things. 5.293 1.454 -.606 -.105
I am usually among the first to try new products. 3.749 1.539 .088 -.627
I don't like to take chances. 4.885 1.548 -.373 -.562
Cronbach Alpha .7779


Importance of PDA functions
Mobile Phone 5.157 1.994 -.839 -.550
Intemet/E-mail 5.942 1.295 -1.422 2.220
Video games 2.670 1.570 .733 -.207
MP 3 and movie file player 4.016 1.761 -.124 -.967
Wordprocessing/Spreadsheet 5.162 1.606 -.807 -.080
Digital camera 4.340 1.749 -.270 -.892
Global Positioning System 3.497 1.954 .122 -1.206


Perceived Risks
The PDA might fail to perform to my satisfaction. 4.026 1.308 -.220 -.199
My friends or relatives will judge my purchase. 2.812 1.578 .404 -.890
I might waste my money. 4.356 1.602 -.256 -.744
I might waste my time or effort getting the product 4.042 1.486 -.082 -.425
repaired or replaced.
Cronbach Alpha .5310 (.6706 if 2nd item deleted)


Attitude
Unfavorable/Favorable 4.733 1.264 -.386 .263
Bad/Good 4.777 1.222 -.423 .271
Negative/Positive 4.712 1.267 -.368 .274
Cronbach Alpha .9472


Purchase Intention
Unlikely/Likely 4.073 1.687 -.163 -.955
Probable/ Improbable 4.099 1.551 -.245 -.608
Possible/ Impossible 4.743 1.473 -.396 -.137
Cronbach Alpha .9091












Ownership and Familiarity of New Technologies

In terms of ownership of new technologies (Table 6-3), a mobile phone is owned

by the largest respondents, among the new technologies measured in this study. 153

respondents personally own a mobile phone. Secondly, 135 respondents have a DVD

player. A digital camera is owned by only 57 respondents (29.8%). A Digital Video

Recorder (DVR), which was deleted from the results of analysis because of normality, is

owned by just two respondents.

Table 6-3: Ownership of new technologies

Mobile Video DD Digital Digital Broadband DVR
DVD Broadband DVR
Phone Game Camera Cable

Owner 153 61 135 57 74 127 2

No owner 37 125 52 131 113 61 189

% owner 80.5% 32.6% 72.2% 30.3% 39.6% 67.6% 1.1%


With respect to familiarity with new technologies, regardless of ownership,

respondents feel familiar with new technologies except that of a DVR. Table 6-2 presents

that all means relative to familiarity with new technologies are more than 4.8. Because

"4" means "neutral" and "7" means "very familiar," it can be verified that a mobile phone,

a video game console, a DVD player, a digital camera, digital cable/satellite TV, and

broadband are communication devices with which respondents feel familiar.

The Results of Hypotheses and Research Questions

Some hypotheses were supported and other hypotheses were not supported. Table 6-4

presents the results of hypotheses and research questions.













Table 6-4: Results of Hypotheses and Research Questions

Hypotheses
H1.1: Relative advantage will be positively related to attitude toward PDA.

H1.2: Compatibility will be positively related to attitude toward PDA.
H1.3: Complexity will be negatively related attitude toward PDA.
H1.4: Trialability will be positively related to attitude toward PDA.
H2: The number of ownership of new technologies will be positively related to
attitude toward PDA.
H3: Personal innovativeness will be positively related to attitude toward PDA.
H4.1: Attitude toward PDA will be positively related to purchase intention.
H4.2: Perceived uncertainty will be negatively related to purchase intention.


Results
Supported
Supported
Not supported

Supported

Not supported

Supported
Supported
Not supported


Research Questions Results
S- ~Attitude: compatibility > personal
RQ1: Which factors influence attitude and perceived At : ci
innovativeness > trialability > relative
uncertainty toward PDA at the persuasion stage of the avante
Innovation-Decision process and is there the relative adva
. a i- Perceived uncertainty: relative advantage >
influence among variables? complex
complexity

RQ2: Which factors will be useful in discriminating two attitude, relative advantage, compatibility,
purchase intention group (High/Low)? trialability, and personal innovativeness

-Attitude: mobile phone, video games, digital
RQ3: Which functions of PDA are related to attitude camera, and global positioning system
toward PDA and purchase intention? Purchase intention: MP3 player, digital
camera, and global positioning system


Factors Influencing Attitude toward Personal Digital Assistant (PDA)

The first research question and six hypotheses were developed to investigate the

relationship between independent variables (relative advantage, compatibility,

complexity, trialability, ownership of new technologies, and personal innovativeness) and

attitude toward PDA. Multiple regression and stepwise regression were performed.

Stepwise regression is one of the ways to compute Ordinary Least Squares (OLS)

(Garson, 2003).


The results of multiple regression analysis of attitude toward PDA on relative

advantage, compatibility, complexity, trialability, ownership of new technology, and












personal innovativeness were presented in Table 6-5, with tolerance and Variance-

Inflation Factor (VIF) provided as supporting information. Multicollinearity means the

linear relation of independent variables, so high multicollinearity may make assessment

of the unique role of independent variable difficult. In order to measure mulitcollinearity,

tolerance or VIF is used (Garson, 2003). According to Garson (2003), generally, if

tolerance is less than .20, a multicollinearity problem is indicated. In other words, the

closer to 0 tolerance is, the higher multicollinearity is. In case of VIF, which is the

reciprocal of tolerance, a value above 5 indicates a problem with multicollinearity. SPSS

suggests another method, called condition indices, to assess if there is too much

multicollinearity in the model (Garson, 2003). If a condition index is over 30, there is a

serious collinearity problem. A condition index over 15 suggests possible collinearity

problems (Garson, 2003). Table 6-5 shows that all independent variables tolerance levels

fall between .590 and .878 and VIF scores range from 1.138 to 1.694. In case of a

condition index (see Table 6-6), there is no factor that exceeds 30. On the basis of this

data, multicollinearity was not found in this analysis.

The results of multiple regression analysis in Table 6-5 present that this regression

equation is significant (F=20.553, p < .001). For this equation, 40.1 percent of the

variance is statistically explained by the independents variables. Among the independent

variables, relative advantage (P =.153), compatibility (3 = .364), trialability (P = .147),

and personal innovativeness (P = .217) were significantly (p < .05) and positively related

to attitude toward PDA. Complexity and ownership of new technologies had no

significant impact on attitude toward PDA.

Based on the results of multiple regression analysis, hypotheses were examined.













H1.1, H1.2, H1.3, and H1.4 stated that perceived attributes of PDA are significantly

related to attitude toward PDA. The results of regression revealed that relative advantage,

compatibility, and trialability had a significant positive relationship with attitude.

Therefore, H1.1, H1.2, and H1.4 are supported, while H1.3 is not supported (see Table 6-

4).

H2 and H3 predicted that ownership of new technologies and personal

innovativeness would be positively related to attitude toward PDA, respectively. The

results from multiple regression analysis supported H3 with a significant positive relation,

but H2 was not supported (see Table 6-5).

Table 6-5: Multiple regression analysis of attitude toward PDA on relative advantage,
compatibility, complexity, trialability, ownership of new technology, and personal
innovativeness.
Dependent variable: Attitude toward PDA
Unstandarized Standardized
Variables Coefficients Coefficients t Sig. Tolerance VIF
B SD Beta
Relative Advantage .154 .069 .153 2.232 .027 .694 1.440
Compatibility .331 .068 .364 4.897 .000 .590 1.694
Complexity -.011 .036 -.019 -.313 .755 .878 1.138
Trialability .201 .088 .147 2.293 .023 .795 1.258
Ownership of new -.041 .147 -.017 -.280 .780 .838 1.193
technologies
Innovativeness .204 .060 .217 3.386 .001 .794 1.259


R: .633
R2: .401
Adjusted R2: .382
F-ratio: 20.553 (p< .001)













Table 6-6: Collinearity diagnostics of multiple regression analysis of attitude and
perceived uncertainty toward PDA on relative advantage, compatibility, complexity,
trialability, ownership of new technology, and personal innovativeness
Dimension Eigenvalue Condition Index
1 6.506 1.000
2 .197 5.753
3 .144 6.726
4 .05524 10.852
5 .04237 12.391
6 .03436 13.760
7 .02175 17.295

Stepwise regression was performed to examine a model with variables (relative

advantage, compatibility, trialability, and personal innovativeness) that contributed

significantly to attitude toward PDA from the previous multiple regression and to

investigate their relative influence on attitude. Table 6-7 presents the results of stepwise

regression. They showed unique and substantial contribution in this study. The model

obtained from this stepwise regression procedure has four variables that statistically

significantly explained 40.1 percent of the variance in attitude toward PDA.

Compatibility had the strongest influence in this equation, while relative advantage,

trialability, and personal innovativeness had weak influence on attitude toward PDA.

Since all tolerance values are not below .20 and all VIF are not above 5, there may be no

serious multicollinearity problem in this model.

Table 6-7: Stepwise multiple regression of attitude toward PDA on relative advantage,
compatibility, trialability, and personal innovativeness
Model 1
Variables R R2 Adj. R2 R2 change F change B Beta t Tolerance VIF
.565 .319 .315 .319 88.492
Compatibility .514 .565 10.76 1.00 1.00
Model 2
Variables R R2 Adj. R2 R2 change F change B Beta t Tolerance VIF
.600 .360 .354 .042 12.213














Table 6-7. Continued
Variables R R2 Adj. R2 R2 change F change B Beta t Tolerance VIF
Compatibility .446 .490 7.875 .880 1.136
Innovativeness .204 .058 3.495 .880 1.136


Model 3
Variables R R2 Adj. R2 R2 change F change B Beta t Tolerance VIF
.620 .384 .374 .024 7.232
Compatibility .402 .441 6.929 .811 1.233
Innovativeness .200 .213 3.483 .880 1.137
Trialability .222 .162 2.689 .906 1.104


Model 4
Variables R R2 Adj. R2 R2 change F change B Beta t Tolerance VIF
.633 .401 .388 .016 5.103
Compatibility .337 .370 5.260 .650 1.540
Innovativeness .199 .212 3.496 .880 1.137
Trialability .190 .138 2.287 .879 1.138
Relative advantage .153 .152 2.259 .710 1.408
*all p <.05


Factors Influencing Perceived Uncertainty toward Personal Digital Assistant (PDA)

In order to examine the relationship between perceived uncertainty and the

independent variables (relative advantage, compatibility, complexity, trialability,

ownership of new technologies, and personal innovativeness), multiple regression and

stepwise regression were used. Table 6-8 shows the results of multiple regression

analysis of perceived uncertainty toward PDA on the independent variables. Results

revealed that this equation is significant (F=5.633, p < .001), but only 15.5 percent of the

variation in perceived uncertainty toward PDA is explained. Contrary to the previous

equation for attitude, there are only two variables which are significantly (p < .05) related

to perceived uncertainty toward PDA: relative advantage (P = -.317) and complexity (P

=.153). Relative advantage is negatively related to perceived uncertainty and complexity













is positively related to perceived uncertainty. Because tolerance values ranged from .590

to .878 and VIF values ranged from 1.138 to 1.694, no serious multicollinearity problem

was found. In addition, a condition index didn't exceed 30 (see table 6-5).

Table 6-8: Multiple regression analysis of perceived uncertainty toward PDA on relative
advantage, compatibility, complexity, trialability, ownership of new technology, and
personal innovativeness
Dependent variable: Perceived uncertainty toward PDA
Unstandarized Standardized
Variables Coefficients Coefficients t Sig. Tolerance VIF
B SD Beta
Relative Advantage -.308 .079 -.317 -3.903 .000 .694 1.440
Compatibility -.047 .077 -.053 -.602 .548 .590 1.694
Complexity .08669 .041 .153 2.122 .035 .878 1.138
Trialability .116 .100 .088 1.155 .250 .795 1.258
Ownership of new .07093 .169 .031 .420 .675 .838 1.193
technologies
Innovativeness -.058 .069 -.064 -.841 .402 .794 1.259


R: .394
R2: .155
Adjusted R2: .128
F-ratio: 5.633 (p< .001)



Stepwise regression was performed to examine a model with variables (relative

advantage and complexity) that significantly contributed to perceived uncertainty toward

PDA from the previous multiple regression and to investigate the relative influence of

two variables (see Table 6-9). The obtained model from stepwise regression explains 14.3

percent of the variation in perceived uncertainty. Relative advantage had the strongest

influence on perceived uncertainty. In addition, there was no serious multicollinearity

problem in this model.













Table 6-9: Stepwise multiple regression of perceived uncertainty toward PDA on relative
advantage and complexity
Model 1
Variables R R2 Adj. R2 R2 change F change B Beta t Tolerance VIF
.330 .109 .104 .109 23.113
Relative Advantage .-.32 -.330 -4.808 1.00 1.00


Model 2
Variables R R2 Adj. R2 R2 change F change B Beta t Tolerance VIF
.378 .143 .134 .034 7.486
Relative Advantage .-.32 -.330 -4.887 1.00 1.00
Complexity .104 .185 2.736 1.00 1.00


*all p <.05


Relationship between the Intervening Variables (Attitude and Perceived
Uncertainty toward PDA) and Purchase Intention

The set of fourth hypotheses addressed whether there are significant relationships

between intervening variables (attitude and perceived uncertainty) and purchase intention.

Multiple regression was also employed to measure the relationship. The prediction

equation for purchase intention explains 43.0 percent of the variance in purchase

intention (see Table 6-10). Purchase intention is significantly predicted by only attitude

toward PDA.

H4.1 and H4.2 predicted that attitude toward PDA would be positively related to

purchase intention and perceived uncertainty would be negatively related to purchase

intention. The results of multiple regression analysis show that attitude has a significant

effect for predicting purchase intention, while perceived uncertainty is not a significant

predictor. So, H4.1 was supported, H4.2 was not supported (see Table 6-4).













Table 6-10: Multiple regression analysis of purchase intention on attitude and perceived
uncertainty toward PDA
Dependent variable: Purchase intention
Unstandarized Standardized
t
Variables Coefficients Coefficients Sig. Variables VIF
B SD Beta
Attitude .774 .071 .634 10.941 .000 .904 1.106
Perceived risks -.0790 .073 -.062 -1.076 .283 .904 1.106


R: .655
R2: .430
Adjusted R2: .424
F-ratio: 70.886 (significant: .000)


Stepwise regression results are presented in Table 6-11, with only attitude as an

independent variable. This regression model predicting purchase intention results in 42.6

percent of variance explained.

Table 6-11: Regression of purchase intention on attitude toward PDA
Model 1

Variables R R2 Adj. R2 R2 change F change B Beta t Tolerance VIF

.653 .426 .423 .426 140.457

Attitude .797 .653 11.85 1.00 1.00


*all p <.05


Factors Discriminating Two Purchasing Intention Group (High/Low)

The second research question addressed which factors are useful in discriminating

among purchase intention groups. In order to examine this research question,

discriminant analysis was employed. All variables (relative advantage, compatibility,

complexity, trialability, ownership of new technologies, personal innovativeness,

perceived uncertainty, and attitude toward PDA) served as the independent variables, and












two purchase intention groups (high and low) were used as the dependent variables in

discriminant analysis. Questionnaires about purchase intention were composed of three

items, which adapt a seven-point scale. In other words, a score of 21 indicates the highest

purchase intention and a score of 3 presents the lowest purchase intention. The mean of

purchase intention scores was 12.9, so this study classified respondents whose scores

were over 12.9, into the high purchase intention group (n = 88), and respondents whose

scores were under 12.9, into the low purchase intention group (n = 103).

Table 6-12 reveals the result of Wilks's lambda of each variable in discriminant

analysis. The larger the Wilks's lambda, the less important the independent variables to

the discriminant analysis (Garson, 2003). Wilks's lambdas of relative advantage,

compatibility, trialability, innovativeness, and attitude are significant by the F test. This

study dropped complexity, ownership of new technologies, and perceived uncertainty

because they were not significant.

Homogeneity of covariance matrices between groups is the assumption of

discriminant analysis. This assumption is measured by Box's M test. On the basis of

Box's M test, a null hypothesis of equal population covariance matrices was rejected (see

Table 6-12). This study has only one discriminant function because there are only two

categories in the dependent variable. The eigenvalue of discriminant function is

statistically significant (p < .001).

Table 6-11 also presents canonical discriminant function coefficients. Standard

canonical discriminant function coefficients indicate the relative importance of the

independent variables in explaining the dependent variable (Garson, 2003). Attitude is

the strongest discriminating factor among the independent variables. Compared to














attitude, the importance of other variables is very weak. In terms of the correlations of

each variable with discriminant function, attitude has the strongest correlation with

discriminant function.

Table 6-12: Discriminant analysis between high-purchase intention group and low-
purchase intention group
Tests of equability of group means


Means


Low intention


High intention


Wilks'
Lambda


F Sig.


Relative advantage
Compatibility
Complexity
Trialability
Ownership of new technology
Innovativeness
Perceived uncertainty
Attitude


Box's M test


Box'sM

75.926


11.7136
11.1893
13.3689
9.9709
3.2233
13.0680
12.9029
12.3204


13.2386
14.2273
12.5227
10.9773
3.4432
14.9318
11.8636
16.4545


F
Approx. dfl df2 sig.
2.014 36 114249.6 .000


Eigenvaules
Function
1


Wilks' Lambda
Function
1


eigenvalue
.536


Wilks' Lambda
.651


% of variance
100.0


Cumulative % Canonical correlation
100.0 .591


Chi-square
79.454


Canonical discriminant function coefficients


Standardized Canonical
Discriminant Function
Coefficients


Unstandarized Canonical
Discriminant Function
Coefficients


Structure Matrix


Relative advantage

Compatibility

Complexity

Trialability


-.047


-.096


.007 .003


9.255
33.704
.925
7.380
1.017
12.218
4.449
96.396













Table 6-12. Continued
Standardized Canonical Unstandarized Canonical
Discriminant Function Discriminant Function Structure Matrix
Coefficients Coefficients
Ownership of new technology -.057 -.038 .100
Innovativeness .040 .011 .347
Perceived risks .047 .014 -.209
Attitude .935 .322 .975


Classification results of discriminant analysis are presented in Table 6-13. 75.9

percent of original grouped cases are correctly classified by discriminant function.

In order to measure the significance of hit rate, t-ratio test was employed, to was

calculated by the equation, and it was 5.40. Therefore, the hit rate is statistically

significant (p < .01).

Table 6-13: Classification results of discriminant analysis
Purchase Intention Predicted Group Membership Total
Low High
Count Low 74 29 103
Count
High 17 71 88
Low 71.8 28.2 100.0
High 19.3 80.7 100.0

Relation between Functions of PDA and Attitude and Purchase Intention

The third research question was developed to explore the relationships among

each function of PDA, attitude, and purchase intention. Pearson correlation was

performed. Questions about the perceived importance of functions of PDA used a seven-

point scale, with "7" meaning that respondents consider that function extremely

important. Respondents answered that Internet/Email (mean=5.517) and mobile phone

(mean=5.942) are important functions in PDA, while Global Positioning System (GPS)













(mean=3.497) and video games (mean=2.670) were not perceived as important functions

(see Table 6-14).

Table 6-14: Descriptive profile of perceived importance of functions of PDA
Mean Median SD Skewness Kurtosis
Mobile Phone 5.517 6.00 1.994 -.839 -.550
Intemet/Email 5.942 6.00 1.294 -1.422 2.220
Video games 2.670 2.00 1.569 .733 -.207
MP3 player 4.016 4.00 1.761 -.124 -.967
Wordprocessing 5.162 6.00 1.616 -.807 -.080
Digital Camera 4.340 4.00 1.749 -.270 -.892
GPS 3.497 4.00 1.954 .122 -1.206

The correlation matrix is presented in Table 6-15. In terms of relationship

between each function of PDA and attitude toward PDA, mobile phone, video games,

digital camera, and global positioning system are significantly correlated to attitude.

Interestingly, Internet/Email, which is perceived as a relatively important function, didn't

have a significant relation with attitude, and video games, which was perceived to be the

least important function, is significantly and positively related to attitude toward PDA.

With respect to the relationship between each function and purchase intention, MP3

player, digital camera, and global positioning system are significantly correlated to

purchase intention, while mobile phone, Internet/Email, video games, and

wordprocessing/spreadsheet are not significantly related to purchase intention. As in the

result of the relationship to attitude, Internet/Email was not significantly related to

purchase intention.















Table 6-15: Pearson correlation between perceived importance of PDA functions and
attitude and purchase intention
Purchase
Variables Fund Func2 Func3 Func4 Func5 Func6 Func7 Attitude Pur
intention
Funcition: 1
1.000
Mobile Phone
Function 2:
Function .354** 1.000
Internet/Email
Function 3:
Function 3: .064 .022 1.000
Video games
Function 4:
Functon 4: .029 .067 .451** 1.000
MP3 player
Function 5:
Function 5: .179* .394** -.002 .131 1.000
Wordprocessing
Function 6:
Function 6: .329** .141 .179* .359** .065 1.000
Digital Camera
Function 7:
Function 7: -.036 -.072 .289** .276** -.113 .357** 1.000
GPS
Attitude
Attitude .187** .071 .180* .096 .030 .246** .178* 1.000

Purchase
Purchase .120 -.001 .123 .180* .059 .343** .260* .653** 1.000
intention

*: p .05, **: p .01


This study classified the dimensions of functions with factor analysis. With


eigenvalues of 1.00 or higher as the criteria, two factors were yielded by explaining


52.721 percent of the variance (see Table 6-16). Factor 1 consists of video games, MP3


player, digital camera, and global positioning system. Factor 2 is composed of mobile


phone, Internet/Email, and wordprocessing/spreadsheet. Factor 1 has more entertainment


aspects than factor 2. On the other hand, factor 2 has more information aspects than


factor 1. So, this study refers factor 1 as an entertainment factor and factor 2 as an


information factor. In addition, entertainment has more significant functions than


information, in terms of the relationship with attitude and purchase intention, even though


respondents thought that functions related to entertainment are less important than those


related to information.













Table 6-16: Factor analysis of perceived importance of PDA functions
Items Mean SD Factor 1 Factor 2
Importance of PDA functions
Mobile Phone 5.157 1.994 .127 .681
Internet/E-mail 5.942 1.295 -.007 .797
Wordprocessing/Spreadsheet 5.162 1.606 -.044 .690
Table 6-16. Continued
Items Mean SD Factor 1 Factor 2
Importance of PDA functions
Video games 2.670 1.570 .692 -.023
MP 3 and movie file player 4.016 1.761 .745 .108
Digital camera 4.340 1.749 .648 .314
Global Positioning System 3.497 1.954 .700 -.204
Table 6-17. Continued
Items Mean SD Factor 1 Factor 2
Eigenvalue 1.962 1.728
Percent of variance explained 28.034% 24.687%
Cumulative percent 28.034% 52.721%
Cronbach Alpha .6490 .5391


Demographic Characteristic of Purchase Intention Group

Differences in demographic characteristics between the high purchase intention

group and the low intention group were investigated to reveal if there were distinct

characteristics for each group. t-test results are shown in Table 6-17. Results indicated

that there was little demographic difference between the high purchase group and the low

purchase intention group. In the case of age, t-value was -.846 (p > .05) and the t-value of

gender was -.948 (p > .05).

Table 6-17: t-test Results Comparing between Purchase Intention Group
Demographics Low purchase intention group High purchase intention group t-Value
Demographics t-Value
Mean SD Mean SD
Age 22.388 4.6510 22.909 3.7035 -.846 (p = .399)
Gender 1.592 .4938 1.659 .4767 -.948 (p = .345)

Gender: Male = 1; Female = 2.
Purchase intention: Low = 0; High = 1.














Final Parsimonious Model

This study examined the relationships among variables. Relative advantage,

compatibility, trialability, and personal innovativeness were the significant factors that

affected attitude toward PDA. In addition, attitude was the significant predictor as to

purchase intention. Based on these results, this current study created a final parsimonious

model like the following:





Relative
Advantage
.152*


Compatibility .370**
.653**


Trialability Attitude Purchase Intention
C ^Toward PDA
.212**

Personal
Innovativenes


Figure 6-1: Final Parsimonious Model

















CHAPTER 7
DISCUSSION

This chapter consists of four parts. The first part will present a brief review of the

theoretical model of this current study. The second part will summarize the findings from

the statistics analysis and the results of hypotheses and research questions. The third part

will discuss the implications of this study. In the fourth part, limitations of this study will

be presented. Finally, conclusion will be addressed in the last part.

Review of the Present Study

This current study had a goal. This study wanted to examine the impact and the

relative influence of perceived characteristics of innovation, ownership of new

technologies, personal innovativeness, and functions of PDA in exploring attitude,

perceived uncertainty, and adoption of PDA. Toward this goal, diffusion theory was used

as the conceptual framework and theoretical paradigm for understanding the adoption of

PDA. Rogers (1995) argued that during the process of the adoption of new technology,

an individual (or other decision-making unit), who passed the knowledge stage of an

innovation, forms his/her attitude toward the innovation, decides to adopt or reject,

implements, uses the new idea, and confirms his/her decision. Following from this focus,

this present study focused on two stages (persuasion stage and decision stage) of the

Innovation-Decision Process of Diffusion theory to examine which variables affect

attitude and perceived uncertainty toward PDA, and whether PDA adoption can be

explained by variables.












According to Rogers (1995), an individual forms a favorable or unfavorable

attitude toward the innovation and seeks information to decrease a perceived uncertainty

about the innovation at the persuasion stage, and decides to adopt or reject the innovation

at the decision stage. This study applied Rogers's theory to predict and examine the

factors influencing adoption of PDA. The independent variables were selected on the

basis of previous adoption researches: relative advantage, compatibility, complexity,

trialability, ownership of new technologies, and personal innovativeness.

There are three research questions and eight hypotheses in the present study.

Hypotheses and research questions presented the relationship among the variables during

the adoption process.

A total of 218 undergraduate and graduate students from a large southeastern

university participated in the survey for this current study and 191 sets of valid data were

acquired.

Summary of Results of Research Questions and Hypotheses

This section will summarize findings and results of each of eight hypotheses and

four research questions.

Hypotheses

Hypotheses 1 predicted the relationship between perceived attributes of PDA and

attitude toward PDA.

HI. 1: Relative advantage will be positively related to attitude toward PDA.

H1.2: Compatibility will be positively related to attitude toward PDA.

HI. 3: Complexity will be negatively related to attitude toward PDA.

HI. 4: Trialability will be positively related to attitude toward PDA.












These hypotheses were created based on diffusion theory. Rogers (1995) asserted

that perceived attributes of innovations are significantly associated with the rate of

adoption and are significant determinants to influence forming an attitude toward the

innovation. Perceived attributes of the innovation have been used as a predictor to explain

adoption of innovations (LaRose and Atkin, 1992; Eastlick, 1993 & 1996; Lin, 1998;

Parthasarathy et al., 1998; Du, 1999). The findings of previous studies indicated that

these variables (relative advantage, compatibility, complexity, trialability, and

observability) are significant factors in predicting the adoption of innovations. Their

findings were consistent with Rogers's assertion that perceived attributes of innovations

play an important role in forming an attitude (Rogers, 1995).

Some results of H1 were consistent with the findings of previous researches and

others were not consistent. In most previous researches, relative advantage was a

significant predictor of adoption of innovations (Ostlund, 1974; Eastlick, 1993 & 1996;

Parthasarathy and Bhattacherjee, 1998; Du, 1999; Sund et al., 2001). As this study

expected, relative advantage was a significant predictor of attitude toward PDA in the

current study. This study also found that compatibility and trialability are significantly

and positively related to attitude. Compatibility (Ostlund, 1974; Eastlick, 1993 & 1996;

Rogers, 1995; Lin, 1998; Parthasarathy and Bhattacherjee, 1998; Du, 1999; Sund et al.,

2001) and trialability (Ostlund, 1974; Holak, 1988; Eastlick, 1996; Rogers, 1995; Sund et

al., 2001) were factors influencing adoption of innovations in other researches. In terms

of complexity, this study didn't support that complexity is significantly associated with

attitude toward PDA. Ostlund (1974), Du (1999), and Sund et al. contended that

complexity is one of the factors predicting an adoption, while Eastlick (1993) and Lin












(1998) found that there was no significant relationship between complexity and adoption.

As long as an individual's technology apprehension is outweighed by the perceived

advantage of innovations, complexity is not a serious concern for consumers (Lin, 1998).

Results of this study mean that relative advantage and compatibility may be a strong

predictor of adoption, while complexity may not be as strong a predictor as it appears to

be in diffusion theory.

H2: The number of ownership of new technologies (Mobile Phone, Video game
player, DVD player, Digital camera, Digital Cable/Satellite TV, Broadband, and
Personal Video Recorder) will be positively related to attitude toward PDA.
H2 expressed that ownership of new technologies would be significantly and

positively related to attitude toward PDA. In order to measure ownership of new

technologies, this study asked respondents about ownership of seven technologies. Lin

(1998) found that communication technology ownership is an important factor in

predicting the personal computer adoption rate and ownership of other communication

technology devices predicted PC adoption. Dupagne (1999) also contended that the

number of home entertainment products was positively related to adoption of HDTV. The

early adopters of personal computers owned other high technology products (Danko and

MacLachlan, 1983). Therefore, it was expected that the more new technologies are

owned, the more favorable attitude the subject would have. However, there was no

significant effect of ownership of new technologies on attitude toward PDA. This result

was not consistent with some past researches (Lin, 1998; Dupagne, 1999). Next

paragraph will explain the reason why the result of this study were not consistent with

previous studies.












The notion of functional similarity and need compatibility was suggested by

several researchers. Atkin (1993) contended that the adoption of innovations is related to

functionally similar media or technologies. Perse and Courtright (1993) asserted that the

adoption of a functionally similar product has an impact on the adoption of new

technologies. According to Henke and Donohue (1989), new technological advancement

tends to affect the way consumers reorganize their view about the established media. In

order to research functional similarity and functional displacement, the number of new

technologies owned as well as exposure time to media, familiarity of established media,

and other variables have been used as predictors. Even though ownership of new

technologies was a significant predictor in previous researches, this study didn't support

that ownership of new technologies would affect attitude toward PDA. Ownership of new

technologies alone may not explain adoption of innovations and functional substitution.

H3: Personal innovativeness will be positively related to attitude toward PDA.

Hypothesis 3 presented that personal innovativeness would be a significant

predictor of attitude toward PDA. In adoption studies, innovativeness has been an

important variable to examine adoption. As in previous studies that suggested that

personal innovativeness would lead to a more positive attitude (Venkatraman, 1991;

Manning et al., 1995; Lin, 1998; Lin and Jeffres, 1998; Donthu and Garcia, 1999; Du,

1999; Citrin et al., 2000; Im et al., 2003), it was shown that personal innovativeness

significantly influenced attitude toward PDA. In other words, consumers who have a

higher level of innovativeness with innovations are more likely to have positive attitude

toward PDA.














H4.1: Attitude toward PDA will be positively related to purchase intention.
H4.2: Perceived uncertainty will be negatively related to purchase intention.
Hypothesis 4 predicted that attitude would positively affect purchase intention and

perceived uncertainty would negatively influence purchase intention at the decision stage.

A person's attitude affects behavioral intention, which is a good forecaster of actual

behavior (Fishbein and Ajzen, 1975; Ajzen, 1985). Previous researches found that

attitude has a strong correlation with intent to purchase (Lutz et al., 1983; Brown and

Stayman, 1992; Rogers, 1995; Ko, 2002). Based on previous studies, this present study

expected that attitude would have a positively significant relation with purchase intention.

As expected, there was significant relationship between attitude and purchase intention.

Respondents were asked to rate their attitude (unfavorable/favorable, bad/good, and

negative/positive) toward PDA, and the mean of attitude score was 4.74. This means that

subjects of this study, by and large, have favorable attitude toward PDA and their

attitudes were significantly related to purchase intention.

Contrary to attitude, perceived risks negatively affect adoption of innovations

(Cox and Rich, 1964; Dowling and Staelin, 1994; Wood and Scheer, 1996; Mitchell,

1998; Pope, 1998; Ko, 2001). According to Cox and Rich (1964), perceived risk is a

function of the amount at stake in the purchase intention. Pope (1998) contended that

perceived risk was related to online purchase of sport products. Therefore, this current

study expected that there would be a significant relationship between perceived risks and

purchase intention. Contrary to the expectation, perceived uncertainty had no significant

relation with purchase intention (p > .05).












Research Questions
RQ1: Which factors influence attitude and perceived uncertainty toward Personal
Digital Assistant (PDA) at the persuasion stage of the Innovation-Decision
process and which variables have the relatively strong or weak influence on
attitude and perceived uncertainty?

Following from hypotheses 1, hypotheses 2, and hypotheses 3, research question

1 investigated which variables affect attitude and perceived uncertainty toward PDA and

whether there are the relative weights of the significant variables on attitude and

perceived uncertainty. The results present that relative advantage, compatibility,

trialability, and personal innovativeness positively affect attitude, and perceived

uncertainty is affected by relative advantage and complexity. The strongest variable in

predicting attitude toward PDA among significant independent variables was

compatibility, which explained 31.9 percent of the variation in attitude toward PDA,

while relative advantage, which statistically explained 1.6 percent of the variation, was

the weakest variable. Contrary to the result of attitude, among the variables which are

significantly related to perceived uncertainty, relative advantage was the strongest

variable, but it could explain only 10.9 percent of the variation in perceived uncertainty

toward PDA. Only relative advantage had a significant impact on both attitude and

perceived uncertainty, while ownership of new technologies was not significantly related

to both attitude and perceived uncertainty.

RQ2: Which factors will be useful for discriminating two purchase intention
groups (lhighl /11)?
Research question 2 examined which factors would be useful in discriminating

between the high purchase intention group and the low intention group. According to the

results of discriminant analysis, discriminant function classified correctly 75.9 percent of

the original grouped cases. As expected, variables (relative advantage, compatibility,












trialability, innovativeness, and attitude), which were found as significant predictors in

hypotheses, were significant discriminant factors. Among the significant factors, attitude

was the strongest discriminating factor, and trialability was the weakest discriminating

factor. In addition, relative advantage, compatibility, and personal innovativeness

significantly differentiated the high purchase intention group from the low purchase

intention group.

RQ3: Which functions ofPDA are related with attitude toward PDA and purchase
intention?
Research question 3 intended to examine if there was any significant relationship

between each function of PDA, and attitude and purchase intention. This study expected

that each function of PDA would be related to attitude toward PDA and purchase

intention. The results of Pearson correlation analysis revealed that attitude toward PDA

was significantly correlated to mobile phone, video games, digital camera, and global

positioning system. In addition, MP3 player, digital camera, and global positioning

system had a significant correlation with purchase intention. Functions of PDA were

classified with two factors by factor analysis. Factor 1 is composed of video games, MP3

player, digital camera, and global positioning system. Factor 2 consists mobile phone,

Internet/Email, and wordprocessing/spreadsheet. Subjects perceived functions of factor 2

as more important function of PDA than those of factor 1(see Table 6-14). A PDA is still

likely to be perceived as a communication device for information rather than an

entertainment. Digital camera and global positioning system, which are factor 1, were

significantly correlated with both attitude and purchase intention, even though they were

not perceived as an important function by respondents. Except two functions, mobile

phone and video games were significantly correlated with attitude and MP3 player was












significantly correlated with purchase intention. Among functions of factor 2, only

mobile phone had a significant relationship with attitude. It means that perceived

importance for functions of PDA is not linked with attitude and purchase intention.

Regardless of perceived importance, more significant correlations were found from

variables of factor 1. This study provides an exploratory attempt to figure out the

difference among each function of PDA.

This study examined only the relationship between attitude and purchase intention,

and functions of PDA. Only perceived importance for each function of PDA is not

sufficient to explain attitude and purchase intention. In order to effectively examine the

relations between functions of PDA and attitude or purchase intention, other factors such

as motivations or gratification linked with each function of PDA should be investigated.

Implications

This section consists of three parts: theoretical implication, practical implication

and future research.

Theoretical Implications

The theoretical background of this present study is diffusion of innovations. In

particular, this study focuses on the innovation-decision process of diffusion of

innovations. Rogers (1995) proposed that "such persuasion will lead to a subsequent

change in overt behavior (that is, adoption or rejection) consistent with the attitude held,

but in many cases attitudes and actions are quite disparate" (p. 169). In other words,

attitude is a significant predictor for adoption, but the relationship between attitude and

adoption is not strong. From this theoretical perspective, this study investigated the effect

of attitude at the decision stage. The result was little different from Rogers's (1995)












assertation. In this study, attitude was a strong significant predictor for purchase intention

(R2=.426). Based on this result, this present study may offer the notion that attitude has a

strong impact on adoption of innovations.

Additionally, this study partially supports the effect of perceived attributes of

innovations in the persuasion stage. According to Rogers (1995), perceived attributes of

an innovation, such as its relative advantage, compatibility, complexity, trialability, and

observability, play an important role at the persuasion stage. This study examined

whether perceived attributes of PDA have a significant relationship with attitude toward

PDA. Consistent with previous findings of adoption researches, this study presents that

perceived attributes, except complexity, are significantly related to attitude. Several

previous researches also found that complexity was not a significant predictor of adoption

(Eastlick, 1993; Lin, 1998; Parthasarathy and Bhattacherjee, 1998). The rapidly changing

media environment provides consumers much opportunity to experience a variety of new

technologies (e.g., Interactive Television, Satellite Radio, etc.). Under this circumstance,

complexity may not be a significant predictor of adoption any longer. Lin (1998) asserted

that "it is apparent that, as long as the perceived advantage of adopting outweighs one's

technology apprehension, perceived complexity is of no real concern" (p. 108). Therefore,

this study may present the notion that complexity is not a significant factor in predicting

adoption of innovations.

Rogers (1995) said that "at the persuasion stage, and especially at the decision

stage, an individual is motivated to seek innovation-evaluation information, the reduction

in uncertainty about an innovation's expected consequences" (p. 168). From this

perspective, this study intended to examine which factors are significantly related to












perceived uncertainty. According to Rogers (1995), in order to reduce uncertainty, an

individual wants to know the innovation's consequences and advantage/disadvantage.

Consistent with Rogers's assertation, the result of this current study shows that among the

independent variables, relative advantage was the strongest factor in predicting perceived

uncertainty. This study supports that an individual has some degree of uncertainty for

innovations, and knowledge about an innovation's expected consequences and

advantages can reduce individual's perceived uncertainty.

Finally, this study used trialability as an independent variable. A number of

adoption studies excluded trialability from perceived attributes of innovations for

particular reasons (e.g., Parthasarathy and Bhattacherjee, 1998), or trialability was not a

significant predictor of adoption in previous studies (e.g., Eastlick, 1993). Agarwal and

Prasad (1997), however, contended that trialability had a significant and important impact

on the acceptance of information technologies. Rogers (1995) asserted that trialability

will speed up the rate of adoption, and most individuals will not adopt an innovation

without trialability in order to know its relative advantage in their own situation. Even

though this study didn't examine if trialability affects adoption (purchase intention) of

PDA, this study shows that trialability has a significant affect in predicting attitude,

which is the strongest predictor of adoption of PDA in the current study. This current

study expects that under the rapidly changing media environment, consumer want to get

more opportunity to try out new technologies before they decide to purchase them. This

study may offer the notion that triability plays an important role in adoption research.












Practical Implications

The current study contributes to the practical field of marketing for a PDA.

First, this study examined whether attitude toward PDA leads to a purchase

intention. According to the results pertaining to causal relationship, there was a strong

relationship between attitude and purchase intention. In the basis of this result, marketers

should have the ability to influence consumers' attitude toward PDA, providing

information on the benefits associated with a PDA. Marketers should identify important

factors influencing attitude toward PDA, because attitude toward PDA has been found as

the foremost predictor of purchase intention.

Second, this study contributes to the practical field of development of PDA. In

this study, relative advantage, compatibility, and trialability appeared to be influential in

forming attitude toward PDA. In addition, perceived uncertainty was influenced by

relative advantage and complexity. Lin (2001) asserted that unless an innovation can

provide better content, superior technical benefits, and cost efficiency to consumers, an

innovation can hardly displace the traditional technologies. The main attributes of PDA

should present dimensions of relative advantage over other technologies (Atkin et al.,

1998). Developers and researchers should focus on identifying which aspects of PDA

lead to better benefits, compared to other technologies.

Third, several types of PDAs should be offered. This current study found that

Internet, video games, digital camera, and GPS were significantly correlated to attitude

toward PDA, and purchase intention is significantly related to MP3 player, digital camera,

and GPS. Functions related to entertainment have stronger impact on attitude and

purchase intention than functions related to information or others. Consumers are likely