Taking down the Walls of Agriculture

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Title:
Taking down the Walls of Agriculture Effect of Transparent Communication and Personal Relevance on Attitudes and Trust within the Elaboration Likelihood Model
Physical Description:
1 online resource (227 p.)
Language:
english
Creator:
Rumble, Joy N
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University of Florida
Place of Publication:
Gainesville, Fla.
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Thesis/Dissertation Information

Degree:
Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Agricultural Education and Communication
Committee Chair:
Irani, Tracy Anne
Committee Members:
Telg, Ricky W
Lamm, Alexa J
Israel, Glenn D
Treise, Deborah M

Subjects

Subjects / Keywords:
agriculture -- attitude -- communication -- elm -- transparency -- trust
Agricultural Education and Communication -- Dissertations, Academic -- UF
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Agricultural Education and Communication thesis, Ph.D.
Electronic Thesis or Dissertation
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )

Notes

Abstract:
As the industrialization of agriculture has occurred, the agricultural industry has struggled to adequately communicate with and address the concerns of consumers. There continues to be a need to communicate effectively with consumers about agriculture, and specifically livestock production. Literature suggests that transparent communication may improve communication effectiveness in the agricultural industry. However, limited quantitative research has assessed the effect of transparent communication. Therefore, the purpose of this study was to assess the effects of transparent communication and personal relevance, in a livestock production context, on the attitudes and trust of college students. The theoretical framework of ELM, trust, and transparency guided the study and research design. To test the research hypotheses, a 2 (transparent communication: high and low) X 2 (personal relevance: high and low) between subjects factorial experimental design was used. Subjects were randomly assigned to receive one of the experimental treatments. The subjects for this study included a convenience sample of 989 college students from a large southeastern university. Usable responses were received form 688 subjects (69.6%). The personal relevance and transparent communication manipulations were presented in a Facebook page for a fictitious poultry farm. The results of this study indicated that manipulated transparent communication, as well as the subject’s perceived transparency had a positive effect on attitudes and trust. However, personal relevance was not found to have a significant effect on attitudes and trust in this study. Findings of this study suggest that transparent communication may have the ability to reconnect agricultural consumers and producers through increased trust and more favorable attitudes toward the industry. Theoretically, this study provides support for connecting transparent communication and trust to ELM. In addition to motivation and ability factors, perceived transparency may have the ability to influence the cognitive processing of a transparent communication message. Further research of transparent communication in the agricultural industry and as part of ELM should be conducted.
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In the series University of Florida Digital Collections.
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Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
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This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis:
Thesis (Ph.D.)--University of Florida, 2013.
Local:
Adviser: Irani, Tracy Anne.
Statement of Responsibility:
by Joy Noel Goodwin.

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UFRGP
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Applicable rights reserved.
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UFE0045318:00001


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1 TAKING DOWN THE WALL S OF AGRICULTURE: EF FECT OF TRANSPARENT COMMUNICATION AND PERSONAL RELEVANCE ON ATTITUDES AND TRUST WITHIN THE ELABORATI ON LIKELIHOOD MODEL By JOY NOEL GOODWIN A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2013

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2 2013 Joy N. Goodwin

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3 To my fianc, Nathan R. Rumble

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4 ACKNOWLEDGMENTS The completion of my doc toral program would not have been possible without the love, support, and guidance I received from numerous individuals. First I would like to thank my adviser Dr. Tracy Irani for her continued guidance, support, and encouragement. I have valued the knowle dge that Dr. Irani has shared with me, and I look forward to continuing to learn from her in the future. I am grateful for the numerous opportunities and experiences that Dr. Irani has provided to me throughout my doctoral program. In addition, I would lik e to thank my committee: Dr. Glenn Israel, Dr. Ricky Telg, Dr. Alexa Lamm, and Dr. Debbie Treise. Each of my committee members has provided a unique expertise that has allowed me to learn and grow as an academic. I would like to thank my committee for chal lenging me and pushing me to think more deeply about theory, our discipline, and my dissertation. I greatly appreciate the feedback provided by Dr. Ed Osborne during the formation and drafting of my dissertation. His wisdom and insight guided my thoughts and productivity. I would also like to thank Dr. Osborne for the financial and professional support he has provided to me throughout my doctoral program. Without this support, I would not have been able to obtain my doctorate. I thank Dr. Emily (Rhoades) Bu ck for believing in me at the beginning of my graduate career and encouraging me to continue my graduate education. I would also like to thank all of my fellow office mates, graduate students and colleagues who have shared laughs, brainstorms, frustration s, and words of encouragement with me. I thank Lauri Baker and Quisto Settle for being great mentors who showed me the ropes, offered advice, and made sure that I was surviving graduate school. Gretchen Wulff deserves special thanks for her creation of t he logo

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5 used in my experimental stimulus. I also thank my various work out buddies throughout my doctoral program Christy Chiarelli, Kate Wilson, Mary Rodriguez, Jessica Holt, Cathy DiBen e detto, Laura Bernheim, Nicole Dodds, Rachel Divine, Avery Culbertso n Vivian a Giraud, Holly Cain, Sarah Burleson, McKenzie Smith, Kate Shoulders, Lauri, Quisto, Gretchen and the Sand Volleyball Crew Working out is more fun with friends and I always look ed forward to the stress relief that these workouts brought Thos e who have helped make Florida our home have also been integral during my doctoral program. I thank the Klientops for hosting us in their south Florida home for several holidays and long weekends. I also thank the Holt family for allowing me to escape to the pig farm and enjoy the farm life that I so dearly miss. Christy and Elio Chiarelli have offered a loving friendship that continually reminds me of Gods blessings. I thank Jessica Gouldthorpe for always offering a helping hand, being the native Gaines ville tour guide, and allowing us to give back. So many people, including those who have not been named, have impacted my life in these short three years and I am thankful for each of them. I thank my family for believing in me and supporting me. Moving away from home was one of the scariest things that I have ever done, and although I am sure they were scared for me and did not want to see me leave they continued to love me, support me, and give me strength. My father is my rock I thank him for everything he has done for me and for his unconditional love. He always knows what to say and continues to amaze me. I also thank my mother for providing her unconditional love My mother provides encouragement and is the calming voice in times of stress and anxiet y. My mother is my role model and I thank her for the example she has set in my life. I thank

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6 my younger brother for his friendship. I am proud of the young man that he has become in these last few years and I look forward to sharing with him as we both continue to grow and mature. I thank my grandm other, aunts, uncles, cousins and long time friends for their support, thoughtful cards, phone calls, and care packages. Finally, I would like to thank Nate Rumble for his continued love and support. I am thankf ul for his confidence in me and for the sacrifices he has made in order for me to succeed. Nate has been the keeper of our household and has always paid attention to things that I have neglected. I thank Nate for his ability to make me smile and to take my mind away from work. Nate adds youthfulness to my old soul and challenges me to partake in new experiences and visit new places. I could not ask for anyone better to share this adventure with and I look forward to our future adventures I also thank Nate s family for guiding him to become the man that he is today and for supporting us as we star t our life together.

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7 TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................................................................................. 4 LIST OF TABLES .......................................................................................................... 10 LIST OF FIGURES ........................................................................................................ 12 LIST OF DEFINITIONS ................................................................................................. 13 ABSTRACT ................................................................................................................... 15 CHAPTER 1 INTRO DUCTION .................................................................................................... 17 History of Agricultural Communications .................................................................. 20 Agriculture Media Coverage ................................................................................... 21 Consumers Perceptions of Modern Agriculture ...................................................... 23 Social Media ........................................................................................................... 24 Social Media and Transparency ....................................................................... 24 Facebook .......................................................................................................... 27 Millennial Generation .............................................................................................. 28 Millennial Generation and Social Media ........................................................... 29 Millennial Generation and Transparency .......................................................... 30 Transparency .......................................................................................................... 31 Transparency and the Agriculture Industry ............................................................. 32 Elaboration Likelihood Model .................................................................................. 34 Attitudes .................................................................................................................. 35 Trust ........................................................................................................................ 3 5 Significance, Problem Statement, Purpose, & Research Questions ....................... 36 Summary ................................................................................................................ 38 2 RELEVANT LITERATURE ...................................................................................... 39 Attitudes .................................................................................................................. 39 Persuasion .............................................................................................................. 42 Elaboration Likelihood Model .................................................................................. 46 Motivation to Process ....................................................................................... 49 Personal Relevance ......................................................................................... 49 Need for Cognition ........................................................................................... 50 Ability to Process .............................................................................................. 51 Prior Knowledge ............................................................................................... 52 Recent ELM Research ..................................................................................... 53 Research Involving ELM and Agriculture and Natural Resources .................... 56 Public Relations ...................................................................................................... 61

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8 Public Re lations and Ethics .............................................................................. 63 Public Relations and Social Media ................................................................... 64 Trust ........................................................................................................................ 64 Trust and Agriculture ............................................................................................... 66 Transparency .......................................................................................................... 69 Organizational Transparency ........................................................................... 70 Communicative Transparency .......................................................................... 70 Transparency and Agriculture .......................................................................... 72 Computer Mediated Transparency ................................................................... 73 Benefits of Transparency .................................................................................. 75 Challenges of Transparency ............................................................................. 76 Research Involving Transparency .................................................................... 77 Research Involving Transparency and Agriculture ........................................... 80 Conceptual Model of Transparency and Trust in ELM ............................................ 84 Summary ................................................................................................................ 85 3 RESEARCH DESIGN AND METHODS .................................................................. 88 Research Objectives and Hypotheses .................................................................... 88 Research Design .................................................................................................... 89 Controlling Threats to Internal and External Validity ......................................... 90 Subjects ............................................................................................................ 94 Independent Variables ............................................................................................ 96 Message Stimuli ..................................................................................................... 97 Message Stimuli Testing ....................................................................................... 100 Manipulation Check .............................................................................................. 100 Instrument Pilot Test ............................................................................................. 108 Dependent Variables ............................................................................................ 110 Attitude ........................................................................................................... 110 Trust ............................................................................................................... 112 Additional Variables of Interest ............................................................................. 113 Attribute Variables ................................................................................................ 114 Instrumentation ..................................................................................................... 114 Instrument Content ............................................................................................... 115 Procedure ............................................................................................................. 117 Data Analysis ........................................................................................................ 117 Summary .............................................................................................................. 118 4 RESULTS ............................................................................................................. 128 Descriptive Analysis .............................................................................................. 128 Demographics ....................................................................................................... 129 Descriptive Analysis of Variables of Interest ......................................................... 130 Attitud e ........................................................................................................... 130 Trust ............................................................................................................... 130 Perceived Transparency ................................................................................. 131 Livestock Industry Values ............................................................................... 131

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9 Manipulation Checks ............................................................................................ 132 Test of Hypotheses ............................................................................................... 133 Post Hoc Analyses ................................................................................................ 137 Post Hoc Analyses for Attitude ....................................................................... 137 Post Hoc Analyses for Trust ........................................................................... 139 5 CONCLUSIONS ................................................................................................... 163 Overview ............................................................................................................... 163 Key Findings ......................................................................................................... 164 Implications ........................................................................................................... 166 Theoretical Implications .................................................................................. 166 Practical Implications ...................................................................................... 169 Limitations ............................................................................................................. 171 Recommendations ................................................................................................ 172 For Theory and Research ............................................................................... 172 For Practitioners ............................................................................................. 177 Summary .............................................................................................................. 179 APPENDIX A EMAIL PRENOTIFCATION ................................................................................. 181 B FIRST CONTACT EMAIL SeNT TO SUBJECTS .................................................. 182 C FIRST, SECOND, AND THIRD REMINDER EMAIL SENT TO SUBJECTS ......... 183 D FOURTH REMINDER EMAIL SENT TO SUBJECTS ........................................... 184 E INSTRUMENT ...................................................................................................... 185 F MESSAGE TREATMENTS ................................................................................... 202 G IRB APPROVAL ................................................................................................... 210 LIST OF REFERENCES ............................................................................................. 211 BIOGRAPHICAL SKETCH .......................................................................................... 227

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10 LIST OF TABLES Table page 3 1. Pretest manipulation recognition .......................................................................... 119 3 2. Pretest means for personal relevance groups ...................................................... 120 3 3. Pretest manipulation checks for personal relevance groups ................................ 121 3 4. Pretest means for transparent communication groups ......................................... 121 3 5. Pretest manipulation checks for transparent communication groups ................... 122 4 1. Subject Demographics ......................................................................................... 142 4 2. Total attitude scale inter item consistency statistics ............................................. 144 4 3. Attitude grand means among treatment groups ................................................... 145 4 4. Total trust scale inter item consistency statistics.................................................. 146 4 5. Trust grand means among treatment groups ....................................................... 147 4 6. Total perceived transparency scale inter item consistency statistics ................... 147 4 7. Perceived transparency grand means among treatment groups .......................... 148 4 8. Total livestock industry values scale inter item consistency statistics .................. 148 4 9. Livestock industry values grand means among treatment groups ....................... 149 4 10. Attentiveness to stimuli affirmative responses ................................................. 149 4 11. Comparison of manipulations ............................................................................. 149 4 12. A ttitude grand means among classes ................................................................ 150 4 13. Trust grand means among classes .................................................................... 150 4 14. Inter correlations between attitude, trust, transparent communication, personal relevance, and perceived transparency ............................................................ 151 4 15. Attitude means by treatment group .................................................................... 151 4 16. Effect of transparent communication and personal relevance on attitude .......... 151 4 17. Trust means by treatment group ........................................................................ 152

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11 4 18. Effect of transparent communication and per sonal relevance on trust ............... 152 4 19. Multiple linear regression analysis for variables predicting attitude .................... 153 4 20. Multiple linear regression analysis for variables predicting trust ........................ 154 4 21. Inter correlations between attitude, trust, transparent communication, personal relevance, perceived transparency, age, gender, livestock industry values, area of resi dence, industry employment, and meat consumption ..................... 155 4 22. Post hoc multiple linear regression analysis for variables predicting attitude, models 1 and 3 ................................................................................................. 157 4 23. Post hoc multiple linear regression analysis for variables predicting attitude, models 3 and 4 ................................................................................................. 158 4 24. Post hoc multiple linear regression analysis for variables predicting trust, models 1 and 3 ................................................................................................. 160 4 25. Post hoc multiple linear regression analysis for variables predicting trust, models 3 and 4 ................................................................................................. 161

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12 LIST OF FIGURES Figure page 2 1. The Elaboration Likelihood Model of persuasion ................................................... 86 2 2. Conceptual model of trust and transparency in ELM .............................................. 87 3 1. Operational framework ......................................................................................... 122 3 2. Transparent communication manipulation 1 ........................................................ 123 3 3. Transparent communication manipulation 2 ........................................................ 123 3 4. Transparent communication manipulation 3 ........................................................ 124 3 5. Transparent communication manipulation 4 ........................................................ 125 3 6. Personal relevance manipulation ......................................................................... 126 3 7. Pe rsonal relevance manipulation 2 ...................................................................... 126 3 8 Personal relevance manipulation 3 ....................................................................... 127

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13 LIST OF DEFINITIONS A TTITUDES Attitudes are defined as underlying dispositions or m ental sets towards some object that are generally measured in terms of verbal res ponses to evaluative statements (McQuail, 2010, p. 515). In this study, attitude was defined as the subjects score on the attitudinal index. This index was created using att itude items suggested by Osgood, Suci & Tannenbaum (1978) and used by Meyers (2008) and Rhoades (2006). C OGNITIVE PROCESSING ROUTE The cognitive route taken by an individual to process persuasive information resulting in attitude change. The two cognitive processing routes include the peripheral and central processing routes (Petty & Cacioppo, 1981). L IVESTOCK Animals kept or raised for use or pleasure; especially farm animals kept for use and profit ( Livestock n.d.). In the context of this study livesto ck includes poultry. M ILLENNIALS Individuals born as early as 1980 and as late as 2002 (Elmore, 2010; Howe & Strauss, 2007; Payment, 2008; Taylor, & Ketter, 2010). In this study Millennials included those currently enrolled in college born between 1980 and 1994. P ERSONAL RELEVANCE The importance and meaning a message has to an individual (Petty & Cacioppo, 1986). In this study personal relevance was be defined by the personal relevance manipulations in the message stimuli. These manipulations were modeled off of the work by Petty and Cacioppo (1979), Petty, et al. (1981), and Petty, et al. (1983). S OCIAL MEDIA Forms of electronic communication (as Web sites for social networking and micro blogging) through which users create online communities to share information, ideas, personal messages, and other content (as videos) (Social Media, n.d.). In this study the treatment intervention was housed on the social media platform Facebook. T RANSPARENT COMMUNICATION Transparent communication is the deliberate attemp t to make available all legally releasable information whether positive or negative in nature in a manner that is accurate, timely, balanced, and unequivocal, for the purpose of enhancing the reasoning ability of publics and holding organizations accountable for their actions, policies, and practices (Rawlins, 2008a, p. 75). In this study transparent communication was defined by the transparent manipulations in the message stimuli. These manipulations were guided by the work of Auger (2011) and Rawlins (2008a).

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14 T RUST A collective judgment of one group that another group will be honest, meet commitments, and will not take advantage of others (Rawlins, 2007, para 13). In this study, trust was measured by a trust index developed based on the work of Driscoll (1978), Paine (2003), Rawlins (2008b), and TschannenMoran and Hoy (2000).

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15 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy TAKING DOWN THE WALL S OF AGRICULTURE: EF FECT OF TRANSPARENT COMMUNICATION AND PE RSONAL RELEVANCE ON ATTITUDES AND TRUST WITHIN THE ELABORATI ON LIKELIHOOD MODEL By Joy Noel Goodwin May 2013 Chair: Tracy Irani Major: Agricultural Education and Communication As the industrialization of agriculture has occurred, t he agricultural industry has struggled to adequately communicat e with and address the concerns of consumers. There continues to be a need to communicate effectively with consumers about agriculture, and specifically livestock production. Literature suggests that transparent communication may improve communication effectiveness in the agricultural industry However, limited quantitative research has assessed the effect of transparent co mmunication. Therefore, the purpose of this study was to assess the effects of transparent communication and personal relevance, in a livestock production context, on the attitudes and trust of college students. The theoretical framework of ELM, trust, and transparency guided the study and research design. To test the research hypotheses, a two (transparent communication: high and low) X two (personal relevance: high and low) between subject s factorial experimental design was used. Subjects were randomly as signed to receive one of the experimental treatments. The subjects for this study included a convenience sample of 989 college students from a large southeastern

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16 university. Usable responses were received form 688 subjects (69.6%). The personal relevance and transparent communication manipulations were presented in a Facebook page for a fictitious poultry farm. The results of this study indicated that manipulated transparent communication, as well as the subjects perceived transparency had a positive effect on attitudes and trust. Personal relevance was not found to have a significant effect on attitudes and trust in this study. Findings of this study suggest ed that transparent communication may have the ability to reconnect agricultural consumers and producers through increased trust and more favorable attitudes toward the industry. Theoretically this study provided support for connecting transparent communication and trust to ELM. In addition to motivation and ability factors, perceived transparency may have the ability to influence the cognitive processing of a transparent communication message. Further research of transparent communication in the agricultural industry and as part of ELM should be conducted.

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17 CHAPTER 1 INTRODUCTION Weve got to open up the door and show it, weve got to open it up and show it, and sell it to the public that we are doing the right things. I find often times the public s views [about agriculture] are way too simple. You know I sit down and explain to them that things ar ent quite as simple as black and white or good and bad. Dr. Temple Grandin (Vance, 2012). The United States has enjoyed a rich agricultural history (Hurt, 2002). During Native American civilization, agriculture production in the United States involved primarily crop production, while protein sources were accessed through hunting and fishing. Livestock production in the United States did not become abundant until the European Settlement and formation of the British American colonies. At this time an esti mated 75 to 90 percent of the United States population was directly involved in agriculture, a percentage that stayed constant through the American Revolution (Hurt, 2002). The American Revolution brought with it commercial livestock production, which was needed to satisfy the meat and wool demands of the army. These demands escalated during the Civil War. Nearing the end of and following the Civil War, the agriculture industry had begun to revolutionize. The United States Department of Agriculture (USDA) was created, farmers began to move away from subsistence farming the livestock population increased, technology advanced, and landgrant universit ies were created (Hurt, 2002). By 1900, approximately 60 percent of the United States population lived in a rural area, and 41 percent of the workforce was employed in agriculture (Dimitri, Effland, & Conklin, 2005). As technology continued to increase throughout the 20th century, more people left the farm and made the move to urban or metropolitan living and

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18 em ployment. Many children who grew up on a farm during the 20th century left for college or to seek employment in alternative industries never to return to farming (Hurt, 2002). The move away from agriculture in the 20th century brought a 63 percent decrease in the number of farms and a 67 percent increase in the size of farms (Dimitri et al., 2005). Today less than 2 percent of the American workforce is employed in agriculture and less than 30 percent of the population lives in a rural area (Dimitri et al., 2005). The majority of United States c onsumers are now generations removed from the farm (American Farm Bureau, 2001; American Farm Bureau, 2007). Commercial livestock production faced many changes throughout the 20th century. In the first half of the century livestock production was prevalent, but was often challenged by the hardships of war, disease, and the Great Depression (Hurt, 2002). Following World War II, the productivity of agriculture rapidly increased (Taylor & Field, 2004). With the abundanc e and availability of feed grains, livestock production was also able to increase. The second half of the 20th century brought vertical integration to the livestock industry, primarily in the poultry and swine industries (Ensminger & Parker, 1997; Hurt, 2 002). Vertical integration is a business structure through which an individual or company controls several parts of the production process For example, in swine production, a vertically integrated company may include the farrowing, growing, and processing segm ents of the production process (Ensminger & Parker, 1997). Additionally, nutrition and breeding knowledge improved, and livestock quality and production increased with the use of fewer resources. Efficiency of livestock production continued to increas e through the end of the 20th century with the development of

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19 biotechnology, growth hormones, improved management practices, and genetic advancements (Hurt, 2002; Taylor & Field, 2004). In 2002, the amount of beef produced from 97 million head was equival ent to the amount produced by 120 million head in the 1970s (Taylor & Field, 2004). This increased production efficiency and the growth of larger farms led to the implementation of biosecurity standards. Biosecurity standards have reduced disease and bioterrorism threats on farms that are intensively managed (Taylor & Field, 2004). In 2007, the livestock population in the United States, according to the Census of Agriculture, included 96.3 million cattle and calves, 206.8 million hogs and pigs, 349.7 millio n laying hens, 1.6 billion broiler chickens, and 5.8 million sheep and lambs (United States Department of Agriculture, 2009). A large percentage of poultry, hog, and dairy farms today exceed $1 million in annual sales (Hoppe, Korb, & Banker, 2008). As the human population has continued to increase, global meat demand is expected to increase by 2 percent each year (Taylor & Field, 2004). In addition to a growing global population, some countries have been documenting an increase in meat consumption. In China, the consumption of meat has more than quadrupled in the last 30 years (Humphries, 2010). The movement away from the farm coupled with advanced technology and efficiency surrounding the livestock industry, has led to a consumer base that is lacking know ledge about current livestock production and agriculture in general (Duncan & Broyles, 2006; Zimbelman, Wilson, Bennett, & Curtis, 1995). This decreased knowledge and lack of connection to livestock production has left todays consumers misinformed, curious, and skeptical about agricultural topics ( Goodwin, 2012; Vance, 2012; Whitaker

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20 & Dyer 2000; Zimbelman et al., 1995) Therefore, the purpose of this study was to explore solutions to reconnecting consumers with livestock production by assessing the effec ts of transparent communication about animal agriculture on attitudes and trust. History of Agricultural Communications Agricultural communications in the United States has existed since the European settlers colonized in America (Boone, Meisenbach, & Tuck er, 2000). However, much of early communication about agriculture included faceto face communication or print based communication (Telg & Irani, 2012). In the 1920s, radio became an important part of agricultural communication (Hurt, 2002). The radio was highly valued by the farm community and was used to gain weather and market information, as well as agricultural information coming from the USDA or a university (Hurt, 2002; Telg & Irani, 2012). Nearly half way through the 20th century agricultural communication began to appear in television broadcasts (Baker, 1981). From 1980 through today computers and the I nternet have revolutionized agricultural communication (Telg & Irani, 2012). Computers not only have aided agricultural communicators in word processing and design, but also have allowed them to take a more interactive approach with target audiences through websites, social media, and email (Telg & Irani, 2012). A changing audience and changing message have also accompanied changing technology in agr icultural communication. The primary audience for much of agricultural communications history included farmers and those involved in the agricultural industry (Telg & Irani, 2012) Agricultural communications was historically used to communicate messages about the industry and various agriculture sectors However, as consumers have become further removed from the farm, they have become the primary audience for agricultural communicators (Telg & Irani, 2012) The goal of agricultural messages

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21 communicated t oday is to equip consumers with information to make inf ormed decisions about agricultur al issues and topics (Telg & Irani, 2012). This concept has proven hard to fulfil l for the agricultural industry. A need to communicate effectively and proactively about the industry has continued to exist (Graves, 2005). Agriculture Media Coverage Due to increased concerns regarding environmental and food safety issues (Whitaker & Dyer, 2000), agricultural stories increasingly received news media coverage in the 1980 s and have continued to appear throughout the 21st century. Meyers and Abrams (2010) said how the media covers agriculture is important because it can influence consumers perceptions (p. 22). The general public is inclined to gain and seek agricultural inf ormation from the news media because the public is lacking direct knowledge and contact with the industry (Zimbelman et al., 1995). The complexity of agricultural technology and issues has caused journalists to report on background information using poor sources, resulting in often inaccurate information (Whitaker & Dyer, 2000). The news media, similar to the general public, has lacked knowledge about and contact with the agriculture industry which has left the majority of agricultural reporting misinform ed and focused on controversy (Zi m belman et al., 1995). Agriculture has not been alone in its struggle to communicate with the public. The broader science industry has also struggled to properly inform and educate consumers (Burns, OConnor, & Stocklmayer 2003; Weigold, 2001). As suggested in science communication literature, the public does not know much about science, and it appears that scientists dont know much about the public (Burns et al., 2003, p. 189). Much like

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22 agricultural stories, considerable background knowledge has been needed by journalist s to provide accurate information about science (Weigold, 2001). A very small percentage of journalists have a science background or education. Additionally science coverage has not always been a prior ity of media organizations (Weigold, 2001) Information needs to be unusual, have high importance, human interest, conflict or controversy and timeliness in order to gain media attention (Shoemaker & Reese, 1991). Occasionally, science information has fal len into these categories, but when it has the stories have commonly been focused on risk or controversy (Weigold, 2001). Bias in reporting of agricultural topics has been found (Whitaker & Dyer, 1998) and the occurrence of activist sources has been hi gh among coverage of agricultural topics (Whitaker & Dyer, 2000). The use of biased information and activists sources has been a concern to the agricultural industry because it has influenced how the general public views agriculture. Specific to media coverage surrounding livestock production, animal activists have been successful in creating public concern with the use of emotionally based information (Zi m belman et al, 1995). Journalists have commonly tried to include sources that are relevant and credibl e (Sundar, 1998). Additionally, it has been common for journalists to include quotes from participant, witness, indirect, official, and unofficial sources throughout their reporting (Abrahamson, 2006). However, due to space and time limitations only a few sources may be used and journalists may only use available or suggested sources (Telg & Irani, 2012). Media information that is vague, biased, or only provides background information causes the audience to draw conclusions based on the information presented. This

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23 process has been shown to cause audience members to react to the information in a misinformed manner (LaMay & Dennis, 1991; Whitaker & Dyer, 2000). Several cases of negative media coverage related to agriculture can be found (Graves, 2005). Consumers Perceptions of Modern Agriculture As agricultural technology has advanced, the average consumer has become more skeptical and unaccepting of agricultural practices (Sparek, Shepherd, & Frewer, 1994) Much of this skepticism and lack of acceptance has been associated with the unknown risks and ethical concerns of advanced agricultural technology (Sparek et al., 1994). Consumers have developed concerns regarding the impacts of modern agricultural practices on the environment, animal welfare, the small scale farmer (Weatherell, Tregear, Allinson, 2003) food safety, utilization of resources, and human health (Zimbelman, et al. 1995) Ethical concerns associated with biotechnology and using animals for human benefit have also topped the minds of consumers (Zimbelman et al., 1995). As a result of concerns associated with industrialized agriculture, consumers have sought alternative food systems and local foods ( Gilg & Battershill, 1998; Hinrichs, 2000; Mardsen, Banks, & Bristow, 2000; Weatherell et al., 2003) Additionally, the agricultural industry has been criticized for ignoring the concerns of consumers (Weatherell et al., 2003; Goodman & DuPuis, 2002). Even after numerous high profile agricultural controversies, such as food safety events, genetically modified organism ( GMO ) protests, mad cow disease, and the use of recombinant bovine somatotropin ( rBST ) the industry has failed to develop an integration of production and consumption needs and preferences into their practices (Goodman & DuPuis, 2002). Much of the research surrounding agriculture has been production focused and has largely neglected the consumption of agricultural products and the consumer,

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24 resulting in an asymmetrical body of literature (Goodman & DuPuis, 2002). In recent years, agriculture literature has begun to include more consumption aspects, but the overall consideration and impact of consumers and their consumption remains under represented (Goodman & DuPuis, 2002). Social Media In todays society, social media is the most popular activity on the Internet (Qualman, 2009). In 2008, 75 percent of I nternet users participated in some form of social medi um (Kaplan & Haenlein, 2010). Social media has become a way for individuals to make sense of and sort through a plethora of information (Qualman, 2009) Social media also offers users a platform to create their own content and share it with others, a process also known as user generated content (Agic htein, Castillo, Donato, Gionis & Mishne, 2008). The structure of social media sites has a llowed users to connect to other users, as well as document based information (Agichtein et al., 2008). Additionally, the social media environment has led people to want to know what the majority is doing and to get involved in the conversation (Qualman, 2009). The implementation of smart phones and mobile technology has increased the access to social media, and consumers can now log on to social media virtually anywhere at any time (Kaplan & Haenlein, 2010). Social Media and Transparency Social media has been described as eliciting mass transparency from both individuals and organizations (Qualman, 2009). Transparency is defined as T he deliberate attempt to make available all legally releasable information whether positive or negative in nature in a manner that is accurate, timely, balanced, and unequivocal, for the purpose of enhancing the reasoning ability of publics and holding organizations accountable for their actions, policies, and practices (Rawlins, 2008a, p. 75).

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25 A t the business level transp arency can be achieved through social media, exposing how a company does business and its actions good or bad while conducting business (Wilms, 2011). Even if an individual or an organization chooses not to be transparent on its social media network, others can easily expose any information the individual or organization chooses not to disclose (Qualman, 2009). Social media has inflated transparency and no secret event or news item has the opportunity to be discrete (Qualman, 2009). Transparency cannot be avoided in the world of social media and has given customers more power than ever before (Meyer, 2003). Social media has provided the public with an avenue to express what they perceive as responsible corporate behavior (Jaques, 2012) This ability has allowed the public to narrow the expectation gap. The expectation gap has been identified as the gap between the actions and performance of an organis ation and the expectations of its stakeholders and the public (Jaques, 2012, p. 37). Corporate socia l responsibility has been discussed as including economic, legal, ethical, and philanthropic categories (Carroll, 1991). Previously corporations have tried to work in these areas to meet their own or perceived stakeholder demands for corporate responsibil ity (Carroll, 1999). Through social media, the public has told corporations what is expected as responsible corporate behavior, thus pushing companies and organizations to meet these demands or risk being distrusted in the public eye (Jaques, 2012) Offeri ng transparency on a social networking site can provide benefits to an organization or business. Qualman (2009) suggested that if an organization or company is transparent, then consumer concerns about that organization or company will

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26 decrease. Transparen t communication has also benefited businesses by allowing them to engage with consumers quickly and easily, offering an impactful influence. Some businesses may feel threatened by the amount of transparency that may be present on social media sites (Qualm an, 2009). Social media has brought with it a consumer that is increasingly more apt to let their frustrations be known. When customer concerns or criticisms are posted on a social media site, many businesses have tried to control them or eliminate them fr om public view. However, responding to these concerns or criticisms is an opportunity for businesses to respond in a timely, transparent, and professional manner, ultimately i mproving customer relationships (Qualman, 2009). Although the benefits of transparency and social media at the organizational level have been discussed, others have suggested that online or computer based transparency has its downfalls (Meijer, 2009). Online transparency has been discussed as having the potential to be unidirectional, therefore, eliciting oneway communication rather than twoway communication. In addition, the discussion has indicated that online transparency can be unsuccessful because decontextualization of the information may occur. These potential problems of onl ine transparency have been discussed as decreasing rather than increasing, trust among the audience (Meijer, 2009). Social media has provided a new avenue for transparency. With the ability of information to be spread quickly and the ability of the public to let their expectations and opinions be known, it is becoming increasingly important for organizations and companies to be transparent and build relationships with their stakeholders through social media.

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27 Facebook One social media outlet that companies can use to be transparent is Facebook. Facebook is a social media site that was launched in 2004. The mission of Facebook is to give people the power to share and make the world more open and connected (Facebook, 2012a, para 2). Facebook rapidly gained popularity and continues to be a popular social media site with 910 million active users. On an average day, 500 million users log in, over 100 million likes are generated, and 250 million photos are uploaded to Facebook (Fisk, 2011). Additionally, the average Facebook user has at least 150 friends (Qualman, 2009). Facebook has given people the tools to stay connected, discover, share, and express (Facebook, 2012b). When one person posts a video, story, or other information on Facebook it has the potenti al to reach thousands of people very quickly (Qualman, 2009). The power of informing a lot of people in a short amount of time has its benefits from a business standpoint. Individuals look to their friends for help with decision making and new ideas (Fisk, 2011). After originally only being available to individual users, Facebook extended its network to organizations in 2006 (Smith, 2006). The power of Facebook has allowed a business to create brand power through voluntary brand advocates that naturally have a greater influence on others through acquaintance connections (Fisk 2011). A 2012 report indicated that Facebook is used by more than half of young adults to keep up with brand or company information that they like and to look for deals from brands or companies (YPulse, 2012). The high frequency use of Facebook, as well as the power it has to influence many has made it a great outlet for companies and organizations to promote their brand.

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28 Similar to other social networking sites, Facebook is a place where organizations can be transparent (Qualman, 2009) Additionally, Facebook is an outlet for the public to demand corporate responsibility from organizations (Jaques, 2012) As Facebook continues to evolve, it is likely that it will continue to be a pla ce for organizations to connect with stakeholders and promote their brand, especially to young adults (YPulse, 2012). Millennial Generation The Millennial G eneration includes individuals born as early as 1980 and as late as 2002, although many dispute the generation parameters (Elmore, 2010; Howe & Strauss, 2007; Payment, 2008; Taylor, & Ketter, 2010). This generation is essentially the children of individuals from the Baby B oomer generat ion and early G eneration X (Elmore, 2010). Some of the individuals in this generation are in the workforce, some are in college, and some are preparing to finish high school. This generation has grown up with technology and uses it more than any other generation (Elmore, 2010; Taylor & Ketter, 2010). Un like previous generat ions, the M illennials do not have to talk to people or read hard copy materials to get information; rather, they can access virtually any information from anywhere (Elmore, 2010). Approxi mately 62 percent of M illennials have report ed regularly connecting t o the I nternet when away from home, work, and school (Taylor & Ketter, 2010). While older generations have not always look ed favorably toward technology, M illennials have indicated that technology has helped them stay connected to family and friends and has made life more manageable (Taylor & Ketter, 2010). Millennials have been described as confident, self expressive, liberal, upbeat and open to change (Taylor & Ketter, 2010, p.1). This generation is expected to be the

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29 most educated generation ever; more M illennials have graduated from high school and attended college than any other generation (Taylor & Ketter, 2010). When accessing news information, M illen n ials report accessi ng news f rom television or the Internet (Taylor & Ketter, 2010). More than half of all adults possess traits of skepticis m and distrust; this has also been true for M illenn ials (Taylor & Ketter, 2010) In addition, only 14 percent of M illennials live in a rural area, a percentage much lower than previously observed from older generat ions when they were the same age (Taylor & Ketter, 2010). This generation has brought with it many different experiences, values, and traits that have not been held by any other generation. Learning how to communicate with and inform this generation has become increasingly important as they begin to make up a larger portion of active consumers. Millennials are estimated to make up around 27 percent of the United States population and have 11 percent more buying power than generations that have come before t hem (Hais & Winograd, 2011). Millennial Generation and Social M edia Grow ing up with technology has led M illennials to be high users of social media. In 2010, 75 percent of M illennials used at least one social networking site (Taylor & Ketter, 2010). Of tho se using social media, more than half accessed their social media site at least once a day, if not multiple times a day. Social media use and activity has been the highest among those M illennials who are currently in or have gone to college. Additionally, females are more likely to be frequent users of social media than males. White individuals are also more likely to have and regularly use social media sites than Black s and Hispanic s (Taylor & Ketter, 2010).

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30 Twitter is used by about 53 percent of the Millennial G eneration, a proportion greater than other generations (Shreffler, 2012). Facebook receives the most M illennial use, with 93 percent of the generation using Facebook (Shreffler, 2012). Additionally, an estimated 20 percent of M illennials have uploaded videos of themselves to a social networking site (Taylor & Ketter, 2010). Other social media sites such as Google+, LinkedIn, Tumblr, Pinterest, Foursquare, and Instagram ar e gaining popularity among the M illennial population (Shreffler, 2012). Millennials commonly share their opinions about and explore brands through these social media sites (Schubarth, 2012). Millennial G eneration and T ransparency Growing up with digital information, the Millennial G eneration has not accepted representative democrac y and secretive competitive advantages (Meyer, 2003, para. 7). Millennials have expected and demanded transparency in all aspects of life (Red McGregor, 2012; Shore, 2011). In a focus group of young opinion leaders a participant made this demand clear by stating: Agricultural corporations could become more transparent so that we dont have to find out about things like five, 10 years later through private investigative journalism by people secretly trying to find out what the quote, unquote farmers are d oing to the food. I would rather that the companies take me seriously. I mean, I dont even know too much about it now about how transparent they are, but I would like to hear that it is becoming a lot more transparent (Goodwin, 2012, p. 36). Millennials v iew companies as more honest and credible if they include transparent characteristics on their website, such as customer feedback (Red McGregor, 2012). Additionally, this generation wants to interact with and know about companies and businesses. Millennial confidence in a company is directly influenced by the companys level of transpar ency (Red McGregory, 2012). If M illennials feel a company has been unfair they will look for a way to expose the company (Shore,

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31 2011). However, this generation forgives eas ily and is not likely to write off a company that has wronged them, as long as the company has taken steps to improve its actions (Shore, 2011). As this generation moves into leadership roles, it is expected that they will lead in an open culture free of s ecrecy (Meyer, 2003). Millennials have demanded transparency and can obtain transparency through the use of social media and other technology. College students represent the middle sector of the Millennial generation, most likely to be born between 1990 a nd 1994. College students participate in social media and are an active portion of this generation (Taylor & Ketter, 2010). College students form their opinions as consumers of agricultural products and solidify their attitudes as they get older (Sears, 1986) Therefore, they are an important audience, within the Millennial Generation, to study and try to understand in regard to attitude formation. Transparency The idea of and discussion surrounding transparency has increased throughout the 21st century (Rawlins, 2008b). Corporate scandals have led to the increased attention surrounding transparency, as well as the increasing consumer distrust of large corporations (Rawlins, 2008a). Several industries now either demand, or are exploring, the use of transparent practices and communication. Some of these industries have include d government (Piotrowski & Van Ryzin, 2007), business (Tapscott & Ticoll, 2003), financial (Mehrez & Kaufman, 2000), nonprofits (Auger, 2011; Bothwell, 2000), health (Shea, Shih, & Davis, 2007), and agriculture (Hoogland, de Boer, & Boersema, 2005; Iles, 2007). However, the academic examination of transparency has remained somewhat limited (Rawlins, 2008b). Of the transparency research that has been done, most has been qualitative. Only a few quantitative studies have examined the

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32 relationship between transparency and trust (Auger, 2011; Rawlins, 2008b). A quantitative measurement for transparency was not developed until 2008 (Rawlins, 2008a). The release of a plethora of information does not equate to transparency (Rawlins, 2008a). Information must be released in a manner that enhances understanding (Wall, 1996) and allows the stakeholder to perceive that the information and the organization are transparent (Gower, 2006). Transparency has i ncluded communicating in a truthful (Martinson, 1996) and substantially complete manner (Klaidman & Beauchamp, 1987). Additionally, stakeholder involvement is a large component of transparency. Stakeholder involvement has allowed consumers to gain control, establish preferences (Stirton & Lodge, 2001), acquire information, distribute that information, and create knowledge (Cotterrell, 1999). If executed and managed well, the use of transparency within an organization can increase trust among consumers (Auger, 2011; Meyer, 2003). Transparency and the Agriculture I ndustry Academic research regarding transparency in the agriculture industry has been limited. A few studies have looked at the transparency of the food chain (Hoogland et al., 2005; Iles, 2006) foo d safety (Beulens, Broens, Folstar & Hofstede, 2005) farm animal welfare (Greef, Stafleu, & De Lauwere, 2006), and food safety regulation (Asamoah & Sharfstein, 2010) and labeling (van Dorp, 2008). Most of this research has been conducted in European countries. The majority of discussion surrounding agricultural transparency in the United States has primarily been held at the industry level In 2008, the American Humane Association (AHA) provided a testimony to a U.S. House Subcommittee, created in

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33 respons e to the beef recall prompted by the mistreatment at Hallmark Westland packing plant, encouraging the implementation of live video feeds in slaughtering facilities (Weir, 2008). AHA testified that the use of live video feeds would improve transparency and reduce instances of inhumane treatment, improve the state of farm animal welfare and restore consumer confidence (Weir, 2008, para. 1). Dr. Temple Grandin, a livestock handling expert, animal science professor, and major proponent of transparency in the industry, has continually suggested that the industry should open farms and slaughter houses to consumers showing them the processes of agriculture production (Garner, 2009; Roybal, 2012). Additionally, others have called for farmers to engage in communic ation and build relationships with their consumers (Weatherell, Tregear, & Allinson, 2003). Some farms and slaughterhouses have taken that step to be more transparent and engage in conversations with consumers Some slaughterhouses have implemented video s urveillance (Raines, 2009) and farms have opened their doors for consumers to visit (Fair Oaks Farms, 2012; Hastings, 2012). However, other s, such as corporations within the industry and politicians, have felt that increased exposure and transparency is not a good idea (Potter, 2011) Five states Iowa, Utah, North Dakota, Montana, and Kansas now ban photography and video graphy on farms ; this legislation is popularly known as Ag Gag laws and other states are currently considering or have previously considered similar legislation ( Flynn, 2012; Mitchell, 2011; Potter, 2011). These pieces of legislation are part of an effor t to prevent under cover footage of farm practices (Mitchell, 2011). Decreasing worry among farmers and eliminating the capture of images that are

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34 misperceived were listed among the benefits. Critics of these bills referred to the public disconnect with agriculture and the need to inform consumers how their food is produced. The bills were discussed as a major step in the wrong direction for transparency in the food system (Mitchell, 2011, para.11). Within the United States agricultural industry it is apparent that there are two sides of the transparency debate. One side includes those who agree with and support Dr. Grandins views of transparency and live video feeds on farms (Garner, 2009; Roybal, 2012; Weir, 2008) and the other side includes those who push for legislation to ban photography on farms (Potter, 2011) There has been a need to understand how transparency or a lack of t ransparency may impact the industry. Researchers may be able to begin looking at the impact of transparency on the agricultural industry by examining communication models such as the Elaboration Likelihood Model. Elaboration Likelihood Model The Elaborati on Likelihood Model (ELM) is a theory of attitude change model ed by cognitive processing and persuasion (McQuail, 2010; Petty & Cacioppo, 1986; Petty & Cacioppo, 1996). The model explains how the level of thinking impacts the process and influence of persu asion for each situation (Petty & Cacioppo, 1981; Petty & Cacioppo, 1986; Petty & Wegener, 1999). Within the model, Petty and Cacioppo (1981) suggest ed there are two routes that can lead to attitude change. These two routes include the central and peripher al route. When an individual receives information, that information is either processed centrally or peripherally ultimately impacting attitude. The central processing route is based on effortful cognitive thinking, while the peripheral route is based on simple cues. The processing route taken is affected by an individuals motivation and ability to process (Pet ty, Brinol, & Priester, 2009).

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35 The ELM takes into account several variables of persuasion and assesses the impact of information on attitudes bas ed on the cognitive processing route for each situation. Attitudes Attitudes are defined as underlying dispositions or mental sets towards some object that are generally measured in terms of verbal res ponses to evaluative statements (McQuail, 2010, p. 5 15). Petty and Cacioppo (1996) offer a different definition of attitude suggesting that attitudes are a general and enduring positive or negative feeling about some person, object, or issue (p.7). Attitudes have become an important construct in mass medi a communication research, because attitudes are the connection between information received and behavioral outcome (Petty et al., 2009). In general, individuals are explicitly aware of their own attitudes, but occasionally individuals are unaware of implic it predisposed attitudes (Petty et al., 2009). Individuals desire to hold and maintain correct attitudes; however the abundance of information limits their ability to process everything eff ectively (Petty & Cacioppo, 1996). Individuals attitudes are not altered easily and when altered, the change process is slow. The presence of new information that does not align with existing attitudes may cause attitude to realign to be more consistent with the new information (McQuail, 2010). By examining attitudes predictions and influences on behavior can be assessed. Trust In recent years, trust has increased in value and is now regarded as essential to successful public relations (Rawlins, 2007). Organizational trust has been defined as a collective judgment of one group that another group will be honest, meet commitments, and will not take advantage of others (Rawlins, 2007, para 13). Trust adds strength to

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36 organizations cooperation, communication, and relationshipbuilding (TschannenMoran & Hoy, 2000). In previous studies of transparency, a positive relationship between trust and transparency has been found (Auger, 2011; Rawlins, 2008b). To improve relationships with consumers, the agricultural industry must examine the trust consumers have in agricultural practices as well as the trustworthiness of the industry (Meijboom, Visak, & Brom, 2006). The relationship between trust and transparency and the importance of trust in relationships, made trust an important variable to explore in this study. Significance, Problem Statement, Purpose, & Research Questions As the industrialization of agriculture has advanced and consumers have become further removed from the farm, farmers and consumers have become disconnected with each other (Duncan & Broyles, 2006; Zimbelman, Wilson, Bennett, & Curtis, 1995). The agricultur al industry has tried to address this disconnect by shifting the communication focus from communicating with those in the industry to those not involved in the industry (Telg & Irani, 2012). However, communicating effectively with the consumer base has proven to be difficult. The limited agricultural experience and knowledge of todays consumer coupled with their concerns regarding industrialized agricultural practices (Weatherell et al., 2003; Zimbelman, et al., 1995) has complicated the conversation between producers and consumers. There continues to be a need to communicate effectively with consumers about agriculture, and specifically livestock production (Graves, 2005). A suggested solution to improving the effectiveness of communication about livestock production is to increase the transparency of the industry (Garner, 2009; Roybal, 2012).

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37 The news media has discussed agricultural topics, but this coverage has tended to be biased and misinformed (Whitak er & Dyer, 2000; Zibelman et al., 1995). With a large influence on consumer percept ions, the news media have often influenced and created misperceptions and concerns that consumers have about agriculture and livestock production (Meyers & Abrams, 2010). Social media have strengthened the influence of news media and the spread of information. Additionally, transparency has evolved as a byproduct of social media. Social media and digital technology have created a culture where it is virtually impossible to keep information and events secret (Qualman, 2009). A growing consumer group, the Millennial G eneration, is known to demand transparency and have become high frequency users of social media (Red McGregor, 2012; Shore, 2011). Within this generation, college s tudents are particularly known to be high users of social media and to have unsolidified attitudes ( Sears, 1986; Taylor & Ketter, 2010) Communicating effectively about livestock production is essential to the future of the industry. Current communication practices within the industry have not been proven to inform and resonate with consumers in a manner t hat provides longlasting impacts ( Goodwin, 2012; Graves, 2005; Zimbelman et al., 1995; Whitaker & Dyer, 2000). Additionally, the effect of transparency w ithin the livestock industry has not been assessed. Further assessment of effective communication methods and transparency in the livestock industry has the potential to impact many. The industry, agricultural communicators, educators, extension agents, co nsumers, and politicians would find value in this type of assessment because it will ultimately lead to a better understanding

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38 of how to create an informed citizenry ensuring the future sustainability of food and the industry needed to support human life (Doerfert, 2011). The purpose of this study was to assess the effec ts of transparent communication and personal relevance, in a livestock production context, on the attitudes and trust of college students, who are an important segment of the Millennial Generation The following research objectives guided this study: RO1: To determine the effects of different levels of transparent communication on attitudes and trust. RO2: To determine the effect of different levels of personal relevance on attitudes and trust. Summary With an increasing disconnect between farmers and consumers, as well as an increasing demand for and exposure to transparency the livestock industry must assess the effects of transparent communication. As the Millennial G eneration increasingly makes up a larger proportion of active consumers the industry must understand how to communicate with them effectively. This study address ed these concerns by exploring the effect s of transparent communication, specific to livestock production, on the attitudes and trust of college students. The results of this study will add to the understanding of how to influence the Millennial Generation so that they are informed and able to make decisions on agricultural topics.

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39 CHAPTER 2 RELEVANT LITERATURE Chapter 1 outlined the need for effective communication in the agricultural industry, as well as the need to explore the use of transparent communication from a contextual perspective Chapter 2 focused on the theoretical perspective and provided a literature re view of attitudes, persuasion, Elaboration Likelihood Model (ELM), public relations, trust, and transparency. Additionally, previous research exploring these topics was also assessed throughout Chapter 2. Attitudes Petty and Cacioppo (1996) defined attitude as the general and enduring positive or negative feeling about some person, object, or issue (p. 7). McQuail (2010) added that attitudes are underlying dispositions or mental sets towards some object that are generally measured in terms of verbal res ponses to evaluative statements (p. 515). Eagly and Chaiken (1993) discussed attitude as a psychological tendency that is expressed by evaluating a particular entity with s ome degree of favor or disfavor (p. 1). Attitude has been the primary focus of persuasion researchers since the work of Gordon Allport in 1935, in which he claimed attitudes were one of the most important concepts in social psychology (Oskamp, 1991; Petty & Cacioppo, 1996). When attitudes are expressed verbally, they have been referred to as opinions (Katz, 1960). Therefore, it has been sugges ted that public opinion compromises the shared attitudes of society (Oskamp, 1991). Although a connection exists between attitudes and opinions, not all researchers agree they are the same. Howeve r, b ecause of its relation to opinion and public opinion, attitude has become a multi disciplinary research topic spanning across

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40 several fields including psychology, sociology, communication, and political science (Oskamp, 1991). Attitudes have been desc ribed as last ing internally for a least a small amount of time if not for an extended period of time (Eagly & Chaiken, 1993). Evaluative tendencies are often prompted by attitudes in response to an object. E valuative tendencies involve attitudinal respons es to stimuli through which evaluative responses and the resulting attitude is formed (Eagly & Chaiken, 1993) In 1960, Katz identified four functions of attitude. These functions included protecting an individuals ego, helping individuals to express thei r values, adding understanding to everyday encounters, and helping individuals to maximize rewards and minimize punishments (Katz, 1960). Additionally, attitudes have been shown to be related to and predictive of behavior ( A jz en & Fishbein, 1977; Fishbein & Ajzen 1974; Petty & Cacioppo, 1996; Petty & Cacioppo, 2009). The theory of planned behavior and the theory of reasoned action have indicated that attitudes and norms guide individuals to develop intentions to perform or not perform certain behaviors (Aj zen, 1991; Petty & Cacioppo, 2009). The process of changing attitudes has also been referred to as persuasion (Petty & Cacioppo, 1996). However, Eagly and Chaiken (1993) discussed that attitude change could occur as a result of affective processes, personal behavior impact, and social influences, in addition to persuasion. Individuals do not purposely try to change their attitudes (Petty & Cacioppo, 1996), and often attitude change has been a slow process that is resistant to change (McQuail, 2010). For lasting attitude change to occur, persuasive communication must gain an individuals attention, be comprehended, mental ly rehearsed, and cognitively stored (Petty & Cacioppo, 1996). Hovland, Janis,

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41 and Kelley (1953) suggested the source, message, recipient, and channel of the communication all play a r ole in this process. The message source is responsible for gaining an individuals attention and causing an initial belief change ( Hovland, et al. 1953; Petty & Cacioppo, 1996). The persuasive message, if compr ehended, can cause the initial attitude change. Factors associated with the individual receiving the message impact and solidify the attitude further. Lastly, the communication channel through w hich the message was delivered will impact the message retenti on and any resulting behavior change (Hovland, et al. 1953; Petty & Cacioppo, 1996). Petty and Cacioppo (2009) suggested motivation and the ability to process are the most important factors in determining whether a persuasive message will result in enduri ng attitude change and influence behavior. Attitude s have been measured directly and indirectly (Petty & Cacioppo, 1996). A direct measurement of attitude has involved individuals self reporting their attitudes. Indirect measurement of attitudes has includ ed a researcher measuring someones attitudes without the individuals knowledge (Petty & Cacioppo, 1996). The direct measurement of attitudes has been shown to be more precise and have higher reliability and validity than indirect measures (Lemon, 1973; P etty & Cacioppo, 1996). The challenge with measuring attitudes directly is that individuals may not share or be truthful about their attitudes. However, this issue is reduced when the research is less sensitive and anonymous (Petty & Cacioppo, 1996). Petty and Cacioppo (1986) concluded that attitudes are affected by behavioral, affective (attractiveness), and cognitive e xperiences.

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42 Persuasion Perloff (2003) defined persuasion as a symbolic process in which communicators try to convince other people to change their attitudes or behavior regarding an issue through the transmission of a message, in an atmosphere of free choice (p. 8). In addition, Petty and Cacioppo ( 1986) defined persuasion as any change in attitudes that results from exposure to communicat ion (p. 5). Commonly persuasion has been thought about as coming from corporations, government, or propaganda, but persuasion can also occur from person to person (Petty & Cacioppo, 1996). Persuasion has been identified as the root of social movements and historical change (Br ock, Shavitt, & Brannon, 1994). With the increasing availability of information, persuasion has become more complex than ever before (Larson, 1992; Perloff, 2003). Every day individuals are exposed to numerous persuasive messages that are continually competing for attention (Larson, 1992). In addition, w ith technological advances and cultural diversity, persuaders are constantly being challenged to identify how their audience will respond to a message (Perloff, 2003) Persuasion seeks to change attitudes and opinions (Brock et al., 1994). T he five components of persuasion must be present in order for persuasion to occur ( Perloff, 2003). The first component suggests that persuaders must use tools such as symbols to connect culturally and personally with the audience. Next, a persuader must recognize that he or she is trying to influence others and that others are susceptible to changing mental processes. The third component relies on the belief that individuals persuade themselves; the r ole of the persuader is to provide the arguments that will lead individuals to change their attitudes or behaviors. Additionally persuaders must

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43 remember that the message transmission, regardless of medium, is required in the persuasion process. Lastly, i ndividuals are free to choose whether to conform to the persuasion or not (Perloff, 2003). Miller identified three different types of persuasion (1980): shaping, reinforcing, and changing. Shaping persuasion involves the use of common associations to attr act and shape the audiences attitude (Perloff, 2003). For example, Nike is known as an athletic brand and commonly uses well known athletes in their ads. The association between the Nike brand and attractive athletes shapes audiences to hold attitudes of e xcellent athletic perf ormance toward the Nike brand. Reinforcing persuasion involves strengthening preexisting attitudes (Perloff, 2003) In the case of reinforcing, an individual may already have an attitude that says smoking is bad, but reinforcement of this attitude will strengthen the attitude when under peer influence or stress. Changing includes persuasion that changes attitudes and behaviors (Perloff, 2003) Evidence of this type of persuasion can be s een through many examples in United S tates histo ry, such as the change in attitudes toward segregation (Perloff, 2003) Persuasion has commonly been researched through the use of experiments and surveys (Perlof f, 2003). Experimental studies focus on the effects of persuasive communication, while application s of persuasion are commonly assessed through surveys (Perloff, 2003) Throughout academic history, several scholars have developed different approaches to studying persuasion ( Chaiken, Liberman, & Eagly, 1989; Chen & Chaiken, 1999; Greenwald, 1968; Ho vland et al., 1953; McGuire, 1989; Petty & Cacioppo, 1986; Petty, Ostrom, & Brock, 1981).

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44 One of the first approaches to study persuasion was the Yale Attitude Change Approach developed by Hovland et al. (Perloff, 2003). This approach assessed the effects of source credibility, message appeals, and recipient personality on attitude (Hovland et al., 1953). Additionally, this appro ach identified five steps to persuasion, which included attention, comprehension, learning, acceptance, and retention. Although mo re recent research has confirmed that learning is a part of persuasion (Chaiken, Wood, & Eagly, 1996), this approach to persuasion assumed that all information would be absorbed and learned (Perloff, 2003) and significant rei nforcement would change attitud es (Larson, 1992) Another early approach to persuasion was McGuires communication/persuasion matrix model. McGuires (1985; 1989) matrix included both inputs and outputs. The inputs included the source, message, recipient, channel, and context. The outputs, similar to the five steps of persuasion in the Yale Attitude Change Approach, included exposure, attention, interest, comprehension, acquisition, yielding, memory, retrieval, decision, action, reinforcement, and consolidation ( McGuire, 1989). In th e ma trix each input could have a different effect on each of the outputs (Petty et al., 2009). Although the matrix included several outputs it was not expected that each message would go through each output (McGuire, 1989). The matrix has been criticized for not addressing the potential independent nature of the outputs or for identifying the components of yielding (Petty et al., 2009). Additionally similar to the Yale Attitude C hange Approach, the matrix assumes learning will result in persuasion and does not account for the possibility that the message may be misunderstood (Petty et al., 2009).

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45 To address some of the weaknesses of McGuires communication/persuasion matrix (1985, 1989), the cognitive response theory (Greenwald, 1968; Petty, Ostrom, & Brock, 1 981) was developed (Petty et al ., 2009). This theory indicated a persons own mental reactions to a message play a critical role in the persuasion process (Perloff, 2003, p. 122). Similarly, Petty et al. (2009) suggested the cognitive response perspecti ve maintains that individuals are active participants in the persuasion process who attempt to relate message elements to their existing repertoires of information (p.163). Thus, an individuals thoughts about a message are more important to the persuasion process than learning (Petty et al., 2009). The theory discussed both favorable (persuasion occurs) and unfavorable (persuasion does not occur) cognitive responses (Perloff, 2003; Petty et al., 2009). Additionally, the cognitive response theory took into account various components that affected cognition, such as, forewarning, distraction, and confidence (Perloff, 2003; Petty et al., 2009). However, the theory lacked explanation of the influence of persuasive messages and it assumed people think carefull y about messages in all situations (Perloff, 2003). The limitations of early persuasion approaches and theories eventually led to the development of dual process models (Perloff, 2003). Dual process models take into account two different influences on atti tude change. The two dominant dual process models to persuasion are the Heuristic Systematic Model (HSM) (Chaiken, Liberman, & Eagly, 1989; Chen & Chaiken, 1999) and the Elaboration Likelihood Model (ELM) (Petty & Cacioppo, 1986). HSM indicated that indivi duals would develop attitudes based on either systematic or heuristic processing (Chen & Chaiken, 1999). Systematic processing involved a comprehensive cognitive thought process, requiring high

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46 cognitive ability and capac ity. Heuristic processing relied on stored memories or cognitive structures, which are applied to the information presented, requiring less cognitive work (Chen & Chaiken, 1999). Although HSM is a sound dual processing model of persuasion, researchers have not used it extensively because it lacked the comprehensive understanding of communication effects that ELM provided (Perloff, 2003). Elaboration Likelihood Model Social psychologists have studied communication messages and the effects messages have had on attitudes and behavior since the discipline began (Allport, 1935; Petty & Cacioppo, 1986; Ross, 1908). Research on these topics was prevalent through the 1960s (Petty & Cacioppo, 1986); however, researchers were finding inconsistent results and began to question if attitudes could be changed and if attitudes could predict behavior ( Himmelfarb & Eagly, 1974; Petty & Cacioppo, 1986). Petty and Cacioppo compiled these inconsistent research findings into one conceptual model to create ELM, a theory of attitude change modeled by cognitive proc essing and persuasion ( McQuail, 2010; Petty & Cacioppo, 1986; 1996). ELM explains the process individuals go through when exposed to persuasive communication, but the model can also be applied to other attitude change contexts not initiated by persuasion (Petty & Cacioppo, 1986). The process and influence of persuasion changes for each situation, and ELM explains how the level of thinking impacts this process (Petty & Cacioppo, 1986; Petty & Wegener, 1999). Cognitive processing has been recognized as an im portant component to persuasion, as scholars have suggested you cannot understand the effects of communication on people without knowing how people process the message (Perloff, 2003, p. 120).

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47 The model (Figure 21) explains this proces s through two cognitive routes, the central and peripheral route. An individuals motivation, ability to process, processing nature, and cognitive structure determine the resulting processing route (Petty et al., 2009). The central route includes a detail ed thought process and careful consideration of the information presented. The peripheral route does not include careful thought and consideration; rather it includes the attraction to and influence of simple cues (Petty & Cacioppo, 1986). Through indepth cognitive process ing, the central route is impacted by previous knowledge and experiences (Petty & Cacioppo, 1986; 2009). The recall of previous knowledge paired with the processing of new information results in cognitive thought s and builds upon ones overall cognitive st ructure (Petty et al., 2009). The thoughts produced during this process will be favorable or unfavorable, which aligns with the process identified in the cognitive response theory (Petty et al., 2009). Individuals will partake in this comprehensive cogniti ve effort to determine if the persuasive message is correct or incorrect when they have the motivation and ability to do so (Petty et al., 2009). The greater the importance an individual associates with the topic discussed in the message, the more likely t hey are to expend cognitive effort ( Petty & Wegener, 1998; Petty, Wheeler, & Bizer, 2000). For example, if a message source is seen as untrustworthy, individuals ar e more likely to think and elaborate further about the message. This occurs because individuals are motivated to hold correct attitudes. It is important for them to think carefully about the message presented from an untrustworthy source to ensure that they form the correct attitude and not just accept the position advocated (Priester & Petty, 1995). This in depth thought process results in

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48 a subsistent attitude, but the accuracy or rationality of the attitude may not be correct. Attitudes formed through the central route are easy to access from memory, held with high confidence, persistent overt ime, predictive of behavior, and resistant to change until they are challenged by cogent contrary information (Petty et al., 2009, p. 134). At times when thorough cognitive processing is not possible or desired, simple cues in persuasive messages influenc e attitude through the peripheral processing route (Petty et al., 2009). Simple cues contained within a message offer individuals a way to evaluate a message without effortful cognitive work. It is possible within the peripheral route for persuasive cues t o produce a sleeper effect For example, an individual may discount a message based on a cue that the message was untrustworthy and develop attitudes with peripheral characteristics. However, the message is later thought about in detail, the cue fades, and the attitude begins to be influenced by more central processing characteristics (Petty et al., 2009). Peripheral processing can lead to effective attitude change, but only for a limited time because the attitude is not incorporated in the overall cognit ive structure as it would be in the central route (Petty et al, 2009). When attitudes are formed through the peripheral route, they are less accessible, enduring, and resistant to subsequent attacking messages (Petty et al., 2009, p. 135) Persuasive impact from peripheral processing is short term (Petty et al., 2009). Petty and Cacioppo (1986) identified seven postulates of ELM. These include: People are motivated to hold correct attitudes. Although people want to hold correct attitudes, the amount and nature of issuerelevant elaboration in which they are willing or able to engage to evaluate a message vary with individual and situational factors.

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49 Variables can affect the amount and direction of attitude change by (a) serving as persuasive arguments, (b) serving as peripheral cues, and/or (c) affecting the extent or direction of issues and argument elaboration. Variables affecting motivation and/or ability to process a message in a relatively objective manner can do so by either enhancing or reducing ar gument scrutiny. Variables affecting message processing in a relatively biased manner can produce either a positive (favorable) or negative (unfavorable) motivational and/or ability bias to the issuerelevant thoughts attempted. As motivation and/or abilit y to process arguments is decreased, peripheral cues become relatively more important determinants of persuasion. Conversely, as argument scrutiny is increased, peripheral cues become relatively less important determinants of persuasion. Attitude changes t hat result mostly from processing issuerelevant arguments (central route) will show greater temporal persistence, greater prediction of behavior, and greater resistance to counter persuasion than attitude changes that result mostly from peripheral cues. (p. 5). Motivation to P rocess Motivation and ability both impact if and how an individual will process a message. Additionally, both motivation and ability must be present for elaboration to occur (Petty & Cacioppo, 1996). Motivational factors in ELM guide individuals intentions when presented with a message to process (Petty & Cacioppo, 1986). Several variables, including personal relevance and need for cognition, can influence an individuals motivation to process. An individuals motivation to process w ill vary for each message they are presented with (Petty & Cacioppo, 1996). Personal R elevance Personal relevance has been described as the most influential motivational factor in determining if an individual will have the motivation to process a message ( Petty & Cacioppo, 1986). Personal relevance refers to the importance and meaning a message has to an individual. As personal relevance increases, motivation to process increases.

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50 However, high personal relevance can be cofounded with other factors such as prior knowledge, making personal relevance a difficult factor to interpret (Petty & Cacioppo, 1986). Several experimental studies have manipulated personal relevance to test it within ELM ( Petty & Cacioppo, 1979; Petty, Cacioppo, & Goldman, 1981; Petty, C acioppo, & Schumann, 1983). In these studies researchers have assigned participants to either high or low personal relevance groups. For example, in a study with college students those in the high personal relevance group were asked to provide feedback on academic changes to be made in the next year at their college, while t hose in the low personal relevance group were asked to provide feedback on academic changes to be made in 10 years (Petty et al., 1981). In another study with college students, those in a high personal relevance group were told that visitation policies were being changed at their university, while the low personal relevance groups were told that visitation policies were being changed at another university (Petty & Cacioppo, 1979) This tactic has also been used in advertising experiments (Petty et al., 1983). These studies have found that the ability of a message to produce persuasion is more important for those in high personal relevance groups (Petty & Cacioppo, 1979), those in high pers onal relevance groups display traits of central processing (Petty et al., 1981), and those in low personal relevance groups are more susceptible to peripheral cues such as an attractive source (Petty et al., 1983). Need for C ognition Individuals have a nee d to cognitively process information in order to understand the world around them; this need varies from person to person (Cohen, Stotland, & Wolfe, 1955; Cacioppo, Petty, Kao, & Rodriguez, 1986). Additionally, the level of

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51 cognition that an individual exh ibits varies according to the issue and information being presented (Cacioppo et al., 1986). Need for cognition is a personality characteristic defined as a need to understand the world and to employ thinking to accomplish this goal (Perloff, 2003, p. 135). Cohen et al. (1955) originally defined need for cognition as a need to structure relevant situations in meaningful, integrated ways. It is a need to understand and make reasonable the experimental world (p. 291). In ELM need for cognition has been i dentified as a motivational factor that influences attitude formation (Petty & Cacioppo, 1986). Researchers have found that those with high need for cognition tend to need substantial information before making a decision (Verplanken, Hazenberg, & Palenewe n, 1992) enjoy thinking (Cacioppo & Petty, 1982) engage in comprehensive cognitive processing when exposed to persuasive communication (Cacioppo et al., 1986) and have more persistent attitudes that are resistant to counter persuasion ( Petty et al., 2009) Therefor e, individuals with high need for cognition are more likely to elaborate on persuasive messages and use the central route to persuasion. Individuals with a low need for cognition will not partake in effortful cognitive thought and are more like ly to be attracted by simple cues and take the peripheral route to persuasion (Petty et al., 2009) In addition to the motivational factors such as need for cognition, ability factors including prior knowledge, also impact the route through which persuasi ve messages will be processed. Ability to P rocess When individuals have the motivation to think about a message, they must also have the ability to process the message in order to develop lasting attitudes (Petty et al., 2009). Ability factors are those t hat naturally affect an individuals processing of a

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52 message, without conscious intent (Petty & Cacioppo, 1986). An individuals ability to process a message may be affected by repetition, distraction, the presentation of the message, and existing knowledg e or previous experience with the topic (Petty & Cacioppo, 1996) Prior K nowledge Prior k nowledge is one variable that affects an individuals ability to process a message. If individuals know about an issue being discussed in a persuasive message, then they are likely to process the information more carefully and with more thought (Perloff, 2003). Prior k nowledge about an issue allows individuals to be able to separate the issuerelevant arguments from information that is less central to the issue at hand (Wood, Rhodes, & Biek, 1995) In addition, prior knowledge allows individuals to identify shortcomings in the information presented (Wood et al., 1995) Individuals with less knowledge about an issue are less likely to be able to identify between strong and weak arguments and will have less confidence in their attitudes and opinions (Perloff, 2003). Therefore, individuals with more knowledge about an issue are more likely to process information through the central route, while those with less knowledge are more likely to process information through the peripheral route (Perloff, 2003). When considering motivation and ability to process persuasive information, Petty et al ( 1981) suggested that any variable that increases the likelihood that people will be motivated and able to engage in the difficult tasks of evaluating the message arguments increases the likelihood of the centr al route to persuasion (p. 854). Those variables that decrease an individuals motivation and ability to cognitively process a mes sage increase the likelihood of peripheral processing (Petty et al. 1981). Now that

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53 ELM has been discussed, the following sections will discuss recent studies that have used ELM as well as agriculturally based studies that have used ELM. Recent ELM R esear ch A review of recent literature found studies examining the use of ELM in a variety of context s. A study by Street, Dougl as, Geiger, and Martinko (2001) sought to examine the amount of cognitive effort that decision makers exhibit when resolving ethical i ssues by presenting an integrated model of ethical decision making. The integrated model which integrates the issue contingent model (ICM) (Jones, 1991) and ELM is titled The Cognitive Elaboration Model of E thical Decision Making. The integrated model was created because the existing ethical decision making models did not address the cognitive effort of decision makers and their likelihood to recognize ethical or moral issues (Street et al., 2001). By incorporating ELM into ICM the authors suggested that those with high cognitive levels would recognize ethical and moral issues and be more likely to act morally, while those with low cognitive levels would be less likely to recognize the issues and act morally. The creation of this model and incorporation of ELM allowe d the authors to incorporate an explanat ory component to moral decisionmaking (Street et al., 2001). In a study by Tam and Ho (2005), researchers used W eb personalization to enhance persuasion and assessed the effects through ELM. Web personalization uses technology to provide the right content in the right format to the right person at the right time (Tam & Ho, 2005, p. 271). At the height of W eb personalization popularity, the researchers identi fied that the effectiveness of W eb personaliz ation had not been examined. To examine if W eb personalization increased persuasion the researcher s specifically looked at preference matching, sort cues, and recommendation set si ze.

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54 Preference matching included the generation of W eb content to match the preference and personality of the user. Sort c ues have been used by websites to sort the most popular information. For example, Amazon.com uses sort cues to recommend items similar to ones that an individual views or buys. Recommendation set size included how many personalized recommendations are given to individuals. In this study researchers used an online experiment to test W eb personalization. Partici pants were first invited to a free ring tone website and wer e initially asked to fill out a short survey to gather information about their demographics and personality. Then participants were assigned to treatment groups and were directed to a website that captured their personalized ring tone preferences. The results of the study indicated that preference m atching increased the persuasion and the likelihood that participants w ould accept personalized offers, therefore providing implications for central processing in ELM. Additionally, the researchers had predicted that sorting cues were equivalent to peripheral cues a nd would result in peripheral processing, but the sorting cues were found to be more influential than would be expected from a peripheral variable. The researchers suggested this finding adds to ELM indicatin g that when participants are given a c hoice those variables that may normally be peripheral become more central to the individual (Tam & Ho, 2005) These researchers demonstrate that as technology advances, theories need to be retested to identify if new variables impact intended results. A re search study conducted in 2000, examined the persuasive influence of group opinions on those with high and low need for cognition using ELM and HSM (Areni, Ferrell, & Wilcox) Using college students, the researchers assessed the influence of group opinions at three points in a semester. To alleviate sensitization over these time

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55 periods participants were asked to answer questions about several social issues without knowledge of the issue being assessed. In the first administration period a narrative was gi ven, in the second period quantitative poll results were given, and in the third administration period arguments surrounding the issue were presented by other university students. The results showed that the influence of group opinions increased throughout the time periods for those with a low need for cognition. However, low need for cognition individuals demonstrated rationalization in their attitudes when asked to report their attitudes. High need for cognition individuals were only influenced by group opinions in the third administration period. The researchers suggested that high need for cognition participants were influenced in the third administration period because the presentation of issuerelevant arguments likely biased their evaluations of the issue. An expanded view of persuasion was recommended as a result of this research. The researchers suggested that low need for cognition individuals may attribute their attitudes to valid information when given the chance, and high need for cognition ind ividuals may be influenced by simple cues when they are linked to issuerelevant information. Therefore a recommendation was made to expand persuasion to include these specific circumstances which may alter the traditional views of persuasion (Areni et al, 2000). Peripheral cues in advertising were the focus of a study conducted by Dotson and Hyatt (2000). These researchers assessed the impact of a religious symbol in advertising for a nonreligious product. The advertisement presented to the participants was for pet insurance and included a picture of a dog sitting in front of a fireplace with a cross hanging above the mantle. All of the participants reported the presence of the

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56 cross in an openended unaided recall of the advertisement. However, those participants with low involvement (not interested in pet insurance) were not influenced by the presence of the cross. The researchers suggested that this finding conflicts with ELM because under E LM the positive association of the cross as a peripheral cue should have influenced low involvement participants to have a positive attitude toward the advertisement. High involvement (interested in pet insurance) individuals reported favorable attitudes toward the advertisement. Researchers determined that the favorability was likely due to cognitive processing and not the cross. In conclusion, the researchers suggested that the influence of a peripheral cue might have boundaries in ELM. The researchers stated, This research indicates that it is not possible to view peripheral cues in a deterministic waythe symbolic associations that a particular cue has with different audiences will affect the way that cue operates (Dotson & Hyatt, 2000, p. 67) A review of recent literature shows that ELM has been used in a var iety of context s. For some researchers ELM provides a missing piece to existing models as demonstrated by Street et al. (2001). Other researchers have identified circumstances that call for a need to expand or revise parts of ELM (Areni et al., 2000; Dots on & Hyatt, 2000; Tam & Ho, 2005). Despite the identification of some circumstances suggesting an expansion or revision to ELM, all researcher s had findings that supported components of ELM. Research I nvolving ELM and Agriculture and Natural R esources E xamining studies that have included ELM in an agriculture or natural resources context is important for this research In 2005, Verbeke conducted a literature review to look at how information about agriculture and food is communicated in the Information

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57 A g e and the challenges associated with communicating about food. The author first bega n by discussing the gap that exists between consumers and the food industry, as well as the associated challenges Verbeke identified that the psychological domains of uncertainty, involvement, personality, and knowledge all affect a consumers need for information. When discussing the need for information, ELM and the processing of information was also discussed. A n examination of previous research found that because of consumer uncertainty regarding food, information and decisions about food have been commonly based on simple cues and processed through the peripheral route ( Frewer, Howard, Hedderley, & Shepherd, 1997; Verbeke, 2005) Additionally, Verbeke suggested that inf ormation processing about food information is contingent on consumers wanting or needing foodrelated information, and thus seeking that information out. In the discussion of involvement, food was identified as a low involvement good (Beharrell & Denison, 1995) again increasing the likelihood of peripheral processing. In conclusion, the author indicated that improving the information communicated to consumers about food and agriculture would only be effective if the consumers were willing to access and process the information (Verbeke, 2005). The literature review by Ver beke (2005) included discussion about a study conducted by Frewer, Howard, Hedderley, and Shepherd (1997) regarding food risks. This study used ELM to look at the effects of source credibi lity, persuasive content, and personal relevance on attitudes about food risk. Using an experiment the researchers studied the effect of food risk messages, which were separated into high and low risk categories. The results of the study showed medi ca l so urces of food risk information were more favorable and produced positive attitudes. Additionally, messages that

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58 contained highly persuasive information were also seen as more favorable and were regarded as having more accurate and factual information. When the food risk information had highpersuasive information, the participants also saw it as more personally relevant In conclusion the authors found that ELM was useful in determining how to most effectively communicate risk information (Frewer et al., 1997). A study by Gore, Knuth, Scherer, and Curtis (2008) used ELM to test the effectiveness of wildlife education and outreach programs. The researchers used ELM to predict environmentally responsible attitudes. Wildlife related risk perceptions served as the attitude of interest for this study. The participants in this study were presented with educational materials about reducing humanblack bear conflict. The materials were presor ted into peripheral and central based categories by the researchers The r esults of the study showed that the majority of the participants in this study processed the materials peripherally. In addition, the participants showed favorability toward the materials that were classified as peripheral by the researchers. The researchers concluded that the peripheral attitudes produced by the educational m aterials would not provide longterm impact and thus would not positively improve behavioral intentions toward wildlife interactions (Gore et al., 2008). A similar stud y examined students attitudes toward two characters in a wilderness education skit (Hendricks, 2000) The two characters in the skit included the impact monster, also known as the bad hiker, who demonstrated inappropriate behavior in the wilderness and the good guy also known as the good hiker or good wilderness ranger, who demonstrated appropriate behavior in the wilderness. Researchers used ELM as the theory base for this study because message source can

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59 provide negative and positive cues, ultimately impacting processing route and attitude. The study was conducted because administrators of the educational program feared that the impact monster could have been vi ewed as cool rather than bad, resulting in the production of unfavorable behaviors. The results of the study showed that students developed correct attitudes in relation to the impact monster. Additionally, the results indicated that the students participating in the study found the wilderness hiker to be more favorable than the wilderness ranger. The researchers suggested this result is likely due to the connection students felt with the hiker who assumed a role similar to that of a peer, thus fitting the personal schema of the students. The authors recommended further examination to determine how the skit impacts behavior (Hendricks, 2000). Using ELM to examine the relationships between message frames and the resulting effects on cognitive processing, Lundy (2004) presented extension agents with persuasive information about the benefits of internati onalizing the extension service. The frames used included mutual benefit and moral norms. The results of the study indicated the frames used in the persuasive communication did not result in differences in extension agent s attitudes toward internationaliz ing extension or the perceived message quality. However, the frames did affect message elaboration differently. Issue involvement and need for cognition influenced attitudes toward internationalizing extension more than the message frames. Lundy (2004) concluded that framing should be considered in ELM because frames were shown to have an influence on elaboration. In 2008, Meyers conducted a study to examine the effect of persuasive communication on influencing media coverage of biotechnology. Using ELM as the

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60 foundation, the study also incorporated positively framed messages on the topics of health, scientific progress, and economics. The participants for this study included both science and agricultural science communicators and journalists. The results of the study indicated that those with high involvement and positive preexisting attitudes toward biotechnology had a higher mean score toward argument quality. Additionally, mean scores for argument quality were higher for those receiving messages from the health or scientific progress frame, while they were lower for those receiving the economic frame. Despite the positive influence on attitudes toward argument quality, the persuasive communication did not significantly affect the communicators likelihood to publish biotechnology stories. Meyers (2008) suggested this result might be due to other factors that make stories newsworthy such as human interest, issue prominence, number of people affected, controversy or conflict, uniqueness, proximity, timeliness, and locality (Meyers, 2008; Shoemaker & Reese, 1991). The science communicators in the study were found to have more unfavorable attitudes toward biotechnology and were thus harder to persuade than the agricultural science communicators. The author concluded that further research should be done to assess the effects of framing within ELM (Meyers, 2008). A review of ELM studies conducted in the agriculture or natural resource context has show n that many of these studies found instances of peripheral proc essing (Frewer et al., 1997; Gore et al., 2008; Hendricks, 2000; Veberke, 2005). The prevalence of peripheral processing suggest s that individuals may not have the motivation and ability to process information about agriculture and natural resources. Addit ionally, studies by

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61 Lundy (2004) and Meyers (2008) indicated that framing can provide an additional link to ELM, which if done correctly may be beneficial to agricultural communication. Public Relations Public relations (PR) is defined by Moore and Canf ield (1977) as a social philosophy of management expressed in policies and practices, which, through sensitive interpretation of events based upon twoway communication with its publics, strives to secure mutual understanding and goodwill (p. 6). Grunig and Hunt (1984) discussed PR as management of communication between an organization and its public s (p. 6). Public relations help organizations understand their audience and fulfill organizational goals (Diggs Brown, 2012). Once known as press agentry and then public relations council ( Grunig & Hunt, 1984), the PR field has evolved over time to be part corporate and nonprofit businesses, politics and government, and social reform movements (Diggs Brown, 2012) Additionally, PR has become an academic c ourse of study and an important discipline in the communication field (Diggs Brown, 2012). Grunig and Hunt (1984) developed four models of public relations including the press/agentry model, public information model, twoway asymmetric model, and twoway symmetric model. The press/agentry model and the public information model are oneway models of communication indicating communication occurs in one direction, from the organization to the public (Grunig & Hunt, 1984). The asymmetric and symmetric two way communication models include communication from the organization to the public, as well as communication from the public to the organization (Grunig & Hunt, 1984). The press/agentry model focuses on communication from the organization to the public. This communication includes information that the organizat ion wants the public

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62 to believe. Truthful information is not seen as essential in this model (Grunig & Hunt, 1984; Diggs Brown, 2012). Under the press/agentry model organizations know little about their target audience (Diggs Brown, 2012). The public information model differs from the press/agentry model in that providing the truth becomes important in the oneway communication from the organization to the public (Grunig & Hunt, 1984). Diggs Brown (2012) regarded the public information model as the journalistic approach to PR, suggesting that truthful information would be given, but damaging information would be left out. When Grunig and Hunt (1984) developed these models, they suggested the public infor mation model was used most frequently. The twoway asymmetric model of communication involves communication back and forth between an organization and the public, but is labeled as asymmetric because th e organization does not change while, the individuals that make up the public do change ( Grunig & Hunt, 1984). Organizations use the twoway asymmetric model to elicit attitude and behavior change. Therefore, organizations carefully and strategically communicat e with the public, but the feedback provided by t he public is not taken into consideration or seen as influential by the organization. Dif fering from this idea, the twow ay model of symmetric communication involves communication back and forth between an organization and the public where both can influence attitude and behavior change in each other (Grunig & Hunt 1984). In other words, the public is just as likely to influence a change in an organization s attitudes and behavior as the organization is to influence and change in the publics attitudes and behaviors With a more balanced communication process, Grunig and Hunt (1984) suggested that symmetrical communication may result in no attitude or behavior change because the increased

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63 communication will add to the understanding of each other decreasing the potential for influence. Diggs Brown (2012) identified the twoway model of symmetric communication as the best PR model and a model that provides benefits to all involved. Public Relations and E thics Gru nig and Hunt (1984) suggested an organization with favorable PR demonstrates public responsibility and is ethical in all aspect of business. Additionally, good PR requires a management that speak[s] truthfully, act[s] fairly, and deal[s] honorably (Moore & Canfield, 1977, p. 23). Those who are ethic al act in ways to ensure honesty and trustworthiness (Grunig & Hunt, 1984). Ethical organizations and individuals do not act in any way that will negatively affect others, except in circumstances where it may be unavoidable (Grunig & Hunt, 1984). Throughout PR literature several codes of ethics and models of ethical communication are discussed (Diggs Brown, 2012). The model of social responsibility or corporate social responsibility (CSR) is related to trust and transparency (Rawlins, 2008a). CSR has been defined as undertaking business in an ethical way in order to achieve sustainable development, not only in economic terms, but also in the social and environmental sphere (Filizoz & Fisne, 2011, p. 1406). CSR deals directly with business and society relationships (Carroll, 1999; Diggs Brown, 2012). Economic, legal, ethical, and philanthropic categories have identified as important to CSR (Carroll, 1991). CSR is expected to continue to become more focused on business responsibilities to communities and s takeholders on a global level (Carroll, 1999). Companies that have incorporated transparency have been seen as being more trustworthy and loyal to stakeholders, thus also being regarded as ethical and having adequate CSR (Rawlins, 2008a).

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64 Public R elations a nd Social M edia A s social media has evolved and I nternet technology has advanced to support two way communication, social media has become an important PR tool (Diggs Brown, 2012) Scholars in the PR field recommended that social media be a part of all pr actitioners PR strategies (Wright & Hinson, 2008). To achieve PR success through social media, the technology and its audiences must be adequately understood (Diggs Brown, 2012). Despite the support for social media in the PR industry, limited research has tested the effectiveness of social media as a PR tool (Taylor & Kent, 2010). A study by Waters, Burnett, Lamm, and Lucas (2009) found that nonprofit organizations were using social media to promote openness and transparency, but were not utilizing the p otential of social media tools from a PR standpoint. In 2009, Wright and Hinson (2009) reported that PR actioners were commending social media as a PR tool to increase two way communication through direct communication channels. Those PR professionals who are skeptical of social media have suggested that the use of social media does not have as much value as practitioners think and the risks of using social media as PR are underestimated (Taylor & Kent, 2010). Taylor and Kent (2010) suggested that social media should be incorporated into PR strategies, but should not be the only P R tool used by practitioners. Trust The academic study of trust has been described as complex and is a construct that many approach with different definitions and measurement strategies (Mayer, David, & Schoorman, 1995; Rawlins, 2007; Rousseau, Sitkin, Burt, & Camerer, 1998; Tschannen Moran & Hoy, 2000) Trust has been studied as a personality trait, as a component to social interactions, and from an organizational standpoint (Rawlins,

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65 2007). TchannenMoran and Hoy (2000) defined trust as one partys willingness to be vulnerable to another party based on the confidence that the latter party is (a) benevolent, (b) reliable, (c) competent, (d) honest, and (e) open (p. 556). Rawlins (2 008b) adjusted this definition slightly to more closely align with the organizationpublic relationship literature. Rawlins (2008b) defined trust as one partys willingness to be vulnerable to another party based on the confidence that the latter party is competent and dependable, has integrity, and acts with goodwill (p. 5). In todays society, the public must seek trust in a variety of organizations and entit ies (TschannenMoran & Hoy, 2000). TschannenMoran and Hoy (2000) provided the example that soci ety depends on those organizations or individuals who grow and supply food, and therefore society must find trust in knowing that quality food will be supplied. When this trust is broken, organizations suffer and the public seeks to find trust in others (T schannenMoran & Hoy, 2000) Americans have been discussed as becoming increasingly distrustful of organizations and leaders (TschannenMoran & Hoy, 2000). Barber (1983) suggested that Americans distrust could be attributed to the fast paced changes of so cietal values including increased knowledge and equality. From an organizational perspective, trust has increased in value and is now regarded as essential to successful public relations (Rawlins, 2007). Organizational trust has been defined as a collect ive judgment of one group that another group will be honest, meet commitments, and will not take advantage of others (Rawlins, 2007, para 13). Trust adds strength to organizations cooperation, communication, and relationship building (TschannenMoran & H oy, 2000). Rawlins (2007) suggested organizational

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66 trust involves both group and interpersonal components. In this context, individuals or the public are trustors who determine how much trust they have toward an organization. Several different components of trust have been discussed. In their review of the literature, Mayer et al. (1995) reviewed 23 different academic studies examining trust. The factors of trust identified throughout these studies varied significantly and represented a widerange of factors and combination of factors. Tschannen Moran and Hoy (2000) discussed that willingness to risk vulnerability, confidence, benevolence, reliability, competence, honesty, and openness were elements that collectively form the concept of trust. These element s of trust were described as having variable influence on trust depending on the situation (Tschannen Moran & Hoy, 2000). Mayer et al. (1995) indicated that the factors of trustworthiness included ability, benevolence, and integrity. They discussed ability as being similar to competence (Mayer et al., 1995) TschannenMoran and Hoy (2000) recognized benevolence as the most agreedupon and common element of trust. Hon and Grunig (1999) identified the factors of trust as being dependability, competence, and i ntegrity. Rawlins (2007) approached trust from an organizational p ublic relationship perspective, modified Hon and Grunigs (1999) factors of trust slightly, and suggested that trust included the factors of competence, integrity, and goodwill. Goodwill, in this context, was discussed as being similar to benevolence (Auger, 2011). C ompetence, integrity, and goodwill were factors used by both Rawlins (2008b) and Auger (2011) to examine the relationship between trust and transparency. Trust and A griculture The role of trust in the agriculture industry has been explored, especially in regards to trus t in food and food production. Trust is important to the agricultural industry because it strengthens relationships with the public and it fills the void in

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67 agricul tural knowledge that has been created by the increasing divide between production and consumption (Jokinen, Kupsala, & Vinnari, 2012). In recent years, the trust in food and food production has declined due to scandals and food recalls (Jokinen et al., 2012). As the food system has become increasingly complex, as the gap between food production and consumers has grown and the industrialized and rationalized food system has been associated with a diversity of new, humanmade risks, the issue of trust has be come a central question in farm animal welfare politics (Jokinen et al., 2012, p. 106). Trust in agricultural and food systems can be explained by three different bases of trust including emotional trust, habitual trust, and reflexive trust ( Bildtgard 2008). Emotional trust refers to the personal trust relations that people who are connected emotionally have with each other. The emotional trust that the public had toward the agricultural industry was historically very strong because they were close to people working in the industry, they were neighbors, they were family, and they were friends (Bildtgard, 2008). However, that emotional connection and trust has been lost for a lot of people because industrialization has removed the majority of the public from the proximity of those in the industry (Bildtgard, 2008). Habitual trust refers to trust that is based on the fact that something has always occurred in the same fashion and nothing has happened to disrupt this trust (Bildtgard, 2008). For example, peopl e may trust a certain food product because nothing has ever happened to disrupt their trust in that food product. Habitual trust in agriculture and food is decreasing, as more agricultural controversies are made public (Jokinen et al., 2012). Lastly, refle xive trust refers to the idea that because of the plethora of information and growth of scientific knowledge people are forced to make decisions about what information, systems, and actors to trust and which to not trust (Bildtgard, 2008). Trust in the agr iculture industry is

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68 increasingly becoming more reflexive (Billdtgard, 2008). Due to the plethora of information and conflicting information, individuals are overwhelmed with information and have to make decisions about who to trust and who not to trust In the agricultural industry trust can also lead to changes in consumption behavior (Baron, Hershey, & Kunreuther, 2000). Consumers are motivated to engage in behavi ors that satisfy their concerns. If consumers develop concerns with a company or industry t hat they used to trust and bu y products from, then they are likely to develop distrust and discontinue buying those products (Baron, Hershey, & Kunreuther, 2000) Meijboom, Visak, and Brom (2006) indicat e that trust will continue to be an issue in agricult ure and food systems because of technology, the distance between production and consumption, and the association with scandals. Technology impacts trust because it is commonly developed without the evaluation, approval, and acceptance of the public (Meijboom et al., 2006) In addition, the distance between production and consumption will continue to be an issue because the distance creates a feeling of loss of control among the public (Meijboom et al., 2006). Controlling the trust of others is difficult and often unsuccessful (Meijboom et al., 2006). The more effective strategy to regaining or strengthening trust is for an organization to become more trustworthy. Increased trustworthiness can be accomplished through increased transparency and traceabilit y ( Meijboom et al. 2006). In addition, demonstrating responsibility, taking steps to be proactive, avoiding the blame game, and incorporating tenants of corporate social responsibility can increase trustworthiness. Increasing trustworthiness of an organizati on also increases the trust the public has in that organization (Meijboom et al., 2006).

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69 Transparency The concept of transparency can be traced back to the 1890s with the work of Henry Carter Adams a public finance academic who discussed the role of public ity in the case of corporate abuses ( Bigelow, Sharfman, & Wenley, 1922; Stoker & Rawlins, 2004). The value of transparency has been growing in interest (Rawlins, 2008a), especially in the fields of business management, public relations, and democratic government (Fairbanks, Plowman, & Rawlins 2007). Transparency has been discussed in public relations literature for several decades (Fairbanks et al. 2007) Despite the discussion s of transparency over time, very little academic research has tes ted the effec ts of transparency due to the challenges involved with defining and measuring the construct (Rawlins, 2008b). A r eview of the literature included several qualitative assessments of transparency ( Barling, Sharpe, & Lang, 2009; Fairbanks et al., 2007; Jahansoozi, 2006; Meijer, 2009). Two quantitative studies were found that tested the relationship between transparency and trust (Auger, 2011; Rawlins, 2008b). Transparency was defined by some as the opposite of secrecy ( Florini, 1998; Rawlins, 2008a) Opennes s was another term often used to describe transparency, especially in organizationpublic relationship literature (Jahansoozi, 2006; Rawlins, 2008b). Cotterrell (1999) defined transparency as the availability of information on matters of public concern, the ability of citizens to participate in political decisions, and the accountability of government to public opinion or legal processes (p. 414). Rawlins (2008a) offered a comprehensive definition of transparency, which was used for this research. Trans parency is the deliberate attempt to make available all legally releasable information whether positive or negative in nature in a manner that is accurate, timely, balanced, and unequivocal, for the purpose

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70 of enhancing the reasoning ability of p ublics and holding organizations accountable for their actions, policies, and practices (p. 75). Organizational Transparency In the development of his transparency instrument, Rawlins (2008a) suggested that transparency could be assessed both from an organizati onal and communicative standpoint. Organizational transparency is identified as transparency that can be controlled through public relations and provides nonfinancial intangible assets (Rawlins, 2008a). Organizational transparency relates to organizationa l traits within an organization, which result from being accurate, timely, balanced, and unequivocal (Auger, 2011). Another name for organizational transparency is reputational transparency (Rawlins, 2008a) Organizational transparency is directly linked t o an organizations reputation and is determined by the stakeholder. The variables included in organizational transparency are integrity, respect, and openness. Respect and openness were proven to be very important to determining organizational transparenc y, while integrity, although still important to transparency, may be more highly related to trust (Rawlins, 2008a). Communicative Transparency Instead of being focused on organizational traits communicative transparency focuses on communicative traits su ch as being accurate, timely, balanced, and unequivocal (Auger, 2011). Communicative transparency includes the variables of substantial information, participation, accountability, and secrecy (opposite of openness) (Rawlins, 2008a). Substantial information has been discussed as the amount and type of information needed by individual s The relevance, clarity, completeness, accuracy,

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71 reliability, timeliness, and comparability of information impact whether or not the information is substantial (Rawlins, 2008b). Without adequate knowledge of the information wanted and needed by the stakeholders, organizations cannot guarantee they are achieving transparency through substantial information (Rawlins, 2008b). Participation includes the interaction and feedback between organizations and stakeholders (Auger, 2011). This trait of communicative transparency includes involvement, feedback, detailed information, the ease of finding information, and the initiative by the organization to understand and ask for stakeholder opinions (Rawlins, 2008b). Additionally, organizations must invite stakeholders to participate, and organizations must provide responses when stakeholders participate. The participation variable of communicative transparency highlights the active partici pation in acquiring, distributing and creating knowledge (p. 419), a requirement of transparency identified by Cotterrell (1999). Accountability in communicative transparency includes information that covers more than one side of controversial issues, mi ght be damaging to the organization, admitting mistakes, and that can be compared to industry standards (Rawlins, 2008b, p. 431). Additionally, accountability includes an organization being open to criticism and being forthcoming (Rawlins, 2008a). Those organizations that are transparent have been identified as being accountable for their words, actions, and decisions ( Rawlins 2008b). Secrecy was a reversecoded variable included by Rawlins (2008a). Therefore, this variable includes minimal openness and s ecretive information (Rawlins, 2008b).

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72 T he secrecy trait is present when information is withheld, blame is placed on others, information is unclear, and an organization is slow to inform (Rawlins, 2008a) Transparency and Agriculture The agricultural indus try has determined that steps must be taken to regain consumer trust and confidence with increasing public concern about food safety and quality, due to scandals, incidents, and ethical concerns with the industry ( Barling et al., 2009; Beulens et al., 2005; Opara & Mazaud, 2011). Transparency has been identified as a potential solution to this problem ( Barling et al., 2009; Beulens et al., 2005; Opara & Mazaud, 2001). Farmers and other upstream operators have been called upon to engage in more direct relat ionships with end consumers (Weatherell, Tregear, & Allinson, 2003, p. 233). Additionally, transparency has been identified as a solution to reconnect people with the origin of their food (Hoogland et al., 2005). Some agricultural researchers have suggest ed that transparency needs have increased, especially in the area of food safety. This has led many organizations within the agricultural industry to look at transparency through the concepts of tracking and tracing through the food supply chain ( Barling e t al., 2009; Beulens et al., 2005; Opara & Mazaud, 2001; Miller & Mariani, 2010; van Dorp, 2003; Wognum et al., 2011). Good quality assurance programs have also been identified as a way to increase transparency and show how food gets from farm to plate (Opara & Mazaud, 2001). Transparency has been discussed as having the potential to benefit all sectors of the agriculture industry (van Dorp, 2003). W hen planning production practices researchers have suggested that agriculturalists should make an effort to i ncrease transparency for all of their stakeholders (DeGreef, Stafleu, & DeLauwerence, 2006).

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73 Despite the benefits that transparency may provide to the agricultural industr y, the literature also discussed some concerns with transparency. Some of these conc erns include d the move from reactive to proactive management, the cost of implementing transparency, and the unauthorized use of information that may result from being transparent ( Beulens et al., 2005). Industry wide buy in toward transparent practices has also been raised as a concern (Barling et al., 2009). Some of these concerns may be heightened when discussing the concept of computer mediated transparency. Computer Mediated Transparency Computer mediated transparency is defined as the ability to look clearly through the windows of an institution through the use of a computerized system (Meijer, 2009, p. 259). Several scholars have identified that advancing technology and the I nternet make transparency a reality, suggesting that no information can be kept secret or discrete in todays environment (Meyer, 2003; Oliver, 2004; Qualman, 2009). However, Meijer (2009) discussed the potential problems associated with computer med iated transparency and addressed the views of proponents and opponents of transparency. Three perspectives of computer mediated transparency were discussed. These include d the premodernists, modernists, and post modernists perspectives (Meijer, 2009) Pre modernists believe that traditional trust mechanisms are threatened by computer mediated transparency. However, modernists have favored computer mediated transparency because they believe that providing objective information to the public will increase trust. Post modernists value the esthetic nature of computer mediated transparency (Meijer, 2009). Online transparency has been discussed as decreasing corruption and increasing accountability, but as suggested by Meijer (2009) a debate exists

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74 surrounding the validity of transparency. The proponents of computer mediated transparency have tended to be modernists (Meijer, 2009) Proponents have suggested that complete and substantial information available online allows for the public to make informed decisions (Meijer, 2009) Additionally, proponents have indicated that increased morality ability, and decreased corruption have also been benefits of computer mediated transparency (Meijer, 2009) Computer mediated transparency has been cited by proponents as being required for success in politic s, business, and personal life (Oliver, 2004; Meijer, 2009). A more democratic and affluent society has also been identified as a benefit resulting from computer mediated transparency (Meijer, 2009) Despite the suggested benefits of computer mediated transparency, opponents have developed an argument against computer mediated transparency. The opponents of computer mediated transparency have typically held the premodern perspective (Meijer, 2009). Opponents have argued that computer mediated transparency cannot offer some of the benefits that have been suggested by the proponents, such as a more affluent and democratic society (Meijer, 2009) Additionally, opponents have indicated that online information can be unsorted and incorrect, leading to increased confusion and decreased understanding (Meijer, 2009; ONeill, 2002). Opponents have also criticized the nature of online communication. They have argued that online communication typically involves oneway communication, rather than the more effective two way model of communication (Meijer, 2009). I n addition, the structure and decontextualization of online communication has been discussed as prohibiting a natural communication experience that individuals may be used to from face to face

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75 communication (Meijer, 2009) Opponents of computer mediated t ransparency have suggested that transparency gives organizations the opportunity to massage the truth (Meijer, 2009; ONeill, 2002). Together these criticisms of transparency have led opponents to believe that computer mediated transparency may reduce r ather than strengthen trust (Meijer, 2009). Although different perspectives have exist ed regarding computer mediated transparency, no research was found that tested the effect of computer mediated transparency. Benefits of Transparency T ransparent practic es have been considered by several individuals and businesses because of the benefits associated with t hese practices (Jahansoozi, 2006; Rawlins, 2008a) One benefit of transparency that has been discussed is the access to information and knowledge that tr ansparency provides individuals enabling them to increase their reasoning ability and make informed decisions ( Fagotto & Graham, 2007; Rawlins, 2008a). For stakeholders, transparent communication has offered increased competence and understanding ( Tampere, 2007). From the business per spective, transparency has been viewed as an intangible asset (Rawlins, 2008a) and regarded by some as essential to business success (Oliver, 2004) Transparency has been identified as a way to reverse distrust and skepticism caused by decept ive practices of corporations (Rawlins, 2008a). Regaining trust after a crisis has also been recognized as a benefit of transparency (Jahansoozi, 2006). Additionally, transparency has been discussed as promoting accountability, commitment, collaboration, and cooperation among organizations ( Jahansoozi, 2006).

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76 These benefits of transparency may increase the ethical nature of a company (Rawlins, 2008a). Transparency offers benefits at both the stakeholder and organization level. These benefits have led businesses to explore the use of transparent communication and practices. However, challenges and barriers to transparency have also been identified. Challenges of Transparency Transparency has the ability to be beneficial to organizations, but t here are also many challenges associated with being transparent. One challenge of transparency identified is that the release of information does not automatically equate to transparency ( Rawlins, 2008a; Rawlins, 2008b). Strathern (2000) suggested that t oo much information could reverse the benefits of transparency, resulting in less understanding and trust. Stakeholders must define the amount and type of information needed (Cotterrell, 1999; Stirton & Lodge, 2001). Once defined, organizations must release substantial and balanced information that is useful to the audience (Rawlins, 2008b) Additionally, to be impactful and effective, Cotterrell ( 1999 ) discussed that transparency has to spread beyond information to the distribution and creation of knowledge. A nother challenge recognized when releasing transparent information is stakeholders may not notice the new information or know what to do with new information (Fagotto & Graham, 2007). Transparency however, is not just dependent on organizations; to be successful the stakeholders must also perceive an organization as being transparent (Gower, 2006). Another challenge of transparency is that it exposes the weaknesses of an organization (Rawlins, 2008a). This exposure has been discussed as leading

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77 organizat ion s to feel uncomfortable and vulnerable. However, transparency will also challenge organizations to recognize their weakness and develop a plan to turn their weaknesses into strengths (Rawlins, 2008a) With the challenge of exposed weaknesses and vulnerability, organizations are also challenged with trusting their stakeholders with the information they provide (Rawlins, 2008b). Beulens et al. (2005) identified challenges of transparency in their case study. These challenges included the unauthorized use of information, the costs associated with transparency, uncertainty regarding the longterm profitab le benefits of transparency, a loss of independence, and a proactive management style ( Beulens et al., 2005). Another barrier of transparency discussed by B arling et al. (2009) was cost. Gaining buy in for transparency throughout an industry, consistent trans parency standards throughout an industry, and identifying areas of ethical concerns related to transparency within a business have also been identified as challenges of transparency (Barling et al., 2009). The benefits and challenges of transparency have been examined by organizations considering the implementation of transparent communication. The effects of transparency, although limited in academic res earch, have begun to be assessed by researchers. Research Involving Transparency Transparency has been know n as a construct that is both difficult to define and difficult to measure. Rawlins (2008a) established that a transparency measurement is only beneficial if measured from a stakeholder perspective. Additionally, Rawlins (2008a) developed an instrument to measure both organizational and communicative transparency. The instrument includes semantic differential scales to measure

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78 organizational transparency and seven point Likert type agreement scales to measure communicative transparency. The instrument was qualitatively pretested for understanding and then underwent an exploratory factor analysis, confirmatory factor analysis using structure equation modeling, and Cronbachs reliability analysis Following these tests, 17 items were eliminated from the instrument due to poor loading, and the final instrument included 40 items (Rawlins, 2008a). Since its creation, the instrument has been used in two transparency studies (Auger, 2011; Rawlins, 2008b) Using his previously developed instrument, Rawlins (2008b) tested the relationship between trust and transparency with 361 employees from a healthcare organization. The research found a positive relationship between trust and transparency. However, Rawlins (2008b) noted that since this study was limited to employees, the results might have been different if the participants had been external stakeholders. Additionally, Rawlins (2008b) noted some multi colli near ity problems with this research. Rawlinss (2008a) instrument was also used to examine the relationship between trust and transparency by Auger (2011). In this study members of the general public were presented with fictitious news articles about how either a nonprofit or for profit organization dealt with a crisis. Different transparency treatments were applied throughout the articles. Participants were then asked about their perceptions of organizational transparency, communicative transparency, and behavioral intentions. The results of this study indicated the organizations with higher organizational and communicative transparency were more trusted and have greater behavioral intentions. Additionally, trust and behavioral intentions were greater for nonprofit organizations

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79 than for profit organizations in most cases (Auger, 2011) No multi collinearity issues were reported by Auger (2011). Prior to Rawlins (2008a) development of a transparency instrument, most transparency research had been qualitative in nature. Fairbanks et al (2007) conducted a qualitative study to examine the role of transparency in government communications. The participants in the study were government communicators and were selected using snow ball sampling. Semi structured i nterviews were conducted with 18 government communicators. During the interview s, the communicators discussed transparency as being essential to government communications and identified that with increased transparency came increased trust and credibility. Additionally, the communicators discussed the factors influencing the level of transparency that was communicated. These factors included personal, organizational, and resource factors For example, the level of transparency could be influenced if the com municator was scared about how the information could be perceived (personal), if the manager or administrator would not let information be released (organizational), and if time or staffing (resources) did not allow for complete transparency. Fairbanks et al. (2007) concluded that government communicators value and recognize the benefits of transparency. A phenomenological study conducted by Jahansoozi (2006) included 18 indepth interviews. The interviews were conducted with both community and industry m embers who were involved with the Sundre Petroleum Operators Group (SPOG). SPOG was developed after a crisis in order to increase transparency and restore trust. During the interviews, the participants recalled leadership training that SPOG had provided. This training and other steps taken by SPOG to be more transparent were identified as

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80 helping to rebuild trust and relationships. Participants in this study indicated the efforts to be transparent increased the accountability of those companies participati ng in SPOG and also gave the community evidence of the companies actions The companies involved in SPOG were identified as having a better understanding of community needs and a renewed value in community participation. In addition, SPOG had put pressure on other companies joining the community to also be transparent. Jahansoozi (2006 ) stated, Transparency has led the community to have a better understanding of what the industry is doing, the precautions they take and consideration for limiting the impac t of their operations on the community (p. 951). In this study transparency was discussed as an essential component to rebuilding trust and relationships following a crisis. With the development of Rawlins (2008a) transparency measure, researchers have quantitatively measured transparency. The tw o studies that have done so, found a positive relationship between trust and transparency. A review of the research completed prior to the development of the t ransparency instrument indicated that transparency has primarily been assessed in a qualitative manner. However, these studies found that transparency was regarded as an important construct and was discussed as increasing trust and building relationships. Research Involving Transparency and Agriculture A stu dy by B eulens et al. (2005) examined the challenges of food safety and transparency through a case study of European small and medium sized egg enterprises. Prior to the case study the researchers arguably stated that a common definition of transparency w as nonexistent ( Beulens et al., 2005) For this study, the research focused on transparency of a supply chain network and defined transparency

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81 as the extent to which all of the networks stakeholders have a shared understanding, and access to, product and process related information, that they request, without loss, noise, delay and distortion (Beulens et al., 2005, p. 482). The research found that transparency of the egg production practices w as commonly initiated and enforced at the retail or supply chain. Additionally the study identified transparency as an essential component for food safety and consumer trust ( Beulens et al., 2005) Through the case study Beulens et al. (2005) identified that organizational and psychological threats could inhibit t he implementation of transparency. Some of these threats included unauthorized use of information, the cost to implement transparency, and the unknown profit driven benefits ( Beulens et al., 2005). The researchers concluded by asking sever al questions about transparency including, h ow to measure transparency and how perceptions and attitudes differ across cultures and borders ( Beulens et al., 2005). Hoogland et al. (2005) discuss ed transparency of animal welfare, animal production, and animal slaughter as a way to encourage more sustainable food choices. The authors suggested that individuals were not exhibiting food consumption behaviors that aligned with their preferred livestock production practices. They stated As a result of the dissociation of the anim al from the food that is consumed, people's buying behaviour may unintentionally provide incentives for activities which they actually dislik e such as factory farming because their concern for animal welfare does not translate into corresponding food choices (Hoogland et al., 2005, p. 16). In order to assess the effect transparency may have on consumers food purchasing decisions, the researchers conducted an experiment that included priming manipulations to cause consumers to think about the origin of meat (Hoogland et al., 2005) The study was conducted in two European grocery stores with European consumers and found consumers are sensitive to reminders of meat origin when

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82 shopping (Hoogland et al., 2005) Therefore, the researchers concluded that incr eased transparency could cause consumers to prefer more sustainable food choices, which would cause the industry to comply with the consumers preference toward more sustainable food (Hoogland et al., 2005). Several studies have addressed transparency through discussion of tracing, tracking, and labeling of food products. Opara and Mazaud (2001) suggested consumers and other stakeholders in agroindustry now demand transparency in the way food is grown and handled throughout the supply chain, resultin g in t he emergence of 'traceability' as an important policy i ssue in food quality and safety (p. 239) A study by Barling et al. (2009) examined traceability and ethical concerns in the United Kingdom wheat bread chain. Through indepth interviews with stakehol ders of the wheat bread chain, participants identified a need for more transparency and they regarded transparency and traceability as the same (Barling et al., 2009) The participants also indicated that transparency would help differentiate each stage of the production chain (Barling et al., 2009) G aining buy in from the whol e industry, cost, and other barriers of transparency were also discussed by the participants (Barling et al., 2009). Another study looked at the emergence of transparency through beef labeling (van Dorp, 2003). The s tudy included two case studies one before and one after a Bovine Spongiform Encephalopathy ( BSE) crisis which sought to find out if a gap existed between the actual information being provided and the desired informat ion wanted by Dutch stakeholders involved in the beef supply chain. The study found that prior to the BSE crisis only product information was provided, but the stakeholders

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83 wanted more of this information. After the BSE crisis traceability increased and l abeling was legislated. I nformation was then provided from the production phase through the distribution phase, resulting in an overall increase in transparency (van Dorp, 2003) Traceability and transparency were also assessed in a study of food supply chains in the Netherlands (Wognum, Bremmers, Trienekens, van der Vorst, & Bloemhof, 2011). In this study the authors discussed the concepts of horizontal and vertical transparency which were originally identified by Kalfagianni (2006) Horizontal transparency was discussed as each company in a supply chain providing information individually to its stakeholders (Wognum et al., 2011) Vertical transparency was discussed as transparent communication provided to the stakeholders from the supply chain as a whole (Wognum et al., 2011) Traceability was discussed as a component of vertical transparency. Through surveys and indepth interviews the researchers identified that most companies were participating in horizontal transparency and traceability was not always accurate within the supply chain (Wognum et al., 2011). A study by Miller and Mariani (2010) examined the traceability and transparency in seafood. The research conducted a case study to examine if a sample of fish was labeled correctly. Using DNA test ing, the researchers found that 39 of 156 pieces of fish were mislabeled, indicating poor transparency. The researchers suggested the fish mislabeling was a huge problem and indicated, If transparency can be established within the seafood industries, focus could then be returned to the development of effective consumer awareness campaigns (Miller & Mariani, 2011, p. 521). The review of literature revealed the majority of studies surrounding the topic of agriculture and transparency were conducted in European countries. Additionally, many

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84 of the studies approached transparency from a qualitative perspective. Commonly, the researchers in these studies identified the lack of definition and research surrounding transparency. Many of the transparency and agricultural studies discuss ed transparency as the tracing, tracking, and labeling of food products. Conceptual Model of Transparency and Trust in ELM To describe how transparency and trust may impact attitude within ELM, a conceptual model ( Figure 22) has been developed based on Petty and Cacioppos ELM (2009), Rawlins transparency work (2008a; 2008b), and Petty and Priesters (1995) discussion of trust in ELM In th e conceptual model of transparency and trust in ELM the communication treatment causes individuals to determine their motivation and ability to process the information in the message. For this study, motivation to process will be determined by personal relevance. Both motivation and ability must be high in order for individuals to process and elaborate on the message. The next part of the conceptual model is perceived transparency. Perceived transparency in this model replaces argument quality from Petty & Cacioppos (2009) ELM. If motivation and ability are high, and perceived transparency increases thinking, then elaboration will lead to attitude and trust formation through central processing. If motivation and/or ability are not high, then at the perceived transparency stage individuals will bypass elaboration and move straight into attitude and t rust formation through peripheral processing. In this instance, perceived transparency may serve as a peripheral cue. At the bottom of the model, the twoway arrow between trust and attitude signifies the impact that these two dependent variables may have on each other. This conceptual model provides a graphical representation of how trust and transparency measures can be incorporated into ELM

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85 Summary Chapter 2 began with a discussion of attitudes and persuasion. Following this discussion, ELM was examined and recent studies using ELM were discussed. The review of recent ELM studies showed findings consistent with ELM, as well as findings suggesting a revision or expansion of ELM. The discussion then focused on public relations, trust, and transparency. The benefits and challenges of transparency were discussed, followed by a discussion of empirical transparency studies. The review of transparency literature showed a lack of quantitative transparency research. In addition, it showed how transparency research has evolved from qualitative studies to the development and early testing of an instrument. Transparency studies in the agriculture context were also assessed. This assessment showed that agricultural studies examining transparency were primarily qualitat ive, conducted in European countries, and focused on the tracing, tracking, and labeling of food products. To conclude Chapter 2 a conceptual model of trust and transparency in ELM was discussed. This model demonstrates how the tenets of transparency and t rust relate to ELM and overall attitude formation.

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86 Figure 21. The Elaboration Likelihood Model of persuasion (Petty et al., 2009)

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87 Figure 2 2. Conceptual model of trust and transparency in ELM

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88 CHAPTER 3 RESEARCH DESIGN AND METHODS The purpose of this study was to assess the effects of transparent communication and personal relevance, in a livestock production context, on the attitudes and trust of college students. To achieve this purpose, quantitative methodology and an experimental design was impleme nted. Quantitative methodology was appropriate for this study because it sought to test theory and hypotheses (Ary, Jacobs, Razavieh, & Sorensen, 2006). Previous research has shown a positive relationship between trust and transparency (Auger, 2011; Rawlin s, 2008b), thus as transparency increases, trust increases. Additiona lly, ELM (Petty & Cacioppo, 1986) predicts the route of attitude formation through processing ability and motivational factors such as personal relevance and prior knowledge. Recent ELM r esearch has shown that ELM and persuasion literature may need to be expanded or adjusted to incorporate technological advances (Areni et al., 2000; Dotson & Hyatt, 2000; Tam & Ho, 2005). ELM research, including agricultural topics has found that peripheral attitudes commonly form in response to agricultural messages (Frewer et al., 1997; Gore et al., 2008; Hendricks, 2000; Veberke, 2005) Therefore, the following objectives and hypothesis were tested. Research Objectives and Hypotheses This study sought to explore the following research objectives: RO1: To determine the effect s of different levels of transparent communication on attitudes and trust. RO2: To determine the effect of different levels of personal relevance on attitudes and trust.

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89 Based on the r esearch objectives the following research hypotheses were developed: H1: Subjects exposed to high transparent communication and high personal relevance will have more positive attitudes than those exposed to low transparent communication and low personal r elevance. H2: Subjects exposed to high transparent communication and high personal relevance will have more positive trust than those exposed to low transparent communication and low personal relevance. H3: When controlling for transparent communication an d personal relevance, perceived transparency will have a positive effect on attitudes. H4: When controlling for transparent communication and personal relevance, perceived transparency will have a positive effect on trust. H5: When controlling for transpar ent communication, personal relevance, and perceived transparency, the interaction between transparent communication and perceived transparency will have a positive effect on attitudes. H6: When controlling for transparent communication, personal relevanc e, and perceived transparency, the interaction between transparent communication and perceived transparency will have a positive effect on trust. Research Design This study included a two ( personal relevance: high and low) x two ( transparent communication: high and low ) betweensubjects factorial experimental design. In a betweensubjects design each subject is exposed to only one treatment condition (Keppel & Wickens, 2004). For this study, s ubjects were randomly assigned to receive both a high or low tran sparent communication and high or low personal relevance treatment Additionally, the factorial nature of this study indicated that each level of the independent variables was combined with all other levels of the other independent variable (Keppel & Wickens, 2004). The factorial design allows for the influence of each independent variable to be assessed separately, as well as in combination with the

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90 other independent variables (Keppel & Wickens, 2004). This experiment sought to determine whether personal r elevance and transparent communication influenced college student s attitudes and trust. The operational framework for the study is in Figure 31. This study included a posttest only design and experimental groups were designed as follows: R = random assignment; O1 = posttest measures; X1 = high personal relevance, high transparent communication; X2 = high personal relevance, low transparent communication; X3 = low personal relevance, high transparent communication; and X4 = low personal relevance, low transparent communication (control) R X 1 O 1 R X 2 O 1 R X 3 O 1 R X 4 O 1 Controlling Threats to Internal and External Validity A measure of validity describes how well an instrument measures what it was designed to measure (Ary et al. 2006). In an experimental design, internal validity examines the extent to which the changes observed in a dependent variable are actually caus ed by the independent variables. H istory, maturation, testing, instrumentation, statistical regression, selection, experimental mortality selection maturation, experimenter effect, subject effects, and diffusion threaten internal validity (Ary et al., 2006). The history effect occurs when something outside of the experiment produces changes in the posttest measure (Ary et al., 2006). Since the design was posttest only the history effect is eliminated. The maturation threat includes biological or

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91 physiological changes in the subject s because of a pass age of time (Ary et al., 2006). The post test onl y design of this experiment eliminated the t hreat of maturation. The posttest only design also eliminated the testing threat, which occurs when subject s could be sensitized by a pretest (Ary et al., 2006). The threat of instrumentation occurs when an instrument does not measure consistently throughout the course of an experiment (Ary et al., 2006). To address this threat a panel of experts was used to establish face and content validity. In addition to the panel of experts, pretesting of the message stimuli and the instrument was conducted. Throughout the course of the experiment no changes were made to the instrument, which further reduced the threat of instrumentation (Ary et al., 2006). Statistical regression includes the tendency for those who score extremely high or low on a pretest to score closer to the mean on the posttest regardless of treatment (Ary et al., 2006). Statistical regression was eliminated in this study because of the posttest only design. The selection threat is of concern when there are important differences between the experim ental and control groups before the experimental treatment takes place (Ary et al., 2006). The selection threat was controlled in this study because subject s were randomly assigned to receive both the high or low transparent communication and high or low personal relevance treatments Experimental mortality is the loss of subject s between the comparison groups (Ary et al., 2006). This threat was eliminated by the posttest only design. The selection maturation interaction occurs when the experimental and control groups are not randomly selected, but are instead preexisting groups (Ary et al., 2006). This threat was controlled by the random assignment of subject s to all treatment groups. The experimenter effect was controlled by the online nature of this expe riment, thus

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92 eliminating any personal characteristics of the researcher. Subject effects can threaten internal validity when subjects are aware that an experiment is going on and provide responses based on the experimental nature (Ary et al., 2006) Althou gh subjects were aware that they were participating in an online survey, they were not aware of the experimental nature of the research. Therefore, the threat of subject effects was limited. When subjects communicate to others about the research treatment this communication can have an effect, called diffusion, on the outcomes between the experimental and control groups (Ary et al., 2006) Since subject s were unaware of the experimental design of the research and the different treatment groups the diffusi on threat to internal validity was reduced. External validity examines the extent to which the research can be generalized to other subjects, settings, and operations (Ary et al., 2006, p. 314). In social science most research designs cannot control all threats to external validity and the reader must determine if the threats are reasonable (McMillan & Schumacher, 2010). External validity is divided into population threats and ecological threats The population threats include the selection of subjects, characteristics of subjects, and the subject treatment interaction (McMillan & Schumacher, 2010) Since the subjects in this study were a convenience sample, the results of this study cannot be generalized to a larger population. In addition, the character istics of the subjects may be different than the characteristics of other college students, thus further limiting the generalizability. To minimize this threat subjects were selected from seven different classes and class effect was recorded. In addition, subjects were randomly assigned to treatment groups. The ecological threats to external validity include multiple treatment inference, setting-

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93 treatment interaction, time of measurement treatment interaction, pretest treatment interaction, and the novelty effect (Ary et al., 2006; McMillan & Schumacher, 2010) The multiple treatment inference threat explains that when there are multiple interventions the generalizability of the results is limited to similar multi intervention situations, as was the case wi th this research (McMillan & Schumacher, 2010) Additionally, the settingtreatment interaction limits the generalizability to the setting of the research. In this study the settingtreatment interaction was reduced because subjects participated in the stu dy in their natural environment (McMillan & Schumacher, 2010) The design and administration of this study reduced the novelty effect. Since a large number of the subjects were familiar with Facebook, the novelty of the treatment intervention was reduced ( YPulse, 2012) The threat of pretest treatment interaction was controlled in this study due to the posttest only design (McMillan & Schumacher, 2010). Lastly, the time of measurement treatment interaction could not be controlled and it cannot be concluded that any effects are long lasting (McMillan & Schumacher, 2010). In addition to internal and external validity, construct validity must also be addressed. Construct validity explains how consistent the research operations and design are with the construct s used (Ary et al., 2006). The threats to construct validity include inadequate explanation of constructs, manipulation of the construct, measure of the construct, reactivity of the experimental situation, and the experimenter effect. These threats were mi nimized through the pretesting, pilot testing, and manipulation checks. In addition, using multiple questions to measure a construct reduced the measure of the construct threat Thorough discussion and definition of the constructs also reduced the t h reat o f inadequate explanation (Ary et al., 2006)

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94 There are limitations associated with a posttest only design. Change as a result of the experiment cannot be assessed in a posttest only design (Ary et al., 2006). In addition, if subjects drop out during the course of the experiment there is no way to tell if those subjects are different from those who did not drop out (Ary et al., 2006). Subjects The population ( N = 33, 513) for this study included college students from a large southeastern university (Universit y of Florida Office of Institutional Planning and Research, 2011b) The desired sample size was determined using an effect size of .02 reported in a similar study (Abrams, 2010). With an effect size of .02, a desired power of .90, and an estimated response rate of 40% a minimum sample of size of 880 was needed (Keppel & Wickens, 2004). A convenience sample was used to sample students from this population. The sample included 989 subjects from seven university c ourses These courses included: RTV 2100: Writ ing for Electronic Media, two sections of AEC 3033: Research and Business Writing, AEC 3030: Effective Oral Communication, AEB 2014: Economic Issues, Food, and You, ENY 3005: Principles of Entomology, and AEC 3414: Leadership Development. These courses inc luded students from a variety of majors and class rank. Subjects were offered 5 points extra credit in their course as incentive to participate. Those enrolled in more than one of the courses participating in this study were only accounted for once, but received extra credit in each course. Due to practical constraints, efficiency, and accessibility, convenience samples are commonly used by both qualitative and quantitative researchers (McMillan & Schumacher, 2010). Additionally, convenience samples have been commonly used in psychology and consumer research studies with college student subjects (Peterson, 2001). Although data from studies using convenience samples are not generalizable to

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95 the larger population, the findings are still useful and can provide valuable insight to better understanding relationships (McMillan & Schumacher, 2010). The debate about whether college students make good research subject s has gone on for quite some time and is composed of strong arguments on both sides of the issue (Pet erson, 2001). However, college students are a common subject pool for social science research including studies of consumer behavior and psychology. Peterson (2001) conducted a metaanalysis to compare the effect sizes of student and nonstudent samples from various studies His results showed that college students tend to be a more homogeneous group than noncollege students. Increased homogeneity can be beneficial because it reduces the effect of extraneous variation. However, Peterson (2001) warned that the increased homogeneity could also decrease the magnitude of differences and relationships. W hen using college students as subjects, caution should be taken when making inferences about c onsumer behavior (Peterson, 2001). Kam, Wilking, and Zechmeister ( 2007) outline four situations when college student samples may be appropriate. These include: Student subjects are appropriate in cases where there is theoretical and/or empirical reason to believe that the effect of the treatment would be the same (or cl osely similar) across student and nonstudent samples. Student samples might be appropriate in cases where student samples provide a more critical test of the hypothesis Student subjects are appropriate when students are the population of interest. Student subjects are appropriate when the principle purpose of the study is to investigate a specific, theoretically interesting causal relationship; in other words when the focus is on internal validity (Kam et al., 2007, p. 421). In this study college students were the population of interest and the purpose of the study was to understand the relationship between transparency and attitude and

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96 transparency and trust; therefore, the use of college students is appropriate for this study based on the guidelines prov ide by Kam et al. (2007). College students were of interest in this study because they are part of the Millennial Generation. Those in the Millennial G eneration have been identified as requesting transparency (R ed McGregor, 2012; Shore, 2011) and are high users of social media (Taylor & Ketter, 2010) inclu ding Facebook (Shreffler, 2012) In addition, college students are known to have less solidified attitudes than older adults (Sears, 1986). Therefore, influencing and shaping attitudes may be more success ful with this population. Personal relevance has also been identified as poorly developed in college students, while need for cognition and knowledge has been discussed as higher in college students (Sears, 1986). Additionally, individuals between the ages 18 to 24 have shown increased trends in voting. In the 2008 election, the 18 to 24 age range was the only agegroup to significantly increase in voting turnout ( U.S. Census Bureau, 2009). Many young adults have reported learning from, participating in, and post ing their own information about political issues on the I nternet (Kohut, 2008; Smith & Lee, 2008) Thus, college students ar e seeking information from the I nternet to make informed policy decisions while also sharing their opinions with others. Independent Variables Two independent variables were used in this study, both of which were manipulated. These independent variables included transparent communication and personal relevance, which were combined to create four treatment groups. These treatment groups included Treatment 1: High personal relevance, high transparency, Treatment 2: High personal relevance, low transparency, Treatment 3: Low personal

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97 relevance, high transparency, and Treatment 4: Low personal relevance, low transparency. Message Sti muli For this research, the experimental manipulation was designed to test levels of transparent communication and personal relevance. To conduct the study, s ubjects were randomly assigned (through Qualtrics survey software) to receive both low or high tra nsparent communication and low or high personal relevance treatments The transparent communication manipulations were presented in a Facebook page for a fictitious poultry farm called Clucking Farms and Hatchery ( Appendix F ) A poultry farm was chosen for the focus of the Facebook page because the transparency debate in the agriculture industry has commonly been focused on animal agriculture segments of the industry. The personal relevance manipulations were presented in a description of the farm and on the farm s Facebook page. The static nature of the Facebook page in this study presents a limitation. However, in order to control the message stimuli for all subjects the Facebook page had to be static. Transparent communication is the deliberate attempt t o make available all legally releasable information whether positive or negative in nature in a manner that is accurate, timely, balanced, and unequivocal, for the purpose of enhancing the reasoning ability of publics and holding organizations accountable for their actions, policies, and practices (Rawlins, 2008a, p. 75). The high transparent communication message on the Facebook page included communicative transparency manipulations suggested by Auger (2011) and Rawlins (2008a). The communication was manipulated to encourage stakeholder feedback, provide detailed information, provide information that could be verified by a third party, was relevant and easy to understand. Additionally,

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98 the communication was manipulated to be forthcoming with information that could be damaging to the industry and acknowledged mistakes and criticisms of the industry. These manipulations were based on the elements of communicative transparency identified by Rawlins (2008a). Four sections of the Facebook page included the t ransparent communication manipulations. The first manipulation included a Facebook post from the farm describing how the chickens were going to be housed at Clucking Farms and Hatchery (Figure 32) The high transparent communication manipulation included a link to a description of enriched chicken housing and a link to the American Veterinary Medical Associations animal care standards. The second manipulation included a Facebook post from the farm discussing criticisms of poultry producers (Figure 33) F or this manipulation, the high transparent communication treatment asked for stakeholder feedback and indicated that the farm would work with their stakeholders to ensure that the animals were cared for in a way that met everyones needs. The third manipul ation included a Facebook post that discussed three options (viewing window, 24 hour live video feed, and demonstration rooms) that the farm was considering in their buil ding plans (Figure 34) The high transparent communication manipulation included a polling feature where stakeholders could vote for which option should be included in the building plans. The fourth manipulation was completed in the Facebook cover picture (Figure 35) The cover picture of the high transparent communication treatment inclu ded an image of white chickens inside a barn. The low transparent communication Facebook page included the reverse of the items mentioned above. For instance, the low transparent communication was manipulated to provide incomplete information, not request stakeholder feedback, and

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99 provide information that was difficult to understand and that could not be verified by a third party. Additionally, the low transparent communication did not disclose information that may be damaging to the industry (Auger, 2011; Rawlins, 2008a) For the first manipulation, the low transparent communication treatment simply stated that an enriched housing system would be used (Figure 32) The second manipulation was adjusted for the low transparent communication treatment to disc uss prior criticism of poultry producers, but the post did not ask for stakeholder feedback ( Figure 33) For the third manipulation, the Facebook page indicated that the farm was considering including one of three options in its building plans, but a poll was not provided and the stakeholders were not asked for feedback (Figure 34) The cover picture for the low transparent communication treatment included a generic image of a pasture and red barn; no chickens were included in this image for the last mani pulation (Figure 35) Personal relevance, the importance and meaning a message has to an individual (Petty & Cacioppo, 1986), was manipulated by changing the location where the farm was going to be built. The high personal relevance manipulations indicated that the farm was going to be built just outside of Gainesville, Florida. The low personal relevance manipulations indicated that the farm was going to be built just outside of Iowa City, Iowa The personal relevance manipulations used in this study were modeled off of personal relevance manipulations used in previous research (Petty & Cacioppo, 1979; Petty et al., 1981; Petty et al., 1983). Three areas of the treatment intervention were manipulated for personal relevance. The first personal relevance manipulation was in cluded in the description that subject s were asked to read before viewing the Facebook page (Figure 36) The description described the farm, the location where it

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100 would be built (High Gainesville, Florida, Low Iowa City, Iowa) and a c all for feedback from college students. The second personal relevance manipulation was in corporated into the information bar on the Facebook page (Figure 37) The address, phone number, and map were manipulated in the information bar to reflect either the high (Gainesville) or low (Iowa City) personal relevance treatments. The last personal relevance manipulation was a post on the Facebook page ( Figure 38) The high personal relevance Facebook page included a picture of land in Florida where the farm woul d be built. The low personal relevance Facebook page included a picture of land in Iowa where the farm would be built. Message Stimuli Testing To ensure that the transparent communication and personal relevance manipulations were successful, the four treatments were pretested. The treatments were pretested with a convenience sample of college students from a large southeastern university who were not part of the final sample. College students were chosen to pretest the communication manipulations because t hey were part of the same population that the sample was selected from. It is common for research developed message stimuli to be tested several times to establish validity and refine the stimuli (Wimmer & Dominick, 1994) The follow section details the pr etest procedure and manipulation check. Manipulation Check The subjects were randomly assigned to one of the four treatment groups Then the subjects were asked if they noticed the different transparent communication and personal relevance manipulations while reading the Facebook page. For example, one of the questions asked, When you were reading the Facebook page did you read

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101 anything about where the farm was going to be located? In addition, the subjects were asked to rate how open and how relevant the communication on the Facebook page was on a four point semantic differential scale. The first scale ranged from not relevant to very relevant, while the second scale ranged from not open to very open. The fir st pretest was completed with 31 college students via an online survey The pretest results indicated that transparent communication manipulation 1 (F igure 32 ) was not recognized by the majority of subjects (T able 31) However, the ratings of how open and how relevant the communication on the Facebook page was showed no significant difference between treatment groups. The high personal relevance groups had a mean of 2.80 ( SD = .94) and the low personal relevance groups had a mean of 2.31 ( SD = .95). A one way betweengroups analysis of variance showed no significant difference between the high and low personal relevance groups, F (1, 30) = 2.065, p = .161 ( T ables 3 2 and 33) The high transparent communication groups had a mean of 3. 36 ( SD = 93) and the low transparent communication groups had a mean of 3.06 ( SD = .90). A oneway betweengroups analysis of variance showed a no significant difference between the high and low transparent communication groups, F (1, 30) = .820, p = 373 (T ables 34 and 35) Following the first pretest emphasis through bolding was added to the manipulations included in the Facebook page. It was thought that the added emphasis would not only ensure recognition by the subjects but also result in larger differences among treatment groups. The second pretest was completed wit h 30 college students via an online survey. The manipulation recognition questions produced favorable result s (T able 31). The manipulation check between personal relevance groups was found to

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102 be significant, but no significance was found between transparent communication groups. The high personal relevance groups had a mean of 3.06 ( SD = .83) and the low personal relevance groups had a mean of 1.69 ( SD = .75). A oneway between groups analysis of variance showed a significant difference between the high and low personal relevance groups, F (1, 29) = 21.75, p = .000 ( T ables 32 and 33) The high transparent communication groups had a mean of 3.53 ( SD = .52) and the low transparent communication groups had a mean of 3.20 ( SD = .68). A oneway betweengroups a nalysis of variance showed no significant difference between the high and low transparent communication groups, F (1, 29) = 2.303, p = .140 ( Tables 3 4 and 35) As a result of pretest 2, further changes were made to the message stimuli. The bolding that w as added during the second pretest was removed. In addition, revisions were made to the manipulation check question that asked how open the communication was. This question was revised to ask how transparent the communication was. A definition of transparent communication accompanied this question. The largest change between the second and third pretest was the changing of the cover photos between the high and low transparent communication groups. Prior to the third pretest both high and low transparent com munication groups had a cover image picturing white chickens inside a barn. For the third pretest, the low transparent communication groups were revised to have a cover image picturing a generic pasture and red barn; no chickens were included in this image. The third pretest was completed with 16 college students via an online survey. The manipulation recognition questions showed favorabl e results which can be seen in Table 31. However, the treatment groups showed no significant difference. The high personal relevance groups had a mean of 3.25 ( SD = 1.04) and the

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103 low personal relevance groups had a mean of 2.38 ( SD = 1.30). A oneway betweengroups analysis of variance showed no significant difference between the high and low personal relevance groups, F (1, 15) = 2.213, p = .159 (T ables 32 and 33) The high transparent communication groups had a mean of 3.43 ( SD = .54) and the low transparent communication groups had a mean of 3.33 ( SD = .50). A oneway betweengroups analysis of variance showed no significant difference between the high and low transparent communication groups, F (1, 15) = .135, p = 719 (T ables 34 and 35) To further investigate the lack of difference between those in the high and low transparent communication groups and those in the high and low personal relevance groups three cognitive interviews were conducted with subject s who participated in the third pretest. Cognitive interviews are used to determine if questions, or in this case the message stimuli, are being correctly understood and comprehended (Dillman, Smyth, & Christian, 2009). The first cognitive interview was conducted with a participant who received the low personal relevance/low transparent communication treatment. This participant indicated in the pretest that the communication provided in the Facebook page was transparent. When asked why she felt the communication was transparent this participant said, I think I felt like they were transparent because from the ground up they were telling us what houses they were going t o use, that the ground was breaking, they were just good at giving us information about what they were doing and just kind of keeping the followers in the loop. Additionally, the participant indicated in the pretest that the communication provided in the Facebook page was not personally relevant. When asked why she felt this way this

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104 participant said, I feel like it wasnt specifically relevant to me because its in Iowa and Im in Florida. The participant was then read the definition of transparent comm unication and was asked if she felt that the definition was represented by the communication on the Facebook page. This participant said, I dont specifically think they gave us any negative information, um they gave us information about their enriched housing and that they were going to begin their construction and things like that, but none of the negatives. They could have also expanded on what actually what enriched housing was. So I guess compared to other sites they are much more transparent, but ther e things that they could continue to do to improve their transparency. To conclude the discussion about transparency, the participant was asked to explain why she ranked the communication as three out of four on the transparency scale, indicating that the communication was closer to very transparent than not transparent. This participant responded and said, I think I ranked it as a three, because like I said they didnt give any negative, but they also didnt expand upon what they were doing, like what is t he enriched housing, what are they feeding their chickens that kind of thing, if theyd expanded a little bit more to give me the actual detailed information, I probably would have given them a four, but I did feel like I said that they are doing better than others. The participant would have warranted a lower ranking of one or two on the transparent communication scale if less information was given. This participant said, I guess if they didnt tell us what kind of houses and they were just like were rai sing chickens and we are starting this up, and this is where we are located that would have been a lot less transparent because it would have raised a lot of questions like what kind of chickens are you going to raise? Are they free range? Are they in thes e enriched houses and things like that, so that would have lowered them in my ranking.

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105 Additionally, the participant discussed that the farm could have replied back to more of the comments that were posted on the Facebook page to further enhance their tran sparency. The second cognitive interview was conducted with a participant who received the high personal relevance/low transparency treatment. In the pretest the participant indicated that he felt the communication provided was transparent. When asked about this answer in the cognitive interview the participant indicated that he thought the question was confusing and that he had to read it a couple of times. He also justified his answer and said, I thought that it presented the information, but did I think that there should have been more information? Yes. But, um I thought that the overall idea was good, but Im just Im a real detailed person and so I just would have liked to see more details In addition, the participant indicated that the communication provided in the Facebook page was personally relevant to him. During the interview the participant was asked if the personal relevance would change if the farm were being built in Iowa City, Iowa instead of Gainesville, Florida. The participant responded and said, Yeah, it would have because to me agriculture is agriculture and I take pride in Florida Ag, but if it comes from a different state I dont really have much of a problem with that. Through the remainder of the interview, the participant further discussed additional details that would have been helpful. Some specific details wanted by the participant included an explanation of enriched housing, details about how the industry had been criticized, and proof of follow through on one of the three bui lding options. During the pretest this participant ranked the transparent communication as a three out four, indicating that the communication was more transparent than not. This participant

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106 provided the following justification for his response and said, There was some transparency, just not as much as I would have liked to seen I guess. The participant indicated that the Facebook page would have earned a lower transparency ranking if no pictures would have been included and if the farm would not have sai d what they were going to do. The last cognitive interview was conducted with a participant who received the high personal relevance/ high transparent communication treatment. This participant indicated that she thought the Facebook page was transparent because the farm was putting information out for everyone to see. This participant said, I feel like they werent trying to hide anything, they were trying to put everything out there, like this is what we are doing, what do you think about it? Similarly, this participant also indicated in the pretest that the communication was personally relevant. The participant indicated that the communication was personally relevant because, I think as a local student, and in the ag industry, it is something good to know and to be aware of, not because I have any particular attachment to poultry or anything. When asked to compare the low and high transparent communication post about how the chickens would be housed the participant indicated that the low transparent com munication post still had elements of transparency. This participant said, Yeah, I mean they are telling you what they are doing, but I think that them providing the links kind of goes that extra step and makes it more transparent. Similarly, when asked to compare the low and high transparent communication post about the three options being considered in the building plans the participant indicated that the absence of the

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107 feedback poll would not affect her view of the transparent communication. This parti cipant said, No, I dont think it would have affected my view of the transparency, but I think that adding that [poll] kind of emphasized the transparency. I think they like kind of went above and beyond really trying to make sure. This participant ranked the Facebook page as very transparent (a four out of four) in the pretest. When asked what would have had to be different about the Facebook page for it to earn a lower transparency rating this participant said, Maybe if they had like not included as muc h information, it seemed like their whole purpose was to make everything very transparent and like really put a lot of detail about what they were doing and to make sure that the community was ok with that, and get their input. So I dont think if they wou ld have done anything less I would have thought less, its just because they did more that I thought more. The lack of significance between high and low personal relevance groups in two of the three pretests may be a result of the subjects existing personal involvement outweighing the manipulated personal involvement. This occurrence was observed in the second cognitive interview. The participant had received the low personal relevance treatment, but still found the communication to have high personal relev ance because of his involvement in agriculture. The results of the cognitive interviews indicated that the lack of significance between the transparent communication groups was a result of at least some transparency being observed in both the high and low transparency groups. It is to be expected that communication of any kind would have some level of transparency. The two participants receiving the low transparent communication treatment both provided suggestions for improved transparency. Most of their s uggestions for improved transparency were part of the high transparent communication treatment, such as

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108 including more detailed information about enriched housing. Additionally, the participant who received the high transparent communication treatment indi cated that the low transparent communication treatments still included elements of transparent communication; they just were not as transparent as the high transparent communication treatments. Therefore, it was concluded that transparency was being observ ed in both treatments, but when shown both the participants would indicate that the high transparent communication treatments were more transparent than the low transparent communication treatments. To account for this conclusion, additional manipulation c heck questions were added to the final survey instrument. The questions that were added included both the high and low transparent communication elements for each manipulation and asked participants to indicate which one was the most transparent. Instrument Pilot Test A pilot test was completed in conjunction with the first pretest in November of 2012 to determine the instruments reliability and validity. A panel of experts which included four tenured professors and one assistant professor, reviewed the instrument for face and content validity prior to the pretest and pilot test. The instrument was piloted with a sample of 76 college students from a large southeastern university who were not included in the final sample. A response rate of 40.8% ( n = 31) was achieved for the pilot test. Item analysis statistics were run to measure the construct validity for each construct scale using SPSS 20.0. A reliability coefficient of .80 or higher, in the social sciences, indicates that an index is measuring what it intends to measure ( Norcini, 1999; Traub, 1994) However, reliability coefficient of .70 is also considered acceptable

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109 (Kline, 1998). The 18item attitude index, modeled off of previous research (Osgood, Suci & Tannenbaum, 1978; Meyers, 2008; Rhoades 2 006), had an alpha reliability of .91. All items were kept in this scale. To measure trust two scales were used. The first scale was modeled off of Driscolls (1978) measure of hierarchical trust The second trust scale was developed and modeled off of g eneral trust scales discussed by Paine (2003), Rawlins (2008b ), Tschannen Moran and Hoy (2000). The combined 10item scale had an al pha reliability of .84. No items were removed from this scale. In addition to testing the reliability of the scales within i nstruments, three cognitive interviews were also conducted. The cognitive interviews were used to assess the overall understanding and c omprehension of the instrument (Dillman et al., 2009). The participants who participated in the cognitive interviews als o participated in the pilot test Four changes were made to the instrument based on the cognitive interviews. The first change was made to the wording of the questions immediately following the stimuli. The participants were instructed to keep the Facebook page open so they could refer back to it throughout the survey, but the wording of the questions read When you were reading To align the wording of the questions with the instructions the questions were revised to read, When reading The second change was made to the demographic questions asking subjects to classify where they lived growing up. One of the cognitive interviews revealed that the participant lived in an urban area that was outside of city limits. This participant described that she did not know which answer would be best because she did not live in

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110 a rural area, but she also did not live in a city or town. Therefore, the answer choice of Urban or suburban area outside of city limits was added. Two questions were deleted from the instru ment due to repetitiveness. Originally, two questions were asked after the subjects saw the stimuli. The first question asked if the information was relevant to them and the second question asked if the information was transparent. Subjects only had the choice of answering yes or no. Since subjects were asked at the end of the survey to rate the relevance and transparency on a semantic differential scale, these two questions were deleted. The last change made to the instrument as a result of the cognitive interviews, was the addition of four manipulation check questions at the end of the instrument. These questions compared each of the four high and low transparent communication manipulations. Each question compared the high and low transparent communicatio n manipulation and asked subjects to indicate which manipulation was more transparent. These questions were added because it was discovered through the cognitive interviews that both high a low transparent communication manipulations had at least some level of transparency, but when compared to each other the subjects would identify one as being more transparent than the other. Dependent Variables This study was interested i n understanding the effect s of the independent variables on the dependent variables of attitude and trust. Attitude The process of changing attitudes has also been referred to as persuasion (Petty & Cacioppo, 1996). For lasting attitude change to occur, persuasive communication must gain an individuals attention, be comprehended, mentall y rehearsed, and

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111 cognitively stored (Petty & Cacioppo, 1996). Petty and Cacioppo (2009) suggested that motivation and the ability to process are the most important factors in determining whether a persuasive message will result in enduring attitude change and influence behavior. The direct measurement of attitudes also known as self reporting, has been found to be more precise and have higher reliability and validity than indirect measures (Lemon, 1973; Petty & Cacioppo, 1996). Several approaches to direct ly measuring attitudes have been taken, such as the Guttman Scale, Thurston Scale, Likert Scale, and the Semantic Differential scale (Eagly & Chaiken, 1993). The Likert Scale approach to measuring attitudes was developed to reduce the complexity and time c ommitment of the Thurston scale, while still maintaining high validity and reliability. The Likert Scale achieved this purpose, but lack s the precision of measurement seen with the Thurston scale (Eagly & Chaiken, 1993). The semantic differential scale has become the popular attitude measure in academic research due to its convenience and ease of development, but also lacks precision measurement (Eagly & Chaiken 1993). For the purposes of this research an 18item semantic differential scale was created to measure attitudes toward Clucking Farms and Hatchery The semantic differential scale was adapted from the bipolar adjectives suggested by Osgood, Suci & Tannenbaum (1978) Additionally, the scale was also adapted from the scales used by M e yers (2008) and Rhoades (2006). T he philosophy of tripartite attitudes guided the formation of the attitude scale (Eagly & Chaiken, 1993) The attitude index response items were categorized to the real limits standard of: 1.00 to 2.33 = least favorable, 2.34 to 3.66 = neutral and 3.67 to 5.00 = most favorable. To measure attitude, an index was

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112 constructed with 18 items remaining after assessing reliability. Averaging the responses for each item in the index, for each subject, created the attitude index. For those subjects who had some missing values in the index, their remaining responses were averaged and put in place of the missing values. This method for dealing with missing values was originally discussed by Cohen and Cohen (Allison, 2009). If a subject had missing values for more than 50% of the index, then the missing values were not replaced. The resulting measurement of attitudes for this study had an alpha reliability of 90. Trust Trust is regarded as essential to successful public relations (Rawlins, 2007). Trust has been defined as a collective judgment of one group that another group will be honest, meet commitments, and will not take advantage of others (Rawlins, 2007, para 13). Several factors of trust have been discussed in the literature. C ompetence, integrity, and goodwill were factors used by Rawlins (2008b) and Auger (2011) to examine the relationship between trust and transparency. To measure trust in this study two scales were used. The first scale was modeled off of Driscolls (1978) measure of gener al hierarchical trust. The second trust scale was developed and modeled off of scales measuring specific trust discussed by Paine (2003), Rawlins (2008 b ), and TschannenMoran and Hoy (2000). General and specific measures are commonly combined to measure ot her variables such as behavior, as observed in the theory of planned behavior research (Krueger & Carsrud, 1993). The trust responses for both of the scales ranged from 1 (strongly disagree) to 2 (disagree) to 3 (neither agree nor disagree) to 4 (agree) to 5 (strongly agree). Combining both scales after assessing reliability created one 10item trust index. The trust index

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113 response items were categorized to the real limits standard of: 1.00 to 1.49 = strongly disagree, 1.50 to 2.49 = disagree, 2.50 to 3.49 = neither agree nor disagree, 3.50 to 4.49 = agree, and 4.50 to 5.00 = strongly agree. Averaging the responses for each item in the index, for each subject, created the index. For those subjects who had some missing values, their remaining responses were averaged and put in place of the missing values. If a subject had missing values for more than 50% of the index, then the missing values were not replaced. The resulting measurement of trust for this study had an alpha reliability of 86. Additional Variables of Interest In addition to the independent variables, two other variables of interest were measured in this study. These included the variables of perceived transparency and livestock industry values. The perceived transparency variable was created fr om a four item index which had an alpha reliability of .86. These items were based on the overall transparency segment from Rawlinss (2008b) transparency instrument. The responses for perceived transparency scale ranged from 1 (strongly disagree) to 2 (disagree) to 3 (neither agree nor disagree) to 4 (agree) to 5 (strongly agree). The perceived transparency index response items were categorized to the real limits standard of: 1.00 to 1.49 = strongly disagree, 1.50 to 2.49 = disagree, 2.50 to 3.49 = neither agree nor disagree, 3.50 to 4.49 = agree, and 4.50 to 5.00 = strongly agree. Averaging the responses for each item in the index, for each subject, created the index For those subjects who had some missing values, their remaining responses were averaged and put in place of the missing values. If a subject had missing values for more than 50% of the index, then the missing values were not replaced.

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114 The livestock industry values variable was created from a fiveitem index adapted from Heleski, Mertig, & Za nella (2004) which had an alpha reliability of .72. The responses for the livestoc k industry value scale ranged from 1 (strongly disagree) to 2 (disagree) to 3 (neither agree nor disagree) to 4 (agree) to 5 (strongly agree) to 6 (I dont know). The livest ock industry values index response items were categorized to the real limits standard of: 1.00 to 1.49 = strongly disagree, 1.50 to 2.49 = disagree, 2.50 to 3.49 = neither agree nor disagree 3.50 to 4.49 = agree, and 4.50 to 5.00 = strongly agree. As comm only demonstrated by researchers in survey research, all I dont know responses were treated as missing values (Holbrook, 2008) Averaging the responses for each item in the index, for each subject, created the index For those subjects who had some miss ing values, their remaining responses were averaged and put in place of the missing values. If a subject had missing values for more than 50% of the index, then the missing values were not replaced. Attribute Variables An attribute variable includes characteristics that subjects have prior to participating in a study (Ary et al., 2006). The demographic attribute variables included in this study were gender, age, college rank (freshman, sophomore, junior, senior graduate), and college major Other attribute variables included area of residence while growing up, employment in the livestock industry by the subject or a close family member of the subjec t, and frequency of meat consumption. Instrumentation The instrument for this study was created in and distri buted t hrough Qualtrics. Qualtrics is survey software that has capabilities to host experimental designs (Qualitrics, 2012). Online administration of surveys has become more common as

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115 technology has advanced and more individuals have adopted Internet use ( Dillman et al. 2009). Additionally, online experiments becoming more frequently used by social science researchers (Babbie, 2010). Instrument Content The survey was the same for all subject s, except for the randomly assigned farm description and Facebook page with the transparent communication and personal relevance treatment s. The complete survey can be found in Appendix E The layout of the survey was as follows. Page 1 included the informed consent. Page 2 included a prompt for the subject s to enter their participant ID number, as well as select the classes that they were currently enrolled in. This information was used to track who qualified for the extra credit incentive. Page 3 included instructions for the remainder of the survey. Page 4 included a description of Clucking Farms and Hatchery, which was manipulated for personal relevance. The high personal relevance description indicated that the farm was going to be built just outside of Gainesville, FL. The low personal relevance description indicated that the farm was going to be built just outside of Iowa City, Iowa. This page also included directions for viewing the Facebook page, a link to a randomly assigned Facebook page, and a question to verify that the subject s were able to view the Facebook page. Page 5 included questions, which asked the subject s if they noticed certain parts of the Facebook page, which included the experimental manipulations. Page 6 asked subject s to list the thoughts and feelings that they had while viewing the Facebook page. Page 7 included a question that populated the thoughts that were listed on page six and the subject s were asked to classify their thoughts as favorable, unfavorable, or neutral. Page 8 included two semantic differential questions asking subject s how im portant the Facebook page was and how motivated they were to view the Facebook page.

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116 Page 9 included an 18 item semantic differential scale to measure subject s attitudes toward the phrase I feel the communication provided by Clucking Farms and Hatchery on its Facebook page was Page 10 included a 5 item likert scale to measure subject s attitudes toward livestock production. Page 11 included a 4 item likert scale to measure subject s different levels of trust. Page 12 included a 6 item likert scale to measure subject s trust in the information provided by Clucking Farms and Hatchery on the Facebook page. Page 13 included a 4 item likert scale to measure subjec ts perceived transparency of the communication provided by Clucking Farms and Hatchery Page 14 asked subject s if they would post something on the Facebook page and if so, what that post would say. Page 15 was a transition page between the treatment related questions and demographic questions. Page 16 asked 7 demographic type questions. Page 17 was a transition page between demographic questions and manipulation check questions. Page 18 included a manipulation check question asking about the relevance of the Facebook page. Page 19 included a manipulation check question asking about the transparent communication of the Facebook page. Page 20 included a manipulation check question that asked subject s to compare the transparency of the cover pages from both the low and high transparency pages. Page 21 included a manipulation check question that asked subjec ts to compare two descriptions of chicken housing from both the low and high transparency pages. Page 22 included a manipulation check question that asked subjec ts to compare two descriptions of prior criticisms of chick producers from both the low and high transparency pages. Page 23 included a manipulation check question that asked subjec ts to compare two descriptions of building plan options from both the low and high transparency pages.

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117 Page 24 was a thank you page detailed the purpose of the st udy and the fictitious nature of Clucking Farms and Hatchery as well as the Facebook page. Procedure Subjects in this study (N = 989) were sent an email with the link to the survey. The transparent communication and personal relevance treatment s that the s ubjects received w ere randomly assigned through the survey software. Administration of the survey followed the procedures for web survey distribution outlined by Dillman et al. (2009). S ubjects were sent a prenotice email of the survey from their instruc tors a few days prior to the initial distribution of the survey (Appendix A) The pre notice email indicated that for participation, the subjects would receive five extra credit points toward their course grade in that class. The survey was distributed via personalized emails to all subjects. The initial email (Appendix B ) included a personalized greeting (Dear [First Name]), information about the survey, directions on accessing the survey, the link to the survey, their participant identification number, and the researchers contact information. T he subjects had 12 days to complete the survey. Four follow up emails ( Appendix C ) were sent to subject s on day three, eight 10, and the morning of day 1 2 The message in the last followup email (Appendix D) varied slightly as recommended by Dillman et al. (2009). Data Analysis SPSS 20.0 was used to analyze the data from this study. Demographic data were analyzed using frequency measures. Additionally, an internal consistency measure of reliability was conducted using Cronbach alpha coefficient. This measure of internal consistency is appropriate for scale measures (Ary et al., 2006). A factorial analysis of variance was used to analyze hypotheses one and two. The assumptions of

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118 ANOVA include normality, homoscedas ticity, independence, and identical distribution of error for all treatment groups (Keppel & Wickens, 2004). The assumptions of independence and the identical distribution of error were satisfied by the random assignment of subjects to the treatment groups (Keppel & Wickens, 2004). Additionally, the assumptions of normality and homoscedasticity were not violated. Hypotheses three through six were analyzed using multiple linear regression. This analysis allowed for the examination of the best possible combination of independent variables to predict the dependent variable outcome (Ary et al., 2006). The assumptions of regression include linearity, independence, homoscedasticity, and normality (Field, 2009) Independence was satisfied by the random assignment of subjects to the treatment groups (Keppel & Wickens, 2004). The assumptions of normal distribution, linearity, and homoscedasticity were not violated and were assessed by examining a scatter plot of the residual values (Field, 2009). Summary To complete this study a two ( transparent communication: high and low) x two ( personal relevance: high and low ) betweensubjects factorial experimental design was used. Subjects for this study included a sample of 989 college students from the University of Florida. P re testing, manipulation checks, and pilot testing validated the instrumentation for the experiment The experiment was administered through an online survey hosted through Qualtrics. Subjects had 12 days to complete the survey and were given a pre notice, initial invitation, and four foll ow up reminders. Data were analyzed using SPSS 20.0. Statistical procedures included analysis of variance (ANOVA) and multiple linear regression.

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119 Table 31. Pretest manipulation recognition Question f % Pretest 1 Did y ou read anything about where the farm was going to be located? 30 96.8 Did you read anything about how the chickens were going to be housed at Clucking Farms and Hatchery? 20 64.5 Did you read anything about the options Clucking Farms and Hatch ery is considering in their building plans? 25 80.6 Did you see a picture of the land where Clucking farms and Hatchery will be built? 31 100.0 Did you read anything about the criticism associated with chicken producers? 29 93.5 Pretest 2 Did you re ad anything about where the farm was going to be located? 29 96.7 Did you read anything about how the chickens were going to be housed at Clucking Farms and Hatchery? 29 96.7 Did you read anything about the options Clucking Farms and Hatchery is consid ering in their building plans? 22 73.3 Did you see a picture of the land where Clucking farms and Hatchery will be built? 29 96.7 Did you read anything about the criticism associated with chicken producers? 28 93.3 Pretest 3 Did you read anything ab out where the farm was going to be located? 15 93.8 Did you read anything about how the chickens were going to be housed at Clucking Farms and Hatchery? 14 87.5

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120 Table 31. Continued Did you read anything about the options Clucking Farms and Hatchery is considering in their building plans? 14 87.5 Did you see a picture of the land where Clucking farms and Hatchery will be built? 16 100.0 Did you read anything about the criticism associated with chicken producers? 14 87.5 Table 32. Pretest means for personal relevance groups Manipulation n M SD Pretest 1 High personal relevance 15 2.80 .94 Low personal relevance 16 2.31 .85 Pretest 2 High personal relevance 17 3.06 .83 Low personal relevance 13 1.69 .75 Pretest 3 High personal relevance 8 3.25 1.04 Low personal relevance 8 2.38 1.30

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121 Table 33. Pretest manipulation checks for personal relevance groups SS df MS F p value Pretest 1 Between groups 1.840 1 1.840 2.065 .161 Within groups 25.838 29 .891 Total 27.677 30 Pretest 2 Between groups 13.756 1 13.756 21.749 .000 Within groups 17.710 28 .633 Total 31.467 29 Pretest 3 Between groups 3.063 1 3.063 2.213 .159 Within groups 19.735 14 1.384 Total 22.468 15 Table 34. Pretest means for transparent communication groups Manipulation n M SD Pretest 1 High transparent communication 14 3.36 .93 Low transparent communication 17 3.06 .90 Pretest 2 High transparent communication 15 3.53 .52 Low transparent communication 15 3.20 .68 Pretest 3 High tr ansparent communication 7 3.43 .54 Low transparent communication 9 3.33 .50

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122 Table 35. Pretest manipulation checks for transparent communication groups SS df MS F p value Pretest 1 Between groups .683 1 .683 2.213 .370 Within groups 24.155 2 9 .833 Total 24.839 30 Pretest 2 Between groups .833 1 .833 2.303 .140 Within groups 10.133 28 .362 Total 10.967 29 Pretest 3 Between groups .036 1 .036 .135 .719 Within groups 3.714 14 .265 Total 3.750 15 Figure 31. Oper ational framework

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123 A B Figure 32. Transparent communication manipulation 1 A) High Transparent Communication, B) Low Transparent Communication A B Figure 33. Transparent communication manipulation 2 A) High Transparent Communication, B) Low Trans parent Communication

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124 A B Figure 34. Transparent communication manipulation 3 A) High Transparent Communication, B) Low Transparent Communication

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125 A B Figure 35. Transparent communication manipulation 4 A) High Transparent Communication, B) Low Transparent Communication

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126 A B Figure 36. Personal relevance manipulation 1 A) High Personal Relevance, B) Low Personal Relevance A B Figure 37. Personal relevance manipulation 2 A) High Personal Relevance, B) Low Personal Relevance T2. Clucking Farms and Hatchery has recently bought land just outside of Gainesville, Florida. This farm will house 168,000 meat chickens and 200,000 laying hens that will lay eggs to be hatched in the hatchery for meat production. The meat produced from the farm will be distributed to north Florida business es including food services at the University of Florida. Clucking Farms and Hatchery is interested in being involved in the Gainesville c ommunity and is particularly interested in building a strong relationship with college students. They have created a Facebook page to interact with their community and specifically reach college students. The farm wants to hear from people like you. Please examine and read the clucking Farms and Hatcherys Facebook page by clicking on the link below. Please keep the new window open throughout the remainder of the survey so you can refer back to the Facebook page. T2. Clucking Farms and Hatchery has recently bought land just outside of Iowa City, Iowa This farm will house 168,000 meat chi ckens and 200,000 laying hens that will lay eggs to be hatched in the hatchery for meat production. The meat produced from the farm will be distributed to eastern Iowa business es including food services at the University of Iowa Clucking Farms and Hatchery is interested in being involved in the Iowa City c ommunity and is particularly interested in building a strong relationship with college students. They have created a Facebook page to interact with their community and specifically reach college students The farm wants to hear from people like you. Please examine and read the clucking Farms and Hatcherys Facebook page by clicking on the link below. Please keep the new window open throughout the remainder of the survey so you can refer back to the Facebook page.

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127 A B Figure 38 Personal relevance manipulation 3 A) High Personal Relevance, B) Low Personal Relevanc e

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128 CHAPTER 4 RESULTS Guided by the theoretical framework which consisted of Elaboration Likelihood Model ( ELM ) trust, and transparency, the objectives of this study were to determine the effects of different levels of transparent communication and different levels of personal relevance on attitudes and trust. The experimental treatments were presented to subject s through an online survey. The subjec ts in this study included a sample of college students from a large southeastern university Transparent communication and personal relevance served as the independent variables for this study. The dependent variables were attitude and trust. An a nalysis of data is presented in Chapter 4. An analysis of demographics, variables of interest, scale reliabilities measuring the dependent variables, an overview of manipulation checks, and tests of hypotheses are also presented throughout Chapter 4 Descriptive Analysis The experimental treatments and questionnaire for this study were distributed to 989 college students at a large southeastern university via an online survey. A total of 793 subject s responded to the survey. However, only 688 of these responses were usable. Responses were removed from the sample because the subject s indicated that they could not see the Facebook page ( n = 78 ), subjec ts took the survey twice ( n = 11), subjec ts were not part of the millennial generation ( n = 10), and subjec t data was missing for the majority of variables ( n = 6 ). The resulting response rate b ased on the accessible population was 69.6% ( n = 68 8) ; however the overall response rate was

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129 80.2% ( n = 793) The overall response rate was above 75% and therefor e contr olling for nonresponse error was not required (Ary et al., 2006) Demographics The demographic type questions included in the instrument were questions about age, gender, area of residence while growing up, self or family employment in the livestock indus try, meat consumption, college rank, and college major. The subject s ranged in age from 17 years old to 32 years old, with a mean age of 20. 6 years old ( SD = 1.94). Females accounted for 465 (67.6%) of the subject s, while males accounted for 221 (32.1%) o f the subjects The majority of the subjects self reported either growing up in a subdivision in a town or city ( n = 306, 44.5 %) or in an urban or suburban area outside of city limits ( n = 220, 32.0 %). Growing up in a rural area (not a farm) was identified by 100 (14.5%) subjects while 39 (5.7%) subject s reported growing up in a downtown area in a city or town and 22 (3.2%) subject s reported growing up on a farm. The majority ( n = 56 3, 81.8%) of the subject s reported that neither they nor an immediate family member worked in the livestock industry. A small percentage of subjects reported eating meat infrequently: 38 (5.5%) subjects reported never eating meat and 23 (3.3%) subjects reported eating meat less than once per week. Eating meat one to three times per week was reported by 103 (15.0%) subjects, while eating meat four to seven time per week was reported by 252 (36.6%) subjects. A fairly large percentage of subjects reported eating meat eight to 14 times per week : 207 ( 30.1%) Eating meat more that 14 times per week was reported by 64 (9.3%) subjects College junior ( n = 263, 38.2%) and senior ( n = 205, 29.8 %) students made up the majority of subjects followed by 163 (23.7%) sophomores, 51 (7.4%) freshman, and

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130 four ( 0.6%) graduate students. A breakdown of the demographics can be seen in Table 4 1. This demographic breakdown was similar to the population of undergraduates from the university that this sample was drawn from. The population of undergraduate students at the university included 33, 513 in dividuals, 18,524 ( 55.3% ) of which were females and 14,978 ( 44.6% ) of which were males (University of Florida Office of Institutional Planning and Research, 2011b). Additionally, seniors accounted for 12,405 (37%) of the total undergraduate students, follo wed by 9,243 (27.6%) juniors, 6,645 (19.8%) sophomores, and 3,715 (11.1%) freshman (University of Florida Office of Institutional Planning and Research, 2011a). Descriptive Analysis of Variables of Interest Attitude One attitude scale was used to measure t he subject s general attitudes toward the message stimuli The scale was developed using bipolar adjectives suggested by Osgood et al. (1978), Meyers (2008), and Rhoades (2006). Att itude was measured using a five point semantic differential scale. The 18 i tems included in the attitude scale had standard deviations ranging from .76 to 1.07. The total reliability for the index was = 90. Based on the inter item consistency statistics, t he reliability could not be significantly improved with the deletion of an item from the index ( Table 42). The complete 18item attitude index had a grand mean of 3.87 ( SD = .53). The results for attitude by treatment group are in Table 43 Trust Two scales were used to measure trust. The first trust scale was used to measure the subject s general hierarchical trust while the second trust scale was used to

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131 measure the subject s specific trust toward the message stimuli The four item hierarchical trust scale was modeled off of Driscolls (1978) scale and t he six item specif ic trust scale was developed and modeled off of scales discussed by Paine (2003), Rawlins (2008 b ) and TschannenMoran and Hoy (2000). Trust was m easured in both scales using a fivepoint Likert scale. These scales were combined together to create one over all trust scale. The 10 items included in the overall trust scale had standard deviations ranging from .55 to 1.02. The total reliability for the index was = .86. Based on the inter item reliability statistics, t he reliability could not be significantly improved with the del etion of an item from the index ( Table 44 ) The complete 10item trust index had a grand mean of 3.19 ( SD = .50). The results for trus t by treatment group are in Table 45. Perceived Transparency Perceived transparency was measured using four item index, which were based on the overall transparency segment from Rawlinss (2008b) transparency instrument. The four items included in the liv estock industry values scale had standard deviations ranging from .78 to .86. The total reliability for the index was = .86. Based on the inter item reliability statistics, the reliability could not be significantly improved with the deletio n of an item from the index ( Table 46 ). The complete perceived transparency index had a grand mean of 3.80 ( SD = .68) The results for perceived transparency by treatment group are in Table 4 7 Livestock Industry Values Livestock industry values were measured using a five item index. The livestock industry value index was adapted from Heleski, Mertig, & Zanella (2004). The five item s included in the livestock industry values scale had standard deviations ranging from

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132 1.04 to 1.22. The total reliability for this index was item reliability statistics, the reliability could not be significantly improved with the deletion of an item from the index (Table 4 8 ). A grand mean of 3.07 ( SD = .77) was observed for this index. The results for livestock industry values by treatment group are in Table 4 9 Manipulation Checks Both the personal relevance and transparent communication manipulations were evaluated through three different sets of manipulation checks. The description of the farm and the Facebook pages were identical except for the m anipulations outlined in F igure 3 2 through Figure 38. The first manipulation check indicated whether subjects were attentive to the stimuli and took place immediately following the presentation of the stimuli. Five questions were asked to see if the subjects were attentive to different parts of the stimuli. Attentiveness and recognition of the different parts of the sti muli was high ( Table 410 ). The second manipulation check included two questions that asked subjects to indicate how relevant and how transparent the information on the Facebook page was to them. These questions were both four point semantic differential scales that ranged from not relevant to very relevant and not transparent to very transparent. The hig h personal relevance groups had a mean of 2.64 ( SD = 1.00) and the low personal relevance groups had a mean of 1.80 ( SD = .94) A one way betweengroups analysis of variance showed a significant difference between the high and low personal relevance groups F (1, 685) = 127.1, p = .00 0 The high transparent communication groups had a mean of 3.13 ( SD = .75) and the low transparent communication groups had a mean of 2.94 ( SD = 80). A oneway betweengroups analysis of variance showed

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133 a significant difference between the high and low transparent communication groups, F (1, 684) = 10.3, p = .001. The last manipulation check compared each transparent communication manipulation through four questions and asked subjects to identify whether the high or low manipulation was more transparent. The majority of subjects indicated that the high transparent communication manipulations (Page A) were more transparent than the low transparent communication manipulations ( Table 411) In addition to examining the message stimuli manipulations, class effect was also assessed. A oneway betweengroups analysis of variance showed no significant difference in attitude between the seven classes, F (6, 660) = 1.563, p = .155. Additionally, no significant difference in trust was found between the seven classes, F (6, 661) = 1.227, p = .290. The results for attitudes by class can be found in Table 412 and the results for trust by class can be found in Table 413. Test of Hypotheses Before hypothesis testing was conducted, a bivariate anal ysis between the variables was conducted to identify any multi collinearity issues. The dependent variables of attitude and trust were included in the bivariate analysis as well as the factors of transparent communication, personal relevance, and perceived transparency. The results of the correlations are in Table 414. No multicollinearity issues were found. H1: Subjects exposed to high transparent communication and high personal relevance will have more positive attitudes than those exposed to low transparent communication and low personal relevance. A two way betweengroups analysis of variance was conducted to determine the effect of the different transparent communication and personal relevance treatments on attitude. The independent variables were tra nsparent communication (high, low) and

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134 personal relevance (high, low). The dependent variable was attitude toward the message stimuli. The assumptions of normality, equal variance, and independence were tested and showed no violations. H ypothesis 1 was par tially supported. The interaction of personal relevance and transparent communication was not significant F ( 1,686) = .001, p < .980. However, t he two way betweengroups analysis of variance did reveal a main effect for transparent communication, F (1,686) = 6.090, p = .014. Those receiving high transparent communication had a slightly more favorable attitude ( M = 3.92, SD = .52), tha n those receiving low transparent communication ( M = 3.82, SD = .53). The means can be found in Table 415. A main effect for personal relevance was not found, F (1,686) = .417, p = .519 (Table 4 16) H2: Subjects exposed to high transparent communication and high personal relevance will have more positive trust than those exposed to low transparent communication and low person al relevance. A two way between groups analysis of variance was conducted to determine the effect of the different transparent communication and personal relevance treatments on trust. The independent variables were transparent communication (high, low) and personal relevance (high, low). The dependent variable was trust. The assumptions of normality, equal variance, and independence were tested and showed no violations. H ypothesis 2 was partially supported. The interaction of personal relevance and transparent communication was not significant F (1,687) = .141, p < .707. However, t he two way between groups analysis of variance did reveal a main effect for transparent communication, F (1,687) = 11.012, p = .001. Those receiving high transparent communication had slightly more positive level of trust ( M = 3.26 SD = .47), than those receiving low transparent communication ( M = 3.13, SD = .52). The means can be

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135 found in Table 417. A main effect for personal relevance was not found, F (1,687) = .040, p = .8 41 (Table 4 18). H3 : When controlling for transparent communication and personal relevance, perceived transparency will have a positive effect on attitudes Multiple linear regression was used to test H ypothesis 3 Transparent communication, personal relevanc e, and perceived transparency served as predictors of attitude in this model. The model was found to be significant F (3,684) = 99.035, p = .000. Additionally, the predictors explained 30.4% of the variance in attitude. Perceived transparency was the only predictor that was significant, t = 16.965, p = .000. This result indicated that a onepoint increase in perceived transparency would result in a .426 increase in attitude. The results of H ypothesis 3 can be seen in Table 419. H4 : When controlling for tr ansparent communication and personal relevance, perceived transparency will have a positive effect on trust. To test Hypothesis 4, transparent communication, personal relevance, and perceived transparency served as predictors of trust. The model was found to be significant F (3, 686) = 101.877, p = .000. However, perceived transparency was the only predictor that was significant, t = 17.027, p = .000. This result indicated that a onepoint increase in perceived transparency would result in a .339 increase in trust The predictors in this model explained 30.9% of the variance in trust. The results for H ypothesis 4 ca n be seen in Table 420. H5 : When controlling for transparent communication, personal relevance, and perceived transparency, the interaction between transparent communication and perceived transparency will have a positive effect on attitudes. The predictors of transparent communication, personal relevance, perceived transparency, and the interaction between transparent communication and perceived

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136 t ransparency were used to test H ypothesis 5. The model was found to be significant F (4, 684) = 74.412, p = .000. However, the H ypothesis 5 was not supported as the interaction between transparent communication and perceived transparency was not significant. Similar to the results of H ypothesis 3, perceived transparency was the only significant predictor t = 11.713, p = .000. The results indicated that a .406 increase in attitude would be observed for each increase in perceived transparency In addition, these predictors explained 30.4% of the variance in attitude. The change in R2 from the between this model and the model used in H ypothesis 3 was not significant, p = .410. Therefore, the addition of the interaction term did not improve this regression model (refer back to Table 419) H6 : When controlling for transparent communication, personal relevance, and perceived transparency, the interaction between transparent communication and perceived transparency will have a positive effect on trust. To test H ypot hesis 6, transparent communication, personal relevance, perceived transparency, and the interaction between transparent communication and perceived transparency served as predictors of trust. The model was found to be significant F (4, 686) = 76.705, p = 000. However, H ypothesis 6 was not supported as the interaction between transparent communication and perceived transparency was not significant. The predictor of perceived transparency was significant t = 13.058, p = .000, indicating that t rust would inc rease by .423 for each single increase in perceived transparency This model explained 31% of the total variance in trust The change in R2 between this model and the model used in Hypothesis 4 was not significant, p = .288. Therefore the addition of the i nteraction term did not improve this regression model (refer back to T able 420)

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137 Post Hoc Analyses To further examine the variables not included in the hypotheses post hoc analyses were conducted. The post hoc analyses included the addition of predictor va riables to the regression model used in H ypothesis 3 and H ypothesis 4 Two additional regression models were run for each dependent variable. The first regression model included the addition of age and gender. The second regression model included the addition of agricultural experience predictors including, belief statement s about the livestock industry, area of residence while growing up, self or immediate family employment in the livestock industry, and meat consumption. In order to understand the rel ationships between the variables to be used in post hoc tests and identify if any multi collinearity issues existed between variables a bivariate analysis was conducted. The dependent variables of attitude and trust were included in the bivariate analysis as well as the factors of transparent communication, personal relevance, and perceived transparency, age, gender, livestock industry values, area of residence, industry employment, and meat consumption. The results of the correlations are in Table 421. A strong relationship was observed between area of residence while growing up and meat consumption. Post Hoc Analyses for A ttitude To test the effect of additional variables on attitude, additional predictors of attitude wer e added to the regression model fo rmed in Hypothesis 3 The first post hoc regression model tested included the predictors of personal relevance, transparent communication, perceived transparency, age, and gender. The gender variable was dummy coded (0 male, 1 female) and the constant repr esented the male gender. The model was found to be significant, F (5,623) = 57.920, p = .000. Additionally, the female

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138 gender ( t = 3.195, p = .001), and perceived transparency ( t = 16.234, p = .000) were found to be significant predictors of attitude. Thes e findings indicated that attitude would decrease by 1 24 if an individual was female, and increase by .434 for each increase in perceived transparency Personal relevance, transparent communication, and age were not found to be significant predictors The predictors in the model explain ed 31.9% of the variance in attitude. The change in R2 between this model and the model used in H ypothesis 3 was significant, p = .006. Therefore, the addition of the age and gender variables did improve the regression model ( T able 422). A second post hoc regression model was tested. This model included the predictors listed in the previous paragraph, as well as the predictors of livestock industry values, area of residence while growing up, self or immediate family employment in the livestock industry, and meat consumption. For this post hoc model, the variables of gender, area of residence while growing up, self or immediate family employment in the livestock industry, and meat consumption were dummy coded. The constant in this model represented a male, who lived in a subdivision in a town or city while growing up, did not personally work or have an immediate family member who worked in the livestock industry, and ate meat four to seven times per week. This model was found to be significant, F ( 18, 623) = 17.095, p = .000. Perceived transparency ( t = 15.623, p = .000) the female gender ( t = 3.118, p = .002) and c urrently working or having an immediately family member work ing in the livestock industry ( t = 2.043, p = .041) were found to be significant predictor s of attitude. These finding s indicated that attitude would increase by .431 for each increase in perceived transparency, decrease by .129 if an individual was female, and decrease

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139 by .124 if an individual was current ly working or had an immediate family member who currently worked in the livestock industry. The remaining variables in the regression model were not found to be significant predictors of attitude. Of the total variance in attitude, 33.7% was explained by the variables in this model. The change in R2 between this model and the first post hoc model was not found to be significant, p = .228 Therefore, the additional variables did not improve the regression model The post hoc analyses result s for attitude ar e in Tables 4 22 and 423. Post Hoc Analyses for Trust To test the effect of additional variables on trust, additional predictors of trust were added to the regression model formed in H ypothesis 4 The first p ost hoc regression model included the predictor s of personal relevance, transparent communication, perceived transparency, age, and gender. The gender variable was dummy coded (0 male, 1 female) and the constant represented the male gender. The model was found to be significant, F (5,625) = 57.856, p = .000. Additionally, transparent communication ( t = 2.052, p =.041), the female gender ( t = 3.913, p = .000), and perceived transparency ( t = 16.246, p = .000) were found to be significant predictors of attitude. These findings indicated that attitude would increase by .067 if an individual received the high transparent communication treatment, by .141 if an individual was female, and by .402 for each increase in perceived transparency. Personal relevance and age were not found to be significant predictors i n this regression model The predictors in the model explained 31.8% of the variance in attitude. The change in R2 between this model and the model used in H ypothesis 3 was significant, p = .001. Therefore, the addition of the age and gender variables did improve the regression model (T able 424).

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140 A second post hoc regression model was tested. This model included the predictors listed in the previous paragraph, as well as the predictors of livestock industry values area of residence while growing up, self or immediate family employment in the livestock industry, and meat consumption. For this post hoc model, the variables of gender, area of residence while growing up, self or immediate family employment in the livestock industry, and meat consumption were dummy coded. The constant in this model represented a male, who lived in a subdivision in a town or city while growing up, did not personally work or have an immediate family member who worked in the livestock industry, and ate meat four to seven times per week. This model was found to be significant, F (18, 625) = 26.124, p = .000. Transparent communication ( t = 2.480, p = .013) perceived transparency ( t = 14.930, p = .000), livestock industry values ( t = 9.645, p = .000) and never eating meat ( t = 2.493, p = .013) were found to be significant predictors of trust. These result s indicated that trust would increase by .076 if an individual received the high transparent communication treatment, increase by .352 for each increase in perceived transparency, inc rease by .205 for each increas e livestock industry values, and decrease by .174 if an individual never ate meat Although the female gender was significant in the first post hoc test, it was not found to be a significant predictor in this post hoc test. Ad ditionally, t he remaining variables in this regression model were not found to be significant predictors of trust Of the total variance in trust, 43.7 % was explained by the variables in this model The change in R2 between this post hoc model and the firs t post hoc model was significant, p = .000. Therefore, the additional variables

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141 did improve the regression model The post hoc analyses results for trust are in Tables 4 24 and 425.

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142 Table 41. Subject Demographics n % Gender Female 465 67.6 Male 221 32.1 Self reported area of residence while growing up Downtown area in a town or city 39 5.7 Urban or suburban area outside of city limits 220 32.0 Subdivision in a town or city 306 44.5 Rural area (not a farm) 100 14.5 Farm 22 3.2 Employment in the livestock industry No 563 81.8 Yes, in the past 61 8.9 Yes, currently 50 7.3 I or someone in my immediate family plans to in the next 4 years 11 1.6 Meat consumption, including meals and snacks More tan 14 times per week 64 9.3 8 14 time s per week 207 30.1 4 7 times per week 252 36.6 1 3 times per week 103 15.0 Less than 1 time per week 23 3.3 Never 38 5.5 Class rank Graduate Student 4 0.6 Senior 205 29.8

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143 Table 41. Continued Junior 263 38.2 Sophomore 163 23.7 Freshman 51 7.4

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144 Table 4 2 Total attitude scale inter item consistency statistics n M SD Corrected Item Total Correlation Alpha if Item Deleted Acceptable: Unacceptable 688 4.38 .81 .56 .89 Favorable: Unfavorable 687 4.04 .86 .65 .89 Right: Wrong 686 3.99 .84 .63 .89 Bad: Good* 686 4.00 .90 .65 .89 Positive: Negative 687 4.18 .82 .62 .89 Harmful: Beneficial* 686 4.05 .81 .67 .89 Necessary: Unnecessary 686 3.65 .99 .56 .89 Unimportant: Important* 686 3.68 .98 .56 .89 Meaningful: Meaningless 688 3.72 .92 .63 .89 Progressive: Regressive 686 4.07 .80 .61 .89 Private: Public* 686 4.36 .87 .39 .90 Reputable: Disreputable 686 3.72 .88 .53 .89 Transparent: Opaque 687 3.50 .92 .37 .90 Complex: Simple* 686 3.93 .88 .16 .90 New: Old 686 3.89 .8 5 .56 .89 Sensitive: Insensitive 687 3.59 .84 .47 .89 Boring: Interesting* 686 3.26 1.07 .53 .89 Wise: Foolish 686 3.67 .76 .58 .89 Note: Responses based on semantic differential scale from 1 = Unacceptable to 5= A cceptable. *Reverse coded item

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145 Table 4 3. Attitude grand means among treatment groups n M SD High personal relevance, high transparency 177 3.93 .52 High personal relevance, low transparency 173 3.83 .55 Low personal relevance, high transparency 165 3.91 .53 Low personal relevance, low transparency 171 3.81 .51 Note: Responses ranged from 1.00 to 2.33 = least favorable, 2.34 to 3.66 = neutral and 3.67 to 5.00 = most favorable (see methods)

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146 Table 44 Total trust scale inter item consistency statistics n M SD Corrected ItemTotal C orrelation Alpha if Item Deleted I can trust Clucking Farms and Hatchery 688 3.32 .74 .70 .83 I can trust the information provided by Clucking Farms and Hatchery 687 3.37 .79 .70 .83 I can trust the chicken/poultry industry 686 2.79 .98 .48 .86 I ca n trust the agricultural industry 686 3.06 1.02 .49 .86 Clucking Farms and Hatchery has a great amount of experience. 687 2.95 .67 .40 .86 Clucking Farms and Hatchery makes truthful claims 686 3.32 .60 .64 .84 Clucking Farms a nd Hatchery has great ex pertise 686 3.14 .56 .56 .85 Clucking Farms and Hatchery is honest 686 3.38 .66 .65 .84 I believe what Cluck ing Farms and Hatchery tells me 688 3.36 .79 .66 .84 Clucking Farms and Hatc hery is skilled at what they do 686 3.25 .55 .54 .85 Note: Responses based on Likert type scale from 1 = Strongly disagree to 5 = Strongly agree

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147 Table 45. Trust grand means among treatment groups n M SD High personal relevance, high transparency 177 3.25 .49 High personal relevance, low transparency 173 3.14 .49 Low personal relevance, high transparency 16 6 3.26 .45 Low personal relevance, low transparency 171 3.12 .54 Note: Responses ranged from 1.00 to 1.49 = strongly disagree, 1.50 to 2.49 = disagree, 2.50 to 3.49 = neither agree nor disagree, 3.50 to 4.49 = agree, and 4.50 to 5.00 = strongly agree (see methods) Table 46. Total perceived transparency scale inter item consistency statistics n M SD Corrected ItemTotal Correlation Alpha if Item Deleted Clucking Farms and Hatchery wants to understand how its decisions affect people like me 687 3.90 .7 8 69 .83 Clucking Farms and Hatchery provides information that is useful to people like m e for making informed decisions 687 3. 66 86 68 .84 Clucking Farms and Hatchery want to be accountable to people like me for its actions 687 3.6 9 79 71 .8 2 Clucking Farms and Hatchery wants people like me to know what it is doing and why it is doing it 687 3.9 6 .81 75 .8 1 Note: Responses based on Likert type scale from 1 = Strongly disagree to 5 = Strongly agree

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148 Table 47 Perceived transparency grand means among treatment groups n M SD High personal relevance, high transparency 177 3.91 66 High personal relevance, low transparency 173 3. 73 68 Low personal relevance, high transparency 16 6 3. 85 66 Low pers onal relevance, low transparency 171 3. 71 70 Note: Responses ranged from 1.00 to 1.49 = strongly disagree, 1.50 to 2.49 = disagree, 2.50 to 3.49 = neither agree nor disagree, 3.50 to 4.49 = agree, and 4.50 to 5.00 = strongly agree (see methods) Table 48. Total livestock industry values scale inter item consistency statistics n M SD Corrected ItemTotal Correlation Alpha if Item Deleted Both large and small farmers work to care for their animals appropriately 688 3.32 .74 .70 .83 The care of farm ani mals lacks proper oversight* 687 3.37 .79 .70 .83 Farm animals are cared for today better than they were 50 years ago 686 2.79 .98 .48 .86 Large scale livestock production is necessary to feed the worlds growing population 686 3.06 1.02 .49 .86 Live stock should be raised for human consumption 687 2.95 .67 .40 .86 Note: Responses based on Likert type scale from 1 = Strongly disagree to 5 = Strongly agree Reverse coded item

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149 Table 49 Livestock industry values grand means among treatment groups n M SD High personal relevance, high transparency 1 69 3. 05 72 High personal relevance, low transparency 166 3. 08 84 Low personal relevance, high transparency 158 3. 06 79 Low personal relevance, low transparency 164 3. 09 72 Note: Responses ranged from 1.00 to 1.49 = strongly disagree, 1.50 to 2.49 = disagree, 2.50 to 3.49 = neither agree nor disagree, 3.50 to 4.49 = agree, and 4.50 to 5.00 = strongly agree (see methods) Tabl e 410. Attentiveness to stimuli affirmative responses n % When read ing the Facebook page did you read anything about where the farm was going to be located? 661 96.1 When reading the Facebook page did you read anything about how the chickens were going to be housed? 605 87.9 When reading the Facebook page did you read anything about the options being considered in the building plans? 571 83.0 When reading the Facebook page did you see a picture of the land where the farm will be built? 665 96.7 When reading the Facebook page did you read anything about criticism as sociated with chicken producers? 622 90.4 Table 411. Comparison of manipulations n % Page A has the most transparent cover photo 547 79.5 Page A has the most transparent description of the chicken housing system to be used 638 92.7 Page A has the most transparent description of prior criticisms of chicken producers and plan to address those criticisms 600 87.2 Page A has the most transparent description of the building plan options 574 83.4

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150 Table 412. Attitude grand means among classes n M SD RTV 2100: Writing for Electronic Media 79 3.72 53 AEC 3033: Research and Business Writing A 57 3. 97 49 AEC 3033: Research and Business Writing B 70 3. 89 50 AEC 3030: Effective Oral Communication 140 3. 86 52 AEB 2014: Economic Issues, Food, an d You 175 3.90 .52 ENY 3005: Principles of Entomology 79 3.88 .56 AEC 3474: Leadership Development 61 3.92 .51 Note: Responses ranged from 1.00 to 2.33 = least favorable, 2.34 to 3.66 = neutral and 3.67 to 5.00 = most favorable (see methods) Table 413. Trust grand means among classes n M SD RTV 2100 : Writing for Electronic Media 79 3.14 5 1 AEC 3033: Research and Business Writing A 57 3. 22 4 8 AEC 3033: Research and Business Writing B 70 3. 23 49 AEC 3030 : Effective Oral Communication 139 3. 21 5 1 AEB 2014: Economic Issues, Food, and You 177 3. 16 .45 ENY 3005 : Principles of Entomology 79 3. 17 48 AEC 3474 : Leadership Development 61 3.33 .5 2 Note: Responses ranged from 1.00 to 1.49 = strongly disagree, 1.50 to 2.49 = disagree, 2.50 to 3. 49 = neither agree nor disagree, 3.50 to 4.49 = agree, and 4.50 to 5.00 = strongly agree (see methods )

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151 Table 414. Inter correlations between attitude, trust, transparent communication, personal relevance, and perceived transparency Variable 1 2 3 4 5 1. A ttitude 1 2. Trust .45 1 3. Transparent communication .09 .13 1 4. Personal relevance .03 .01 .02 1 5. Perceived transparency .55 .55 .12 .03 1 Table 415. Attitude means by treatment group High personal relevance Low personal relev ance Total High transparency 3.93 (.52) 3.91 (.53) 3.92 (.52) Low transparency 3.83 (.55) 3.81 (.51) 3.82 (.53) Total 3.88 (.54) 3.86 (.52) Table 4 16. Effect of transparent communication and personal relevance on attitude Source SS df MS F p Pers onal relevance .116 1 .116 .417 .519 Transparent communication 1.694 1 1.694 6.090 .014 Personal relevance*Transparent communication .000 1 .000 .001 .980 Error 189.760 682 .278 Total 3301.003 686

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152 T able 417. Trust means by treatment group High personal relevance Low personal relevance Total High transparency 3.25 (.49) 3.26 (.45) 3.26 (.47) Low transparency 3.14 (.49) 3.12 (.54) 3.13 (.52) Total 3.20 (.49) 3.19 (.50) Table 418. Effect of transparent communication and personal rele vance on trust Source SS df MS F p Personal relevance .010 1 .010 .040 .841 Transparent communication 2.684 1 2.684 11.012 .001 Personal relevance*Transparent communication .034 1 .034 .141 .707 Error 166.470 683 .244 Total 7181.690 687

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153 Table 419. Multiple linear regression analysis for variables predicting attitude Variable Model 1 B t p Model 2 B t p Constant 2.235 22.965 .000 2.308 17.491 .000 Transparent communication .027 .796 .426 .131 .672 .502 Personal relevance .010 .281 779 .009 .268 .789 Perceived transparency .426 16.965 .000 .406 11.713 .000 Transparent communication*perceived transparency .041 .825 .410 R2 .304 .304 F 99.035 .000 74.412 R2 .001 F .680 .410

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154 Table 4 20. Multiple linear regression analysis for variables predicting trust Variable Model 1 B t p Model 2 B t p Constant 1.653 18.193 .000 1.565 12.690 .000 Transparent communication .058 1.828 .068 .248 1.367 .172 Personal relevance .008 .259 .795 .008 2.41 .810 Perce ived transparency .339 17.027 .000 .423 13.058 .000 Transparent communication*perceived transparency .050 1.063 .288 R2 .309 .310 F 101.877 .000 76.705 R2 .001 F 1.130 .288

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155 Table 421. Inter correlations between attitude, trust, transparent communication, personal relevance, perceived transparency, age, gender, livestock industry values, area of residence, industry employment, and meat consumption Variable 1 2 3 4 5 6 7 8 9 10 11 1. Attitude 1 2. Trust .45 1 3. Transparent communication .09 .13 1 4. Personal relevance .03 .01 .13 1 5. Perceived transparency .55 .55 .12 .03 1 6. Age .01 .02 .02 .05 .01 1 7. Gender .16 .07 .00 .07 .01 .17 1 8. Livestock indu stry values .14 .44 .02 .01 .18 .05 .20 1 9. Farm .06 .02 .04 .04 .01 .12 .05 .08 1 10. Rural .05 .02 .04 .04 .02 .02 .00 .08 .99 1 11. Urban or Suburban .06 .03 .05 .04 .01 .03 .06 .07 .99 .99 1 12. Downtown .06 .02 .04 .04 .0 1 .05 .06 .08 .99 .99 .99 13. Past employment .00 .03 .02 .03 .07 .01 .02 .06 .58 .57 .58 14. Current employment .00 .03 .02 .02 .07 .01 .02 .06 .58 .57 .58 15. Future employment .00 .03 .02 .02 .07 .01 .02 .06 .58 .57 .58 16. Never .06 .01 .04 .04 .02 .01 .09 .06 .99 .99 .99 17. Less than 1 .06 .02 .04 .04 .01 .01 .09 .07 .99 .99 .99 18. 1 3 times .06 .02 .04 .04 .02 .01 .12 .07 .99 .99 .99 19. 8 14 times .06 .04 .04 .04 .00 .01 .14 .10 .99 .99 .99 20. more than 14 .06 .03 04 .03 .01 .04 .23 .08 .99 .99 .99

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156 Table 421. Continued Variable 12 13 14 15 16 17 18 19 20 12. Downtown 1 13. Past employment .58 1 14. Current employment .58 .99 1 15. Future employment .58 .99 .99 1 16. Never .99 .58 58 .58 1 17. Less than 1 .99 .58 .58 .58 .99 1 18. 1 3 times .99 .58 .58 .58 .99 .99 1 19. 8 14 times .99 .57 .57 .57 .99 .99 .99 1 20. more than 14 .99 .58 .58 .58 .99 .99 .99 .99 1

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157 Table 422. Post hoc multiple linear regression analysis for variables predicting attitude, models 1 and 3 Variable Model 1 B t p Model 3 B t p Constant 2.195 20.922 .000 2. 06 0 8.649 .000 Transparent communication .023 .659 .510 .026 .72 5 .468 Personal relevance .008 .223 .824 .017 .493 .622 Perceive d transparency .440 16.387 .000 .43 4 16.234 .000 Age .009 .95 3 .341 Gender Female .1 24 3.195 .001 R2 .308 .3 19 F 91.836 .000 57.920 .000 R2 .011 F 5.185 .006 Note: Values for model one differ from the model observed in hypotheses testing due to the exclusion of additional cases as a result of model comparison

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158 Table 423. Post hoc multiple linear regression analysis for variables predicting attitude, models 3 and 4 Variable Model 3 B t p Model 4 B t p Constant 2 070 9.431 .000 1.961 8.548 .000 Transparent communication .026 .725 .468 .025 .708 .479 Personal relevance .017 .493 .622 .014 .401 .689 Perceived transparency .434 16.234 .000 .431 15.623 .000 Age .009 .953 .341 .009 898 .36 9 Gender Female .124 3.195 .001 .1 29 3.1 18 .002 Livestock industry values index .0 43 1.730 .084 Self reported area of residence while growing up Farm .121 1. 093 .275 Rural area (not farm) .099 1.853 .0 64 Urban or suburban area outside of city limits .017 .4 20 .6 75 Downtown area in a town or city .012 .1 53 .8 78 Employment in the livestock industry Yes, in the past .002 .037 .971

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159 Table 423. Continued Yes, currently .124 2.043 .041 I or someone in my immediate family plans t o in the next 4 years .121 1.555 .121 Meat consumption, including meals and snacks Never .047 .5 81 .562 Less than once per week .085 872 .3 84 1 3 times per week .005 .0 85 .9 32 8 14 times per week .040 .915 .361 More than 14 times per week .011 .16 7 .867 R2 .3 19 .337 F 57.920 .000 17.095 .000 R2 .011 .018 F 5.185 .006 1.268 .228

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160 Table 424. Post hoc multiple linear regression analysis for variables predicting trust, models 1 and 3 Variable Model 1 B t p Model 3 B t p Constant 1.673 17.123 .000 1. 743 8.599 .000 Transparent communication .069 2.096 .036 067 2.052 .0 41 Personal relevance .039 1.196 .232 .0 29 .887 376 Perceived transparency .396 15.851 .000 .4 02 16.246 .000 Age .0 07 .783 434 Gender Female .141 3.913 .000 R2 .301 .3 18 F 89.407 .000 57.856 .000 R2 017 F 7.659 .00 1 Note: Values for model one differ from the model observed in hypotheses testing due to the exclusion of additional case s as a result of model comparison

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161 Table 425. Post hoc multiple linear regression analysis for variables predicting trust, models 3 and 4 Variable Model 3 B t p Model 4 B t p Constant 1. 743 8.599 .000 1.322 6.744 .000 Transparent communication 067 2.0 52 .0 41 076 2.480 013 Personal relevance .0 29 .887 376 .022 .720 .472 Perceived transparency .4 02 16.246 .000 .3 52 14.930 .000 Age .0 07 .783 434 .007 .8 20 412 Gender Female .141 3.913 .000 .048 1.3 59 .1 75 Livestock industry values in dex .205 9. 645 .000 Self reported area of residence while growing up Farm .105 1. 110 267 Rural area (not farm) .0 51 1. 107 269 Urban or suburban area outside of city limits .004 122 .9 03 Downtown area in a town or city .087 1.3 00 .1 94 Employment in the livestock industry Yes, in the past .021 .4 36 .6 63

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162 Table 425. Continued Yes, currently .026 .5 03 615 I or someone in my immediate family plans to in the next 4 years .002 .0 35 .9 72 Meat consumption, inclu ding meals and snacks Never .1 74 2. 493 .013 Less than once per week .115 1.3 87 .1 66 1 3 times per week .007 158 874 8 14 times per week .053 1.4 17 .1 57 More than 14 times per week .051 .8 92 .3 73 R2 .3 18 .437 F 57.856 000 26.124 .000 R2 017 .118 F 7.659 .00 1 9.809 .000

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163 CHAPTER 5 CONCLUSIONS Overview This study sought to determine the effect of different levels of transparent communication and personal relevance on the attitudes and trust of college students. Literature surrounding Elaboration Likelihood Model ( ELM ) trust, and transparency informed and guided this study. The independent variables of transparent communication and personal relevance were both manipulated. All of the transparent communicati on manipulations were part of a Facebook page for a fictitious poultry farm called Clucking Farms and Hatchery. The personal relevance manipulations were included in a description show n to participants, as well as the Facebook page. Refer to F igures 3 2 through 38 to view both the transparent communication and personal relevance manipulations. The dependent variables included attitude and trust which in the case of this study were situated in the context of a poultry farm A two (personal relevance: high and low) x two (transparent communication: high and low) betweensubjects factorial experimental design was used for this study. The experiment was conducted through an online survey. The survey was sent to a convenience sample of college students from a l arge southeastern university. The subjects were randomly assigned to the treatment conditions. In C hapter 4, the data analysis results were discussed. A total of 688 subject responses were usable and analyzed. The mean age of the subjects was 20.6 and the sample included a majority of females (67.6%). Most of the subjects either grew up in a subdivision or in an urban or suburban area outside of city limits. Many of the subjects reported eating meat four to seven times per week and the majority did not per sonally

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164 work or have a family member who worked in the livestock industry. Key findings, implications, limitations, recommendations, and conclusions are presented in Chapter 5. Key Findings The descriptive analysis showed that all of the treatment groups had a mean attitude score above 3.67, which indicated that all of the mean attitude scores were most favorable. The high personal relevance, high transparent communication group had the highest mean attitude score of 3.93 ( SD = .52). The low personal relev ance, high transparent communication group had the next highest mean score of 3.91 ( SD = .53). Additionally, the analysis showed that all of the treatment groups had a mean trust score between 2.50 and 3.49, which indicated that all of the mean trust scores fell into the neither agree nor disagree category. The low personal relevance, high transparent communication group had the highest mean trust score of 3.26 ( SD = .45). The next highest mean trust score was 3.25 ( SD = .49), which was the mean of the high personal relevance, high transparent communication treatment group. Although each treatment group mean fell into the same real limits standards, it was the treatment groups with high transparent communication that had the two highest mean scores for both attitude and trust. In this study, six hyp otheses were tested. H ypothesis 1 formed based on the theoretical framework of ELM and transparency, predicted that those receiving the high transparent communication and high personal relevance treatment would have a more positive attitude than those who received low transparent and low personal relevance treatments. Hypothesis 1 was partially supported. The interaction of personal relevance and transparent communication was not significant. However, the main eff ect for transparent communication was significant. This suggested that those who received

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165 high transparent communication would have higher mean attitude scores than those who received low transparent communication. Although the main effect of personal rele vance was not significant, an inspection of the means in Table 415 indicated that those who received high personal relevance treatments had slightly more favorable attitudes than those who received low personal relevance treatments. Hypothesis 2 made simi lar predictions to the H ypothesis 1 but instead of examining the dependent variable of attitude, H ypothesis 2 examined the dependent variable of trust. Like H ypothesis 1 H ypothesis 2 was also partially supported. The interaction between personal relevanc e and transparent communication was not found to be significant. However, the main effect for transparent communication was significant. This main effect indicated that those who received high transparent communication would have a higher mean score for tr ust than those who received low transparent communication. A main effect for personal relevance was not found to be significant but an inspection of the means in Table 417 indicated that those who had received high personal relevance treatment had slight ly more positive trust than those who received low personal relevance treatments. Hypothesis 3 predicted that when controlling for transparent communication and personal relevance, perceived transparency would have a positive effect on attitudes. H ypothes is 3 was supported. Perceived transparency was found to be a significant predictor of attitude. This finding indicated that those with a higher perceived transparency score had a higher attitude score than those with a lower perceived transparency score.

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166 H ypothesis 4 made the s ame prediction as H ypothesis 3 but used the dependent variable of tr ust rather than attitude. Like H ypothesis 3, H ypothesis 4 was also supported. Perceived transparency was found to be a significant predictor of trust. Those with a higher perceived transparency score had a higher trust score than those with a lower perceived transparency score. H ypothesis 5 predicted that the interaction between transparent communication and perceived transparency would have a positive effect on attit ude when controlling for transparent communication, personal relevance, and perceived transparency. The interaction between perceived transparency and transparent communication was not found to be significant and H ypothesis 5 was not supported. However, perceived transparency was found to be a significant predictor of attitude as identified in H ypothesis 3 H ypothesis 6 made the same predictions as H ypothesis 5 but used the dependent variable of trust rather than attitude. As was found in H ypothesis 5 the interaction between perceived transparency and transparent communication was not significant and H ypothesis 5 was not supported. Perceived transparency was identified as a significant predictor of trust as indicated in H ypothesis 4 Implications The results of this study provide both theoretical and practical implications. The study offers theoretical implications to ELM, trust, and transparency literature. Additionally, practical implications are offered from the results. Theoretical I mplications As p revious research has indicated, a positive relationship exists between trust and transparency ( Auger, 2011 ; Rawlins, 2008b). The perceived transparency measure

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167 used in this study was adapted from the transparency measure used in both Rawlins (2008b ) and A ugers (2011) studies. The findings of this study are consistent with previous research, as perceived transparency was shown to be a significant predictor of trust. The results showed that trust increases positively when perceived transparency increases In addition to perceived transparency, manipulated transparent communication also had a significant impact on trust in Hypothesis 2 and in the post hoc tests for trust When examining the means in Table 417, those who received high transparent communication had slightly more positive trust than those who received low transparent communication. Thus the results suggest that not only can perceived transparency have a positive effect on trust, as was found in previous studies (Auger, 2001; Rawlins, 2008b ), but also that manipulated levels of transparent communication can also impact trust. However, the lack of significance of manipulated transparent communication in Hypothesis 4 and Hypothesis 6 suggest that some multi collinearity exists between transparent c ommunication and perceived transparency. This finding confirms that the action of an organization to communicate in a more transparent manner can positively impact its t rust worthiness and Millennials trust in the organization (Meijboom et al., 2006) Similarly, perceived transparency was found to have a significant impact on attitude. The relationship between transparency and attitude had not been previously explored. The findings from this study suggest that attitude increases positively when perceived tr ansparency increases Additionally in Hypothesis 1, manipulated transparent communication was also found to have a significant impact on attitude. When examining the means in Table 415, those receiving the high transparent

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168 communication manipulation had slightly more favorable attitudes than those who received the low transparent communication manipulations. Transparent communication was not found to be significant in any tests that included the variable of perceived transparency, thus suggesting that these two variables are multi collinear. The positive influence of perceived transparency on attitude is important to understand because previous research has shown that attitudes are predictive of behavior (Ajzen & Fishbein, 1977; Fishbein & Ajzen, 1974; Petty & Cacioppo, 1996; Petty & Cacioppo, 2009). With higher level of perceived transparency, stronger attitudes will form which may also have the potential to result in desired behavioral outcomes. Using ELM, the design of the research and conceptual model suggested that the manipulation of personal relevance would impact the subjects motivation to process the communication. However, no significant differences were found between those who received high and low personal relevance treatments and personal r elevance did not have a significant impact on trust or attitude. Transparent communication may have been more salient than personal relevance to the subjects participating in this study. The lac k of significance may be explained by previous identification of low involvement associations with fo od (or in this case foodrelated information) (Beharrell & Denison, 1995). In addition, this lack of significance may be due to personal relevance cofounding with prior knowledge, a problem observed previously with personal relevance (Petty & Cacioppo, 1986). Since personal relevance was not significant and no other measures of motivation were collected in this study it cannot be concluded that the subjects had the motivation to process the communication. Thus, it is likely that the attitudes and trust formed were based on the peripheral processing route. In this

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169 case, perceived transparency likely served as a peripheral cue that led to peripheral processing. However, further examination of the subjects elaboration would need to be conducted to confirm this conclusion. Previous research has found a prevalence of peripheral processing in studies of ELM and agriculture (Frewer et al., 1997; Gore et al., 2008; Hendricks, 2000; Veberke, 2005). Practical I mplications A need existed to understand how transparency could impact the livestock industry, in or der to bring together those who support (Garner, 2009; Roybal, 2012; Weir, 2008) transparency in the industry and those who oppose it (Potter, 2011). The results of this study indicate the use of transparent communication, specifically when communicating with those in the Millennial Generation, would be beneficial to the industry. Transparent communication is likely to result in more favorable attitudes and higher levels of tr ust among the Millennial Generation. Additionally, the findings suggest that transparent communication in the agricultural industry should go beyond the tracking and tracing ( Barling et al., 2009; Beulens et al., 2005; Opara & Mazaud, 2001; Miller & Marian i, 2010; van Dorp, 2003; Wognum et al., 2011) of food products and should encompass transparent communication practices throughout the production process, as this study found transparent communication about poultry production to be effective. Previous lit erature indicated that t rust in agricultur e and food systems c ould be explained by three different bases of trust including emotional trust, habitual trust, and reflexive trust (Bildtgard, 2008). It is suspected that the use of transparent communication in this study likely increased the reflexive trust of the subjects. However, the trust and transparency literature suggests that over time transparency may also be

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170 able to increase emotional and habitual trust. Because transparency can positively influence trust (Auger, 2011; Rawlins, 2008b) and trust improves relationship building Tschannen Moran & Hoy, 2000), it is expected that over time the improved relationships between the communicator and receiver would develop components of emotional and habitual trust (Biltgard, 2008). Additionally, organizations who communicate more transparently will not only be more trusted, but may also be identified by consumers as having adequate corporate social responsibility (Filizoz & Fisne, 2011). The post hoc analyses indicated that gender was a significant predictor for attitude. If a subject was female her attitude was likely to be less favorable than that of a male Gender was also found to be a significant predictor of trust in the first post hoc test, but was not significant in the second post hoc test. In the first post hoc test, a females trust was likely to be more favorable than that of a male. The lack of significance in the second post hoc test is likely due to multi collinearity issues with a variable that was added in the second post hoc test for trust. The occurrence of females having less favorable attitudes, but higher levels of trust may imply that females form attitudes on a different set of values, beliefs, and experiences that they use to form trust. Ad ditionally, the females in this study may have been more trusting because the message stimuli were presented within a Facebook page. Literature suggests that females are more likely to regularly use social media than males (Taylor & Ketter, 2010). Therefor e, the familiarity of the social media interface may have been more familiar to the females, leading to increased levels of trust. The post hoc analyses also indicated that those who currently worked or had an immediate family member who worked in the liv estock industry had a less favorable

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171 attitude toward the communication than those who did not work in the industry. It can be implied that those who currently worked in or had an immediate family member who worked in the livestock industry may have been fr om the premodernist view of transparency (Meijer, 2009) Thus, suggesting that these individuals may have felt that transparent communication presented through social media may threaten traditional trust mechanisms (Meijer, 2009) Additionally, the opposition to transparency by a segment of the agricultural industry (Potter, 2011) or the challenges associated with transparency may explain the less favorable attitude among these individuals (Barling et al., 2009; Beulens et al., 2005). The livestock industry values index was found to be a significant predictor of trust in the post hoc analyses. This finding may indicate that values may have more of an impact on trust rather than attitudes. Although literature suggests that attitudes help one express thei r values (Katz, 1960), further exploration should be done to investigate how trust and values interact The post hoc analyses also found that those who reported that they never ate meat had less favorable levels of trust compared to those who ate meat regularly. The literature suggests that individuals engage in behaviors that satisfy their concerns (Baron et al., 2000) Therefore, it is implied from the findings and the literature that those who never ate meat likely had concerns associated with eating mea t or the livestock industry. Lower levels of trust toward meat products and the livestock industry was likely developed as a result of these concerns (Baron et al., 2000). Limitations Although this study provided valuable theoretical and practical insight s, limitations of this study must also be discussed. The convenience sample used in this study limits the generalizability of these results (McMillan & Schumacher, 2010). In addition, the

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172 characteristics of the subjects may be different than the characteri stics of other college students, thus further limiting the generalizability. Although this study looked at the effects of transparent communication on the Millennial Generation, it cannot be assumed that the results from this study are representative of the Millennial Generation as a whole. In addition, these results are not transferable to other age groups The one time exposure to t he message stimuli also presented limitations to this study. Attitudes especially those formed through the central processi ng route, are often resistant to change and require multiple exposures to persuasive communication before changes in attitude may occur (Perloff, 2003; Petty et al., 2009) It is likely that the attitudes formed through this study were formed through the peripheral route; indicating that the attitudes are likely not enduring, accessible, and resistant to change (Petty et al., 2009). The effects of transparent communication and personal relevance on attitude may have been different if subject s had been expos ed to the message stimuli multiple times. Additionally, the static nature of the Facebook page, which presented the message stimuli to the participants, is a limitation. On an active Facebook page the participants would have been able to comment, post, and interact with Clucking Farms and Hatchery. However, in order to control the message stimuli for all subjects the Facebook page had to be static. An active Facebook page may have produced different results than what was observed in this study. Recommendations For Theory and R esearch As this study found that transparency has a significant effect on attitude, further research should be done to connect transparency to ELM. In this study, it was

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173 concluded that perceived transparency likely served as a peripheral cue. The conceptual model developed for this study hypothesized that perceived transparency would either act as a peripheral cue to influence attitudes and trust or it would act as argument quality does in ELM and further strengthen resulting attitudes and trust. Further research should examine if perceived transparency always acts as a peripheral cue or if it can assume the role of argument quality when motivation and ability are present. Since personal relevance was not significant and no other measures of motivation were collected, i t was concluded that the attitudes and trust observed in this study resulted from peripheral processing. However, it cannot be concluded that the same results would be present if central processing were to occur. A followup study should be done to determine if central processing of transparent communication would lead to the same effects on attitudes and trust. It is recommended that a followup study measure subjects motivational and ability factors, rather than trying to manipulate them, and then assess how those subjects who scored high on the motivational and ability factors differ from those who scored low on the motivational and ability factors. This type of assessment would provide insight to the formation of two different types of attitudes and trust as a result of transparent communication, t hus, allowing researchers to determine which processing route should be targeted for transparent communication in the agricultural context. Additionally, further research exam ining transparency in ELM should measure elaboration to provide further insight to the processing route, as well as the strength and endurance of the resulting attitudes and trust. A pretest posttest design

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174 would also be beneficial in determining the impac t of transparent communication on attitudes and trust. Personal relevance was not found to have significant effects on attitude and trust in this study. Previous literature has suggested that manipulations similar to those used in this study have been suc cessful in affecting attitude and increasing persuasion (Petty & Cacioppo, 1979; Petty et al., 1981; Petty et al., 1983) However, previous literature has al so indicated that food and foodrelated information is a low involvement topic (Beharrell & Denison 1995) Further research should be done to determine if high personal relevance on the topic s of food or livestock production can be achieved with the Mille n nial Generation. Future research should include different methods of operationalizing personal rel evance. Researchers should explore if high personal relevance can be achieved through the direct administration of transparent communication, perhaps in a farmers market setting or other natural setting where producers and consumers would interact with one another. A measure of the subjects perceived personal relevance might also be considered. I n addition, future research should also explore how other factors such as need for cognition, prior knowledge, and repetition influence subjects motivation and ability to process food or livestock related information. The post hoc analyses showed a strong relationship between area of residence while growing up and meat consumption as well as signs of some multi colleniarity issues. Further work should be done to examine these variables and eliminate any underlying multi collinearity issues as well as remove any individuals, such as those involved in the industry, who may be biasing the results.

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175 The post hoc analyses conducted in this study suggested that different variables influence attitude and trust. However, the hypotheses tests showed that perceived transparency could significantly influence both attitude and trust. Therefore, further research should be done to explore the relationship between attitude and trust in the context of transparent communication. In addition, previous research has shown that attitudes are predictive of behavioral intentions (Ajzen & Fishbein, 1977; Fishbein & Ajzen, 1974; Petty & Cacioppo, 1996; Petty & Cacioppo, 2009). Other researc hers have suggested that transparency improves the reasoning ability of individuals and enables them to make informed decisions (Fagatto & Graham, 2007; Rawlins, 2008a). The link between transparency, attitude, trust, and behavioral intentions should also be explored. Trust improves relationship building ( TschannenMoran & Hoy, 2000) and has been discussed as a way to fill the void in agricultural knowledge that has been created by the increasing divide between production and consumption (Jokinen, Kupsala, & Vinnari, 2012). Therefore, it is important to continue to explore trust in the agricultural context. Further research should be done to examine emotional trust, habitual trust, and reflexive trust (Bildtgard, 2008) and determine how transparent communication may impact these three types of trust. In addition, the relationship between the livestock industry or agricultural values and trust should be further researched. This study found that livestock values had a significant impact on trust, but research ers should gain a better understanding of how these values are formed by individuals. This study found that perceived transparency influenced attitude and trust as did manipulated transparent communication in certain situations The interaction between transparent communication and perceived transparency was not a significant predictor

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176 indicating that although they both can independently influence attitude and trust in certain situations together their impact is not significantly greater. More work shoul d be done to determine how individuals perceive transparent communication and what variables influence their perceptions. Finally, further academic work should be done to examine transparency and its effects (Rawlins, 2008b). This research should include m ore quantitative studies of transparency (Rawlins, 2008a). In addition, more studies of transparency in the United States agricultural industry should be completed, since much of the existing work has been completed in the European Union. Transparency research should also be conducted with a variety of populations and though a variety of communication channels Exploring the effect of transparent communication with different generations as well as general consumers would provide insight to the breadth of tr ansparent communication effectiveness. In addition, examining the effect of transparent communication when presented through different media channels, such as print, video, social media, and interactive channels, will allow researchers to identify if trans parent communication is more effective when presented through a certain medi a channel as compared to others. Further research should also explore the use of alternative images in the experimental stimulus. Exploring the effect of transparent communication when the positivity or negativity of images used in the communication varies could provide insight to whether or not there is a transparency threshold. For example, we cannot conclude that the results of this study would have been the same if the cover photo used in the high transparency treatment showed confined chickens in cages. Because of the relationship between trust and transparency, it is recommended that researchers

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177 explore the use of transparent communication in the event of an agricultural crisis Previous researchers have suggested that transparency may be effective in regaining trust after a crisis such as crises associated with petroleum drilling (Jahansoozi, 2006). For P ractitioners In this study perceived transparency and in certain situati on manipulated transparent communicationhad a significant effect on the attitudes and trust of the subjects participating in the study. These results suggest that agricultural practitioners should utilize transparency in their communication practices, especially when communicating with those who are part of the Millennial Generation. Literature indicates that Millennials find confidence in companies that communicat e transparently (Red McGregor 2012) and this study suggests that Millennials will have more favorable attitudes and higher levels of trust toward those who communicat e in a transparent manner. Through increased trust, it is likely that the gap between those who produc e and those who consum e food will narrow (Jokinen et al., 2012). However, it is recommended that practitioners narrow their audience segments further as sub groups within the Millennial generation may not develop positive trust in response to the transparent communication. For example, in this study those subjects who reported never eating meat had significantly lower levels of trust when compared to those who ate meat regularly. Because of the need to control the message stimuli in this experiment, this study was not able to utilize a symmetrical twoway model of communication. How ever, it is recommended that practitioners use t he symmetrical twoway model of communication (Grunig & Hunt, 1984) when communicating transparently. This model of communication will allow for increased communicative transparency by giving

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178 stakeholders the opportunity to participate in conversations about agriculture, to express what information they need regarding agriculture, and to hold agricultural practitioners accountable (Rawlins, 2008a; Rawlins, 2008b). Through this process, those who produce agricultural products and those who consume agricultural products should become more aware of each others needs (Goodman & DuPuis, 2002). Practitioners should work to meet the needs of consumers while also addressing the concerns co nsumers have about the indust ry (Goodman & DuPuis, 2002). However, practitioners should be cautioned that communicating in a transparent manner does not ensure improved attitudes and trust. The target audience of the communication must first access and attend to the communication ( Fagotto & Graham, 2007) In addition, the target audience must perceive the communication to be transparent (Gower, 2006) In this study, the subjects were incentivized with extra credit to participate in the study and read the message stimuli. Outside the experimental setting, the transparent communication must be presented in a manner that the target audience would attend to. Strategies, such as t he use of consumer or audience testimonials may be effective in attracting a broader target audience to attend the communication. If targeting Millennials, it may be appropriate to use a social media interface such as Facebook because this is a media channel that the Millennial Generation is motivated to use (Shreffler, 2012). Practitioners should assess the needs of their target audience and the media that they commonly use. This assessment will allow practitioner s to determine the best channel to communicate through in order to reach their target audience before implementing transparent practices.

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179 Practitioners should plan for the additional challenges associated with the implementation of transparent communication. The literature suggests that the exposure of weaknesses, unauthorized use of information, loss of independence, a proactive management style, and addit ional costs are all challenges associated with transparency (Barling et al., 2009; Beulens et al., 2005; Rawlins, 2008a; Rawlins, 2008b). Practitioners should be prepared to deal with each of these challenges and address them as they arise. As observed in the post hoc analyses, those currently working in the livestock industry may not have favorable attitudes toward transparent communication. Practitioners should be aware of this and know that buy in for transparent communication m a y not be industry wide ( Barling et al., 2009). Workshops, trainings, promotion of successful case studies, and communication about transparent practices may be necessary to increase industry buy in of transparent communication. Summary Based on a theoretical framework of ELM, trust, and transparency, this study sought to determine the effect of transparent communication and personal relevance on the attitudes and trust of college students. The results suggest ed that perceived transparency si gnificantly impacted attitudes and trust In addition, manipulated transparent communication also significantly impacted attitudes and trust in certain situations These findings are important because they provide foundational evidence that transparent communication may have the ability to create an informed citizenry, ensuring the future sustainability of food and the industry needed to support human life (Doerfert, 2011) As was observed with the Millen n ial population used in this study,

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180 t ransparent communication may have the potential to reconnect agricultural producers and consumers, while increasing trust in the industry. In addition, conversations created by transparent communication may be able to address consumer concerns regarding agricultural practices. Within the livestock industry, t hese findings provide evidence to suggest that the industry should come together on the use of transparent communication. Transparent communication positively influences trust and attitudes; this connection could provide many benefits to the industry. Thes e findings indicate that a move away from transparent communication may be harmful to the relationships between producers and consumers particularly those consumers in the Millennial generation. In addition, the findings indicate that the Ag Gag legislation that exists in five states and has been proposed in others may be harming the trust Millennials have in the industry. The gap between agricultural producers and consumers will continue to exist. However, with further research and exploration transparent communication may provide a successful strategy to narrow the gap between agricultural producers and consumers

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181 APPENDIX A EMAIL PRENOTIFCATION Hi [insert course number] students, I wanted to let you all know about an opportunity to earn 5 extra cr edit points for AEC 3030. Here are the details: Extra Credit Opportunity 5 points You are being asked to participate in a 15minute online survey to gather students opinions about livestock production. You will receive an email from Joy Goodwin with a link to the survey. It will be sent to your GATORLINK e mail account by Monday November 19th. The survey will expire on November 30th at 11:59 p.m. Contact Joy with questions/problems: goodwin.4@ufl.edu Take the survey to earn extra credit points. Joy will track who takes the survey and will give me a list of people who took the survey for extra credit Thanks! [instructor name]

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182 APPENDIX B FIRST CONTACT EMAIL SENT TO SUBJE CTS Dear First_Name, I am conducting a study about communication and livestock production and would like to gather your opinions about them through an online survey. The survey will take approximately 15 minutes of your time. You will get 5 extra credit points in Class for your participation. To take the survey, you will need to know your unique participant ID number which is ID_number. Please take extra care to type in the correct ID number. The survey is online. The link will only be active until 11:59 p.m. on November 30th. If you have questions or problems accessing the survey, please email me at goodwin.4@ufl.edu or call 3522732614. Follow this link to the Survey or copy and paste the URL below into your internet browser: Link Thank you, Joy Goodwin Doctoral Candidate University of Florida

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183 APPENDIX C FIRST, SECOND, AND T HIRD REMINDER EMAIL SENT TO SUBJECTS Dear Firs t_Name, You are receiving this email because I do not have record of you completing the Communication and Livestock Production survey. You certainly are not obligated to complete the survey; this is simply a reminder message. It will take approximately 15 minutes of your time. To take the survey, you will need to know your unique participant ID number which is ID_number. Please take extra care to ensure you type in the correct participant ID number. The survey is online a nd will be active until 11:59 p.m. on November 30th. If you have questions or problems accessing the survey, please email me at goodwin.4@ufl.edu or call 3522732614. Follow this link to the survey or copy and paste the URL below into your internet br owser: Link Thank you, Joy Goodwin Graduate Assistant University of Florida

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184 APPENDIX D FOURTH REMINDER EMAI L SENT TO SUBJECTS Dear ${m://FirstName}, Today is the final day to take the online survey for extra credit in ${e://Fiel d/course}. The survey will take approximately 15 minutes of your time. To take the survey, you will need to know your unique participant ID number, which is ${e://Field/ID}. Please take extra care to ensure you type in the correct participant ID number. The survey is online and will only be active until 11:59 p.m. tonight. If you have questions or problems accessing the survey, please email me at goodwin.4@ufl.edu or call 3522732614. Follow this link to the survey or copy and paste the URL below into your internet browser: ${l://SurveyLink?d=Take the Survey} Please note you must have your popup blocker turned off in order for the survey to work properly. Thank you, Joy Goodwin Graduate Assistant University of Florida

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202 APPENDIX F MESSAGE TREATMENTS

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203 High Transparent Communication, High Personal Relevance Treatment

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210 APPENDIX G IRB APPROVAL

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211 LIST OF REFERENCES Abrams, K. M. (2010). The power of food labels: Marketing environmental impacts and anim al welfare on meat labels and gains vs. nonlosses and the influence on attitudes and voting intentions (Doctoral Dissertation) Retrieved from ETD Theses and Dissertations. ( http://purl.fcla.edu/fcla/etd/UFE0041946). Abrahamson, D. (1996). The problem with sources, a source of the problem. Journal of Magazine and New Media Research, 9(1), 1 6. Retrieved from http://aejmcmagazine.arizona.edu/fall2006.html Agichtein, E., Castillo, C., Donato, D., Gionis, A., & Mishne, G. (2008). Finding highquality content in social media. Proceedings of the International Conference on Web Search and Web Data Mining, USA, 183 194. doi : 10.1145/1341531.1341557 Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179211. Retrieved from http://www.sciencedirect.com/science/journal/07495978 Ajzen, I., & Fishbein, M. (1974). Attitudebehavior relations: A theoretical analysis and review of empirical research. Psychological Review, 18 5974. Allis on, P. D. (2009). Missing Data. In R. E. Millsap & A. MaydeuOlivares (Eds.), Handbook of quantitative methods in psychology (pp. 7289). Thousands Oaks, CA: Sage Publications Inc. Allport, G. W., (1935). Attitudes. In C. Muchinson (Ed.), Handbook of social psychology (Vol. 2). Worchester, MA: Clark University Press. American Farm Bureau (2007, January). Activists attack animal agriculture. The Voice of Agriculture. Retrieved from http://www.fb.org/index.php?fuseaction=newsfocus&year=2007&file=nr0107g.html American Farm Bureau. (2001, November). Agriculture must reach out and communicate with urban Americans. The Voice of Agriculture. Retrieved from http://www.fb.org/index.php?fuseaction=newsroom.newsfocus&year=2001&file=nr 1106.html Areni, C. S. Ferrell, M. E., & Wilcox, J. B. (2000). The persuasive impact of report ed group opinions on individuals low vs. high in need for cognition: Rationalization vs. biased elaboration. Psychology & Marketing, 17(10), 855875. doi: 10.1002/15206793(200010) Ary, D., Jacobs, L. C., Razavieh, A., & Sorenson, C. (2006). Introduction t o research in education (7th ed.). Belmont, CA: Thomson Wadsworth.

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212 Asamoah, A. K., & Sharfstein, J. M. (2010). Transparency at the Food and Drug Administration. The New England Journal of Medicine, 326(25), 23412343. Retrieved from http://www.nejm.org/ Auger, G. A. (2011). An experimental analysis of the effect of transparency on charitable nonprofit and for profit business organizations (Doctoral dissertation). Retrieved from ETD Theses and Dissertations. ( http://purl.fcla.edu/fcla/etd/UFE0042826). Babbie, E. (2010). The practice of social research (3rd ed.). Belmont, CA: Wads worth Baker, J. C. (1981). Farm broadcasting: The first sixty years Ames, IA: Iowa State University. Barber, B. (1983). The logic and limits of trust New Brunswick, NJ: Rutgers University Press. Barling, D., Sharpe, R., & Lang, T. (2009). Traceability a nd ethical concerns in the UK wheat bread chain: From food safety to provenance to transparency. International Journal of Agricultural Sustainability, 7(4), 261278. doi: 10.3763/ijas.2009.0331 Baron, J., Hershey, J. C., & Kunreuther, H. (2000). Determinants of priority for risk reduction: The role of worry. Risk Analysis, 20 (4), 413427. doi : 10.1111/02724332.204041 Beharrell, B. & Denison, T. J. (1995). Involvement in a routine food shopping context. British Food Journal, 97(4): 24 29. doi: 10.1108/00070709510085648 Beulens, A. J. M., Broens, D., Folstar, P., & Hofstede, G. J. (2005). Food safety and transparenc y in food chains and networks. F ood Control, 16, 481486. doi: 10.1016/j.foodcont.2003.10.010 Bigelow, S. L., Sharfman, I. L., & Wenley, R. M. (1922). Henry Carter Adams. Journal of Political Economy, 30 (2), 201211. Retrieved from http://www.press.uchicago.edu/ucp/journals/journal/jpe.html Bildtgard, T. (2008). Trust in food in modern and latemodern societies. Social Science Information, 47(1) 99 128. doi: 10.1177/0539018407085751 Boone, K., Meisenbach, T., & Tucker, M. (2000). Agricultural communications: Changes and challenges Ames, IA: Iowa State University Press. Bothwell, R. O. (2000). Trends in self regulation and transparency of nonprofits in the U.S. The International Journal of Not for Profit Law, 2(3 ), 1 20. Retrieved from http://trustenablers.com/local/Trends_in_self regulation_and_transparency_of_nonprofits_in_the_US.pdf

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227 BIOGRAPHICAL SKET CH Joy Noel Goodwin w as born in 1986 in Marion, Ohio, daughter of Christopher and Laura Goodwin. She was raised on a small farm just outside of Mt. Gilead, Ohio. After graduating high school in 2004, Joy attended The Ohio State University Agricultural Technical I nstitute where she obtained an a ssociates degree in livestock s cience. Joy transferred to The Ohio State University main campus in the fall of 2006 where she continued her education and earned a bachelors degree in animal science. While in college Joy worked for two large hog operations, the USDA Farm Service Agency, and the Institutional Animal Care and Use Committee (IACUC) at Ohio State. Following graduati on in December of 2007, Joy began working for The Ohio State University Institutional Revi ew Board (IRB) as a protocol analyst for oncology research. Joy worked in this position through July of 2010. While working for the IRB offi ce at Ohio State, Joy earned a m asters degree in agricultural communications from Ohio State. In August of 2010, Joy began her doctoral program at the University of Florida in agricultural education and communication. Joy will begin her career as an assistant professor in the Department of Agricultural Education and Communication at the University of Florida, focusing her efforts in the Center for Public Issues Education in Agriculture and Natural Resources (PIE Center) in May of 2013. Joys research focus is on consumers perceptions of agriculture, message testing, and transparent communication.