Effects of Source Credibility Via Social Media on the Risk Perception

EFFECTS OF SOURCE CREDIBILITY VIA SOCIAL MEDIA ON THE RISK PERCEPTION

AND PURCHASE INTENTION TOWARD GENETICALLY MODIFIED FOODS: A CROSS-

CULTURAL STUDY BETWEEN YOUNG MILLENNIALS IN CHINA AND THE U.S.

RUOYU SUN

(Under the Direction of Juan Meng)

ABSTRACT

A two by four between-subjects design was used to investigate the effects of source credibility and risk attitude on young millennials’ risk perception of Genetically Modified (GM) foods, perceived benefits of GM foods and purchase intention toward GM foods in different countries (China and the United States). Results indicated young millennials’ risk attitude significantly influenced their purchase intention toward GM foods. In both China and the U.S., risk-seeking participants had a higher purchase intention for GM foods than risk-aversion participants. In addition, in China, risk-seeking participants perceived more benefits of GM foods than risk-aversion participants. There was also a significant interaction effect of source credibility and risk attitudes on Chinese young millennials’ risk perception of GM foods.

Specifically, among the risk-seeking participants, those who viewed the scientist stimuli had a significantly lower risk perception of GM foods than whose who saw the company stimuli.

INDEX WORDS: GM Foods, Source Credibility, Risk Perception, Purchase Intention,

Young Millennials, Experiment, Social Media

EFFECTS OF SOURCE CREDIBILITY VIA SOCIAL MEDIA ON THE RISK PERCEPTION

AND PURCHASE INTENTION TOWARD GENETICALLY MODIFIED FOODS: A CROSS-

CULTURAL STUDY BETWEEN YOUNG MILLENNIALS IN CHINA AND THE U.S.

RUOYU SUN

B.A., BNU-HKBU United International College, China, 2015

A Thesis Submitted to the Graduate Faculty of The University of Georgia in Partial Fulfillment

of the Requirements for the Degree

MASTER OF ARTS

ATHENS, GEORGIA

2017

Ruoyu Sun

EFFECTS OF SOURCE CREDIBILITY VIA SOCIAL MEDIA ON THE RISK PERCEPTION

AND PURCHASE INTENTION TOWARD GENETICALLY MODIFIED FOOD: A CROSS-

CULTURAL STUDY BETWEEN YOUNG MILLENNIALS IN CHINA AND THE U.S.

RUOYU SUN

Major Professor: Juan Meng Committee: Bryan H. Reber Michael A. Cacciatore

Electronic Version Approved:

Suzanne Barbour Dean of the Graduate School The University of Georgia May 2017 iv

DEDICATION

This thesis work is dedicated to my parents, Long Sun and Yinghua Dong, who have always loved me unconditionally and whose endless support and encouragement have made me concentrate on my study and finally finish this thesis.

ACKNOWLEDGEMENTS

I would first like to express my deepest gratitude to my advisor, Dr. Juan Meng, for her unwavering support and encouragement throughout this project. This thesis would not have happened with her guidance and help. Under her mentorship I have learned the essential knowledge needed to do social science research, which is an invaluable tool to have as my academic career moves forward. Thank you, Dr. Meng for offering so much of your precious time and knowledge to me.

I would also like to thank my thesis committee members, Dr. Michael A. Cacciatore and

Dr. Bryan H. Reber, for their contributions and support. Both of them have generously given their time and expertise to better my work. I am so fortunate to have these intelligent professors on my committee. In addition, I would show my appreciation to Dr.Yan Jin, who has generously shared her time, idea and advice with me in the past two years.

Furthermore, I would also like to thank Dr. Wenhui Shen, Dr. Yuan Li, Dr. Yanshu Sun,

Dr. Qiuling Yang and my dear friends for helping me collect data in China. I would like to thank

Xingzi Huang for helping me proofread and revise the translation of questionnaire. I would like to thank Yiwei Gao for sharing her Photoshop skills with me. Without their help this thesis would not have been possible.

Finally, many thanks go to my dear friends Wenbo Li, and Mengwen liu, who have persistently encouraged me and helped me in the past two years. I feel so lucky and cheerful having you as my friends.

TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS ...... v

LIST OF TABLES ...... ix

CHAPTER

1 INTRODUCTION ...... 1

2 BACKGROUND ...... 3

GM Foods ...... 3

Labeling GM foods in the U.S. and China ...... 4

Leading Social Media Platforms in the U.S. and China ...... 5

3 LITERATURE REVIEW ...... 7

Source Credibility ...... 7

Risk Attitude – Risk Aversion and Risk Seeking ...... 9

Risk Perception ...... 10

Risk Perception with Perceived Benefits and Purchase Intention ...... 11

4 RESEARCH QUESTIONS AND HYPOTHESES ...... 13

5 METHOD ...... 15

Study Design ...... 15

Population and Samples ...... 16

Demographic ...... 17

vii

Procedure ...... 17

Manipulation Checks ...... 20

Independent Variables ...... 20

Dependent Measures ...... 21

6 RESULTS ...... 22

Data Analysis ...... 22

Frequency of Social Media Use - the U.S...... 22

Frequency of Social Media Use - China ...... 24

Reliabilities of Independent Measures ...... 26

Reliabilities of Dependent Measures ...... 26

Level of Awareness on Health Risk Issues ...... 27

Source Credibility ...... 27

Risk Attitudes ...... 29

Results of Manipulation Checks ...... 29

Testing Correlations among Three Dependent Variables ...... 31

Testing Main Effects of Source Credibility - the U.S...... 31

Testing Main Effects of Source Credibility - China ...... 32

Testing Main Effects of Risk Attitudes - the U.S...... 33

Testing Main Effects of Risk Attitudes - China ...... 34

Testing Interaction Effects of Source Credibility and Risk Attitudes - the U.S. ...34

Testing Interaction Effects of Source Credibility and Risk Attitudes - China ...... 35

Uncertainty Avoidance ...... 36

7 DISCUSSION ...... 37

viii

Summary of Research Findings ...... 37

Examining Effects of Source Credibility ...... 37

Examining Effects of Risk Attitudes ...... 39

Examining Interaction Effects of Source Credibility and Risk Attitudes ...... 40

Effects of gender ...... 41

Limitations and Suggestions for Future Study ...... 43

8 CONCLUSION ...... 45

REFERENCES ...... 46

APPENDICES ...... 52

A Tables ...... 52

B Experiment Questionnaire and Stimulus for Participants in the U.S...... 58

C Experiment Questionnaire and Stimulus for Participants in China ...... 68

LIST OF TABLES

Page

Table 1: Demographic profiles of participants in the U.S...... 52

Table 2: Demographic profiles of participants China ...... 54

Table 3: Scale Reliabilities – Independent Measures ...... 55

Table 4: Scale Reliabilities – Dependent Measures ...... 55

Table 5: Means and Standard Deviation of Source Credibility of Each Information Source ...... 55

Table 6: Cases of Risk Aversion VS. Risk Seeking ...... 56

Table 7: Correlations among the Three Dependent Variables ...... 56

Table 8: Results of ANOVA /MANOVA of Source Credibility and Risk Attitude (The U.S.) ....56

Table 9: Results of ANOVA /MANOVA of Source Credibility and Risk Attitude (China) ...... 57

CHAPTER 1

INTRODUCTION

Food choices are usually very personal. Considering that food choices have important implications for human wellbeing and health, the public’s perceptions of food benefits and risks will lead to simple behavioral changes about food categories (Phillips & Hallman, 2013).

Recently, the rapid development of genetic engineering technology has made genetically modified foods (GM foods) a prominent public concern. The scientific community and the food industry endorse genetic engineering technology because of its ability to solve the problems of food shortage and production (Clarke, 1996; Shapiro, 1999), improve the nutrient contents of foods (Burke, 1997) and help in removing known food-borne allergens from foods (Jones, 1996).

Although GM foods carry out notable benefits, the public still remains skeptical about

GM foods, which is especially obvious on social media, such as Twitter. With the popularity of social media in the late 2000s, it has become a place full of anti-GMO discussions (Munro, Hartt,

& Pohlkamp, 2015). In a new and open media environment, brought by the popularity of social media, where information has not been filtered and everyone can communicate to the public directly, faulty and inconsistent information about the GM foods from unverified sources on social media has left a sense of scientific uncertainty in consumers’ mind and made consumers view GM foods negatively. As GM foods is a topic that concerns consumers, policy regulators, researchers, and marketing managers, it is the context of this study.

In mass communication literature, many forms of media-delivered messages can influence people’s opinions and behaviors on different issues (Kareklas, Muehling, & Weber,

2015), including social media posts (Mangold & Faulds, 2009). Nowadays, many consumers use social media as an important information source to search for messages and suggestions. Young millennials, those between the ages of 18 and 25 as defined by the Pew Research Center (2016), spend a considerable amount of time using their digital devices. One study found that the U.S. millennials who have Internet access it 3.1 hours a day through their mobile devices (Kelly,

2015). Thus, investigating the persuasive effect of social media posts on young millennials’ opinions and behaviors may generate insightful findings.

Many researchers have studied how the public perceives the risks of GM foods (e.g.,

Phillips & Hallman, 2013) and the influence of source credibility on consumers’ acceptance of

GM foods (e.g., Zhang, Chen, Hu, Chen, & Zhan, 2016). Some studies also examined the relationship between source credibility and general food risk perception (e.g., Frewer, Howard,

Hedderley, & Shepherd, 1997). However, the effects of source credibility via social media on young millennials’ risk perception and purchase intentions for GM foods remain unclear. To address this research gap, this study conducts an experiment to investigate the effects of source credibility via social media on risk perception and purchase intention of young millennials for

GM foods.

Given that GM foods is a hot topic discussed by both Chinese and American social media users, and both China and the U.S. are the world leading countries that produce GM crops

(James, 2006), this study will also identify the differences between Chinese and American young millennials’ risk perception and purchase intention toward GM foods. The research results of this study will contribute to behavioral science literature in the field of health and risk communication.

CHAPTER 2

BACKGROUND

GM Foods

GM foods are produced from genetically modified organisms (GMOs). GMOs are organisms in which “the genetic material (DNA) has been altered in a way that does not occur naturally by mating and/or natural recombination. The technology is often called ‘modern biotechnology’ or ‘gene technology,’ sometimes also ‘recombinant DNA technology’ or ‘genetic engineering’ (World Health Organization, 2016, “Frequently asked questions,” para.2).

GM foods were released into the market in the 1990s. Within two decades, modern biotechnology has developed rapidly. Both China and the U.S. lead the world in producing GM crops (James, 2006). According to the U.S. Department of Agriculture, in 2016, 89% of corn,

94% of soybeans, and 89% of cotton produced in the U.S. are genetically modified crops

(Economic Research Service, 2016). Similarly, in 2015, the total area of GM crops in China was

9.1 million acres. China is the sixth largest grower of GM crops in 2015 (Pollack, 2016). Many of the foods available to the general public are made from GM crops. Thus, this topic has become a main subject of discourse in China and the U.S.

To date, no substantially adverse health effects brought by GM foods have been documented. According to a comprehensive report released by the National Academies of

Sciences, Engineering, and Medicine (2016), the GM foods on the market are safe to eat and do not injure the environment; however, many people remain afraid of the potential long-term side effects. Likewise, the long-term effects of GM foods have not been exhaustively investigated.

Most Americans and Chinese maintain a negative attitude toward GM foods and assume that a risk is involved in eating GM foods. This particular phenomenon reveals a significant knowledge gap between what is considered acceptable in science and what is socially acceptance.

Labeling GM foods in the U.S. and China

Consumers’ concerns about the potential risks of GM foods and the validity of risk assessments of GM foods have triggered heated debate about the desirability of labeling GM foods, allowing for informed public choices (World Health Organization, 2016). Labeling GM foods is a frequently mentioned issue in the U.S. The discussion about federal labeling standards for GM foods is ongoing. According to a Modern Farmer’s report, on July 29, 2016, President

Barack Obama signed a new bill into law, which demands the labeling of foods containing GM ingredients (Amelinckx, 2016). The vague and controversial content of the new labeling law

(e.g., when will the law take effect; what are the finalized regulations; what kinds of products will have to be labeled) has resulted in a hot debate between pro-labeling groups and the foods industry.

China issued regulations in January 2002, which demanded “labeling and safety certification for all GM animals and plants entering China for sale, production, processing, or research” and “foreign companies selling genetically modified organisms (GMO) for seed, production, or processing and importers intending to use GMOs for research purposes are required to obtain a GMO safety certificate from China’s Ministry of Agriculture” (Gale, Lin,

Lohmar, & Tuan, 2002, p.35, 36).

Despite the efforts for mandatory labeling, regulation makers are concerned that consumers will see the labels as warnings (Phillips & Hallman, 2013). According to the Grocery

Manufacturers Association (2012), labels will suggest that GM foods are not in good condition

5 and thus, are unsafe to eat. Polling indicates that half consumers (52%) are unlikely to buy foods with GM labels (Hallman, Hebden, Aquino, Cuite, & Lang, 2003). In addition, studies show that consumers are likely to pay more for food products without GM ingredients (Runge & Jackson,

2000; VanWechel, Wachenheim, Schuck, & Lambert, 2003).

Leading Social Media Platforms in the U.S. and China

Twitter, a micro blogging site that was launched in 2006, allows users to communicate timely and succinctly within the 140-character limit per tweet (Zhang, Tao, & Kim, 2014).

Internet users in China cannot access major global social media platforms, such as Twitter,

Facebook, and YouTube, because of the government’s censorship. However, China has its’ own version of Twitter, which is Sina Weibo. Sina Weibo is “a Twitter-like microblogging service provided by Sina corporation.... It is reported to have over 300 million registered users and generate about 100 million posts per day” (Yuan, Feng, & Danowski, 2013, p.1014). Weibo shares a lot of similarities with Twitter, such as the “number of users,” “character limit,” and functions like “following trending topics,” “photograph uploading,” “share links”, and “private message” (Ngai & Jin, 2016).

However, Weibo is also different from Twitter in several ways; “unlike Facebook and

Twitter, which are both globally used, Sina Weibo has a user population that is concentrated in the Greater China region” (Ngai & Jin, 2016, p.6). In addition, although both Twitter and Sina

Weibo have limited length of 140 characters, a piece of 140-character Weibo post can transmit more message than a tweet, which contains the same characters, because one or two Chinese characters is equal to a word in English (Zhang et al., 2014; Beattie, 2012).

Although there are some differences between Weibo and Twitter, their functions are almost same, for example, facilitating the creation and exchange of user-generated contents

(Kaplan & Haenlein, 2010). Therefore, Twitter was chosen as the social media platform in the

U.S. and Weibo was selected as the social media platform in China to test in this study.

CHAPTER 3

LITERATURE REVIEW

Source Credibility

Source credibility has been an important area of research in persuasion communications for quite some time. Credibility is defined as “the judgments made by a message recipient concerning the believability of a communicator” (Callison, 2001, p.220). According to Hovland,

Janis, and Kelly (1953), the credibility of a communicator influences the response to a communication: “The very same presentation tends to be judged more favorably when made by a communicator of high credibility than by one of low credibility” (Hovland et al., 1953, p.35).

“This variable, the source’s role in communication effectiveness, has been given many names: ethos, prestige, charisma, image, or, most frequently source credibility” (Berlo, Lemert, & Mertz,

1969, p.563).

Existing literature demonstrates that expertise and trustworthiness are the two major factors of the credibility of a communicator (e.g., Hovland et al., 1953; Grewal, Gotlieb, &

Marmorstein, 1994). Expertise refers to “the extent to which a communicator is perceived to be a source of valid assertions” (Hovland et al., 1953, p. 21). Researchers have found some dimensions of expertise, such as “authoritativeness” (McCroskey, 1966), “competence”

(Whitehead, 1968), “qualification” (Berlo et al., 1969), “knowledgeable” and “experienced”

(Ohanian, 1991).

Trustworthiness refers to “the degree of confidence in the communicator’s intent to communicate the assertions he considers most valid” (Hovland et al., 1953, p.21). According to

Ohanian (1991), “dependable,” “reliable,” “sincere,” “trustworthy,” and “honest” are the dimensions of trustworthiness. Source trustworthiness also deals with the self-interest of communicators (Kareklas, Muehling, & Weber, 2015). For example, individuals tend to judge a communicator as less trustworthy when they find that the communicator gains benefits from persuading them, which leads to a less persuasive effect (Kelman & Hovland, 1953).

Likewise, Eagly, Wood, and Chaiken (1978) found that message recipients may perceive two types of communicator bias, that is, knowledge bias and reporting bias, which will affect their causal inferences. Knowledge bias refers to “a recipient’s belief that a communicator’s knowledge about external reality is nonveridical,” and reporting bias refers to “the belief that a communicator’s willingness to convey an accurate version of external reality is compromised”

(Eagly, Wood, & Chaiken, 1978, p.424). These biases will also influence the persuasive effects of messages because message recipients will attempt to examine the cause of the events (Gotlied

& Sarel, 1991).

Perceived source credibility is likely to be one of the most important determinants of public reactions to risk information (Frewer, Howard, Hedderley, & Shepherd, 1997). Research has demonstrated that when receivers perceive the source credibility of a communicator to be high, they are more likely to receive the information being communicated (Mizerski, Golden, &

Kernan, 1979), and then show more positive attitudes and behavioral intentions (Goldsmith,

Lafferty, & Newell, 2000; Biswas, Biswas & Das, 2006).

Karelkas, Muehling, and Weber (2015) investigated the effectiveness of health messages on affecting consumers’ attitudes and behavioral intentions for vaccination under the influence of source credibility. The research found that “online commenters who are perceived to be credible are instrumental in influencing consumers’ response to pro- versus anti-vaccination online

PSAs,” and “when the relevant expertise of online commenters is identified, the effectiveness of the PSA’s advertising message is moderated by the interactive effect of the online commenters and their associated perceived credibility” (Karelkas et al., 2015, p.88).

Zhang, Chen, Hu, Chen, and Zhan (2016) investigated the influence of source credibility on consumers’ acceptance of GM foods in China based on the persuasion model developed by

Hovland. According to Zhang’s research, biotechnology research institutes, government offices devoted to the management of GMOs, and GMO technological experts are three professional and credible sources, which can effectively persuade consumers to accept GM foods. However, non-

GMO experts, foods companies, and anonymous information found on the Internet are information sources perceived as having bias and vested interests; they hardly had any effect on the consumer acceptance of GM foods (Zhang et al., 2016)

As source credibility is an important factor that influence how consumers will respond to the information being communicated, to help inform risk communication related to GM foods, this study focused on the effects of source credibility via social media on the risk perception and purchase intention for GM foods which has not been systematically examined by previous study.

Risk Attitude – Risk Aversion and Risk Seeking

People’s risk attitude is another antecedent to the formation of risk perception apart from source credibility (Phillips & Hallman, 2013). According to MacCrimmon and Wehrung (1990),

“risk attitude, a person’s standing on the continuum from risk aversion to risk seeking is commonly considered to be a personality trait” (see in Weber, Blais, & Betz, 2002, p.264).

According to Goldstein, Johnson, and Sharpe (2008), this research direction indicates that

“individuals generally fall into two subgroups of individuals: those who have a tolerance and even a preference for risk and those who are more cautious and would prefer to avoid risk” (see

10 in Phillips & Hallman, 2013, p.739, 740). Stewart and Roth (2001) compared the personality traits between managers and entrepreneurs. They found that entrepreneurs are more willing to take risks, prefer to think flexibly, and bear more responsibility (Stewart & Roth, 2001). Weber,

Blais, and Betz (2002) also found that “women appeared to be more risk averse in all domains

(financial, health/safety, recreational, ethical, and social) except social risk” (p. 263). Therefore, people’s personality traits, that are, risk aversion and risk seeking, may influence their risk perception.

Risk Perception

Originating from psychology and decision science, psychometric paradigm is one of the dominant approaches in the research area of risk perception (Sjöberg, Moen, & Rundmo, 2004).

According to Slovic and Weber (2002), “risk is seen as a concept that human beings have invented to help them understand and cope with the dangers and uncertainties of life” (p.4).

According to Sjöberg, Moen, and Rundmo (2004), “risk perception is the subjective assessment of the probability of a specified type of accident happening and how concerned we are with the consequences” (p.8). Psychometric paradigm “uses psychophysical scaling and multivariate analysis techniques to produce quantitative representations of risk attitudes and perceptions”

(Slovic & Weber, 2002, p.7).

Slovic (1987) suggested certain risk characteristics can be used to explain people’s risk perceptions and attitudes, such as voluntariness, dread, knowledge, and controllability.

According to Yeung and Morris (2001), three antecedent factors, namely “dread,” “unknown,” and “number of people exposed to the risk,” would significantly affect people’s risk perception.

Such linkage has been approved in several recent studies (e.g. Raats & Shepherd, 1996). Based on Slovic’s (1987) work, Fife-Shaw and Rowe (1996) used “dread” to capture variables, such as

11 harm to vulnerable groups, likely effect on future generations, potential to cause serious harm to health, likely delayed effects, and causes of worry. Fung, Namkoong, and Brossard (2011) defined “dread” as the “feelings of fright evoked when people perceive the consequences of risk exposure to be fatal, involving large numbers of people and leading to severe damages” (p.891).

Familiarity, knowledge, and uncertainty are the three risk characteristics closely related to the

“unknown” factor (Yeung & Morris, 2001).

The larger the number of people exposed to the risk, the higher the perceived risk is.

Renn (1990) defined catastrophic potential as “the ability of a hazard to cause large numbers of deaths in a short period of time” (see in Fung et al., 2011, p.891). The public pays more attention to large-scale catastrophic consequences than individual small harmful consequences reported in the media (DoE, 1995). Frewer and Shepherd (1998) further suggested that the media tends to highlight the catastrophic potential of technology. The case of GM foods also demonstrated this point. By applying the psychometric paradigm as an analytical framework, Fung and his colleagues compared the risk dimensions being transmitted in news coverage of avian flu between The New York Times and South China Morning Post from 2003 to 2007, they found that news reports on NYT are more likely to emphasize the dimension of dreadfulness, catastrophic potential, uncertainty and unfamiliarity of avian flu than the news reports from SCMP (Fung et al., 2011)

Risk Perception with Perceived Benefits and Purchase Intention

According to Alhakami and Slovic (1994), “risk and benefit may be inversely related in people’s minds because an affective feeling is referred to when the risk or benefit of specific hazards is judged” (see in Finucane, Alhakami, Slovic & Johnson, 2000, p.4). Research found there was a significant negative relationship between perceived benefit and perceived risk

(Siegrist, 2000). Given that perceived benefit has a positive relationship with tolerance of risk

(Wandel, 1994), people are less willing to take risks when the benefits are uncertain (Frewer,

Howard, & Shepherd, 1998). The case of GM foods can be used to demonstrate this point.

Not surprisingly, risk perception has been researched in various marketing contexts.

Research has indicated that risk perception is causally related to purchase intention as “the former is an important explanatory variable of the latter” (Yeung & Morries, 2001, p.179). Based on the studies done by Cox, Cox, and Mantel (2010) and Cox, Cox, and Zimet (2006), Phillips and Hallman (2013) concluded: “when faced with uncertainty, consumers often view a new product as either a set of benefits received or as a set of losses avoided;” therefore, “marketing managers often use communications strategies to frame new products in terms of benefits gained or losses avoided” (p.739).

Food safety is one of the major issues that the public are concerned about. Food safety is intended to “acquire food products which have the desired consumption attributes, are safe to eat, and are free of contamination and therefore free of worry to the consumer” (Yeung & Morries,

2001, p.179). According to Rozin, Pelchat, and Fallon (1986), psychological factors have more effects on food choice than food’s physical properties. Risk perception of food safety is one kind of psychological factors that affects consumers’ attitudes and behaviors regarding food purchasing (Yeung & Morris, 2001). Therefore, risk perception of food safety has an important influence on consumers’ food choice behaviors.

According to Frewer, Shepherd, and Sparks (1994), the way in which consumers perceive the risks and benefits of biotechnology and its applications may influence their response on the food produced by technology. Therefore, investigating consumers’ risk perception of GM foods have important implications’ on understanding consumers’ food purchasing behavior.

CHAPTER 4

RESEARCH QUESTIONS AND HYPOTHESES

Based on the literature review, the following research questions and hypotheses were proposed:

RQ1a: Will different sources of information demonstrate different levels of source

credibility?

RQ1b: If so, how would such differences influence participants’ risk perception,

perceived benefits and purchase intention for GM foods?

More specifically, the following hypotheses were proposed to test RQ1b:

H1a: Participants exposed to message advocating the benefits of GM foods from

a source with higher credibility on social media will have lower risk perception of

GM foods.

H1b: Participants exposed to message advocating the benefits of GM foods from

a source with higher credibility on social media will perceive more benefits of

GM foods.

H1c: Participants exposed to message advocating the benefits of GM foods from

a source with higher credibility on social media will have higher purchase

intention toward GM foods.

RQ2: Will participants with different levels of risk attitudes have different risk

perception, perceived benefits, and purchase intention for GM foods?

More specifically, the following hypotheses were proposed to test RQ2:

H2a: Risk-seeking participants will have lower risk perception toward GM foods

than risk-aversion participants

H2b: Risk-seeking participants will perceive more benefits of GM foods than risk-

aversion participants.

H2c: Risk-seeking participant will have higher purchase intention toward GM

foods than risk-aversion participants

RQ3: For participants who have different risk attitudes, what are the similarities and differences between their risk perception of GM foods, perceived benefits of GM foods and purchase intention for GM foods in response to different levels of source credibility via social media?

More specifically, the following hypotheses were proposed to test RQ3:

H3a: Participants’ risk attitudes and source credibility will have an interaction

effect on risk perception of GM foods.

H3b: Participants’ risk attitudes and source credibility will have an interaction

effect on perceived benefits of GM foods.

H3a: Participants’ risk attitudes and source credibility will have an interaction

effect on purchase intention toward GM foods.

CHAPTER 5

METHOD

Study Design

This study used a 2 (risk attitudes: risk aversion vs risk seeking) x 4 (source credibility: government vs food company vs social media influencer vs scientist) between-subjects design. In total, eight conditions were created. In each experiment, individual participant was randomly assigned to an experimental condition which presented one of the four different information sources. Since this thesis is a cross-cultural study, the same experimental design was adopted separately in the U.S. and China.

Different sources (government, food company, social media influencer and scientist) exposed to participants in the two countries were incorporated into the identical fictional social media posts in English and Mandarin Chinese. The fictional social media posts are designed for

Microblog (Twitter for the English version and Sina Weibo for the Chinese version), in which elaborates the benefits of GM foods. To better approximate real-world exposure to a Tweet or a

Weibo message, all the messages were designed to look like they appear on each information sources’ respective Twitter/Weibo desktop version homepage.

The questionnaire and the fictional social media post were first created in English and were then translated into Chinese by a bilingual researcher. The translated version was checked by another bilingual researcher and proofread by a Chinese master student majoring in English

Translation. Two versions of the questionnaire (Chinese and English) were pretested in each country (n = 40 per country) to allow final adjustments before the fully execution of the study.

For the experiment conducted in the U.S., FDA was used as the government source since it regulates the safety of foods and supervises the production of GM foods. Louisa Stark was used as the scientist source since she is the director of Genetic Science Learning Center in the

University of Utah and she is an expert in the field of biology. Monsanto Company was used as the food company source because Monsanto is a leading producer of genetically modified seeds.

Rolf Degen was used as the social media influencer source. He is a science writer and book author in psychology, neuroscience and evolution and he has over 3K followers on Twitter. In order to differentiate social media influencer and scientist, specific modifications were made on the social media influencer stimuli. Rolf Degen was framed as “bestselling author, interested in science, evolution and history” with 125K followers.

For the experiment conducted in China, China’s Ministry of Agriculture was used as the government source whose function is like FDA including regulating GM foods and issuing GMO safety certificate. Ning Yan, a professor of Tsinghua University whose research interest is structural biology, was used as the scientist source. Monsanto Company (China) was used as the food company source. Sijin Chen was used as the social media influencer source and he is a financial consultant and book author in finance and economics. Sijin has over 500K followers on

Sina Weibo. Since the concentrated Chinese social media users on Weibo, a popular account on

Weibo usually has followers over 500K.

Population and Samples

The population studied in this research are young millennials in the U.S. and China.

Participants in the U.S. were recruited from two large universities in the Southeastern region of the United States. Participants in China were recruited from three different universities in mainland China. Only young millennials students, those between the ages of 18 and 25 are

17 selected. A total of 517 millennials participate in this study, 279 from China and 238 from the

U.S. Among them, 242 in China and 207 in the U.S. have completed the experiment. Therefore, a total of 449 questionnaires were analyzed.

Demographic

In the U.S., the total number of participants was (n = 207). Among them, 42 were male participants and 165 were female participants. The majority of participants (n = 97) were in their second year of college. The mean age was 20. The ethnicity of most participants (n = 162) was

Caucasian or white. The majority of participants (n = 82) were majoring in humanities and arts.

The majority of participants (n = 107) considered themselves as republican. The majority of participants (n = 76) described their political view as moderate.

In China, the total number of participants was (n = 242). The final sample included 89 male participants and 153 female participants. Most participants were 20 and the mean age was

21. The majority of participants (n = 86) were in their second year of college. The major of most participants (n = 110) was humanities and arts. The majority of participants (n = 114) described their political view as moderate.

Tables 1 and 2 summarize the demographic characteristics of the sample.

Procedure

In this experiment, government, scientist, food company and social media influencer were used as the message source for the fictional social media post which elaborates the benefits of GM foods. Microblog (Twitter in the U.S. and Sina Weibo in China) was chosen as the communicational channel.

In the first section, participants were asked about their frequency of social media use in daily life. The first section consisted of five questions, for example in English version, “how often do you visit or use (a) Twitter (b) Instagram (c) Pinterest (d) LinkedIn (e) Facebook (f)

Snapchat?” In Chinese version, the equivalent question was “how often do you visit (a) Weibo

(b) WeChat (c) LinkedIn (d) Renren (f) Qzone?” Participants were asked to respond by “Several times a day,” “About once a day,” “A few days a week,” “Every few weeks,” “Less often,”

“Don’t know,” “Refused,” “I don’t use at all.”

The second section measured participants’ level of awareness on a few typical health risk issues. GM foods, flu vaccine and HPV vaccine were chosen as the typical health risk issues because the recently hot debates on these issues and public’s concern about these issues (Kloor,

2014). Two questions were included: “How well-informed do you feel you are about each of the following (a) Genetically modified foods (b) Flu vaccine (c) HPV vaccine?” which was rated by the seven-point Likert scale from “Not at all informed” to “Very informed” and “How would you rate your overall attitude to these same topics?” which was measure by the seven-point Likert scale from “Very negative” to “Very positive.”

The third section was used to assess the level of perceived credibility of the four different information sources. A brief description of the information source adapted from dictionary definition or the organization’s mission statement appeared before the participants answered the related questions. For example, participants would read the definition of FDA: “The Food and

Drug Administration is a federal agency of the United States Department of Health and Human

Services, whose mission is to protect the public health and advance the public health” before they answered the FDA related questions. Participants were asked to assess the perceived credibility of each information source by rating seven 7-point item pairs, including Not at all

19 credible/ very credible, Not at all reliable/ very reliable, Not at all honest/ very honest, Not at all dependable/ very dependable, Not at all believable/ very believable, Not at all truthful/ very truthful, Not at all trustworthy/ very trustworthy. These items were adapted from Ohanian’s source credibility- trustworthiness subscale (Ohanian, 1990).

In the fourth section, participants were asked about their attitudes toward risk in order to measure their willingness to take risk. Eight items were included, for example, “buying an illegal drug for your own use,” which were measured by the seven-point Likert scale from “Very unlikely” to “Very likely.” Items in this section were adapted from the health/safety subscale of

Weber, Blais and Betz’s (2002) risk taking scales.

After the fourth section, participants were randomly assigned to one of the four stimulus.

Participants were invited to read the designed Tweet/Weibo message sent from one of the four different information sources. The message elaborates the benefits of GM foods.

After viewing the stimuli for approximately 20 seconds, participants were asked to answer the manipulation check items and several survey questions about their perceived risks and benefits of GM foods and purchase intentions for GM foods. Since this study set the timing function so participants had to spend 20 seconds to read the stimuli before answering the following questions.

Then, two items were used to measure the uncertainty avoidance, including “in this society, orderliness and consistency are stressed, even at the expense of experiment” and “in this society, societal requirements and instructions are spelled out in detail so citizens know what they are expected to do.” These items adapted from the uncertainty avoidance subscale of the global study done by House, Hanges, Javidan, Dorfman and Gupta (2004).

Finally, participants were asked to provide some demographic information including gender, age, education, ethnicity, major, religion and political party affiliation and political ideology.

Manipulation Checks

Two items were used to check the manipulation of information source: (a) “As best you can recall, the tweet you just saw was sent by?” by selecting one of the following options: the government, a scientist, a food company, a social media influencer or not sure, and (b) “As best you can recall, which of these categories best describes the profile photo of the Twitter account that you just saw?” by selecting one of the following options: personal picture, the name of a government agency, corporate logo, other (please specify) or not sure. In the Chinese version, the options for item (b) were: personal picture, the name of a government agency, corporate logo, animal, other (please specify) and not sure.

Independent Variables

Risk attitude was measured by the following items: “Buying an illegal drug for your own use,” “Consuming five or more servings of alcohol in a single evening,” “Engaging in unprotected sex,” “Not wearing a seatbelt when being a passenger in the front seat,” “Not wearing a helmet when riding a motorcycle,” “Exposing yourself to the sun without using sunscreen,” “Walking home alone at night in a somewhat unsafe area of town,” and “Regularly eating high cholesterol foods.” These items were adapted from the health/safety subscale of

Weber, Blais and Betz’s (2002) risk taking scales and measured by the seven-point Likert scale from “Very unlikely” to “Very likely.”

An additive scale of risk attitude was used to divide participants into two subgroups: risk aversion and risk seeking. The additive scale was created by first combining all scales and then averaging it.

Dependent Measures

Participants were asked to rate items on risk perception of GM foods, perceived benefits of GM foods and purchase intentions towards GM foods by using seven-point Likert scales from

“strongly disagree” to “strongly agree”.

Perceived risk of GM foods was measured by five items, which were adapted from perceived risk scales developed by Thelen, Yoo, and Magnini (2011): (a) “Eating a genetically modified food is risky,” (b) “Genetically modified foods can lead to bad health results,” (c)

“Genetically modified foods have uncertain outcomes,” (d) “Eating a genetically modified foods makes feel anxious,” and (e) “Eating a genetically modified foods would cause me to worry.”

Five items were used to measure perceived benefits of GM food: (a) “Genetically modified food are safe to eat,” (b) “Genetically modified foods dose not present risks for human health,” (c) “Genetically modified foods can help to solve the problems of food shortage and production,” (d) “Some genetically modified foods contain more nutrient content,” and (e)

“Genetically modified crops are more resistant to crop diseases.” These measurements were created by the researcher of this study based on the content of the stimuli.

Purchase intentions toward GM foods were measured by one item. The item was adapted from Natascha, Geertjie, and Klaus (2015): “How likely are you to buy genetically modified foods in the future?”.

CHAPTER 6

RESULTS

Data Analysis

This study used SPSS 19.0 for Mac. The significance level p <.05 was used as the basis for rejecting the null hypothesis for all tests conducted. First, two-way multivariate analysis of variance (MANOVA) was adopted to investigate the effects of source credibility and risk attitudes on the dependent variables. Second, two-way analysis of variance (ANOVA) test was used to answer the RQ1a, RQ1b, RQ2 and RQ3 and test H1, H2 and H3 sets of hypotheses.

The following part reported the results of frequency of social media uses among young millennials, reliabilities, level of awareness on health risk issues, credibility of information source, risk attitudes, hypotheses, and uncertainty avoidance.

Frequency of Social Media Use – the U.S.

Based on the 213 completed questionnaires in the United States, Snapchat (M = 1.40, SD

= 1.19), Instagram (M = 1.48, SD = 1.45), and Facebook (M = 1.67, SD = 1.28) are the three most popular social media among the participants in the United States. Eighty-three percent

(N=171) used Instagram several times a day. Eighty-three percent (N=172) used Snapchat several times a day and 66% (N=136) visited Facebook several times a day. Twitter is the fourth popular social media. Among 207 respondents, 36% (N=74) visited Twitter several times a day;

9% (N=19) used Twitter about once a day; 8% (N=16) reported they used Twitter a few days a week; 7% (N=15) used Twitter every few weeks; 6% (N=13) used Twitter less often and 34%

(N=70) chose the choice “I don’t use at all”. Pinterest (M = 4.74, SD = 2.44) and LinkedIn (M =

5.68, SD = 2.28) are the less popular social networking sites among the participants in the U.S.

Only 10% (N=21) visited Pinterest several times a day and 8% (N=17) used Pinterest about once a day while 30% (N=63) didn’t use Pinterest at all. Only 2% (N=4) used LinkedIn several times a day and 5% (N=10) chose the choice “About once a day.” However, 45% (N=93) didn’t use

LinkedIn at all.

Participants frequently used social media to see others’ updates (M = 1.68, SD = 1.10).

They also commented on others’ social media posts often (M = 2.79, SD = 1.35). However, they do not share the contents created by themselves very often (M = 3.51, SD = 1.51) nor share the updates about themselves (M = 3.79, SD = 1.44) on social media.

Among the 213 participants in the U.S., 10% (N=20) shared the contents (e.g. artwork, photos and videos) created by themselves several times a day; 11% (N=22) shared the contents about once a day; 31% (N=65) shared the contents a few days a week; 30% (N=63) posted the contents created by themselves every few weeks; 13% (N=27) posted the contents created by themselves less often and 5% (N=10) didn’t use social media to share the contents created by themselves. For the question “how often do you use social networking sites to share updates about yourself,” 10% (N=21) shared updates about themselves several times a day; 6% (N=12) share updates about once a day; 21% (N=43) chose the option “A few days a week;” 27%

(N=56) reported every few weeks; 32% (N=67) shared updates about themselves less often and

2% (N=5) didn’t use social media to share updates about themselves.

Among the 213 participants in the United States, 61% (N=126) used social media to see the updates of others several times a day; 20% (N=42) saw others’ updates on social media about once a day; 9% (N=19) saw others’ updates on social media a few days a week; 4% (N=8) used social media to see others’ updates every few weeks and 3% (N=7) saw other’s updates on social

24 medial less often. For the question “how often do you comment on people’s social media posts,”

17% (N=35) answered several times a day; 25% (N=51) reported about once a day; 35% (N=73) chose the option “A few days a week;” 11% (N=22) replied every few weeks; 9% (N=19) answered less often and 3% (N=7) chose the option “Don’t know,” “Refused” and “I don’t use at all.”

Frequency of Social Media Use – China

According to the 242 finished questionnaires in China, WeChat (M = 1.53, SD = 1.12) and Weibo (M = 2.68, SD = 2.22) are the most popular social media among the participants in

China. Qzone (M = 3.19, SD = 2.55) is the third popular social media and a fair number of participants used Qzone. Only a few participants used LinkedIn (M = 6.20, SD = 1.63) and

Renren (M = 7.00, SD = 1.64). Seventy-four percent (N=180) used WeChat several times a day;

11% (N=27) used WeChat about once a day while less than 1% (N=1) didn’t use WeChat at all.

Forty-nine percent (N=118) visited Weibo, the Chinese version of Twitter, several times a day;

13% (N=31) used Weibo about once a day; 12% (N=29) visited Weibo a few days a week; 3%

(N=7) used Weibo every few weeks; 14% (N=33) visited Weibo less often; 1% (N=2) didn’t know Weibo and 9% (N=22) didn’t use Weibo at all. Forty-five percent (N=108) used Qzone several times a day; 7% (N=18) visited Qzone about once a day while 16% (N=38) didn’t use

Qzone at all. Most participants in China didn’t familiar with LinkedIn. Only 2% (N=5) and 1%

(N=2) used LinkedIn several times a day and about once a day whereas 46% (N=112) didn’t know LinkedIn and 33% (N=80) didn’t use LinkedIn at all. Renren, the Chinese version of

Facebook, was the out of date social media in China. Only 3% (N=6) used Renren several times a day. One percent (N=2) visited Renren about once a day. However, 67% (N=161) didn’t use

Renren at all and 16% (N=38) didn’t know Renren.

Participants in China preferred to use social media to see others’ updates (M = 1.90, SD =

1.34). They do not share the updates about themselves (M = 3.95, SD = 1.48) very often nor share the contents created by themselves (M = 3.96, SD = 1.39) on social media. They were less likely to comment on others’ social media posts (M = 4.47, SD = 1.84).

Among the 242 participants in China, 41% (N=105) shared the contents created by themselves less often; 12% (N=30) shared the contents every few weeks; 4% (N=9) shared the contents created by themselves several times a day; 8% (N=20) shared the contents generated by themselves about once a day; 31% (N=76) shared the contents a few days a week and 3% (N=7) didn’t use social media to share the contents created by themselves. For the question “how often do you use social networking sites to share updates about yourself,” 6% (N=14) and 5% (N=11) chose the option “Several times a day” and “About once a day;” 34% (N=81) answered a few days a week; 17% (N=40) reported every few weeks; 34% (N=81) shared updates about themselves less often and 5% (N=11) didn’t use social media to share updates about themselves.

Among the participants in China, 58% (N=140) used social media to see updates about others several times a day; 16% (N=38) saw others’ updates about once a day; 16% (N=39) saw the updates of others a few days a week; 7% (N=16) saw the updates about others less often and less than 1% (N=1) didn’t use social media to see updates about others. For the question, “how often do you comment on people’s social media posts,” 24% (N=59) chose the option “A few days a week;” 7% (N=16) replied every few weeks; 43% (N=104) answered less often; 12%

(N=30) reported didn’t comment on people’s social media posts, while only 7% (N=16) and 4%

(N=10) chose the option “Several times a day” and “About once a day.”

Reliabilities of Independent Measures

A series of reliability analyses were run to test the reliability coefficients of the source credibility scale and the risk attitude scale (see Table 3).

For the United States data, the results of Cronbach’s alpha for the source credibility scale of each information source was: CredibilityGovernment: α = .976; CredibilityCompany: α = .977;

CredibilityScientists: α = .959; CredibilitySocial media influencers: α = .954. The Cronbach’s alpha for the risk attitude scale was .770.

For the Chinese data, the reliability coefficients (Cronbach’s alpha) for the source credibility scale of each information source was: CredibilityGovernment: α = .949;

CredibilityCompany: α = .954; CredibilityScientists: α = .954; CredibilitySocial media influencers: α =

.949. In the risk attitude scale, the Cronbach’s alpha was .842. Results indicated that the internal consistency of all above scales was acceptable.

Reliabilities of Dependent Measures

A series of reliability analyses were also run to test the reliability coefficients of the risk perception scale, perceived benefits scale, and the purchase intention scale (see Table 4).

For the American data, in the risk perception scale, reliability coefficient (Cronbach’s alpha) was .899. The Cronbach’s alpha for perceived benefits scale was .794.

For the data from China, the Cronbach’s alpha for the risk perception scale was .848. and

Cronbach’s alpha for perceived benefits scale is .807. These results suggested all the dependent measures’ internal consistency was acceptable.

Level of Awareness on Health Risk Issues

In the United States, participants felt that they were more informed about Flu vaccine (M

= 4.10, SD = 1.60) and GM foods (M = 3.94, SD = 1.58) than HPV vaccine (M = 3.37, SD =

1.74). Participants had a more positive attitude toward HPV vaccine (M = 4.64, SD = 1.45) and

Flu vaccine (M = 4.48, SD = 1.58) than GM foods (M = 3.45, SD = 1.45).

In China, participants felt they were more informed about Flu vaccine (M = 3.02, SD =

1.37) and GM foods (M = 2.90, SD = 1.36) than HPV vaccine (M = 2.13, SD = 1.48).

Participants had a more positive attitude toward Flu vaccine (M = 4.17, SD = 1.70) than HPV vaccine (M = 3.81, SD = 1.76) and GM foods (M = 3.51, SD = 1.36).

The information above suggested that participants in the U.S. had a better understanding about these health risk issues than participants in China. Since China just approved the use of

HPV vaccine in July 2016, therefore, participants in China were not familiar with it. In addition, both participants in China and the U.S. had a general negative attitude toward GM foods.

Source Credibility

Each information source had different levels of source credibility (see Table 5). For the study conducted in the U.S., participants perceived scientist (M = 5.39, SD = 1.10) and government (M = 5.29, SD = 1.36) had a higher level of source credibility. On the contrary, participants believed company (M = 3.78, SD = 1.43) and social media influencer (M = 3.56, SD

= 1.22) had a lower level of source credibility.

For the study conducted in China, participants perceived scientist (M = 4.53, SD = 1.24) and government (M = 4.17, SD = 1.19) were the information sources with higher credibility.

However, company (M = 3.44, SD = 1.15) and social media influencer (M = 3.23, SD = 1.15) were perceived by participants as the information sources with lower credibility.

Paired-samples T Test was used to test the differences among the levels of credibility of the four different information sources. For the study conducted in the U.S., there was a significant difference between government’s credibility (M = 5.29, SD = 1.36) and company’s credibility (M = 3.78, SD = 1.43); t (206) = 12.31, p < .001. There was a significant difference between government’s credibility (M = 5.29, SD = 1.36) and the social media influencer’s credibility (M = 3.56, SD = 1.22); t (206) = 15.30, p < .001. There was a significant difference between company’s credibility (M = 3.78, SD = 1.43) and scientist’s credibility (M = 5.39, SD =

1.10); t (206) = -13.65, p < .001.There was a significant difference between company’s credibility (M = 3.78, SD = 1.43) and social media influencer’s credibility (M = 3.56, SD = 1.22); t (206) = 2.00, p = .047. There was a significant difference between scientist’s credibility (M =

5.39, SD = 1.10) and social media influencer’s credibility (M = 3.56, SD = 1.22); t (206) = 17.89, p < .001. However, there was a nonsignificant differences between government’s credibility (M

= 5.29, SD = 1.36) and scientist’s credibility (M = 5.39, SD = 1.10); t (206) = -.991, p = .323; since both scientist and government are perceived having a higher level of source credibility.

For the study conducted in China, there was a significant difference between government’s credibility (M = 4.17, SD = 1.19) and company’s credibility (M = 3.44, SD =

1.15); t (241) = 8.97, p < .001.There was a significant differences between government’s credibility (M = 4.17, SD = 1.19) and scientist’s credibility (M = 4.53, SD = 1.24); t (241) = -

4.52, p < .001. There was a significant difference between government’s credibility (M = 4.17,

SD = 1.19) and the social media influencer’s credibility (M = 3.23, SD = 1.15); t (241) = 10.47, p

< .001. There was a significant difference between company’s credibility (M = 3.44, SD = 1.15) and scientist’s credibility (M = 4.53, SD = 1.24); t (241) = -12.79, p < .001. There was a

29 significant difference between company’s credibility (M = 3.44, SD = 1.15) and social media influencer’s credibility (M = 3.23, SD = 1.15); t (241) = 2.54, p = .012. There was a significant difference between scientist’s credibility (M = 4.53, SD = 1.24) and social media influencer’s credibility (M = 3.23, SD = 1.15); t (241) = 13.40, p < .001.

Therefore, for RQ1a, different information sources demonstrated different levels of source credibility. In both countries, the U.S. and China, the order of information sources’ level of credibility was exactly same. However, participants in China tended to perceive lower level of credibility of each information source.

Both participants in the U.S. and China perceived scientists had the highest level of source credibility. Government had the second highest level of source credibility and then the company which had less level of source credibility. Social media influencers were perceived to have the lowest level of source credibility.

Risk Attitudes

An additive scale of risk attitude was created by first combining all scales and then averaging it. According to the results of frequencies of the additive risk attitude scale, median was used to divide participants into two different groups—risk seeking and risk aversion (see

Table 6). Generally speaking, participants in the U.S. (M = 3.08, SD =1.14) had a higher tendency to take risk than participants in China (M = 2.63, SD = 1.20).

Results of Manipulation Checks

This study used crosstab to check the manipulations. For the study conducted in the U.S., the results of manipulation check for the first manipulation item, “as best you can recall, the tweet you just saw was sent by”, indicated among the 53 participants who viewed the

30 government stimuli, 62% (N = 33) of them chose the right answer which means they answered the tweet was sent by government. Forty-eight participants saw the food company stimuli and

73% (N = 35) of them chose the right answer for the first manipulation item. Among the 53 participants who saw the scientist stimuli, 55% (N = 29) chose the right option for the first manipulation item. Fifty-three participants were exposed to the social media influencer stimuli and 70% (N = 37) answer the first manipulation item correctly.

For the second manipulation item “as best you can recall, which of these categories best describes the profile photo of the Twitter account that you just saw,” results showed that among the 53 participants who viewed the government stimuli, 60% (N = 32) of them chose the right option for this manipulation item. Among 48 participants who saw the food company stimuli,

81% (N = 39) of them chose the right answer for the second manipulation item. Fifty-three participants were invited to view the scientist stimuli and 76% (N = 40) answered this manipulation item correctly. Among the 53 participants who were invited to view the social media influencer stimuli, 93% (N = 39) of them answer the second manipulation item correctly.

For the study conducted in the China., the results of manipulation check for the first manipulation item “as best you can recall, the tweet you just saw was sent by” indicated among the 59 participants who viewed the government stimuli, 44% (N = 26) of them chose the right answer. Sixty-four participants saw the food company stimuli and 47% (N = 30) of them chose the right answer for the first manipulation item. Among the 58 participants who saw the scientist stimuli, 24% (N = 14) chose the right option for the first manipulation item. Sixty-one participants were exposed to the social media influencer stimuli and 71% (N = 41) answer the first manipulation item correctly.

For the second manipulation item “as best you can recall, which of these categories best

31 describes the profile photo of the Twitter account that you just saw,” results showed that among the 59 participants who viewed the government stimuli, 44% (N = 26) of them chose the right answer for this manipulation item. Among 64 participants who saw the food company stimuli,

53% (N = 34) of them chose the right option for the second manipulation item. Fifty-eight participants were invited to view the scientist stimuli and 38% (N = 22) answered this manipulation item correctly. Among the 61 participants who were invited to view the social media influencer stimuli, 85% (N = 52) of them answer the second manipulation item correctly.

Testing Correlations among Three Dependent Variables

Pearson correlation was performed to test whether three dependent variables were significantly correlated. The results indicated that in both countries, all of the three dependent variables were linearly related to each other (see Table 7). The risk perception scale and perceived benefits scale were negatively correlated. The risk perception scale and purchase intention scale were negatively correlated. The perceived benefits scale and the purchase intention scale were positively correlated.

Testing Main Effects of Source Credibility- the U.S.

Since all three dependent variables were significantly correlated, a two-way MANOVA was conducted and a two-way ANOVA was used to measure the main effects of source credibility on the three dependent variables for RQ1a, RQ1b and the H1set of hypotheses (see

Table 8).

For the study conducted in the U.S., according to the results of the two-way MANOVA, there was no significant differences among the four different information sources, which had

32 different level of source credibility, on the dependent variables (F (9, 480) = 1.66, Λ = .93, p =

.096, η = .03).

According to the results of the two-way ANOVA conducted for the study in the U.S., there were no significant main effect of the level of source credibility on risk perception (F (3,

199) = 2.26, p = .083, η = .03); perceived benefits (F (3, 199) = 1.39, p = .247, η = .02) and purchase intention (F (3, 199) = 1.43, p = .235, η = .02).

The results indicated that different level of source credibility did not generate different risk perception of GM foods, different perceived benefits of GM foods and different purchase intention for GM foods. Therefore, in the United States, H1a, H1b, H1c were not supported.

Testing Main Effects of Source Credibility- China

For the study conducted in China, according to the results of the two-way MANOVA, there was no significant differences among the four different information sources on the dependent variables (F (9, 595) = 1.45, Λ = 0.95, p = .162, η = .02).

The results of the two-way ANOVA (see Table 9) found that there were no main effects of the level of source credibility on risk perception (F (3, 234) = .65, p = .584, η = .01) and purchase intention (F (3, 234) = .54, p = .658, η = .01). Thus, the hypotheses H1a and H1c were not supported in China.

The results of the two-way ANOVA indicated there was a main effect of source credibility on perceived benefits (F (3, 234) = 2.96, p = .033, η = .04). Therefore, Tukey was chosen as the post hoc tests. The result indicated that participants who viewed the government stimuli (M = 20.20, SD = 4.83) perceived more benefits than the participants who saw the scientist stimuli (M = 17.43, SD = 4.78). However, since result suggested that scientist was

33 perceived having a higher level of credibility than government, H1b was not supported in

China.

Testing Main Effects of Risk Attitudes- the U.S.

Since all three dependent variables were significantly correlated, a two-way MANOVA was conducted and a two-way ANOVA was used to measure the main effects of risk attitudes on the three dependent variables for RQ2 and the H2 set of hypotheses (see Table 8).

For the study conducted in the U.S., the results of the two-way MANOVA suggested that there was significant differences between the two different risk attitudes (risk aversion and risk seeking) on the dependent variables (F (3, 197) = 2.81, Λ = 0.96, p = .041, η = .04).

Results of the two-way ANOVA showed that there were no significant differences between the two risk attitudes on risk perception (F (1, 199) = .021, p = .884, η < .001), or on perceived benefits (F (1, 199) = 2.30, p = .131, η = .01). However, there was significant main effect of risk attitude on purchase intention (F (1, 199) = 6.98, p = .009, η = .03). Risk-seeking participants (M = 4.54, SD = 1.45) had more purchase intention for GM foods than risk-aversion participants (M = 4.03, SD = 1.47). Therefore, in the U.S. the hypothesis H2c was supported while H2a and H2b were not supported.

The above results indicated that people’s risk attitudes had influence on their purchase intention for GM foods but did not have significant impact on their risk perception of GM foods and perceived benefits of GM foods.

Testing Main Effects of Risk Attitudes- China

For the study conducted in China, the result of two-way MANOVA suggested that there was significant difference between the two risk attitudes on the three dependent variables (F (3,

232) = 5.78, Λ = 0.93, p = .001, η = .07).

Results of the two-way ANOVA (see Table 9), conducted for the study in China, showed that there was a nonsignificant main effect of risk attitudes on risk perception (F (1, 234) = 1.55, p = .214, η = .01). However, there were a significant main effect of risk attitude on perceived benefits (F (1, 234) = 8.49, p = .004, η = .04) and on purchase intention (F (1, 234) = 15.81, p

< .001, η = .06). Risk-seeking participants (M = 19.88, SD = 5.23) perceived more benefits of

GM foods than risk aversion participants (M = 17.83, SD = 5.40). In addition, risk-seeking participants (M = 3.60, SD = 1.46) had a higher purchase intention for GM foods than risk- aversion participants (M = 2.90, SD = 1.21).

The above results indicated that, in china, people’s risk attitudes had influence on their perceived benefits of GM foods and purchase intention for GM foods but did not have significant impact on their risk perception of GM foods. Therefore, in China, the hypothesis H2b and

H2c were supported but H2a was not supported.

Testing Interaction Effects of Source Credibility and Risk Attitudes - the U.S.

Since all three dependent variables were significantly correlated, a two-way MANOVA was adopted to measure the interaction effects of source credibility and risk attitudes on the dependent variables (risk perception, perceived benefits, and purchase intention). The results indicated that there was no significant interaction effect between source credibility and risk attitudes on the dependent variables (F (9, 480) = .83, Λ = 0.96, p = .59, η = .01).

In order to investigate the interaction effect of source credibility and risk attitude on each dependent variable for RQ3 and H3 set of hypotheses, three two-way ANOVA were conducted on each dependent variable (see Table 8).

Results found that there were no significant differences among the four different information sources and two different risk attitudes on risk perception (F (3, 199) = .78, p = .508,

η = .01); perceived benefits (F (3, 199) = .88, p = .453, η = .01) and purchase intention (F

(3, 199) = .01, p = .998, η < .001). Therefore, the result showed that risk-aversion and risk seeking-participants in the U.S. were not affected differently by source credibility on risk perception, perceived benefits and purchase intention. Therefore, H3a, H3b and H3c were not supported in the U.S.

Testing Interaction Effects of Source Credibility and Risk Attitudes – China

Similarly, for the study conducted in China, a two-way MANOVA was adopted to measure the interaction effects of source credibility and risk attitudes on the dependent variables.

The results showed that there was no significant interaction effect between source credibility and risk attitudes on the dependent variables (F (9, 565) = 1.81, Λ = 0.96, p = .30, η = .02).

In order to investigate the effect of source credibility and risk attitude on respective dependent variable, three two-way ANOVA were conducted on each dependent variable (see

Table 9). Results found that there was a significant difference among the four different information sources and two different risk attitudes on risk perception (F (3, 234) = 2.72, p =

.045, η = .03). This results suggested that risk-seeking participants and risk-aversion participants were affected differently by source corrodibility. Pairwise comparisons were used to test the simple effects. Specifically, risk-aversion participants’ risk perception of GM foods was

36 similar no matter they saw government stimuli (M = 19.17, SD = 1.13); social media influencer stimuli (M = 20.57, SD = 1.11); scientist stimuli (M = 20.53, SD = 1.04) or company stimuli (M

= 18.03, SD = 1.13). However, among the risk-seeking participants, those who viewed the scientist stimuli (M = 16.96, SD = 1.24) had a significantly lower risk perception of GM foods than whose who saw the company stimuli (M = 20.58, SD = 1.09). Therefore, H3a was supported in China.

Results found that there were no significant differences among the four different information sources and two different risk attitudes on perceived benefits (F (3, 234) = .97, p =

.408, η = .01) and purchase intention (F (3, 234) = .76, p = .520, η = .01). Therefore, H3b and H3c were not supported in China.

Uncertainty Avoidance

Participants in the U.S. had a neutral attitude (M = 3.99, SD = 1.42) toward the statement

“in this society, orderliness and consistency are stressed, even at the expense of experiment and innovation.” For the item “in this society, societal requirements and instructions are spelled out in detail so citizens know what they are expected to do,” participants in the U.S. also had a neutral attitude (M = 4.05, SD = 1.39).

Participants in China had a neutral attitude (M = 3.80, SD = 1.70) for the statement “in this society, orderliness and consistency are stressed, even at the expense of experiment and innovation.” They also had a neutral attitude (M = 3.90, SD = 1.62) attitude the statement “in this society, societal requirements and instructions are spelled out in detail so citizens know what they are expected to do.” The results indicated that participants in both the U.S. and China had a similar level of uncertainty avoidance.

CHAPTER 7

DISCUSSION

Summary of Research Findings

The aim of this thesis is to investigate the influence of source credibility and risk attitudes on risk perception, perceived benefits and purchase intention toward GM foods. In addition, this thesis also seeks to find the differences of the effects of source credibility and risk attitudes on

Chinese and American young millennials’ risk perception, perceived benefits and purchase intention toward GM foods.In sum, three sets of research questions and three sets of hypotheses were tested. The research questions and hypotheses investigated and assessed the effects of (1) source credibility, (2) risk attitude, and (3) source credibility and risk attitude. The three dependent variables which have been analyzed were (1) risk perception of GM foods, (2) perceived benefits of GM foods, and (3) purchase intention for GM foods.These hypotheses were tested independently in each country. The differences of the effects of risk attitudes on perceived benefits of GM foods and the differences of the interaction effects of source credibility and risk attitudes on risk perception of GM foods were found between American young millennials and

Chinese young millennials. In this chapter, the key findings of this thesis, limitations of this thesis and suggestions for future research will be discussed.

Examining Effects of Source Credibility

Results indicated that, for both American young millennials and Chinese young millennials, source credibility via social media didn’t have significant influence on their risk

38 perception of GM foods, perceived benefits of GM foods and purchase intention toward GM foods.

A cross-cultural study conducted before found “information source characteristics contributed very little to attitude change,” and “trust in the information source was not driving risk perception” (Frewer, Scholderer, & Bredahl, 2003, p.1129). However, researchers found, in

China, the credible sources like Biotechnology research institutes, government offices devoted to the management of GMOs, and GMO technological experts, could effectively persuade consumers to accept GM foods (Zhang et al., 2016).

There could be various reasons of the finding of difference in the influence of source credibility. First, the dependent variable in these three studies were not exactly the same. The different measurements of the dependent variable may yield different results. The study done by

Zhang and other scholars generally measured consumers’ acceptance of GM foods but the study done by Frewer and her colleagues specifically measured the risk perception of GM foods and perceived benefits of GM foods. Similar, this study also measured young millennials’ risk perception of GM foods and perceived benefits of GM foods like Frewer’s study. Therefore, this study and Frewer’s study yielded the same results.

Second, Zhang’s study used GM soybean oil representing GM foods which was eaten by people indirectly. However, Frewer’s study used GM beer and GM yoghurt representing GM foods which were eaten by people directly. Thus, GM beer and GM yoghurt are different from

GM soybean oil in essence. For the stimuli in this study, no specific GM foods were used to elaborate the benefits of GM foods. Therefore, different stimulus could also lead to the inconsistent results.

The finding that source credibility via social media didn’t have significant influence on young millennials’ risk perception of GM foods, perceived benefits of GM foods and purchase intention toward GM foods is perhaps understandable if we consider their knowledge about GM foods and their overall attitude to the GM foods. Results of this study show that both young millennials in the U.S. and China felt that they were not very informed about GM foods and they had a general negative attitude towards GM foods which means simply changing the information source or using an information source that is perceived as having higher source credibility is not sufficient to persuade young millennials to change their risk perception of GM foods, perceived benefits of GM foods and purchase intention for GM foods. Thus, if we want to change young millennials’ attitudes toward GM foods, concern only with the information source is not enough.

Long-term education about GM foods and genetic engineering technology to millennials is necessary, like Uzogara (2000) said “the public needs to be sufficiently educated on genetic engineering of any product to enhance acceptability of such a food.” (p.202).

Examining Effects of Risk Attitudes

Results found that American young millennials’ risk attitudes significantly influenced their purchase intention for GM foods, but didn’t have impact on their risk perception of GM foods and perceived benefits of GM foods. Results also found Chinese young millennials’ risk attitudes significantly influenced their purchase intention for GM foods but had nonsignificant effects on their risk perception of GM foods. In both countries, risk-seeking participants had a higher purchase intention for GM foods than risk-aversion participants. In addition, in both countries, there were no significant influence of risk attitude on participants’ risk perception of

GM foods. The result suggested that even though risk-seeking participants and risk-aversion participants had similar risk perception of GM foods, risk seeking participants would be more

40 likely to buy GM foods. One possible explanation for this result could be that risk-aversion participants and risk-seeking participants are more different in their actions rather than their cognitions. Thus, their actions, like purchase intention, could be influenced significantly by risk attitude rather than their cognitions like risk perception.

According to the results, Chinese young millennials’ risk attitudes not only significantly influenced their purchase intention for GM foods, but also had significant impact on their perceived benefits of GM foods. Thus, there were differences between the influence of risk attitudes on young millennials in the U.S. and China. Various reasons could lead to the difference. Once possible explanation for this may be participants in China were less informed about GM foods than participants in the U.S., therefore their opinion of GM foods maybe not as firm as American participants’ opinion of GM foods. Thus, risk-seeking participants in China tended to perceive more benefits of GM foods than risk-aversion participants in China. Second, participants in China were very familiar with Weibo, but participants in the U.S. were less familiar with Twitter. Thus, risk-seeking participants in China were more likely to perceive more benefits of GM foods when they received the message that advocated the benefits of GM foods through Weibo.

Examining Interaction Effects of Source Credibility and Risk Attitudes

For the study conducted in the U.S., there was nonsignificant interaction effects of source credibility and risk attitudes on the three dependent variables, which suggested that risk-aversion and risk-seeking participants in the U.S. were not affected differently by source credibility on their risk perception of GM foods, perceived benefits of GM foods and purchase intention toward GM foods.

However, for the study conducted in China, there was significant interaction effect of source credibility and risk attitudes on Chinese young millennials’ risk perception of GM foods.

Specifically, among the risk-seeking participants, those who viewed the scientist stimuli (M =

16.96, SD = 1.24) had a significantly lower risk perception of GM foods than whose who saw the company stimuli (M = 20.58, SD = 1.09). Therefore, in China, strategic communicators should consider using scientists as the information source in order to successfully transmit the benefits of GM foods.

Effects of Gender

In this study, risk attitude was found to have a significant influence on young millennials’ perceived benefits and purchase intention for GM foods. In the studies conducted before, researchers found women are more risk averse than men (Weiber, Blais, & Betz, 2002). Since gender is highly correlated to risk attitude, although this study was designed to find the effects of source credibility and risk attitudes on millennial’s risk perception, perceived benefits and purchase intention for GM foods, the effects of gender on these dependent variables were analyzed in this part.

For the study conducted in the U.S., three one-way ANOVA were conducted to test the effects of gender on each dependent variable. Results found that there was a significant effect of gender on young millennials’ risk perception of GM foods (F (1, 205) = 4.67, p = .032 η =

.02). Female participants (M = 19.20, SD = 6.45) had higher risk perception of GM foods than male (M =16.69, SD = 6.49). Results indicated that there was a significant effect of gender on young millennials’ perceived benefits of GM foods (F (1, 205) = 19.88, p < .001, η = .09).

Male participants perceived more benefits (M = 23.43, SD = 4.83) of GM foods than female participants (M =19.56, SD = 5.06). Results also indicated that there was a significant effect of

42 gender on young millennials’ purchase intention for GM foods (F (1, 205) = 8.05, p = .005,

η = .04). Male participants (M = 4.86, SD = 1.41) had a higher purchase intention toward GM foods than female participants (M =4.15, SD = 1.46).

For the study conducted in China, three one-way ANOVA were also conducted to measure the effects of gender on respective dependent variables. Results found that there was a nonsignificant effect of gender on young millennials’ risk perception of GM foods (F (1, 240) =

3.31 , p = .070 η = .01), although on average, male participants(M = 18.22, SD = 6.46) had less risk perception of GM foods than female participants (M = 19.70, SD = 5.85). ). Results also indicated that there was a nonsignificant effect of gender on young millennials’ purchase intention for GM foods (F (1, 240) = 2.65, p = .107, η = .01) although male participants (M =

3.43, SD = 1.60) had a higher purchase intention toward GM foods than female participants (M =

3.13, SD = 1.22).

However, results indicated that there was a significant effect of gender on young millennials’ perceived benefits of GM foods (F (1, 240) = 6.85, p = .009, η = .03). Male participants perceived more benefits (M = 19.99, SD = 5.46) of GM foods than female participants (M = 18.12, SD = 5.29).

The above results showed that gender had a significant influence on young millennials’ perceived benefits of GM foods. In both countries, male participants perceived more benefits of

GM foods than female participants which confirmed the study conducted before that women are more risk averse than men (Weiber, Blais, & Betz, 2002). Therefore, strategic communicators should spend more time to communicate and educate young female millennials about the benefits of GM foods in order to increase the acceptance of GM foods.

Limitations and Suggestions for Future Study

Although this study found the influence of risk attitude on young millennials’ perceived benefits of GM foods and purchase intention for GM foods, it had some limitations that prevent the generalization of these results.

The first limitation is the student sample and convenient sample, which is also a limitation of many social science researches. Since the student sample is not randomly selected and also is not representative, thus, the findings in this study cannot be generalized to a larger population. Future study should include more diverse participants and a greater sample size to investigate the influence of source credibility on young millennials’ risk perception of GM foods, perceived benefits of GM foods and purchase intention toward GM foods.

The second limitation is the unbalanced sample of female participants and male participants. Although this study found significant effects of gender on young millennials’ risk perception of GM foods, perceived benefits of GM foods and purchase intention toward GM foods, future studies should try to use a balanced sample of female participants and male participants to investigate the effects of gender on risk perception, perceived benefits and purchase intention toward GM foods.

The third limitation is the desktop version stimulus. The stimulus of this research were designed for the desktop version microblogs (Twitter and Weibo). Since many participants used their mobile phones to access the questionnaire, mobile version stimulus might suit the phones’ screen better. Therefore, future research could specifically design the mobile version stimulus.

This study only examined the influence of government, company, scientists and social media influencers as information sources on young millennials’ risk perception of GM foods, perceived benefits of GM foods and purchase intention toward GM foods. It hasn’t investigated

44 the impacts of family members and friends as information sources on the three dependent variables. Future study can investigate whether family members and friends as information sources can generate significant influence on young millennial’s risk perception of GM foods, perceived benefits of GM foods and purchase intention toward GM foods.

The nonsignificant effects of source credibility on young millennials’ risk perception of

GM foods, perceived benefits of GM foods and purchase intention toward GM foods may also due to the less salient information to young millennials. The stimulus used in this study only generally introduce the benefits of GM foods which may be unable to raise young millennials’ attention of the benefits of GM foods. Future research can repeat this study by using more information-salient stimuli.

Research has found that message strength has influence on audience’s attitudes and behavior. However, this study only used a singular message to communicate the benefits of GM foods. In order to better understand how to effectively communicate the benefits of GM foods, future study could examine the message strengths’ effects on risk perception of GM foods, perceived benefits of GM foods and purchase intention toward GM foods.

Different information formats may generate different results. This study only used pictures to communicate the benefits of GM foods. Therefore, future study could adopt other ways to transmit the benefits of GM foods, such as PSAs or videos.

CHAPTER 8

CONCLUSION

This is the first study investigating the influence of source credibility and risk attitude on young millennials’ risk perception of GM foods, perceived benefits of GM foods and purchase intention toward GM foods in different countries (China and the United States). The results indicated that source credibility via social media had little effect on millennials’ risk perception of GM foods, perceived benefits of GM foods and purchase intention toward GM foods, no matter whether in China or in the United States.

However, young millennials’ risk attitude significantly influenced their purchase intention toward GM foods. In both China and the U.S., risk-seeking participants had a higher purchase intention for GM foods than risk-aversion participants. In addition, in China, participants’ risk attitude also significantly influenced their perceived benefits of GM foods.

Risk-seeking participants tended to perceive more benefits of GM foods than risk-aversion participants. For the study conducted in China, there was also a significant interaction effect of source credibility and risk attitudes on Chinese young millennials’ risk perception of GM foods.

Specifically, among the risk-seeking participants, those who viewed the scientist stimuli had a significantly lower risk perception of GM foods than whose who saw the company stimuli.

The results of this study are valuable for strategic communicators as they plan information campaigns and also aid in our understanding the effects of source credibility and risk attitudes on young millennials’ risk perception of GM foods, perceived benefits of GM foods and purchase intention toward GM foods.

REFERENCE

Amelinckx, A. (2016). What You Need To Know About the New GMO Labeling Law. Retrieved from: http://modernfarmer.com/2016/08/gmo-labeling-law/

Beattie, C. A. (2012). Twitter vs. Weibo: How the world’s leading microblog platforms stack up. Advertising Age. Retrieved from: http://adage.com/article/digital/twitter-weibo-world-s- leading-microblog-platforms-stack/237231/.

Berlo, D. K., Lemert, J. B., & Mertz, R. J. (1969). Dimensions for evaluating the acceptability of message sources. Public Opinion Quarterly, 33, 563–576.

Biswas, D., Biswas, A., & Das, N. (2006). The differential effects of celebrity and expert endorsements on consumer risk perceptions. The role of consumer knowledge, perceived congruency, and product technology orientation. Journal of Advertising, 35(2), 17-31.

Botterill, J., Bredin, M., & Dun, T. (2015). Millennials' media use: It is a matter of time. Canadian Journal of Communication, 40 (3), 537-551.

Burke, D. (1997). What biotechnology can do for the food industry. FOOD SCIENCE AND TECHNOLOGY TODAY, 11, 202-209.

The Parties on the Eve of the 2016 Election: Two Coalitions, Moving Further Apart. (Pew Research Center, September 13, 2016). Retrieved from

Cox, A. D., Cox, D., & Mantel, S. P. (2010). Consumer response to drug risk information: The role of positive affect. Journal of Marketing, 74(4), 31–44.

Cox, A. D., Cox, D., & Zimet, G. (2006). Understanding consumer responses to product risk information. Journal of Marketing, 70(1), 79–91.

Clarke A. The impact of biotechnology in a changing world. Aust Biotechnol 1996, 7: 96—100

Callison, C. (2001). Do PR practitioners have a PR problem?: The effect of associating a source with public relations and client-negative news on audience perception of credibility. Journal of Public Relations Research, 13, 219–234.

DoE, U. K. (1995). A guide to risk assessment and risk management for environmental protection. Department of the Environment HMSO: London, UK.

Eagly, A. H., Wood, W., & Chaiken, S. (1978). Causal inferences about communicators and their effect on opinion change. Journal of Personality and Social Psychology, 36(4), 424.

Fife-Schaw, C., & Rowe, G. (1996). Public perceptions of everyday food hazards: A

psychometric study. Risk analysis, 16(4), 487-500

Finucane, M. L., Alhakami, A., Slovic, P., & Johnson, S. M. (2000). The affect heuristic in judgments of risks and benefits. Journal of behavioral decision making, 13(1), 1.

Frewer, L. J., Howard, C., Hedderley, D., & Shepherd, R. (1997). The elaboration likelihood model and communication about food risks. Risk analysis, 17(6), 759-770.

Frewer, L., Howard, C. and Shepherd, R. (1995), “Consumer perceptions of food risks”, Food Science and Technology, Vol. 9 No. 4, pp. 212-16.

Frewer, L. J., Howard, C., & Shepherd, R. (1998). Understanding public attitudes to technology. Journal of Risk Research, 1(3), 221-235.

Frewer, L. J., Scholderer, J., & Bredahl, L. (2003). Communicating about the risks and benefits of genetically modified foods: The mediating role of trust. Risk analysis, 23(6), 1117- 1133.

Frewer, L. J., & Shepherd, R. (1998). Consumer perceptions of modern food biotechnology. In Genetic Modification in the Food Industry (pp. 27-46). Springer US.

Frewer, L. J., Shepherd, R., & Sparks, P. (1994). Biotechnology and food production: knowledge and perceived risk. British Food Journal, 96(9), 26-32.

Fung, T. K., Namkoong, K., & Brossard, D. (2011). Media, social proximity, and risk: A comparative analysis of newspaper coverage of avian flu in Hong Kong and in the United States. Journal of health communication, 16(8), 889-907.

Gale, F., Lin, W., Lohmar, B., & Tuan, F. (2002). Is Biotechnology in China’s Future?. China’s Food and Agriculture: Issues for the 21st Century. Fred Gale, editor, 34.

Goldsmith, R. E., Lafferty, B. A., & Newell, S. J. (2000). The impact of corporate credibility and celebrity credibility on consumer reaction to advertisements and brands. Journal of advertising, 29(3), 43-54.

Goldstein, D. G., Johnson, E. J., & Sharpe, W. F. (2008). Choosing outcomes versus choosing products: Consumer-focused retirement investment advice. Journal of Consumer Research, 35(3), 440-456.

Gotlieb, J. B., & Sarel, D. (1991). Comparative Advertising Effective- ness: The Role of Involvement and Source Credibility. Journal of Advertising, 20 (1), 38–45.

Grewal, D., Gotlieb, J. B., & Marmorstein, H. (1994). The Moderating Effects of Message Framing and Source Credibility on the Price-Perceived Risk Relationship. Journal of Consumer Research, 21 (1), 145–53.

Grocery Manufacturers Association (2012). Grocery Manufacturers Association position on the use of agricultural biotechnology in the food supply. Retrieved June 1, 2012, from

http://www.gmaonline.org/file-manager/ Food_Safety/GMAPositionOnGMO_secure.pdf.

Hallman, W. K., Hebden, W. C., Aquino, H. L., Cuite, C. L., & Lang, J. T. (2003). Public perceptions of genetically modified foods: A national study of American knowledge and opinion. Food Policy Institute, Cook College, Rutgers, the State University of New Jersey, New Brunswick, NJ. Available at: www. foodpolicyinstitute. org.

House, Robert J., Paul J. Hanges, Mansour Javidan, Peter W. Dorfman, and Vipin Gupta, eds. Culture, leadership, and organizations: The GLOBE study of 62 societies. Sage publications, 2004.

Hovland, C. I., Janis, I. L., & Kelley, H. H. (1953). Communication and persuasion: psychological studies of opinion change. New Haven, CT: Yale University Press.

Hovland, C. I., & Weiss, W. (1951). The influence of source credibility on communication effectiveness. Public Opinion Quarterly, 15(4), 635-650.

James, C. (2006). Global status of comecialized biothech/GM crops: 2006. ISAAA Brief, (35).

Jones L Food biotechnology: current developments and the need for awareness. Nutr Food Sci 1996; 6: 5-11

Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! the challenges and opportunities of social media. Business Horizons, 53(1), 59-68.

Kareklas, I., Muehling, D. D., & Weber, T. J. (2015). Reexamining health messages in the digital age: a fresh look at source credibility effects. Journal of Advertising, 44(2), 88-104.

Kelly, R. (2015). Study: Millennials Spend More Than 3 Hours a Day on Mobile Phones. Retrieved from: http://blog.apastyle.org/apastyle/2010/11/how-to-cite-something-you- found-on-a-website-in-apa-style.html

Kelman, H. C., & Hovland, C. I. (1953). " Reinstatement" of the communicator in delayed measurement of opinion change. The Journal of Abnormal and Social Psychology, 48(3), 327.

Kloor, K. (2014). Why vaccine and GMO denial should be treated equal [Web blog post]. Retrived from http://blogs.discovermagazine.com/collideascape/2014/08/07/vaccine- gmo-denial-treated-equally/#.WOvP6VKZOfQ

Kuttschreuter, M. (2006). Psychological determinants of reactions to food risk messages. Risk Analysis, 26(4), 1045–1057.

MacCrimmon, K. R., & Wehrung, D. A. (1990). Characteristics of risk taking executives. Management science, 36(4), 422-435.

Mangold, W. G., & Faulds, D. J. (2009). Social media: The new hybrid element of the promotion

mix. Business horizons, 52(4), 357-365.

McCroskey, J. C. (1966). Scales for the measurement of ethos.

Mizerski, R. W., Golden, L. L., & Kernan, J. B. (1979). The attribution process in consumer decision making. Journal of Consumer Research, 6(2), 123-140.

Munro, K., Hartt, C. M., & Pohlkamp, G. (2015). Social Media Discourse and Genetically Modified Organisms. The Journal of Social Media in Society, 4(1), 38-65.

National Academies of Sciences, Engineering, and Medicine. (2016). Genetically Engineered Crops: Experiences and Prospects. Washington, DC: The National Academies Press. doi: 10.17226/23395.

Ngai, C. S., & Jin, Y. (2016). The Effectiveness of Crisis Communication Strategies on Sina Weibo in Relation to Chinese Publics’ Acceptance of These Strategies. Journal of Business and Technical Communication, 1050651916651907.

Ohanian, R. (1990). Construction and validation of a scale to measure celebrity endorsers' perceived expertise, trustworthiness, and attractiveness. Journal of advertising, 19(3), 39- 52.

Phillips, D. M., & Hallman, W. K. (2013). Consumer Risk Perceptions and Marketing Strategy: The Case of Genetically Modified Food. Psychology & Marketing, 30(9), 739-748. doi:10.1002/mar.20642

Pollack, A. (2016, April 13). Acreage for Genetically Modified Crops Declined in 2015. Retrieved June 15, 2016, from http://www.nytimes.com/2016/04/13/business/acreage- for-genetically-modified-crops-declined-in-2015.html?_r=0

Raats, M. M., & Shepherd, R. (1996). Developing a subject-derived terminology to describe perceptions of chemicals in foods. Risk Analysis, 16(2), 133-146.

Renn, O. (1990). Risk perception and risk management: A review. Risk Abstract, 7, 1–9.

Rozin, P., Pelchat, M. L., & Fallon, A. E. (1986). Psychological factors influencing food choice. The food consumer, 5, 85-106.

Runge, C. F., & Jackson, L. A. (2000). Labelling, trade and genetically modified organisms. Journal of World Trade, 34(1), 111-122.

Shapiro R. (1999). How genetic engineering will save our planet. Futurist 1999, 33/34: 28-9

Siegrist, M. (2000). The influence of trust and perceptions of risks and benefits on the acceptance of gene technology. Risk analysis, 20(2), 195-204.

Sjöberg, L., Moen, B. E., & Rundmo, T. (2004). Explaining risk perception. An evaluation of the psychometric paradigm in risk perception research. Trondheim, Norway: Rotunde.

Slovic, P. (1987). Perception of risk. Science, 236, 280–285. doi:10.1126/science.3563507

Slovic, P. (1999). Trust, emotion, sex, politics, and science: Surveying the risk-assessment battlefield. Risk Analysis, 19, 689–701.

Slovic, P., & Weber, E. U. (2002). Perception of risk posed by extreme events. Regulation of Toxic Substances and Hazardous Waste (2nd edition)(Applegate, Gabba, Laitos, and Sachs, Editors), Foundation Press, Forthcoming.

Stewart Jr, W. H., & Roth, P. L. (2001). Risk propensity differences between entrepreneurs and managers: a meta-analytic review. Journal of applied psychology, 86(1), 145.

United States Department of Agriculture, Economic Research Service. (2016). Recent trends in GE adoption. Retrieved from: http://www.ers.usda.gov/data-products/adoption-of- genetically-engineered-crops-in-the-us/recent-trends-in-ge-adoption.aspx

Uzogara, S. G. (2000). The impact of genetic modification of human foods in the 21st century: A review. Biotechnology advances, 18(3), 179-206.

VanWechel, T., Wachenheim, C. J., Schuck, E., & Lambert, D. K. (2003). Consumer valuation of genetically modified foods and the effect of information bias. Agribusiness and Applied Economics Report, (513).

Wandel, M. (1994). Understanding consumer concern about food-related health risks. British Food Journal, 96(7), 35-40.

Weber, E. U., Blais, A. R., & Betz, N. E. (2002). A domain-specific risk-attitude scale: Measuring risk perceptions and risk behaviors. Journal of behavioral decision making, 15(4), 263-290.

Whitehead Jr, J. L. (1968). Factors of source credibility. Quarterly Journal of Speech, 54(1), 59- 63.

World Health Organization (2016). Frequently asked questions on genetically modified foods. Retrieved from: http://www.who.int/foodsafety/areas_work/food-technology/faq- genetically-modified-food/en/

Yeung, R. M., & Morris, J. (2001). Food safety risk: Consumer perception and purchase behaviour. British Food Journal, 103(3), 170-187.

Yuan, E. J., Feng, M., & Danowski, J. (2013). ‘“Privacy” in semantic networks on Chinese social media: The case of Sina Weibo. Journal of Communication, 63, 1011–1031.

Zhang, M., Chen, C., Hu, W., Chen, L., & Zhan, J. (2016). Influence of Source Credibility on Consumer Acceptance of Genetically Modified Foods in China. Sustainability, 8(9), 899.

Zhang, X., Tao, W., & Kim, S. (2014). A comparative study on global brands’ micro blogs between China and USA: Focusing on communication styles and branding strategies. International Journal of Strategic Communication, 8(4), 231-249.

APPENDICES

Appendix A: Tables

Table 1 Demographic profiles of participants in the U.S. (N = 207) Frequency Percentage Gender Male 42 20.3% Female 165 79.7% Age 18 9 4.3% 19 57 27.5% 20 80 38.6% 21 41 19.8% 22 14 6.8% 23 4 1.9% 25 2 1.0% Academic Rank Freshman 22 10.6% Sophomore 97 46.9% Junior 67 32.4% Senior 21 10.1% Master N N Ph.D. N N Other N N Major Natural Science 4 1.9% Humanities and Arts 82 39.6% Engineering 4 1.9% Social Sciences 59 28.5% Economics 48 23.2% Policy 10 4.8% Ethnicity Asian 12 5.8% Asian American 6 2.9% Black/African American 17 8.2% Caucasian/White 162 78.3% Native American 1 .5% (continued)

Table 1 Demographic profiles of participants in the U.S. (continued) Frequency Percentage Latino/Hispanic 3 1.4% Multi-racial 5 2.4% Other 1 .5% Religion Guidance 1(No guidance at all) 22 10.6% 2 22 10.6% 3 17 8.2% 4 36 17.4% 5 21 10.1% 6 40 19.3% 7 (A great deal of guidance) 49 23.7% Evangelical Christian Yes 78 37.7% No 129 62.3% Political Affiliation Democratic 57 27.5% Republican 107 51.7% Independent 30 14.5% Something Else 13 6.3% Political View Very Conservative 8 3.9% Conservative 64 30.9% Moderate 76 36.7% Liberal 38 18.4% Very Liberal 21 10.1% Total 207 100%

Table 2 Demographic profiles of participants in China (N = 242) Frequency Percentage Gender Male 89 36.8% Female 153 63.2% Age 18 19 7.9% 19 44 18.2% 20 69 28.5% 21 28 11.6% 22 22 9.1% 23 14 5.8% 24 34 14.0% 25 12 5.0% Academic Rank Freshman 57 23.6% Sophomore 86 35.5% Junior 27 11.2% Senior 15 6.2% Master 43 17.8% Ph.D. N N Other 14 5.8% Major Natural Science 20 8.3% Humanities and Arts 110 45.5% Engineering 52 21.5% Social Sciences 26 10.7% Economics 32 13.2% Policy 2 .8% Religion Guidance 1(No guidance at all) 103 42.6% 2 64 26.4% 3 45 18.6% 4 13 5.4% 5 8 3.3% 6 4 1.7% 7(A great deal of guidance) 5 2.1% Political View Very Conservative 6 2.5% Conservative 31 12.8% Moderate 114 47.1% Liberal 80 33.1% Very Liberal 11 4.5% Total 242 100% 55

Table 3 Scale Reliabilities – Independent Measures The U.S. China CredibilityGovernment .976 .949 CredibilityCompany .977 .954 CredibilityScientists .959 .954 CredibilitySocial media influencers .954 .949 Risk Attitude .770 .842

Table 4 Scale Reliabilities – Dependent Measures The U.S. China Risk Perception .899 .848 Perceived Benefits .794 .807

Table 5 Means and Standard Deviation of Source Credibility of Each Information Source Countries The U.S. China Source Credibility n M SD n M SD Government 207 5.30 1.36 242 4.17 1.19 Company 207 3.78 1.43 242 3.44 1.15 Scientist 207 5.39 1.10 242 4.53 1.24 Social Media 207 3.56 1.23 242 3.23 1.15 Influencer 56

Table 6 Cases of Risk Aversion VS. Risk Seeking Countries The U.S China Risk Attitudes n (M = 1.51, SD = .50) n (M = 2.63, SD = 1.20) Risk Aversion 101 126 Risk Seeking 106 116 Total 207 242

Table 7 Correlations among the Three Dependent Variables The U.S. Perceived benefits Risk perception Risk perception -.293** (N=207) Purchase Intention .546** (N=207) -.357**(N=207)

China Perceived benefits Risk perception Risk perception -.189* (N=242) Purchase Intention .493** (N=242) -.182**(N=242) ** p < .001; * p < .05

Table 8 Results of ANOVA /MANOVA of Source Credibility and Risk Attitude (The U.S.) Dependent Variables Sum of d.f Mean F P value Eta squares square Value squared Source Credibility Risk perception 285.29 3 95.10 2.26 .083 .03 Perceived benefits 113.95 3 37.98 1.39 .247 .02 Purchase intention 9.19 3 3.06 1.43 .235 .02 Risk Attitude Risk perception .89 1 .89 .02 .884 .00 Perceived benefits 62.78 1 62.78 2.30 .131 .01 Purchase intention 14.95 1 14.95 6.98 .009* .03 Source Credibility * Risk Attitude Risk perception 98.27 3 32.76 .78 .508 .01 Perceived benefits 72.03 3 24.01 .88 .453 .01 Purchase intention .08 3 .03 .01 .998 .00 ** p < .001; * p < .05 57

Table 9 Results of ANOVA /MANOVA of Source Credibility and Risk Attitude (China) Dependent Variables Sum of d.f. Mean F P value Eta squares square value Squared Source Credibility Risk perception 71.42 3 23.81 .65 .584 .01 Perceived benefits 245.00 3 81.67 2.96 .033 .04 Purchase intention 2.90 3 .97 .54 .658 .01 Risk Attitude Risk perception 56.91 1 56.91 1.55 .214 .01 Perceived benefits 234.64 1 234.64 8.49 .004* .04 Purchase intention 28.47 1 28.47 15.81 .000** .06 Source Credibility * Risk Attitude Risk perception 299.36 3 99.79 2.72 .045* .03 Perceived benefits 80.37 3 26.79 .97 .408 .01 Purchase intention 4.08 3 1.80 .76 .520 .01 ** p < .001; * p < .05 58

Appendix B: Experiment Questionnaire and Stimulus for Participants in the U.S.

Section 1: Frequency of social media use among millennials

These questions pertain to respondent’s frequency of social media use in daily life (questions adapted from Princeton Survey Research Associates International for the Pew Internet & American Life Project 2009 & 2014)

1. Think about the social networking sites you use, about how often do you visit or use…? Several times a day About once a day A few days a week Every few weeks Less often Don’t know Refused I don’t use at all 1. Twitter 2. Instagram 3. Pinterest 4. LinkedIn 5. Facebook 6. Snapchat

2. How often do you share content on social networking sites that you created, such as your own artwork, photos, stories or videos? Several times a day About once a day A few days a week Every few weeks Less often Don’t know Refused I don’t use at all

3. How often do you use Twitter or any other social networking sites to share updates about yourself? Several times a day About once a day A few days a week Every few weeks Less often Don’t know Refused I don’t use at all

4. How often do you use Twitter or any other social networking sites to see updates about others? Several times a day About once a day A few days a week Every few weeks Less often Don’t know Refused I don’t use at all

5. How often do you comment on people’s social media posts (e.g. Facebook/ Twitter)? Several times a day About once a day A few days a week Every few weeks Less often Don’t know Refused I don’t use at all

Section 2: Level of awareness on risk issues These questions assess respondents’ level of awareness on risk issues (Cacciatore Dissertation, 2013)

6. We would like to ask how well-informed you feel about each of these topics. How well informed would you say you are about: Not at all informed Very informed 1 …… 7 59

1. Genetically modified foods 2. Flu vaccine 3. HPV vaccine

7. Do you tend to have a positive attitude toward the following topics when people are talking about them or when you read news about them? Not positive at all Very positive 1 …… 7

1. Genetically modified foods 2. Flu vaccine 3. HPV vaccine

Section 3: Credibility of information source

These questions assess the level of perceived credibility of each information source (Ohanian, 1990). All items in this section are measured on a 7-point Likert-type scale, ranging from “1” (not at all …) to “7” (very …).

The following part provides a description of each information source first, and then asks questions. Ø The Food and Drug Administration is a federal agency of the United States Department of Health and Human Services, whose mission is to protect the public health and advance the public health.

Ø Monsanto Company is a leading producer of genetically modified seed. The company delivers agricultural products that used by farmers all around the world.

Ø Scientist is “a person who is trained in a science and whose job involves doing scientific research or solving scientific problems” (adopted from http://www.merriam- webster.com/dictionary/scientist ).

Ø Social media influencer is “a person who has the power to influence many people, as through social media”(adopted from http://www.dictionary.com/browse/influencer).

8. In my opinion, the Food and Drug Administration (FDA) is … 1. Not at all credible/ very credible 2. Not at all reliable/ very reliable 3. Not at all honest/ very honest 4. Not at all dependable/ very dependable 5. Not at all believable/ very believable 6. Not at all truthful/ very truthful 7. Not at all trustworthy/ very trustworthy

9. In my opinion, Monsanto is ...

1. Not at all credible/ very credible 2. Not at all reliable/ very reliable 3. Not at all honest/ very honest 4. Not at all dependable/ very dependable 5. Not at all believable/ very believable 6. Not at all truthful/ very truthful 7. Not at all trustworthy/ very trustworthy

10. In my opinion, scientists are … 1. Not at all credible/ very credible 2. Not at all reliable/ very reliable 3. Not at all honest/ very honest 4. Not at all dependable/ very dependable 5. Not at all believable/ very believable 6. Not at all truthful/ very truthful 7. Not at all trustworthy/ very trustworthy

11. In my opinion, social influencers are … 1. Not at all credible/ very credible 2. Not at all reliable/ very reliable 3. Not at all honest/ very honest 4. Not at all dependable/ very dependable 5. Not at all believable/ very believable 6. Not at all truthful/ very truthful 7. Not at all trustworthy/ very trustworthy

Section 4: Risk attitudes These questions assess participants’ willingness to take risk (Weber, Blais, & Betz, 2002)

12. For each of the following statement, please indicate your likelihood of engaging in each activity or behavior. Provide a rating from 1 to 7, using the following scale: Very Very unlikely likely 1 …… 7

1. Buying an illegal drug for your own use. 2. Consuming five or more servings of alcohol in a single evening. 3. Engaging in unprotected sex. 4. Not wearing a seatbelt when being a passenger in the front seat. 5. Not wearing a helmet when riding a motorcycle. 6. Exposing yourself to the sun without using sunscreen. 7. Walking home alone at night in a somewhat unsafe area of town. 8. Regularly eating high cholesterol foods.

Section 5: Stimuli Invite participants to read stimuli 61

Section 6: Manipulation check In this section, participants will complete 2 manipulation check items corresponding to the information source and followers of social media account

13. As best you can recall; the tweet you just saw was sent by? Government Scientist Food company Social media influencer Not sure Cannot see the screenshot

14. As best you can recall, which of these categorical best describes the profile photo of the Twitter account that you just saw? Personal picture The name of a government agency Corporate logo Other please, specify Cannot see the screenshot

Section 7: Perceived risks & perceived benefits

15: perceived risks

After viewing the stimuli, participants will be asked to report their agreement on a 7-point Likert-type scale, ranging from “1” (Strongly disagree) to “7” (Strongly agree), with a series of statements regarding the potential risks of eating genetically modified foods (Cox, Cox, & Zimet, 2006; Thelen, Yoo, & Magnini, 2011). Strongly disagree Strongly agree 1 … 7 1. Eating a genetically modified food is risky. 2. Genetically modified foods can lead to bad health results. 3. Genetically modified foods have uncertain outcomes. 4. Eating a genetically modified food makes feel anxious. 5. Eating a genetically modified food would cause me to worry.

16: perceived benefits

After viewing the stimuli, participants will be asked to report their agreement on a 7-point Likert-type scale, ranging from “1” (Strongly disagree) to “7” (Strongly agree), with a series of statements regarding the potential of benefits of eating genetically modified foods. Strongly disagree Strongly agree 1 … 7 1. Genetically modified foods are safe to eat. 2. Genetically modified foods dose not present risks for human health. 3. Genetically modified foods can help to solve the problems of food shortage and production. 4. Genetically modified foods contain more nutrient content. 5. Genetically modified crops are more resistant to crop diseases.

Section 8: Purchase Intentions 62

This question evaluate participants’ purchase intentions towards genetically modified food (Natascha, Geertjie, & Klaus, 2015). Respondents will express their purchase intention on a 7- point Likert-type scale ranging from “1” (very unlikely) to “7” (very likely).

17 How likely are you to buy genetically modified foods in the future? Very unlikely Very likely 1 … 7

Section 9: Uncertainty avoidance This question measures the cultural dimension of uncertainty avoidance (House, Hanges, Javidan, Dorfam, & Gupta, 2004). The item in this section is measured on a 7-point Likert-type scale, ranging from “1” (Strongly agree) to “7” (Very disagree).

18_1. In this society, orderliness and consistency are stressed, even at the expense of experiment and innovation. Strongly agree Strongly disagree 1 … 7

18_2. In this society, societal requirements and instructions are spelled out in detail so citizens know what they are expected to do. Strongly agree Strongly disagree 1 … 7

Section 10: Demographics In the demographic section, we will ask respondents basic demographic questions such as age, gender, ethnicity, major, religion, etc.

This is the final section of the survey. Please complete the following demographic questions. All information will only be used to categorize the data and will be confidential.

19. Your gender: Female Male Other

20. Your age: ______

21.. Which year are you? Freshman (Class of 2020) Sophomore (Class of 2019) Junior (Class of 2018) Senior (Class of 2017) Master student Ph.D student 63

Other

22. Which of the following best describes your ethnicity? Asian Asian American Black/African American Caucasian or white Native American Pacific Islander Latino/Hispanic Multi-racial Other, please specify______

23. Which of these fields best describes your major, or your anticipated major? Natural Science Humanities and Arts Engineering Social Sciences Economics Policy

24. How much guidance dose religion provide in your everyday life? (Cacciatore, 2016) No guidance at all … A great deal of guidance 1 … 7

25. Do you consider yourself to be an Evangelical Christian? (Cacciatore, 2016) Yes No

26. In politics today, do you consider yourself a: Democratic Republican Independent Something else

27. In general, would you describe your political view as… Very conservative Conservative Moderate Liberal Very liberal 64

Stimulus One: Government

Stimulus Two: Company 66

Stimulus Three: Scientist

Stimulus Four: Social Media Influencer 68

Appendix C. Experiment Questionnaire and Stimulus for Participants in China

第一部分：千禧一代的社交媒体使用频率

1. 你对以下社交网站的访问和使用频率是？一天多次一天一次一周偶尔几次每隔几周较少不知道不愿回答不使用

1. 微博 2. 微信 3. 领英 4. 人人 5. QQ 空间

2. 你经常在社交媒体上分享自己的原创内容，比如照片、视频、文章吗？一天多次一天一次一周偶尔几次每隔几周较少不知道不愿回答不使用

3. 你经常使用以上社交媒体分享自己的近况吗？一天多次一天一次一周偶尔几次每隔几周较少不知道不愿回答不使用

4. 你经常使用以上社交媒体去浏览别人的动态吗？一天多次一天一次一周偶尔几次每隔几周较少不知道不愿回答不使用

5. 你经常评论网民在社交媒体上发的帖子吗？一天多次一天一次一周偶尔几次每隔几周较少不知道不愿回答不使用

第二部分：对风险问题的认知水平

6. 我们想知道你对以下议题的了解程度，你觉得你对以下议题的了解程度是？

非常不了解非常了解 1 …… 7

1. 转基因食品 2. 流感疫苗 3. HPV 疫苗（人乳头状瘤病毒疫苗）

7. 当你听到人们讨论以下议题的时候或者当你读到关于以下议题的新闻的时候，你的感受是什么？请以“消极”，“积极”为评价标准从1到7选择最符合你自身观点的答案：

（“积极”意味着你觉得以下事物是好的，有益的; “消极”意味着你觉得以下事物是不好的，有害的。）

非常消极非常乐观 1 …… 7

1. 转基因食品 2. 流感疫苗 3. HPV 疫苗（人乳头状瘤病毒疫苗）

第三部分：信息来源的可信度

Ø 农业部是主管农业与农村经济发展的国务院组成部门，农业部管理农业转基因的研究和转基因食品的生产与审批。 Ø 孟山都公司是一家跨国农业公司。总部设于美国密苏里州圣路易斯市。孟山都公司是全球转基因种子的领先生产商。目前在中国销售包括番茄、菠菜、辣椒、甜椒、西兰花、白菜花、洋葱、甜玉米、生菜、甘蓝、黄瓜等传统蔬菜种子产品。 Ø 科学家是受过某领域系统地科学教育的人，他们的工作包括科学研究和解决科学难题。 Ø 社交媒体影响者是社交网络上的舆论领袖，在某种程度上社交媒体影响者也可以被理解为网络红人。他们在社交媒体上拥有大量的粉丝，其言论能够影响很多人。

你将使用 7 个标准来评价……作为信息来源的可信度，“1”代表 “根本不..." 和 “7” 代表 “ 非常...”。请指出你觉得……符合的程度是？

8. 我认为农业部

1. 根本不可信／非常可信 2. 根本不可靠／非常可靠 3. 根本不诚实／非常诚实 4. 根本不值得信赖／非常值得信赖 5. 根本不值得相信／非常值得相信 6. 根本不真实／非常真实 7. 根本不守信用／非常守信用

9. 我认为孟山都公司

1. 根本不可信／非常可信 70

2. 根本不可靠／非常可靠 3. 根本不诚实／非常诚实 4. 根本不值得信赖／非常值得信赖 5. 根本不值得相信／非常值得相信 6. 根本不真实／非常真实 7. 根本不守信用／非常守信用

10. 我认为科学家

11. 我认为社交网络影响者

第四部分：风险态度

12. 请指出你参与以下每一个活动或者行为的的可能性:

非常不可能非常可能 1 ……….. 7 1. 购买非法药品供自己使用 2. 在一个晚上喝 5 瓶或者更多的酒 3. 无保护措施的性行为 4. 坐前排不系安全带 5. 骑摩托车不戴头盔 6. 在日晒强的时候不涂防晒霜 7. 夜晚在不安全地带独自走回家 8. 经常食用胆固醇高的食品

第五部分：stimuli

一共有四个不同版本的 stimuli Q33-政府 Q34-社交媒体影响者 Q35-科学家 Q36-公司

第六部分： manipulation check

13. 请仔细回想，你刚才所看到的微博是由谁发出的？政府部门科学家跨国公司社交网络影响者不确定无法看到图片

14. 请仔细回想，以下哪一个种类最能概括你所看到的微博账号的头像？个人照片政府机构名称公司商标动物其它（请具体说明）无法看到图片

第七部分：感知风险与感知收益

15. A: 感知风险

根据你自身的认知，你是否同意以下说法? 请根据以下测量标准从 1 到 7 选择最符合你自身认知的答案

1=非常不同意 7=非常同意

1. 食用转基因食品是有危险的 2. 转基因食品可能有害健康 3. 转基因食品的影响还不确定 4. 食用转基因食品让我感到焦虑 5. 食用转基因食品让我担心

16. B: 感知收益

请从 1 到 7 根据以下测量标准提供一个评级非常不同意非常同意 1 …….. 7 72

1. 食用转基因食品是安全的 2. 转基因食品不会对人体产生危害 3. 转基因食品可以帮助解决食品短缺和食品生产的问题 4. 一些转基因食品有更高的的营养含量 5. 转基因食品风能抵抗作物病害

第八部分：购买意愿请从 1 到 7 根据以下测量标准提供一个评级

17. 在未来你有多大的可能性会购买转基因食品？非常不可能非常可能 1 …….. 7

第九部分：规避不确定性我们想知道你对以下陈述的看法，请以“同意”，“不同意”为标准从 1 到 7 选择最符合你自身观点的答案：

18_1. 当今中国社会强调秩序和一致性，甚至以牺牲实验和创新为代价。非常同意非常不同意 1 …….. 7

18_2. 在当今中国社会，社会的需求和指令相当明确，因此公民很清楚地知道他们该干什么。非常同意非常不同意 1 …….. 7

第十部分：个人信息

19. 你的性别：

男女其他

20. 你的年龄：______

21. 你是哪一个年级的？大一大二大三大四研究生博士生

22. 以下哪一个领域最能准确地描述你的专业，或者你想学习的专业？自然科学人文与艺术工程学社会科学经济学政策

23. 宗教信仰在你的日常生活中提供了多少指导？ 73

一点指导也没有大量的指导 1 ………… 7

24. 一般来说，你会怎么样描述自己的政治观点是？非常保守保守温和自由非常自由 74

Stimulus One: Government

Stimulus Two: Company

Stimulus Three: Scientist 77

Stimulus Four: Social Media Influencer