This research investigates the effects of source credibility
and risk attitude on young millennials’ risk and benefit perceptions and
purchase intentions toward GM foods. Results from two samples (young
millennials in the U.S. and China) confirmed individuals’ risk attitude
significantly influences their purchase intentions toward GM foods. Results
also revealed a significant interaction effect of source credibility and risk
attitude on risk perception of GM foods among Chinese respondents. Practical
and research implications are discussed.
Food choices can be personal or social-influence driven. Considering that food choices have important implications for human wellbeing and health, the public’s perceptions of food benefits and risks can lead to simple behavioral changes for broad 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, 1997; Shapiro, 1999), improve the nutrient content of foods (Burke, 1997) and help in removing known food-borne allergens (Jones, 1996).
Although GM foods carry notable
benefits, the public still remains skeptical about GM foods, as can be readily
seen via the anti-GMO opinions expressed about the issue on social media, such
as Twitter (Munro, Hartt, &
Pohlkamp, 2015). The mass communication literature has convincingly established
that various forms of media-delivered messages, including social media posts,
can influence people’s opinions and behaviors on different issues (Kareklas,
Muehling, & Weber, 2015).
Not surprisingly, the social media
environment has created opportunities for factually inaccurate and inconsistent
information about GM foods to proliferate, possibly contributing to the
uncertainty skepticism that is often linked to GM foods. Previous research has
confirmed that source credibility is one of the most important factors exerting
influence on consumers’ evaluation on the quality of health information (Kunst,
Groot, Latthe, Latthe, & Khan, 2002). To reduce the scientific uncertainty
surrounding GM foods felt by consumers, educating consumers to identify the
quality as well as the credibility of health information on social media is
imperative due to the fact that there’s a significant amount of consumers using
social media to obtain health information as relevant to themselves. Such need
is particularly urgent when communicating with young millennials, those between
the ages of 18 and 25 defined by the Pew Research Center (Fry, 2016), as they
are the heaviest users of social media as sources of information and news
(Greenwood, Perrin, & Duggan, 2016).
Although existing research has studied
general public’s risk perceptions toward GM foods (e.g., Phillips &
Hallman, 2013) and found that source credibility did influence general
consumers’ acceptance of GM foods (e.g., Zhang, Chen, Hu, Chen, & Zhan,
2016), no specific research has been designed to find out the effects of source
credibility via social media on young millennials, nor has any research focused
specifically on social media as an outlet for such health information and news.
Therefore, to address this research gap in the literature, we designed a study
to investigate the effects of source credibility via social media on young
millennials’ risk perception toward GM foods. More significantly, such
investigation is designed to compare perceptions from two different groups of
samples, young millennials in the U.S. and those in China. The central research
question that we asked in this study is: What
kind of influence via social media do source credibility and risk attitude have
on young millennials’ risk perceptions, benefit perceptions, and purchase
intentions toward GM foods?
By fulfilling the overall research
goal, the present study contributes to the literature on health communication
in at least three important ways: First, researchers have ascertained that the
inquiry of health information via social media is an unusually complex one
(e.g., Kunst et al., 2002). Our study contributes to the important role of
sources in shaping young millennials’ knowledge and perceptions toward GM foods
as an important antecedent of their purchase intention, which has yet to be
adequately examined. Second, it is necessary to educate young millennials to
effectively evaluate the credibility of various sources in online health
communication, which may eventually link to any health-related
decision-making. More concretely, taking
into consideration source credibility in identifying the quality of social
media posts may lead to a more rational decision-making process. Third, our
study extends earlier research on general public risk perceptions toward GM
foods by integrating the influence of social media. Furthermore, results
provide important information for health communicators because identifying
sources that amplify credibility and reduce negative perceptions are relevant
for designing health communication messages in reaching target consumers, especially
when such risk perceptions may have a global presence.
Genetically modified (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, 2014, “Frequently asked questions,” para.2). GM foods were
released into the market in the 1990s. Since that time, modern biotechnology
has developed rapidly.
The U.S. now leads the
world in producing GM crops (James, 2016). According to the U.S. Department of
Agriculture, Economic Research Service (2017), currently 89% of U.S. corn acres
are planted with genetically engineered, herbicide-tolerant (HT) seeds and 85%
of domestic cotton acres are produced with genetically engineered, insect-resistant
seeds. Similarly, China also leads the world in the research and development of
biotechnology (Zheng, Gao, Zhang, & Henneberry, 2017). GM crops is the core
product of this technology and has been widely breeding in China (Zhang et al;
2016). According to a report, China is the eighth largest grower of GM crops in
2016 (James, 2016). Although biotechnology has been advanced rapidly, there has
been increasing concern and discussion about the regulation and safety of GM
foods in China.
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. Regulatory authorities such as China’s
Ministry of Agriculture and the Food and Drug Administration (FDA) in the U.S.
have also insisted that GM foods sold on the markets have met the safety
requirements (e.g., FDA, 2018). Furthermore, scientific research has indicated
biotechnology can help to solve the problems of food shortage and production
(Clarke, 1997; Shapiro, 1999), improve the nutrient contents of foods and
reduce the use of toxic pesticides (Noussair, Robin, & Ruffieux, 2004).
However, many Americans and
Chinses still maintain a negative attitude toward GM foods and assume that risk
is involved in eating GM foods. They persisted that the potential side effects
have not yet discovered, and GM crops can cause environmental pollution (Zheng
et al., 2017). These controversies have led to increasing criticism of GM foods
in the U.S. and China, which is especially obvious on Twitter and Weibo, a
Chinese version of Twitter (Munro et
al., 2015). This particular phenomenon reveals a significant knowledge
gap between what is considered acceptable in science and what is socially
accepted.
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,
2016), by examining the similarities and differences between young U.S. and Chinese
millennials’ perceptions and purchase intentions toward GM foods, the results
of this study can contribute to behavioral science literature in the field of
health and risk communication, meanwhile, provide practical suggestions for
heath communicators in both the U.S. and China.
Source credibility has been
an important area of research in persuasion communication for quite some time. Hovland
and Weiss (1951) conducted the first study considering source credibility as a
theoretical construct. This construct indicated that individuals are more
likely to be persuaded when the information source appears to be credible
(Hovland, Janis, & Kelly, 1953). That is, the credibility of the
communicator influences the response to the communication. As stated by
Hovland, Janis and Kelly (1953): “the very same presentation tends to be judged
more favorably when made by a communicator of high credibility than by one of
low credibility” (p. 35).
Credibility is defined by
Callison (2001) as “the judgments made by a message recipient concerning the believability
of a communicator” (p.220). Existing literature demonstrates that expertise and
trustworthiness are the two major factors of the credibility of a communicator
(e.g., Grewal, Gotlieb, & Marmorstein, 1994; Hovland et al., 1953).
Expertise refers to “the extent to which a communicator is perceived to be a
source of valid assertions” (Hovland et al., 1953, p. 21). Research has
confirmed the following dimensions can be used to explain the concept of
expertise: authoritativeness (McCroskey, 1966), competence (Whitehead, 1968),
knowledge and experience (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).
Source credibility is a key
construct in public relations research since it is a positive characteristic of
public relations’ message source and can act as buffer in crisis communication
(DiStaso, Vafeiadis, & Amaral, 2015). Previous research has suggested that
the selection of a credible spokesperson can enhance the acceptance of the message
and may result in desirable attitude changes (Chebat, Filiatrault, &
Perrien, 2001; Coombs, 2014). In addition, Coombs (2014) indicated that
organizations perceived as having high credibility by stakeholders can be more
effectively in dealing with rumors attacking the organizations.
Source credibility has also been
widely studied in health communication. Research finds that consumers evaluate
the quality of health information by looking into the accuracy, believability, trustworthiness,
truthfulness, readability, and completeness of the information (Bates, Romina,
Ahmed, & Hopson, 2006; Hu & Sundar, 2010). It is interesting to see
that previous research has generated inconsistent findings about the effects of
source credibility on people’s evaluation of the quality of health information.
Some studies found people took source credibility into account when judging the
quality of the health information. One study used semi-structured interviews
and found some people use scientific evidence as a cue for trustworthiness to
identify the quality of health information (Stavir, Freeman, & Burroughs,
2003). Another study found accuracy, credibility, currency, clarity, and ease
of understanding the health content are the crucial criteria for college
students to assess the quality of health websites (Escoffery et al., 2005).
However, other work has indicated
people often make little use of source credibility in evaluating the quality of
the health information on the Internet. For example, Bates and colleagues
(2006) used the web pages of the National Cancer Institute (NCI), the American
Lung Association (ALA) and the American Cancer Society (ACS) as credible
information sources and a generic web page without specific information source to
test source credibility. The results indicated that participants who received
lung cancer prevention information from the web page of the NCI, ALA and ALS
did not perceive the information to be more trustworthy, truthful or complete
than participants who received the same information from a generic web page. In
addition, Hu and Sundar (2010) found people did not perceive health information
concerning “the use of sunscreen” and “raw milk consumption” to be more
credible when the information is from a professional source (e.g., a doctor)
than from a layperson. If people pay little attention to source credibility
when evaluating the quality of health information, the persuasive effects of
health information from a credible source would be jeopardized.
Previous research has also suggested
that health risk messages provided by a credible source can lead to a greater
message compliance (e.g., Schouten, 2008; Umeh, 2012). For example, Karelkas et
al. (2015) found that source credibility significantly influences consumers’
attitudes and behavioral intentions for vaccination.
Although previous studies
have examined the effects of perceived source credibility on public reactions
to the health risk information related to GM foods (Frewer, Scholderer, &
Bredahl, 2003; Zhang et al., 2016), mixed results were generated. Based on the
persuasion model developed by Hovland, Zhang et al (2016) investigated the
influence of source credibility on consumers’ acceptance of GM foods in China.
The study found 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 in China to
accept GM foods. However, sources such as non-GMO experts, foods companies, and
anonymous information found on the Internet were considered as less believable
sources, which are unable to lead to favorable attitudes toward GM foods. In
contrast, Frewer, Scholderer and Bredahl (2003) found that the perceptions of
the information source characteristics (expertise and trustworthiness) didn’t
have a significant influence on changing participants’ risk attitude towards GM
foods. Rather, they argued that pre-existing attitudes toward GM foods influenced
participants’ perceptions of source credibility.
All told, there is a lack of
consistency about the effects of source credibility on consumers’ attitudes and
behavioral intentions toward health issues. Further, there is a dearth of
findings on the effects of source credibility on young U.S. and Chinese
millennials’ risk perceptions – whether concerning GM foods or other health
issues. Thus, this study focused on the effects of source credibility via
social media on young American and Chinese millennials’ perceptions toward GM
foods.
People’s risk attitude is
another antecedent to the formation of risk perceptions (Phillips &
Hallman, 2013). Risk attitude is “a person’s standing on the continuum from risk
aversion to risk seeking” and can be seen as a personality trait (Weber, Blais,
& Betz, 2002, p.264). Considering risk attitude as a trait characteristic,
there are generally existing two subgroups of individuals (Goldstein, Johnson,
& Sharpe, 2008): “those who have a tolerance and even a preference for risk
and those who are more cautious and would prefer to avoid risk” (Phillips &
Hallman, 2013, pp.739-740).
Comparing the personality
traits between managers and entrepreneurs, Stewart and Roth (2001) have found
entrepreneurs are more willing to take risks, prefer to think flexibly, and
bear more responsibility than managers. Furthermore, research has found that
risk seeking individuals are more likely to achieve personal success
(MacCrimmon & Wehrung, 1990). In addition, Weber, Blais, and Betz (2002)
found people’s risk attitude was associated with gender, specifically “women
appeared to be more risk averse in all domains (financial, health/safety,
recreational, ethical, and social) except social risk” than men (p. 263).
However, there have not been
sufficient studies to date that have identified the role of risk attitude in
consuming the information related to GM foods. Given the fact that the GM foods
market is rapidly growing and continues influencing people’s daily food
consumption, it is necessary to understand how people’s personality traits such
as risk attitude may influence their perceptions toward GM foods.
Therefore, drawing from
previous research on source credibility and risk attitude, we proposed the
following set research questions and hypotheses to guide our study:
RQ1: Will different sources of information demonstrate
different levels of source credibility?
RQ2: If different sources of information demonstrate
different levels of source credibility, how would such differences influence
participants’ risk perceptions, benefit perceptions, and purchase intentions
for GM foods?
RQ3: Will participants with different levels of risk attitudes
have different risk perceptions, benefit perceptions, and purchase intentions
for GM foods?
H1a: Risk-seeking participants
will have lower risk perceptions
toward GM foods than risk-averse
participants
H1b: Risk-seeking
participants will perceive more benefits
of GM foods than risk-averse participants.
H1c: Risk-seeking
participant will have higher purchase
intentions toward GM foods than risk-averse
participants.
In addition, we also want
to know whether source credibility and risk attitude will have an interaction
effect on participants’ risk perceptions, benefit perceptions, and purchase
intentions for GM foods. Thus, the following research question is proposed:
RQ4: For participants who have different risk attitudes, what
are the similarities and differences between their risk perceptions, benefit
perceptions, and purchase intentions for GM foods in response to different
levels of source credibility via social media?
This study used a 2 (risk
attitude: risk averse vs risk seeking) x 4 (source credibility:
government vs food company vs social media influencer vs scientist)
between-subjects design. The first factor, risk attitudes, was measured. The
other factor, source credibility, was manipulated in the experiment. Each
individual participant was randomly assigned to one of the four experimental
condition which presented one of the four different information sources.
Different sources (government, food company, social media influencer and
scientist) were incorporated into the identical fictional social media posts.
The fictional social media posts were designed for Twitter and Weibo. The tweet
and Weibo message outline 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’ Twitter/Weibo
desktop version homepage.
The questionnaire and the
fictional social media post were first created in English and were then
translated into Chinese and verified by two bilingual researchers. Two versions
of the questionnaire (Chinese and English) were pretested in each country (n =
40 per country) to allow final adjustments before carrying out the main 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 was a science writer and book author in psychology,
neuroscience and evolution and he had over 3K followers on Twitter. The reason
for choosing Rolf Degen as the source of social media influencer is because he
has tweeted information about GM foods in real life. 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. The
scientist was framed as “Ph.D. Director, Genetic Science Learning Center,
University of Utah. Research Professor, Human Genetics, University of Utah.”
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 selected as the social
media influencer source since he has over 500K followers on Weibo and has
posted information related to GM foods on Weibo previously. Due to the
concentrated Chinese social media users on Weibo, a popular account on Weibo
usually has followers over 500K.
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 selected. A total of 517 millennials participated in
this study with 279 from China and 238 from the U.S. Among them, 242 in China
and 207 in the U.S. completed the study. Thus, the final sample consists of 449
completed responses from both countries.
Upon beginning the study, participants
were asked about their frequency of social media use in daily life. The second
section measured participants’ level of awareness for several controversial
health and risk issues, such as GM foods.
The third section was used
to assess the level of perceived credibility of the four different information
sources. The measure of perceived source credibility was adapted from Ohanian’s
source credibility- trustworthiness subscale (Ohanian, 1990). A brief description
of the information source adapted from dictionary definitions 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.
In the fourth section,
participants were asked about their attitudes toward risk in order to measure
their willingness to take risk. The measure of risk attitude was adapted from
the health/safety subscale of Weber, Blais and Betz’s (2002) risk taking
scales. 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.” Reponses to these items were averaged together. A
median split was used to divide participants into two different groups: risk
seeking and risk averse.
After the fourth section,
participants were randomly assigned to one of the four stimuluses. Participants
were invited to read the designed tweet/Weibo message sent from one of the four
information sources. After viewing the stimuli for a minimum of 20 seconds,
participants were asked to answer the manipulation check items and several
survey questions about their perceptions of the risks and benefits of GM foods
and future purchase intentions.
Two items were used to
check the manipulation of information source: (a) “As best you can recall, the
tweet/Weibo message 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/Weibo 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.
Risk perception of GM foods
was measured by adapting the perceived risk scales developed by Thelen, Yoo,
and Magnini (2011), for example, “Eating a genetically modified food is risky.”
Benefit perception of GM foods was measured by asking participants to rate five
benefit-related items created by researchers of this study to fit the scenario
of GM foods. One example is “Genetically modified foods can help to solve the
problems of food shortage and production.” Purchase intention was measured by
adapting the item from Loebnitz,
Schuitema and Grunert (2015), which asked “How likely are you to buy genetically modified
foods in the future?” All items were measured using seven-point Likert scales
ranging from “1 = strongly disagree” to “7 = strongly agree” or from “1 = very
unlikely” to “7 = very likely.” Finally, participants were asked to answer
basic demographic questions such as gender, age, education, ethnicity, major,
religion and political party affiliation and political ideology.
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
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. Among
the 53 participants who saw the scientist stimuli, 55% (N = 29) chose the right
option. Fifty-three participants were exposed to the social media influencer stimuli
and 70% (N = 37) answered 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 answer. Among 48 participants who saw the food
company stimuli, 81% (N = 39) of them chose the right answer. Fifty-three
participants viewed the scientist stimuli and 76% (N = 40) answered this
manipulation item correctly. Among the 53 participants who saw the social media
influencer stimuli, 74% (N = 39) of them answered the second manipulation item
correctly.
For the study conducted in China., the
results of manipulation check for the first manipulation item 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. Among the 58 participants who
saw the scientist stimuli, 24% (N = 14) of them chose the right option.
Sixty-one participants were exposed to the social media influencer stimuli and
71% (N = 41) answered this manipulation item correctly. For the second
manipulation item, results showed that among the 59 participants who viewed the
government stimuli, 44% (N = 26) of them chose the right answer. Among 64
participants who saw the food company stimuli, 53% (N = 34) of them chose the
right option. Fifty-eight participants were invited to view the scientist
stimuli and 38% (N = 22) answered this manipulation item correctly. Among the
61 participants who viewed the social media influencer stimuli, 85% (N = 52) of
them answered the second manipulation item correctly.
A series of reliability analyses were
run to test the reliability coefficients of the source credibility scale, the
risk attitude scale, the risk perception scale, and the benefit perception scale
(see Table 1). Results indicated that the internal consistency of all above
scales was acceptable.
RQ1: Will different sources of
information demonstrate different levels of source credibility?
Results indicated that each
information source had different levels of source credibility. 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 a higher level of
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 a lower level 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., results
suggested there was a significant difference between the government’s
credibility (M = 5.29, SD = 1.36) and the company’s credibility
((M = 3.78, SD = 1.43); t (206) =
12.31, p < .001). There were also
significant differences between the 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), the scientist’s
credibility (M = 5.39, SD = 1.10) and the company’s credibility
((M = 3.78, SD = 1.43); t (206) = 13.65,
p <. 001), the scientist’s credibility (M
= 5.39, SD = 1.10) and the social
media influencer’s credibility ((M =
3.56, SD = 1.22); t (206) = 17.89, p < .001), and the company’s credibility (M = 3.78, SD = 1.43) and
the social media influencer’s credibility ((M
= 3.56, SD = 1.22); t (206) = 2.00, p = .047). However,
there was a nonsignificant difference between the government’s credibility (M = 5.29, SD = 1.36) and the scientist’s credibility ((M = 5.39, SD = 1.10); t (206) = -.991, p = .323).
For the study conducted in China, results indicated there was a
significant difference between the government’s credibility (M = 4.17, SD = 1.19) and the company’s credibility ((M = 3.44, SD = 1.15); t (241) = 8.97, p < .001). There were
also significant differences between the 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), the
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), the scientist’s credibility (M = 4.53, SD = 1.24) and the company’s credibility ((M = 3.44, SD = 1.15); t (241) = 12.79, p <. 001), the
scientist’s credibility (M = 4.53, SD = 1.24) and the social media
influencer’s credibility ((M = 3.23, SD = 1.15); t (241) = 13.40, p < .001), and the company’s credibility (M = 3.44, SD = 1.15) and the social media influencer’s credibility ((M = 3.23, SD = 1.15); t (241) =
2.54, p = .012).
To answer RQ 1, we found that in both
countries different sources of information demonstrate different levels of
source credibility.
RQ2: If different sources of information
demonstrate different levels of source credibility, how would such differences
influence participants’ risk perceptions, benefit perceptions, and purchase
intentions for GM foods?
To test RQ2, we ran three two-way
ANOVA tests to measure the main effects of perceived source credibility on risk
perceptions, benefit perceptions and purchase intentions. For the study
conducted in the U.S. (see Table 2), according to the results of the two-way
ANOVA, there were no significant main effects of perceived source credibility
on participants’ risk perceptions (F
(3, 199) = 2.26, p = .083, ); benefit perceptions (F (3, 199) = 1.39, p = .247, ) and purchase intentions (F (3, 199) = 1.43, p = .235, ) for GM foods.
For the study conducted in China (see Table 3), there were no
significant main effects of perceived source credibility on participants’ risk
perceptions (F (3, 234) = .65, p = .584,)) and purchase intentions toward GM
foods (F (3, 234) = .54, p = .658,)). However, we did find a significant
main effect of perceived source credibility on benefit perceptions of GM foods
(F (3, 234) = 2.96, p = .033, ). Tukey was chosen as the post hoc
tests. The result indicated that participants who viewed the government stimuli
(M = 4.04, SD = .97) perceived more benefits of GM foods than the participants
who saw the scientist stimuli (M =
3.49, SD = .96), although the result
of this study has suggested that scientist was perceived as having a higher
level of credibility than government.
Therefore, to answer RQ2, the results indicated that in both countries,
different levels of perceived source credibility did not generate different
risk and benefit perceptions of GM foods and different purchase intentions for
GM foods. However, we found participants in China who viewed the government
stimuli perceived more benefits than the participants who saw the scientist
stimuli.
Three two-way ANOVA were
used to measure the main effects of risk attitudes on each dependent variable
for RQ3 and the H1 set of hypotheses. For the study conducted in the U.S. (see
Table 2), results of the two-way ANOVA showed that there were no significant
differences between the two risk attitudes on risk perceptions (F (1, 199) = .021, p = .884, ), and benefit perceptions (F (1, 199) = 2.30, p = .131, ) of GM foods. However, there was a significant
main effect of risk attitude on purchase intentions toward GM foods (F (1, 199) = 6.98, p = .009, ). Risk-seeking participants (M = 4.54, SD = 1.45) had more purchase intention for GM foods than
risk-averse participants (M = 4.03, SD = 1.47). Therefore, H1c was supported while H1a and H1b were not supported in the U.S sample.
For the study conducted in
China (see Table 3), results of the two-way ANOVA showed that there was no significant
difference between the two risk attitudes on risk perception of GM foods (F (1, 234) = 1.55, p = .214, ). However, there were significant
main effects of risk attitude on benefit perceptions (F (1, 234) = 8.49, p =
.004, ) and on purchase intentions (F (1, 234) = 15.81, p < .001, ) toward GM foods. Risk-seeking
participants (M = 3.98, SD = 1.05) perceived more benefits of GM
foods than risk aversion participants (M
= 3.57, SD = 1.08). 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).
Therefore, the hypothesis H1b and H1c were supported but H1a was not supported.
To answer RQ3, the above results indicate that participants in the U.S. with
different levels of risk attitudes had different purchase intentions for GM
foods but had similar risk and benefit perceptions. In addition, participants
in China with different levels of risk attitudes had different benefit
perceptions and purchase intentions for GM foods while had similar risk
perception.
RQ4: For participants who have different risk attitudes, what
are the similarities and differences between their risk perceptions, benefit
perceptions, and purchase intentions for GM foods in response to different
levels of source credibility via social media?
In order to investigate the
interaction effects of perceived source credibility and risk attitude on the
three dependent variables for RQ4, a two-way ANOVA was conducted on each
dependent variable. For the U.S. sample (see Table 2), the results indicated
that there were no significant differences among the four different information
sources and the two different risk attitudes on risk perceptions (F (3, 199) = .78, p = .508, ); benefit perceptions (F (3, 199) = .88, p = .453, ) and purchase intention (F (3, 199) = .01, p = .998, ).
For the sample in China
(see Table 3), results found that there was a significant interaction effect of
perceived source credibility and risk attitude on risk perceptions of GM foods
(F (3, 234) = 2.72, p = .045, = .03). This result suggested that
risk-seeking participants and risk-aversion participants were affected
differently by perceived source credibility on risk perceptions of GM foods.
Pairwise comparisons were used to test the simple effects. Specifically,
risk-aversion participants’ risk perceptions of GM foods were similar no matter
they saw government stimuli (M = 3.83,
SD = 1.37); social media influencer
stimuli (M = 4.11, SD = 1.35); scientist stimuli (M = 4.11, SD = 1.42) or company stimuli (M
= 3.61, SD = 1.00). However,
among the risk-seeking participants, those who viewed the scientist stimuli (M = 3.39 SD = .96) had a significantly lower risk perception of GM foods
than whose who saw the company stimuli (M
= 4.12, SD = 1.24). However, results
found that there were no significant differences among the four different
information sources and two different risk attitudes on benefit perceptions (F (3, 234) = .97, p = .408, = .01) and purchase intentions (F (3, 234) = .76, p = .520, = .01).
Therefore, to answer RQ4, the results showed that in the U.S.
risk-averse and risk-seeking participants were not affected differently by
source credibility on any of the three dependent variables. However, the
results showed that in China risk-averse and risk-seeking participants were
affected differently by perceived source credibility on risk perceptions of GM
foods but not on benefit perceptions and purchase intentions for GM foods.
The aim of this study is to investigate the influence of perceived
source credibility and risk attitude on young Chinese and U.S. millennials’
risk perceptions, benefit perceptions, and purchase intentions toward GM foods.
In sum, four research questions and one set of hypotheses were tested. The
results of this study suggested that government, scientist, food company and
social media influencer were perceived as having different levels of source
credibility. For the four information sources adopted in this study, we found
scientists and the government were perceived by participants as high
credibility information sources in both the U.S. and China. In contrast, food
company and social media influencer were perceived as low credibility
information sources in both the two countries.
In addition, this study also found risk attitude had a significant
influence on participants’ benefit perceptions and purchase intentions for GM
foods. Specifically, risk-seeking participants had higher benefit perceptions
and purchase intentions for GM foods than risk-averse participants. This study also
found risk-averse and risk-seeking participants in China were affected
differently by perceived source credibility on risk perceptions of GM foods.
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.
Previous research in persuasion has well established that many forms of
media-delivered messages, such as online PSA and social media posts, are able
to affect public opinion on various issues (e.g., Kareklas et al., 2015;
Mangold & Faulds, 2009). This current study extends this literature in a
health risk-related context (i.e., the current debate on the safety of GM
foods) through investigating the effects of perceived source credibility via
social media and risk attitude on young Chinese and U.S. millennials’ risk
perceptions, benefits perceptions and purchase intentions towards GM foods.
Recognizing a growing amount of people using social media to obtain health
information as relevant to themselves (Witteman & Zikmund-Fisher, 2012),
this study carefully designed the stimuluses as they would appear on the
respective information sources’ Weibo/Twitter account. Since to our knowledge,
this is the first study examining the interaction effects of perceived source
credibility and risk attitude on young Chinese and U.S. millennials’ risk
perceptions, benefits perceptions and purchase intentions, we believe our study
can make important theoretical and practical contributions to the health risk
communication literature in the field of public relations research.
The results of this study indicated that there was a significant
interaction effect of perceived source credibility and risk attitude on young
Chinese 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. This finding provided evidence that the two antecedents of risk
perceptions, perceived source credibility and risk attitude, can exert joint
effect on risk perception. In addition, this finding can also provide a
practical suggestion for public relations practitioners in China that is public
relations practitioners in China should consider using scientists as the
information source in order to successfully transmit the benefits of GM foods
via social media.
This study also found young Chinese millennials’ risk attitude
significantly influenced their benefits perceptions and purchase intentions for
GM foods, but did not have an impact on their risk perceptions of GM foods. The
result suggested that even though risk-seeking
participants
and risk-averse participants had similar risk perceptions of GM foods,
risk-seeking participants can perceive more benefits of GM foods and would be
more likely to buy GM foods. One possible explanation for this result could be
that risk-averse participants and risk-seeking participants are different in
their benefit perceptions rather than risk perceptions. Compared to risk-averse
people, risk-seeking individuals can see more benefits of a risk, thus they are
more willing to take the risk. Therefore, public relations practitioners should
spend more time to communicate the benefits of GM foods to risk-averse
individuals.
The results of this study indicated perceived source credibility via
social media has little or no effect on young Chinese and U.S. millennials’
risk perceptions, benefit perceptions and purchase intentions for GM foods.
Here we provide two reasons that may explain the results. Our findings are
perhaps understandable if we consider those young millennials’ knowledge and
attitude toward GM foods. Results of this study show that young millennials in
China and the U.S. felt that they were not very informed about GM foods and
they had a general negative attitude towards GM foods. Therefore, it is not
particularly surprising that the use of even a highly credible source is
insufficient for persuading this audience to change their attitudinal
evaluations of GM foods. Rather, a long-term education strategy may be needed
to truly shift prevailing opinions. Such sentiments have been shared by Uzogara
(2000), who noted that “the public needs to be sufficiently educated on genetic
engineering of any product to enhance acceptability of such a food.” (p.202).
In addition, according to Frewer et al. (2003), the reason risk communication
sometimes failed is because the message was sent from the expert view of what
should be known by the public rather from the view of what the public are
really concerned about. Therefore, in order to make young millennials be more
willing to accept the positive message related to GM foods, researchers and
public relations practitioners should understand what are the things that this
audience is really concerned about.
Another reason that source credibility via social media has little or no
effect on young millennials’ risk perceptions, benefit perceptions and purchase
intentions for GM foods is that participants may not take source credibility
fully into account when consuming the information from Weibo and Twitter. As suggested
by some former studies, Internet users often make little use of source
credibility in evaluating the quality of the health information on the Internet
(e.g., Bates et al., 2006; Hu & Sundar, 2010). Therefore, the persuasive
effects of the health information from a credible source would be jeopardized.
Since that researchers and health public relations practitioners should
continuously search for ways to improve the health literacy and health
information-seeking skills of young millennials, which might influence how
young millennials use cues like source credibility when forming opinions.
The first limitation of this work is the student sample, which is also a
limitation of many social science research projects. Future studies should
include more diverse participants to investigate the influence of perceived
source credibility and risk attitude on perceptions of GM foods. The second
limitation is the design of the stimulus as the desktop version. Since
participants can also use their mobile phones to access the questionnaire, it
would be better if the stimulus was also designed for the mobile version of
Weibo. As the desktop version stimulus may not suit the phones’ screen very
well, the visual impacts of the stimulus maybe jeopardized. Third, this study
only examined government, company, scientist and social media influencer as
information sources. The impacts of, for example, family members and friends on
the dependent variables remain to be investigated.
Research has found that the persuasive strength of message has a
significant influence on persuasive appeal and attitude change (Frewer, Howard,
Hedderley, & Sheperd, 1997). Since this study only used one message to
communicate the benefits of GM foods, it didn’t manipulate the level of the
persuasive strength of the message. Thus, in order to effectively communicate
the benefits of GM foods and other health topics, future studies should examine
the effects of persuasive strength on public attitudes and behaviors. In
addition, the format (video, audio, text) of the message might also influence
the persuasive appeal of the message. Research has found that video can
generate stronger and more enduring attitude change compared to text since
video has a higher level of vividness than text (Coyle & Thorson, 2001). As
this study relied solely on text-based stimuli to communicate the benefits of
GM foods, future studies could test whether using alternative formats enhances
the persuasiveness appeal of the message.
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