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What Factors Influence Farmers
’
Adoption of Transgenic
Technology under the Background
of Risk Amplification?
Li Zhao , Shumin Liu , Haiying Gu , Chengyan Yue * , David Ahlstrom
Posted Date: 18 August 2023
doi: 10.20944/preprints202308.1308.v1
Keywords: Risk amplification effect; Risk preference; GM agricultural products; China
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Article
What Factors Influence Farmers’ Adoption of
Transgenic Technology under the Background of
Risk Amplification?
Li Zhao *, Shumin Liu, Haiying Gu, Chengyan Yue and David Ahlstrom
Abstract: Consumer preference for products made from transgenic technology has been widely studied, yet
few studies exist exploring the factors influencing producers’ adoption of transgenic technology. Based on field
surveys in Chinese provinces of Shanxi, Henan and Shandong, we employed a gambling experiment to capture
producers’ risk preferences by estimating their risk aversion coefficients. We further estimated producers’ risk
amplification and risk perception of GM technology. Using ordered logit model and Poisson model we
identified the major factors influencing producers’ adoption of transgenic technology. We found the factors
impacted the decision of producers from different regions in different ways. The results showed that over 60%
of participants amplified the risk of transgenic crops. When there was potential risk, producers might not be
rational even if they had high level of knowledge and cognition about the technology. Our results shed light
on government policies aiming to increase the adoption of new technologies by producers.
Keywords: risk amplification effect; risk preference; GM agricultural products; China
1. Introduction
Innovation and reform promote economic and societal development [1], but it is often
accompanied by uncertainty and risk [2]. According to the society amplification framework of risk
(SARF), the risk of events can be amplified through government authority, expert opinions,
traditional mass media, relatives and friends, internet communities and other channels [3,4]. This
“groundless worry” behavior will hinder the effectiveness of market or policy interventions, which
can slow down product or technology adoption and even the speed of economic growth [5]. This
paper uses transgenic crops as an example product to investigate the factors influencing producers’
adoption of new technologies under risk amplification. In the 1990s, transgenic cash crops such as
insect-resistant cotton were introduced into in China’s Henan and Shandong provinces and achieved
great success [6]. Transgenic crops mainly include soybean, corn, cotton and rapeseed. China has
carried out transgenic research for these crops and developed transgenic varieties. However, few
varieties have yet to be commercialized on a large scale.
Risk involves uncertainty about the effects of an activity with respect to something of value. Risk
amplification theory says that certain aspects of a risk portrayed in mediated sources can interact
with psychological processes in ways that might decrease or amplify people’s perceptions of the risk
and, in turn, shape their behaviors. Based on previous research, three aspects of risk are key to
understand producers’ technology adoption behavior: risk amplification effects, individual risk
preferences, and individual characteristics.
The risk amplification effect was first proposed by Kasperson et al. [4]. A risk signal may
strengthen or weaken the public’s cognitive ability through the joint action of a variety of factors.
Risks are amplified at different levels and links. Taking gender imbalance as an example, Slovic stated
that in group events, the risk has been amplified in information fermentation, psychological identity,
and absence of government to public response [7]. According to the “anchoring effect,” Tversky and
Kahneman believed that in uncertain situations, people often estimate the target value through the
initial value, and the public’s risk perception is difficult to correct itself [8]. As a topic most closely
related to people’s health, the risk of food safety is very easy to be amplified. In addition, SARF is
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2
often used as a classic auxiliary tool for learning risk concepts. Using SARF, Kim et al. discussed the
influence of risk perception of pesticide residues on purchase intention [9]; Lee et al. studied
consumers’ risk perception and willingness to pay for genetically modified food [10]; and Li and
Zhang examined Chinese consumers’ willingness to buy GM papaya, and investigated how different
dimensions of media-reported risk impacted consumers’ acceptance of GM agricultural products
[11].
Individual and household characteristics of producers are also important factors impacting their
risk perception. When describing individual characteristics, the most frequently used variables are
age, farming experience (in years), gender, capital status, education level, etc. Awotide et al. found
that in reality, men are more likely to adopt technology than women [12]. Tanaka et al. investigated
how wealth, political history, occupation and other demographic variables are related to risk
perception, time discounting and trust in Vietnam [13]. The study showed people in richer villages
are less averse to loss and more patient compared to those in poorer ones. Farmers with more
household members are in the labor force are more willing to adopt new technologies [14]. Korir et
al. found that the higher the level of education, the higher the likelihood of adoption [15]. The
financial status affects the adoption of new technologies to a certain extent [16]. Huang et al. studied
how cotton farmers’ knowledge, risk preference, market regulation, and pesticide price and
education level influence their pesticide application [17]. More recent research added more variables
to individual characteristics.
Finally, producer risk preference plays an important role in their new technology adoption
decision. Previous research has found producers are generally risk averse and the higher the degree
of risk aversion, the more likely the producers do not adopt or delay the adoption of new
technologies. Liu discussed how Chinese farmers’ risk attitudes impact Chinese farmers’ adoption of
a new form of agricultural biotechnology and concluded risk-averse farmers adopt GM cotton later
than the farmers who are less risk-averse [6]. When asked why insect-resistant cotton was not
adopted immediately, 97% farmers indicated it was mainly because of its uncertainty in reducing
insect pests [18]. Risk aversion is an important factor hindering the extension of agricultural
technology [19,20]. Luo et al. found that risk seeking has a significant positive impact on the adoption
of new technologies [21].
Researchers have also reached similar conclusions when studying consumer new product
adoption decisions. Lusk and Coble, Zhou et al. and Zhang et al. found that the higher the degree of
risk aversion, the less likely for consumers to adopt new technologies [22–24]. The former two studies
found that most individuals are risk averse, and only a small number of individuals are risk seeking;
the third study concluded that risk-seeking consumers are more likely to choose uncertain
promotional activities. Gambling experiments, which asks individuals to make lottery choices, are
often used capture an individual’s risk preference. The most classic methods are Binswanger’s
orderly lottery selection design (OLS design) [25] and Holt and Laury’s multiple price sequence
design (MPL design) [26], in which MPL design is regarded as the “gold standard” for risk preference
measurement experiment. The former designs eight different lotteries and then sorts them. The
subjects choose one of them, and the income is determined by the selected lottery. In MPL, subjects
see a series of paired options and choose one for each row. Harrison et al. expanded MPL and
developed turning multiple price sequence design (sMPL) and iterative multiple price sequence
design (iMPL) [27]. The difference between sMPL experimental design and MPL design is that in
sMPL subjects only need to choose the switching point from lottery A to lottery B.
Based on the risk amplification degree and risk preference of Chinese producers for GM crops,
this paper explores the major factors influencing producers’ adoption of new technologies.
Specifically, we aim to find answers the following questions: Are producers’ perceived risks of GM
crops amplified? What are the factors influencing producers’ adoption of GM crops? What the
government can do to speed up GM technology adoption by producers? Using data collected through
questionnaire survey and economic experiments with producers in three Chinese provinces this
study measures producers’ risk aversion degree, explores the impacts of producers’ risk
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3
amplification, individual characteristics, risk cognition and preference on their adoption of transgenic
technologies.
2. Materials and Methods
2.1. Field Surveys
Field surveys were carried out with producers in rural areas of China’s Henan, Shanxi, and
Shandong provinces to understand producers’ cognition and attitude towards GM crops. After
dropping unusable questionnaires, 338 valid questionnaires were used in this analysis, including 95
participants in Henan, 149 participants in Shanxi, and 94 participants in Shandong. Each participant
who completed the survey received 5 yuan, plus the earnings from lottery series in the gambling
experiment.
The questionnaire consisted of three sections: The first section is about individual and household
characteristics. Participants need to answer eight questions about household and individual
information. Before filling in the questionnaire, the experiment moderator repeatedly stressed that
the information obtained was completely confidential and only used for academic research, to obtain
real information. The second part included multiple-choice questions on consumption habits, genetic
knowledge, and attitude towards the risk of GM technology, risk perception and the acceptance of
GM agricultural products. Participants were told in advance that there was no right or wrong choice
for these questions1. The third part consisted of the sMPL experiment. The experimental difficulty of
this part mainly lies in the complexity of the rules of the game and the way to determine the income.
To help participants better understand the sMPL experiment, two moderators provided detailed
explanations about how the experiment would work and answered questions related to mechanism
of the experiment. The sMPL experiment consisted of three series of lotteries. Participants needed to
select a switching point for each series. After the selection was done by a participant, one of the three
selected lotteries was randomly drawn to determine the participant’s monetary return. The
participants were paid off according to the lottery randomly drawn. Because participants did not
know which lottery will be drawn in advance, this experimental method made participants pay
attention to each choice and were more likely to reveal their true preferences. This experiment will
be described in detail in the next section.
The second part of the questionnaire was deliberately designed to check the validity of
participants’ answers. Some questions were asked multiple times in different ways, and some options
were reverse coded.
2.2. Economic Experiment Design
In the gambling experiment using experimental economics, participants needed to answer
questions in the following form:
“I choose lottery A from line 1 to line n”; “I choose lottery B from lines n to 14.”
Under the hypothesis of rationality of economics, in each series, participants can only switch
from option A to option B once, or would not switch at all (only choose A or only choose B)2. The
first two series had only positive returns. In series 1, option A is “30% chance to get 8 yuan and 70%
chance to get 2 yuan;” In option B, when moving down, the return with small probability was
increasing. In series 2, option A is “90% chance to get 8 yuan and 10% chance to get 6 yuan;” in option
B, when moving down, the return with high probability was increasing. The return in option B of
series 2 was lower than that of series 1. In prospect theory, people’s risk preference in the domain of
gain differs from that in the domain of loss. Therefore, series 3 was to measure whether producers
1 Some participants with low education level said that some questions were beyond the scope of knowledge and could not be
answered, so the experiment moderator needed to make some necessary explanations without affecting the authenticity of the
results.
2 We dropped the observations in which participants switched between options A and B repeatedly.
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maintained the same risk preference when facing both gain and loss. The three lottery series used in
the experiment are shown in Table 1.
Table 1. Lottery series used in the gambling experiment.
TL Option A Option B
Series1
1 get 8 yuan with 30%,get 2 yuan with 70% get 0.5 yuan with 90%,get 10 yuan with 10%
2 get 8 yuan with 30%,get 2 yuan with 70% get 0.5 yuan with 90%,get 13 yuan with 10%
3 get 8 yuan with 30%,get 2 yuan with 70% get 0.5 yuan with 90%,get 16 yuan with 10%
4 get 8 yuan with 30%,get 2 yuan with 70% get 0.5 yuan with 90%,get 19 yuan with 10%
5 get 8 yuan with 30%,get 2 yuan with 70% get 0.5 yuan with 90%,get 22 yuan with 10%
6 get 8 yuan with 30%,get 2 yuan with 70% get 0.5 yuan with 90%,get 25 yuan with 10%
7 get 8 yuan with 30%,get 2 yuan with 70% get 0.5 yuan with 90%,get 28 yuan with 10%
8 get 8 yuan with 30%,get 2 yuan with 70% get 0.5 yuan with 90%,get 33 yuan with 10%
9 get 8 yuan with 30%,get 2 yuan with 70% get 0.5 yuan with 90%,get 38 yuan with 10%
10 get 8 yuan with 30%,get 2 yuan with 70% get 0.5 yuan with 90%,get 45 yuan with 10%
11 get 8 yuan with 30%,get 2 yuan with 70% get 0.5 yuan with 90%,get 55 yuan with 10%
12 get 8 yuan with 30%,get 2 yuan with 70% get 0.5 yuan with 90%,get 65 yuan with 10%
13 get 8 yuan with 30%,get 2 yuan with 70% get 0.5 yuan with 90%,get 80 yuan with 10%
14 get 8 yuan with 30%,get 2 yuan with 70% get 0.5 yuan with 90%,get 100 yuan with 10%
Series 2
1 get 8 yuan with 90%,get 6 yuan with 10% get 0.5 yuan with 30%,get 9 yuan with 70%
2 get 8 yuan with 90%,get 6 yuan with 10% get 0.5 yuan with 30%,get 10 yuan with 70%
3 get 8 yuan with 90%,get 6 yuan with 10% get 0.5 yuan with 30%,get 11 yuan with 70%
4 get 8 yuan with 90%,get 6 yuan with 10% get 0.5 yuan with 30%,get 12 yuan with 70%
5 get 8 yuan with 90%,get 6 yuan with 10% get 0.5 yuan with 30%,get 13 yuan with 70%
6 get 8 yuan with 90%,get 6 yuan with 10% get 0.5 yuan with 30%,get 14 yuan with 70%
7 get 8 yuan with 90%,get 6 yuan with 10% get 0.5 yuan with 30%,get 15 yuan with 70%
8 get 8 yuan with 90%,get 6 yuan with 10% get 0.5 yuan with 30%,get 17 yuan with 70%
9 get 8 yuan with 90%,get 6 yuan with 10% get 0.5 yuan with 30%,get 19 yuan with 70%
10 get 8 yuan with 90%,get 6 yuan with 10% get 0.5 yuan with 30%,get 21 yuan with 70%
11 get 8 yuan with 90%,get 6 yuan with 10% get 0.5 yuan with 30%,get 23 yuan with 70%
12 get 8 yuan with 90%,get 6 yuan with 10% get 0.5 yuan with 30%,get 25 yuan with 70%
13 get 8 yuan with 90%,get 6 yuan with 10% get 0.5 yuan with 30%,get 29 yuan with 70%
14 get 8 yuan with 90%,get 6 yuan with 10% get 0.5 yuan with 30%,get 35 yuan with 70%
Series 3
1 get 12 yuan with 50%,lose 2 yuan with 50% get 15 yuan with 50%,lose 10 yuan with 50%
2 get 2 yuan with 50%,lose 2 yuan with 50% get 15 yuan with 50%,lose 10 yuan with 50%
3 get 0.5 yuan with 50%,lose 2 yuan with 50% get 15 yuan with 50%,lose 10 yuan with 50%
4 get 0.5 yuan with 50%,lose 2 yuan with 50% get 15 yuan with 50%,lose 8 yuan with 50%
5 get 0.5 yuan with 50%,lose 4 yuan with 50% get 15 yuan with 50%,lose 8 yuan with 50%
6 get 0.5 yuan with 50%,lose 4 yuan with 50% get 15 yuan with 50%,lose 7 yuan with 50%
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7 get 0.5 yuan with 50%,lose 4 yuan with 50% get 15 yuan with 50%,lose 5 yuan with 50%
*percentage means probability， TL means transition line
2.3. Basic Information of the Sampled Area
This study selected three provinces in China as the research areas. From west to east, they were
Yangquan City, Shanxi Province, Anyang City, Henan Province and Rizhao City, Shandong Province.
The information of these three provinces is shown in Table 2.
Table 2. Basic information of the sampled area.
Shanxi Province Henan Province Shandong Province
landforms
West of central
Taihang
Mountain
Mountains in the West and
plains in the East
Back to the mountain and facing the
sea, mountains, hills and plains are
distributed alternately
Resident population 1408.8thousand 5192.2thousand 2959.5thousand
Per capita GDP 47790yuan 42936yuan 58110yuan
Number of universities 3 7 6
Proportion of primary
industry 3in GDP 1.50% 11.80% 8.40%
Due to the mountainous terrain around Yangquan City in Shanxi, large-scale agricultural
production is very limited. During the experiment, we found that 90% of the cultivated land was
terraced, the crops were mainly cash crops, and the proportion of the primary industry was relatively
low. Anyang City, Henan Province was selected in the eastern plain. The proportion of primary
industry in GDP was 11.8%, which was the highest among the three regions, but the per capita GDP
was the lowest. Rizhao City, Shandong Province has a port. Thanks to its export-oriented economy,
Rizhao City’s per capita GDP was the highest among the three selected cities, and the proportion of
primary industry in GDP was the second. Regional differences between inland and coastal cities may
also affect producers’ risk preference. Henan Province and Shandong Province are the “hardest hit
areas of the college entrance examination” and they invest more resources in education, and there
are a large number of colleges and universities. Transgenic insect-resistant cotton was also first
introduced into Henan province and Shandong province.
2.4. Empirical Analysis
The dependent variable was the acceptance of genetically technology and the willingness to
purchase GM crops. Building upon Lusk (2005) [22], we developed questions about the acceptance
of genetically technology using five Likert-scale statements: I am willing to accept GM crops; I am
willing to purchase GM crop seeds; I will recommend GM crops to others; I agree with China’s large-
scale import of GM crops; I support the development of GM crops. Participants chose “strongly
disagree”, “disagree”, “neutral” “agree”, or “strongly agree” based on their levels of agreement to
these statements. For each participant, a score was generated by taking the mean of these five
variables and rounded to integer of five levels: 1, 2, 3, 4, and 5. A higher score indicates a higher level
of willingness to grow and the acceptance of GM agricultural products.
The explanatory variables include three core explanatory variables and control variables.
The first explanatory variable is the degree of risk amplification of GM crops（ra）. A health
hazard ranking question was used to measure whether participants overestimate the harm of GM
crops. 4Participants were asked to rank the following five hazards from high to low according to
their perceived harmfulness of these five items.
3 The primary industry is mainly agriculture, including hunting, fishery, animal husbandry and forestry.
4 Simple and understandable examples - "excessive preservatives" and "moldy rice" are given in " food additives that exceed
the regulatory limit " and " bacteria infected food and expired food,” respectively.
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a. Smoking hazards in the workplace;
b. Food additives that exceed the regulatory limit;
c. GM agricultural products;
d. Bacteria infected food and expired food;
e. The impact of avian influenza on humans through live birds.
The scientifically proved harm of GM agricultural products was the smallest among these five
hazards. The widely held opinions about the harmfulness of GMO varieties are groundless, and GMO
food can be considered to be the most thoroughly tested in the world [28]. If it was ranked first, the
risk amplification is recorded as 4; by analogy, if ranked fifth, the risk amplification is recorded as 0.
Therefore, the risk amplification had the value of 0, 1, 2, 3 and 4. The greater the value, the higher the
degree of risk amplification. The frequency distribution is shown in Figure 1.
Figure 1. Frequency distribution diagram of producers’ risk amplification degree.
Figure 1 shows that only 37.3% of participants did not amplify the GM risk (ra = 0) and 9.8% of
participants strongly exaggerated the risk of GM (ra = 4). In terms of regions, the percentage of
participants who amplify the GM risk were 65.3%, 62.4% and 60% respectively in Henan, Shanxi and
Shandong.
The second explanatory variable is the relative risk preference coefficient. We assume the
producer has the utility function shown in Equations (1), (2) and (3).
𝑈(𝑥,𝑝; 𝑦,𝑞) = ()()()();＜＜
()()()();＞＞ ＜＜ (1)
𝑣(𝑥) =
（
-x）
；
＜
；
＞
(2)
𝜋
（
𝑝
）
=𝑒𝑥𝑝
−(−𝐿𝑛(𝑝)) (3)
𝑈(𝑥,𝑝; 𝑦,𝑞) is producers’ utility function, 𝑣(𝑥) is value function, x is the high income obtained
by the producer when the “unexpected luck” occurs, y is the low income obtained when the
“unexpected luck” does not occur, p is the probability of obtaining the high income, and q is the
probability of obtaining the low income. 𝜋(𝑝)、𝜋(𝑞) are weights of two probabilities in the utility
function. 𝜎、𝜆、𝛼 represent three risk preference coefficients. 1−𝜎 measures the curvature of the
value function; the higher
σ
value, the lower the producer’s willingness to take risks. 𝜆=
Curvature of value function below 0
Curvature of value function above 0 , the higher the value, the greater the negative utility brought by the loss
than the positive utility brought by the same amount of gain, and the lower the producers’
willingness to take risks.
α
is the attraction of “unexpected luck” to producers. The higher the
value, the lower the attraction of such events to producers, that is, the lower the willingness to take
risks.
0
20
40
60
80
100
120
140
01234
33 25 19 12 6
56
37 29
16 11
37
14 19
816
126
76 67
36 33
Henan
province
Shanxi
province
Shandong
province
whole
The degree of risk amplification of GM crops
Number of
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The calculation of relative risk preference coefficient follows the formula of Lusk (2005): 𝑈
（
𝑥
）
=𝑥
/(1 − 𝑟𝑟)，𝑈
（
𝑥
）
indicates the utility of a benefit. The range of relative risk preference
coefficient is calculated using MATLAB 2016 software. The greater the coefficient, the more risk
averse. Taking the first line of series 2 as an example, if a participant chooses option A, it must be
because the utility of option A exceeds that of option B i.e., () ()UA UB>, the following inequality
can be obtained5:
11 1 1
86 90.5
0.9 0.1 0.7 0.3
11 1 1
−− − −
+> +
−− − −
rr rr rr rr
rr rr rr rr
Following the same logic, the relative risk coefficient estimation results are obtained and shown
in Table 3. The TL (transform Line) is the line that the subject made a change when choosing the
lottery ticket. TL=1 means that the subject chose option B on each row; TL=2 means that the subjects
all chose option B starting from the second row. Similarly, TL= “NEVER” indicates that the subject
chose option A in each row and never chose option B. Frequency is the number of people on its
corresponding transform line. For example, and in series1, the number of people who choose option
B starting from line 1 is 29.
Table 3. Relative risk coefficient distribution of Series 1 and Series 2.
TL Range of rr1 rr1 Frequency Range of rr2 rr2 Frequency
1 rr<-3.93 -3.93 29 rr<-1.57 -1.57 91
2 -3.93<rr<-1.42 -2.675 12 -1.57<rr<-0.42 -0.995 7
3 -1.42<rr<-0.96 -1.19 9 -0.42<rr<0.02 -0.2 7
4 -0.96<rr<-0.52 -0.74 10 0.02<rr<0.27 0.145 8
5 -0.52<rr<-0.34 -0.43 7 0.27<rr<0.43 0.35 7
6 -0.34<rr<-0.21 -0.275 10 0.43<rr<0.55 0.49 9
7 -0.21<rr<-0.12 -0.165 13 0.55<rr<0.63 0.59 10
8 -0.12<rr<-0.01 -0.065 16 0.63<rr<0.77 0.7 20
9 -0.01<rr<0.07 0.03 12 0.77<rr<0.84 0.805 11
10 0.07<rr<0.15 0.11 17 0.84<rr<0.91 0.875 18
11 0.15<rr<0.24 0.195 31 0.91<rr<0.96 0.935 16
12 0.24<rr<0.30 0.27 24 0.96<rr<1 0.98 18
13 0.30<rr<0.36 0.33 33 1<rr<1.06 1.03 15
14 0.36<rr<0.42 0.39 43 1.06<rr<1.12 1.09 16
Never 0.42<rr 0.42 72 1.12<rr 1.12 85
SUM
338
338
*rr1 represents the relative risk coefficient of series 1 and rr2 represents the relative risk coefficient of series 2.
Table 3 demonstrates the relative risk coefficient increases with the higher transform line, and a
larger rr indicates that subjects are more risk-averse. Over 230 farmers are risk-averse in both Series
1 and Series 2, which accounts for over two thirds of the whole subjects.
The third explanatory variable measures the risk perception of GM technology（rp）. Seven
statements were presented to participants, including: Genetic modification in food production may
bring risks to me and my family; The national control over the safety of GM agricultural products is
sufficient; Genetic modification in food production may bring new diseases to human; The promotion
of GM agricultural products will cause gene pollution or environmental pollution; I am worried that
transgenic technology will destroy natural selection; Eating GM agricultural products will cause
allergic reaction; Regular consumption of GM agricultural products will have an uncertain impact on
human offspring. Participants were asked to indicate their degree of agreement to these statements
using a Liker-scale with 1 meaning “strongly disagree” and 5 meaning “strongly agree.” The mean
5 0.9, 0.1, 0.7 and 0.3 are the probability of obtaining different benefits.
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of these variables was used to measure producers’ risk perception of GM technology. The frequency
distribution of rp is shown in Figure 2.
Figure 2. Frequency distribution diagram of producers’ risk perception.
A lower score indicates a higher perceived risk by participants. Overall, the perceived risk by
participants follows a normal distribution. Among the 338 observations, one observation in
Shandong Province had the highest perceived risk; The number of observations at the medium level
(rp = 3) is the largest, accounting for 49.4% of the total sample.
(2) Individual and household characteristic variables
The demographic variables included gender, age, marital status, household size, whether there
were children under 7 years old in the household, whether there were elderly people over 60 years
old in the household, education level and monthly household income.
We also included a series of control variables. Variable char measures whether a participant was
in charge of purchasing food in the household. Variable freq captures how often a participant read
the production date, shelf life or ingredient and nutrition information on food package when buying
food. The options were: “every time,” “often,” “sometimes,” “not often,” and “not at all.” freq was
measured on a 1-5 Likert scale, and the higher the score, the lower the frequency. The third variable
trust measures participants’ trust in Chinese food industry. Two 5-Likert scale questions were
included. The first question was “Are you confident with the safety provided by the national food
quality and safety certification?” The options are “completely confident,” “confident,” “neutral,”
“unconfident,” and “completely unconfident.” The second question was “What do you think of the
current food safety problems in China?” The options were “very problematic,” “problematic,”
“neutral,” “not problematic,” and “not problematic at all.” The variable trust took the mean of the
answers to these questions after the first question was reversely coded. A higher score indicates a
greater degree of trust in the Chinese food industry. Variable cog measures participants’ knowledge
or cognition of GM agricultural products. Five questions related to participants’ knowledge about
GM crops were asked. The first question was “Do you agree with the following statements? The GM
products allowed to grow in China are disease-resistant papaya and insect-resistant cotton; and
additionally China is allowed to import five kinds of GM products, including cotton, corn, soybean,
rape and sugar beet.” A participant got one point if he/she selected “agree.” The second question was
“Before this survey, have you heard of GM agricultural products?” A participant got one point if
she/he selected “yes.” The third question was “Have you ever heard of homologous transgene?” If a
participant selected “yes,” he/she got one point. The fourth question was “It is said on the Internet
that virgin fruit, large colored pepper and small pumpkin are GM agricultural products. Do you
agree?” If a participant selected “disagree,” he/she got one point. The last Question was “What is the
most widely cultivated GM crop in the world?” If a participant selected “soybean,” he/she got one
0
20
40
60
80
100
120
140
160
180
12345
09
47
32
7
0
24
83
34
8
1
13
37 30
13
1
46
167
96
28
Henan province
Shanxi province
Shandong
province
whole
Risk
p
erce
p
tion
Number of Participants
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point. These points were summed up to get a score (cog) measuring participants’ knowledge about
GM agricultural products. A higher score implies a better understanding of GM crops.
Variable atti measures participants’ attitudes towards GM agricultural products. Two 5-Likert
scale questions were asked to measure participants’ attitude: “Are you worried about the safety of
GM agricultural products?” and “Do you have confidence in the future of GM agricultural
products?” Three statements related to different world organizations’ views about GM agricultural
products were included: “The World Health Organization believes that GM agricultural products
currently available on the international market have passed the safety assessment and will not pose
a risk to human health;” “The EU believes that only controlling GM products is not enough to ensure
the safety of GM products. Only by controlling from the source can the harm of GM products be
controlled within the minimum range;” and “The US Food and Drug Administration clearly
stipulates that the management methods of foods derived from GM crops and foods derived from
traditional crops are exactly the same.” Participants were asked to indicate their levels of agreement
with these statements on 5-Liker scales. Variable atti takes the average of the answers to these
questions. The higher the score, the more likely a participant have a negative attitude towards GM
agricultural products. Variable knowl measures participants’ genetic knowledge. Participants chose
right or wrong for the following four statements: “The gender of a child is determined by the father’s
genes;” “Tomatoes do not contain genes, and transgenic tomatoes contain genes;” “It is impossible
to transfer animal genes to plants;” “Hybrid rice uses transgenic technology.” A higher score
indicates better genetic knowledge. Variable label measures participants’ attitude toward labeling of
GM agricultural products. The question was “Do you think GM agricultural products need to be
labeled mandatorily?” Participants need to answer “yes” or “no” to this question.
The details of these variables are shown in Table 4.
Table 4. Summary of explanatory variables.
Variable
symbol Explanatory variable Notes Expected
direction
Core explanatory variable
ra Risk amplification 0-4，the larger the value, the higher the degree
of risk amplification. -
rr Relative risk preference coefficient The larger the value, the more risk averse. -
rp Risk perception 1-5，the higher the score, the stronger the risk
perception. -
Demographic variables
g
ender The gender of participant Dummy variable, male=0，female=1 -
age The age of participant Continuous variable, 13-70 years old -
marry The marital status Dummy variable, unmarried =0，married =1 -
nm Number of people in the household continuous variable -
Under7 If there are children under 7 years old
in the household Dummy variable, no=0，yes=1 -
Byond60 If there are elderly people who are 60
years and older in the household Dummy variable no=0，yes=1 -
edu Participants’ education level
Categorical variable, primary school =1，junior
high school =2，high school
=3，undergraduate and junior college
=4，master degree or above =5
+
mhi Monthly household income 1-10 levels，the higher the level, the higher the
monthly income of the household. +
Other control variables
char If a participant was in charge of
purchasing food no=0，yes=1 -
f
req
How often participants read
production date, shelf life and
nutrition information on food package
when purchasing food
1-5, the higher the score, the lower the
frequency. +
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trust Degree of trust in Chinese food
industry 1-5, the higher the score, the higher the trust +
cog Cognition of GM agricultural products 0-5, the higher the score, the higher the
understanding. +
atti Attitude towards GM agricultural
products
1-5, the higher the score, the more negative the
attitude -
knowl Genetic knowledge 0-4, the higher the score, the richer the genetic
knowledge +
label GM agricultural products must be
labeled no=0，yes=1 -
According to the risk amplification theory and experimental design, we aim to test the following
hypotheses:
H1: The degree of risk aversion and risk perception significantly impact producers’ willingness to
adopt GM crops;
H2: The more attention is paid to food information on packages (production data, shelf life, and
nutrition information) when shopping, the less likely for the producers to accept GM technology;
H3: The more knowledgeable about GM technology, the more willing for producers to adopt the
technology;
H4: The higher the level of trust in Chinese food industry, the less likely the amplification of GM risk
would impact producers’ willingness to adopt GM technology.
3. Results
3.1. Descriptive Statistics
The summary statistics of the variables measuring producer’s acceptance of GM crops are shown
in Table 5.
Table 5. Descriptive statistics of explanatory variables.
Henan province Shanxi province Shandong province whole
numbe
r
proportion numbe
r
proportion numbe
r
proportion numbe
r
proportion
Level of willingness to accept GM agricultural products
1 32 33.68% 58 38.93% 38 40.43% 128 37.87%
2 23 24.21% 23 15.44% 27 28.72% 73 21.60%
3 33 34.74% 45 30.20% 21 22.34% 99 29.29%
4 3 3.16% 12 8.05% 5 5.32% 20 5.92%
5 4 4.21% 11 7.38% 3 3.19% 18 5.33%
Level of willingness to recommend GM agricultural products to others
1 35 36.84% 47 31.54% 38 40.43% 120 35.50%
2 27 28.42% 37 24.83% 34 36.17% 98 28.99%
3 32 33.68% 45 30.20% 11 11.70% 88 26.04%
4 1 1.05% 16 10.74% 7 7.45% 24 7.10%
5 0 0.00% 4 2.68% 4 4.26% 8 2.37%
Level of agreement with importing a large number of GM agricultural products
1 36 37.89% 46 30.87% 35 37.23% 117 34.62%
2 27 28.42% 30 20.13% 32 34.04% 89 26.33%
3 27 28.42% 56 37.58% 14 14.89% 97 28.70%
4 3 3.16% 10 6.71% 11 11.70% 24 7.10%
5 2 2.11% 7 4.70% 2 2.13% 11 3.25%
Level of support for the development of GM agricultural products
1 33 34.74% 42 28.19% 34 36.17% 109 32.25%
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2 22 23.16% 18 12.08% 26 27.66% 66 19.53%
3 32 33.68% 47 31.54% 20 21.28% 99 29.29%
4 3 3.16% 22 14.77% 8 8.51% 33 9.76%
5 5 5.26% 20 13.42% 6 6.38% 31 9.17%
Level of willingness to purchase GM agricultural products
1 36 37.9% 39 26.2% 28 29.8% 103 30.5%
2 19 20% 24 16.1% 39 41.5% 82 24.3%
3 36 37.9% 45 30.2% 18 19.1% 99 29.3%
4 2 2.1% 19 12.8% 5 5.3% 26 7.7%
5 2 2.1% 22 14.8% 4 4.3% 28 8.3%
A higher the score indicates a higher level of acceptance. Overall, ten percent of participants
were willing to adopt GM agricultural products at relatively higher degrees (4-5). But there were
differences in different regions: Shanxi had the highest level, followed by Shandong, and then Henan;
Overall, less than 10% of participants were (strongly) willing to recommend GM agricultural
products to others. In terms of regions, Shanxi participants had the highest willingness to recommend
(13.42%), followed by Shandong (11.73%), then by Henan (only 1.05%). About 35% participants
(strongly) disagreed that China should import a large number of GM agricultural products, and
highest percentage of participants in Shanxi province (strongly) disagreed with the imports of GM
agricultural products. About 20% of participants (strongly) supported the development of GM
agricultural products, which was the highest among the four acceptance measurements. It can be
seen that although participants had some reservations about GM crops, they showed some level of
support for the development of GM agricultural products. The acceptance of GM crops was the
highest in Shanxi, the lowest in Henan and with coastal Shandong in the middle. However, the degree
of agricultural development in Shanxi is not as high as that of the other two provinces. The results
showed the different levels of acceptance of GM crops between large agricultural and non-
agricultural provinces in China. Participants who strongly disagreed with the purchasing GM plant
seeds accounted for the largest proportion (30.5%), and the proportion in the other three provinces
was 37.9%, 26.2% and 29.8% respectively. Relatively high levels of agreement (4-5) accounted for 16%,
Henan province had the lowest percentage（4.2%）， Shanxi province had the highest percentage
(27.6%), and Shandong province accounted for 9.6%.
3.1.1. Summary Statistics of Core Risk-Related Explanatory Variables
The summary statistics of core risk-related explanatory variables are shown in Table 6. For the
whole sample, the average risk amplification (ra), risk perception (rp), and relative risk coefficients
(rr1 and rr2) were 1.33, 3.31, -0.09 and 0.30, respectively. All these risk measures had the highest value
for participants from Shandong province and lowest value for those from Shanxi province, indicating
participants form Shandong province tended to amplify the risk of GM crops the most, perceived the
risk of GM crops the most and were most risk averse among the three provinces.
Table 6. Summary statistics of core risk-related explanatory variables.
Mean Std. Error Mean Std. Error
ra rr1
Henan province 1.29 1.26 Henan province -0.07 1.08
Shanxi province 1.26 1.27 Shanxi province -0.12 1.10
Shandong province 1.49 1.50 Shandong province -0.06 0.74
Whole sample 1.33 1.33 Whole sample -0.09 1.00
rp rr2
Henan province 3.39 0.77 Henan province 0.22 1.16
Shanxi province 3.17 0.76 Shanxi province 0.13 1.20
Shandong province 3.44 0.93 Shandong province 0.66 0.45
Whole sample 3.31 0.82 Whole sample 0.30 1.06
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3.1.2. Participants’ Sociodemographic Background
The summary statistics of individual and household characteristics are shown in Table 7. Men
and women often have different risk preferences and consumption habits, which may lead to
different choices. Therefore, we tried to balance the number of male and female participants Overall,
the gender distribution was relatively balanced with men slightly more than women.
Table 7. Summary statistics of participants’ sociodemographic background.
Henan province Shanxi province Shandong province Whole sample
numbe
r
proportion numbe
r
proportion numbe
r
proportion numbe
r
proportion
Gender(0=male，1=female)
1 46 48.42% 80 53.69% 37 39.36% 163 48.22%
0 49 51.58% 69 46.31% 57 60.64% 175 51.78%
Age
≤25 18 18.95% 24 16.11% 3 3.19% 45 13.31%
25-35 21 22.11% 33 22.15% 15 15.96% 69 20.41%
35-45 12 12.63% 47 31.54% 36 38.30% 95 28.11%
45-55 29 30.53% 33 22.15% 34 36.17% 96 28.40%
＞55 15 15.79% 12 8.05% 6 6.38% 33 9.76%
Marital status (0=unmarried，1=married)
1 77 81.05% 111 74.50% 85 90.43% 273 80.77%
0 18 18.95% 38 25.50% 9 9.57% 65 19.23%
Number of family members
≤3 35 36.84% 57 38.26% 49 52.13% 141 41.72%
4-5 52 54.74% 81 54.36% 40 42.55% 173 51.18%
＞5 8 8.42% 11 7.38% 5 5.32% 24 7.10%
Members under the age of 7
no 69 72.63% 110 73.83% 60 63.83% 239 70.71%
yes 26 27.37% 39 26.17% 34 36.17% 99 29.29%
Members beyond the age of 60
no 54 56.84% 66 44.30% 43 45.74% 163 48.22%
yes 41 43.16% 83 55.70% 51 54.26% 175 51.78%
Education (1=primary school degree，2=junior high school degree，3=high school degree，4=college
degree，5=Master’s degree or above)
1 13 13.68% 13 8.72% 12 12.77% 38 11.24%
2 30 31.58% 40 26.85% 42 44.68% 112 33.14%
3 22 23.16% 31 20.81% 25 26.60% 78 23.08%
4 27 28.42% 59 39.60% 15 15.96% 101 29.88%
5 3 3.16% 6 4.03% 0 0.00% 9 2.66%
Monthly household income (1=4000-5999yuan, 2=6000-9999yuan, 3= 10000 yuan above)
1 56 58.95% 107 71.81% 38 40.43% 201 59.47%
2 23 24.21% 30 20.13% 35 37.23% 88 26.04%
3 16 16.84% 12 8.05% 21 22.34% 49 14.50%
In the survey, we asked participants to choose their age categories. Since the average marriage
age in rural China is 25 years old, we took 25 years old as the first dividing point, and then takes 10
years as the interval for the age categories. Although there were differences among the three regions,
most participants’ ages were between 25 and 55 years old. Among them, there were full-time
agricultural producers and part-time ones. For example, there is a large number of migrant workers
in Henan, but they return home to work on their farms during busy farming season.
The percentage of married participants was 80%, which was as high as 90.43% in Shandong. The
household size was about 4-5 people, which is in line with the basic situation in rural areas.
Households with fewer than three people accounted for 41.72% of the sample, this was because most
of participants were in their early stage of marriage and had no children and elderly people living
with them. More than 70% of participants had no children under the age of 7, and more than half of
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the sample had elderly people over the age of 60 in their households. About 67% of participants had
education below undergraduate level, and the education levels of older participants were mostly
primary school and junior middle school. With the implementation of relevant national policies and
economic development, the education level of farmers has also been improved, so participants with
senior high school and undergraduate education accounted for more than 50% of the sample. Sixty
percent of participants had low-income, but there is a trend that the higher the level of education, the
higher the income. For participants with master’s degrees, the monthly income can be more than
20000 yuan, which is not different from urban households’ income.
3.1.3. Producer Risk Preference when Facing Loss
In the gambling experiment, series 3 was used to test participants’ risk preference when there
was 50% probability of loss. The number of participants switching at various rows are shown in
Figure 1. Switching row 1 means option B was selected at the first row, switching row 2 means option
B was selected at the second row and so on.
Figure 3. Number of participants switching at different rows in the gambling experiment series 3
6
.
The later the switching row, the more sensitive a participant was to loss. The frequency
distribution has “U” shape. Nineteen point eight percent of participants made the choice of “large
negative return but large positive return” at row 1. This group of participants did not have obvious
loss aversion. At row 2-6, the negative return and return of option B were higher than that of option
A, but the gap between the returns brought by the two options narrows down, and about 40.9%
participants switched at row 2-6. At the last row, the negative returns of the two options were similar,
but the positive return of option B was 30 times that of option A, and 16.8 % participants switched at
the last row. The remaining 22.6% of participants always chose option A and never switched. When
considering those who switched at row 4 and 5 as “risk neutral,” 55.8% of participants were risk
averse.
3.2. Full Sample Estimation Results
The full sample estimation results of how different factors impact GM crop acceptance by
producers are showed in Table 8.
6
Switching at row 1 means that only option B is selected. The number of valid observations is 328.
0
20
40
60
80
1234567Never
switch
65
37
28
15
25 29
55
74
Number of Participants
Switching Row
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Table 8. Full sample estimation results using ordered logit model and Poisson model.
Explanatory variable
Ordered logit model Poisson regression
Model 1 Model 2 Model 3 Model 4
ra -0.249***(0.088) -0.691***（0.214） -0.050***(0.030) -0.194***（0.101）
rp -0.488***（0.169） -0.471***（0.170） -0.092*(0.054) -0.091*（0.055）
rr -0.300***（0.104） -0.307***（0.112） -0.053**(0.036) -0.059***（0.036）
g
ender 0.26（0.230） 0.295（0.231） 0.067(0.080) 0.067（0.080）
age -0.022*（0.012） -0.020*（0.012） -0.004**(0.004) -0.004*（0.004）
marry -0.297（0.279） -0.243（0.275） -0.062(0.085) -0.048（0.084）
nm 0.049（0.102） 0.049（0.102） 0.015(0.035) 0.014（0.035）
under7 -0.049（0.257） -0.075（0.257） -0.028(0.089) -0.019（0.090）
beyond60 -0.203（0.226） -0.201（0.226） -0.037(0.078) -0.040（0.078）
edu 0.366***（0.119）0.363***（0.119） 0.062*(0.041) 0.057*（0.042）
mhi 0.007（0.059） 0.004（0.059） 0.007(0.020) 0.009（0.020）
char -0.387*（0.242） -0.409*（0.244） -0.090**(0.084) -0.091**（0.085）
f
req 0.132*（0.082） 0.137*（0.082） 0.025**(0.028) 0.091**（0.028）
trust 0.063（0.151） 0.015(0.051) 0.068（0.075）
cog -0.029（-0.090） -0.008(0.032) -0.094（0.066）
atti -0.097***（0.183） -0.988***（0.184） -0.190***(0.058) -0.186***（0.058）
knowl -0.254**（0.112） -0.266**（0.112） -0.051*(0.038) -0.051*（0.038）
label -0.815***（0.304） -0.992***（0.373） -0.142***(0.096) -0.269***（0.172）
ra*trust 0.178**（0.077） 0.058**（0.038）
cog*label 0.063（0.095） 0.059*（0.074）
Note: *, **, ***Significant at 0.1, 0.05, or 0.01, respectively. Standard errors are in parentheses.
Columns 2-3 are the estimation results of the whole sample using ordered logit models, columns
4 and 5 show Poisson model estimation results. Compared to model 1 and 3, model 2 model 4 add
two interaction items, ra*trust and label * cog. The results show that three core risk-related explanatory
variables, two demographic variables, four other control variables and two interactive items
significantly affect the acceptance of GM agricultural products by producers.
3.2.1. Ordered Logit Estimation Results
(1) Hypotheses testing results
H1: The degree of risk aversion and risk perception significantly impact producers’ willingness to
adopt GM crops.
The two variables, relative risk coefficient (rr) and risk perception (rp), have significantly
negative impacts on GM acceptance, that is, participants who are more risk averse and with higher
perceived risks of GM agricultural products are less likely to accept GM crops. The results are
consistent across the two models and the coefficients are highly significant. Thus, the null hypothesis
1 cannot be rejected. Most of the coefficients of demographic variables and the interaction terms are
not significant.
The results are intuitive because the more conservative the attitude towards transgenic crops,
the lower the acceptance of them. Producers’ risk preferences affect their decisions to a great extent.
This indicates producers’ risk amplification might lead to irrational decisions in the adoption of GM
crops.
H2: The more attention is paid to food information on packages (production date, shelf life, and
nutrition information) when shopping, the less likely for the producers to accept the transgenic
technology.
We found that participants who read information on packages when buying food had a low
acceptance of GM agricultural products. The coefficients of freq are significant and are consistent
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15
across the two models. The coefficient of char is significantly positive. Because participants in charge
of household food purchase had more responsibility for food safety of their households, their
acceptance of GM crops is significantly lower than that of participants who were not in charge.
Participants who paid more attention to production date, shelf life and nutrition information of food
cared more about the safety of their food. So it is intuitive that those participants were more
conservative on the acceptance of GM crops. Obviously, participants who had more trust in the food
industry believe the industry only allows the production of GM crops if the crops are safe so they
would have higher acceptance of GM technology.
H3: The more knowledgeable about GM technology, the more willing for producers to adopt the
technology.
We found that the knowledge and cognition level of GM agricultural products does significantly
impact the acceptance of GM crops. After adding interaction term cog*label cognitive level has a
significantly positive impact on the acceptance of GM agricultural products and the coefficient
increases significantly. The coefficient of the interaction is positive, indicating that among
participants who think GM agricultural products should be labeled, those who are more
knowledgeable about GM agricultural products are more likely to accept GM technology.
H4: The higher the level of trust in Chinese food industry, the less likely the amplification of GM risk
would impact producers’ willingness to adopt GM technology. There is a negative relationship
between the degree of risk amplification (ra) and the acceptance of GM crops. The psychological
amplification of the risk for GM crops would significantly reduce the possibility of accepting GM
crops. After adding interaction term between risk amplification and trust in the food industry in
model 2 (ra*trust), the coefficient is doubled and becomes very significant. This shows that when the
trust in the food industry is high, it is very likely to alleviate the inhibitory effect of risk amplification
on producers’ GM crop acceptance.7
(2) Demographic variable coefficients
Among the demographic variables, only age and education significantly affected the acceptance
of transgenic technology. Older participants are less likely to accept GM crops than younger ones.
On one hand, the education level of the older generation in China’s rural areas is relatively low; On
the other hand, older participants’ witnesses of the hard times before the Chinese economic reform
and open-up make them very cautious about adopting new things with uncertainty. Therefore, their
possibility of accepting GM crops is low. As expected, participants with higher education level are
more likely to accept GM crops.
3.2.2. Poisson Regression Results
In this study, we ran Poisson regression to validate the results of ordered logit model.
Model 3 and model 4 show that the coefficients of three core risk-related explanatory variables
(ra, rp, and rr) are significantly negative at the levels of 1%, 10% and 1%, respectively. In addition,
age, education, being the person in charge of household food purchase, frequency of looking at
information on food package, attitude towards GM agricultural products, genetic knowledge, views
on whether GM agricultural products should be labeled and the interaction terms are significant and
have the same signs as the results from the ordered logit model.
The calculation of incident rate ratios (IRR) value can help us more intuitively understand the
impact of each explanatory variable on the dependent variable. Suppose a participant’s original
probability of accepting GM crops is p0, the IRR results show that the probability of accepting GM
crops will be reduced to 0.951p0, 0.913p0 and 0.948p0 for each level increase in risk amplification (ra),
75% of the subjects made it clear that many agricultural products in life are genetically modified, and there are no major safety
problems.
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risk perception (rp) and relative risk coefficient (rr), respectively. The probability of acceptance
decreases to 0.996p0 when the age increases by one year. When the household size increases by one,
the acceptance probability increases to 1.015p0. With the education level and income increase by 1
level, the probability of GM acceptance level will rise to 1.064p0 and 1.007p0, respectively. When the
frequency of reading the food package information decreases by one level, the acceptance degree
increases to 1.025p0. When trust in the food industry increase by one level, the GM acceptance
probability increases to 1.015p0. The GM acceptance probability increases to 0.992p0, 0.827p0 and
0.951p0 when the GM cognition, GM attitude and genetic knowledge scores increase by 1 level,
respectively.
3.3. Estimation Results for the Three Provinces
To understand how the factors impact the detailed producers’ response to GM agricultural
products in different regions, we further ran the models for three individual questions which
measures participants’ acceptance intention (willingness to accept GM agricultural products),
purchase intention (willingness to purchase GM plant seeds) and recommendation intention
(willingness to recommend GM agricultural products to others). Table 9 reports the estimation results
for the whole sample and the three provinces.
Table 9. Estimation results of factors impacting the willingness to accept, willingness to purchase,
and willingness to recommend GM technology of producers in different provinces.
Willingness to accep
t
Willingness to purchase seeds Willingness to recommend
Explanator
y variable
Full
sample Shanxi Henan Shandong
Full
sample Shanxi Henan Shandong
Full
sample Shanxi Henan Shandong
ra
-
0.920***（0
.238）
-
1.212***（0
.446）
-1.693*
（1.058）
-
4.383***（1
.142）
-0.402*
(0.216)
-0.865**
(0.370)
-0.873
(0.750)
-0.891*
(0.518)
-0.579***
（0.219
）
-0.345
（0.361）
-1.627
(1.043)
-1.074
(0.747)
rp -0.320**
（0.170）
-0.361
（0.270）
-
1.896***（0
.447）
1.043**
（0.408）
-0.686***
(0.171)
-0.647**
(0.274)
-1.425***
(0.406)
-0.159
(0.365)
-0.522***
（0.171
）
-0.389
（0.271）
-1.429***
(0.412)
-0.226
(0.356)
rr
-
0.298***（0
.105）
-0.395**
（0.155）
-0.244
（0.212）
-0.230
（0.340）
-0.246**
(0.108)
-0.226
(0.158)
-0.231
(0.206)
-0.800**
(0.323)
-0.199*
（0.107
）
-0.330**
（0.159）
-0.227
(0.210)
-0.209
(0.322)
gender 0.288
（0.234）
0.418
（0.376）
0.732
（0.594）
0.096
（0.546）
0.139
(0.227)
0.745**
(0.369)
-0.261
(0.574)
-0.345
(0.519)
0.293
（0.233
）
0.404
（0.366）
0.412
(0.577)
0.279
(0.524)
age -0.006
（0.012）
-0.011
（0.021）
0.076***
（0.028）
0.052
（0.051）
-0.023*
(0.012)
-0.016
(0.020)
0.063**
(0.027)
-0.086**
(0.043)
-0.030**
（0.012
）
-0.024
（0.020）
0.048*
(0.028)
-0.058
(0.045)
marry 0.080
（0.230）
-0.630
（0.479）
2.259**
（0.939）
-0.597
（1.192）
-0.540*
(0.289)
-0.703
(0.485)
1.387
(0.924)
-1.506
(1.076)
-0.482*
（0.301
）
-0.398
（0.463）
1.519*
(0.918)
-0.919
(1.035)
nm 0.124
（0.102）
-0.183
（0.162）
0.311
（0.243）
0.642**
（0.277）
0.085
(0.098)
0.089
(0.156)
0.420*
(0.238)
0.118
(0.242)
-0.057
（0.102
）
-0.006
（0.160）
0.105
(0.249)
-0.158
(0.243)
under7 -0.245
（0.255）
0.222
（0.487）
-0.514
（0.509）
0.196
(0.675)
-0.083
(0.253)
0.929**
(0.467)
-0.502
(0.497)
-0.384
(0.639)
0.194
（0.250
）
0.490
（0.451）
-0.203
(0.514)
0.554
(0.616)
beyond60 -0.504**
（0.225）
-0.788*
（0.405）
-0.766
（0.501）
-1.350**
(0.611)
-0.101
(0.221)
-0.720*
(0.403)
-0.291
(0.485)
-0.630
(0.553）
0.049
（0.226
）
-0.301
（0.409）
-0.451
(0.498)
0.239
(0.539)
edu 0.198*
（0.120）
0.495**
（0.199）
0.358
（0.304）
0.134
(0.335)
0.386***
(0.120)
0.52***
(0.186)
0.245
(0.299)
-0.186
(0.308)
0.317***
（0.120)
0.549***
（0.187）
0.374
(0.305)
-0.016
(0.313)
mhi 0.057
（0.059）
0.111
（0.105）
0.019
（0.142）
-0.039
(0.146)
-0.0019
(0.057)
-0.054
(0.099)
0.157
(0.147)
0.136
(0.134）
0.031
(0.058)
0.033
（0.105）
-0.054
(0.139)
0.111
(0.126)
char -0.497**
（0.244）
-0.425
（0.417）
-0.369
（0.568）
-0.271
(0.566)
-0.088
(0.241)
-0.426
(0.417)
0.317
(0.564)
0.333
（0.515）
-0.513**
(0.246)
-0.031
（0.425）
-0.515
(0.573)
-0.703
(0.512)
freq 0.146*
（0.087）
0.152
（0.127）
0.094
（0.189）
-0.180
(0.258)
0.112
(0.083)
0.017
(0.130)
0.128
(0.188)
-0.068
（0.228）
0.144*
(0.082)
0.194*
（0.118）
0.092
(0.196)
0.03
(0.229)
cog -0.230
（0.197）
-0.488*
（0.293）
0.473
（0.488）
0.148
(0.566)
-0.069
(0.199)
-0.184
(0.288)
0.435
(0.501)
0.037
（0.575）
-0.540***
(0.208)
-0.648**
（0.288）
-0.272
(0.543)
-0.546
(0.611)
atti
-
0.653***（0
.184）
-0.842**
（0.313）
0.171
（0.377）
-
1.358***（0
.429）
-0.953***
(0.187)
-1.845***
(0.356)
-0.171
(0.392)
-0.676*
（0.406）
-0.893***
(0.188)
-1.160***
（0.333）
-0.012
(0.342)
-1.129***
(0.395)
knowl -0.186*
（0.111）
-0.116
（0.182）
-0.002
（0.259）
-0.035
（0.293）
-0.244*
(0.108)
-0.200
(0.172)
0.026
(0.249)
-0.311
（0.271）
-0.147
(0.111)
0.025
（0.173）
-0.413
(0.263)
-0.093
(0.260)
label -1.056** -1.593** 0.21 -0.674 -0.460 -0.773 0.331 1.002 -1.181** -1.722** -0.855 -0.190
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（0.513） （0.775） （0.986） （1.683） (0.522) (0.821) (0.990) （1.611） (0.528) （0.799） (1.114) (1.690)
ra*trust 0.225***
（0.084）
0.445**
（0.183）
0.513
（0.422）
1.193***
（0.351）
0.052
(0.079)
0.291*
(0.156)
0.171
(0.277)
0.099
（0.155）
0.165**
(0.079)
0.134
（0.154）
0.493
(0.416)
0.290
(0.241)
cog*label 0.240
（0.219）
0.577*
（0.322）
-0.458
（0.533）
-0.237
（0.617）
-0.030
(0.221)
0.005
(0.329)
-0.538
(0.541)
-0.265
（0.604）
0.456**
(0.229)
0.420
（0.325）
0.374
(0.580)
0.548
(0.650)
Pseudo R2 12.46% 16.41% 22.76% 27.72% 14.58% 21.34% 19.73% 22.34% 14% 18.61% 21.24% 17.8%
Sample
size 338 149 95 94 338 149 95 94 338 149 95 94
Note: *, **, ***Significant at 0.1, 0.05, or 0.01, respectively. Standard errors are in parentheses.
For willingness to accept GM agricultural products, the coefficients of the degree of risk
amplification (ra) are significant and negative for the whole sample as well as the samples from the
three provinces. For Shandong Province, risk perception (rp) has a significant positive impact on
willingness to accept. The relative risk coefficient (rr) and education level have more significant
effects on the willingness to accept GM agricultural products in Shanxi province than the other two
provinces. For participants from Henan province and Shandong province, the older the age, the
higher their willingness to accept GM crops, but the coefficient of age for Henan province is more
significant. For participants from Henan province, the marital status significantly impacts their
willingness to accept GM crops: married participants are more likely accept GM crops, which is
opposite to other two places. For participants from Shandong province, household size significantly
impacts participants’ degree of GM crop acceptance. For participants from Shanxi and Shandong, the
acceptance of GM crops by those who had people “over 60 years old” in the household is significantly
lower, indicating that participants are more cautious about the choice of food for elderly people. For
the whole sample, the acceptance level by participants who are in charge of food purchase, read
information on food package more frequently and have richer genetic knowledge is significantly
lower, but coefficients are not significant for the three provinces. For participants from Shanxi
province, the higher the cognitive level about GM technology, the lower acceptance level of GM
agricultural products. For Henan participants who thought GM crops should be labeled, the more
negative their attitudes towards GM technology, the lower the degree of their acceptance GM crops
(negative and significant coefficient of atti*label). The interaction term between the degree of risk
amplification and the degree of trust in the food industry (ra*trust) is significantly positive, similar to
the results in Table 8.
For purchase intention, the degree of risk amplification has no significant impact on participants
from Henan province; For participants from Shandong province, the risk perception has no
significant impact on the purchase intention. The relative risk coefficient for Shandong participants
is significantly higher than those of the other two samples. Gender has a significant impact on
willingness to accept for Shanxi participants: women are more willing to accept GM agricultural
products than men, while gender has not significant impact on purchase intention for Shandong and
Henan participants. For the Henan sample, older participants have stronger purchase intention than
younger ones. For the Shanxi sample, the GM agricultural products purchase intention of participants
with family members younger than 7 years old is significantly higher, but the purchase intention of
participants with family members over 60 years old is significantly lower; The higher the level of
education, the higher the purchase intention. For the three provinces, only the coefficient for the
interaction between the degree of risk amplification and the degree of trust in the food industry for
Shanxi participants is significantly positive. It indicates that the higher the degree of trust in food
industry, the greater the Shanxi participants’ willingness to buy GM plant seeds.
For recommendation intention, the coefficient for risk perception (rp) of Henan participants is
significantly negative, the coefficient for relative risk coefficient (rr) of Shanxi participants is
significantly negative, and the coefficients for the risk-related variables are not significant for the
sample of Shandong province; Older and married participants from Henan province have a
significantly higher willingness to recommend GM agricultural products. The coefficient of education
level for Shanxi participants is significantly positive, similar to the that of the whole sample. In the
whole sample, the recommendation intention by participants in charge of food purchase is
significantly lower than those who are not in charge, but it is not significant for the three individual
provinces. For participants from Shanxi province, those who read information on food package more
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frequently and have a higher the degree of cognition of GM technology are less willing to recommend
GM agricultural products, which is the same as the willingness to accept and willingness to purchase
estimation results. The coefficient for label is significantly negative for the sample of Shanxi province,
which means that participants who believe that GM agricultural products should be labeled have
lower willingness to recommend GM agricultural products. The coefficients for the two interaction
terms in the whole sample are significantly positive, which is consistent with the results in Table 8.
These results show the risk-related factors, producers’ demographic background and food
purchasing behavior impact new technology acceptance, purchase, and recommendation intentions
of producers in different areas in different ways.
4. Discussion
Factors impacting consumers’ willingness to accept or consume GM food have been analyzed
extensively. The early literature includes Bredahl (1999), Lusk (2004), De Steur (2010) [29–31]. Bredahl
showed that risk perception of GM food impacted purchase willingness of GM food [29]. Based on
surveys from the United States, the United Kingdom and France, Lusk found that information has a
significant impact on their consumption behavior, and the benefits to the environment brought by
transgenic technology will greatly improve consumers’ willingness to purchase [30]. De Steur
conducted a survey on consumer groups in Shanxi, China, and found that consumers have a
relatively high willingness to accept GM rice, and that objective knowledge and risk perception will
affect consumers’ acceptance of GM rice [31].
More recently, Kubisz et al. found that the negative attitude of Polish society towards GM food
could be considered irrational which was based on fears [28]. Guo et al. surveyed 573 consumers from
Shandong Province, China and found that perceived risk negatively impacted purchase intention of
GM food and that risk communication was vital for acceptance of GM foods using structural equation
model [32].
There are abundant researches on consumer risk perception and consumer purchase intention.
However, there are few literatures from aspects of farmers.
It mainly focuses on economic benefit of GM crops and the planting intention of producers.
Much evidence has shown transgenic technology increases crop yields [33]. There has also been much
success with crop varieties that are drought and cold resistant [34]. Economic benefits and low
planting cost have been shown [35,36]. Regarding the planting intention, Lu and Sun believed that
education was an important factor affecting farmers’ adoption of new technologies [37]. Zhu found
that high yield was an important factor affecting farmers’ planting intention of transgenic rice [38],
but and Qaim and De Janvry proposed that farmers had realized that high yield and high profit were
not exactly the same [39].
The present study studied the impact of producers’ amplified risk and risk preferences on
adoption of GM technology.
Figure 1 shows that only 37.3% of participants did not amplify the GM risk and about 10% of
participants strongly exaggerated the risk of GM from the whole sample. In terms of regions, the
percentage of participants who amplify the GM risk were 65.3%, 62.4% and 60% respectively in
Henan, Shanxi and Shandong. This conclusion is consistent with the findings of Kubisz et al., who
demonstrated that the low level of understanding and acceptance of GMO technologies in Polish
society is based on stereotypes rather than on scientific knowledge to a large extent [28]. This result
is also supported by Li and Zhang, who believed that Media promoted the social amplification of
GMO risk and had a serious impact on residents’ perception of GMO risk and purchase intention
[40].
Regarding the risk preferences, Table 3 demonstrates the relative risk coefficient increases with
the higher transform line, and a larger variable of rr indicates that subjects are more risk-averse. Over
two thirds of farmers are risk-averse in both Series 1 and Series 2. This result is consistent with Jin et
al. [41] and Sulewski and Kloczko-Gajewska [42].
In accordance with Table 8, relative risk coefficient and risk perception have significantly
negative impacts on GM acceptance. In another word, participants who are more risk averse and with
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higher perceived risks of GM agricultural products are less likely to accept GM crops. The results are
consistent across the ordered logit model and Poisson regression and the coefficients are highly
significant. The results are intuitive because the more conservative the attitude towards transgenic
crops, the lower the acceptance of them. Producers’ risk preferences affect their decisions to a great
extent. This indicates producers’ risk amplification might lead to irrational decisions in the adoption
of GM crops. This result is supported by Jin et al. [41] which showed that farmers’ risk preferences
play an important role in agricultural production decisions.
5. Conclusion
In this paper, using producer samples from three Chinese provinces, we estimated participants’
relative risk preference coefficient through gambling experiments, and explored participants’ risk
amplification degree and risk perception of GM crops. We then investigated how these risk-related
variables impacted producers’ willingness to adopt GM crops. We also included a series of other
variables in the analysis including: the attitude towards GM crops, the degree of trust in Chinese food
industry, the level of genetic knowledge, the cognition of GM agricultural products, socio-
demographic information and some purchasing behavior variables.
We had several findings. First, in the context of risk, higher levels of knowledge and cognition
does not necessarily make a producer more rational. Risk perception and attitude significantly affect
how producers make decisions and play a key role in their risk management and decision-making.
Second, labeling GM agricultural products helps producers with high cognitive level reduce
psychological uncertainty and improve their acceptance of GM agricultural products. Third, a higher
level of trust in a country’s food industry can help prevent the non-adoption-behavior caused by the
excessive risk amplification of transgenic technology. Fourth, producers’ demographic background
and purchasing behavior impact their acceptance intention, purchase intention and recommendation
intention of GM agricultural products in different ways. For example, the marital status of a producer
impacts the acceptance intention differently from its impact on purchase intention and
recommendation intention. Similarly, there are regional differences in terms of which variables
impact the acceptance, purchase and recommendation intentions.
Based on the results, we make the following recommendations to improve producers’ adoption
of new technologies such as GM crops. First, a supportive production environment and consumer
market will reduce producers’ risk aversion and producers can adopt new technologies with less
hesitation [43]. Government should improve agricultural insurance, improve risk dispersion
mechanisms, and provide sufficient financial support to ensure that producers will not suffer large
losses due to the adoption of new technologies. Second, government or organizations can conduct
trainings to educate producers about new technologies to help them establish accurate risk
perceptions and improve their risk management strategies and set up effective risk avoidance
mechanisms. Third, although some producers have a correct understanding of new technologies,
they are still affected by the uncertainty of new technologies, reducing their enthusiasm of adopting
them. Therefore, the government should be open and transparent in the efficacy, advantages and
disadvantages of new technologies. Take GM technology as an example, government should remain
transparent and keep producers informed about the management and labeling GM agricultural
products. Only by eliminating producer concerns and improving their trust in the food industry can
we minimize the risk amplification of GM technology. Fourth, different regions have different sub-
culture, education levels and economic development levels. When promoting new technologies to
different areas, government should develop differentiated strategies and adopt publicity methods
accordingly by considering the distinct key factors impacting producers of different areas’ adoption
decisions [44]. Fifth, China’s rural labor force is aging considerably and older producers are more
reluctant to adopt new technologies. The government should develop policies to attract young talents
to build villages, improve the income of producers relying on new agricultural technologies and
value-added agriculture, and make young people more receptive to emergent technologies, which is
also conducive to the adoption of new technologies.
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Author Contributions: Conceptualization: Li Zhao, Shumin Liu, Chengyan Yue, Haiying Gu, David Ahlstrom;
Methodology: Li Zhao, Shumin Liu, Haiying Gu; Writing: Li Zhao, Shumin Liu; Providing idea: Li Zhao;
Chengyan Yue; Haiying Gu; Providing revised advice: Haiying Gu; Chengyan Yue; David Ahlstrom.
Funding: Thank you for the supports from the National Science Foundation of China (Grant No. 71803132) and
National Social Science Foundation of China (Grant No. 22ZDA058).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: A consent was waived-all respondents voluntarily replied to questions, there
were no sensitive private data collected, identification of persons responding is not possible.
Data Availability Statement: Data collected are deposited in an Excel file at Shanghai Maritime University.
Conflicts of Interest: The authors declare no conflict of interest.
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