(2021) 2:14
Kato‑Nitta et al. CABI Agric Biosci
https://doi.org/10.1186/s43170-021-00029-8

CABI Agricultureand Bioscience
Open Access

RESEARCH

Effects of information on consumer attitudes
towards gene‑edited foods: a comparison
between livestock and vegetables
Naoko Kato‑Nitta1,2* , Yusuke Inagaki1,2,4, Tadahiko Maeda1,2 and Masashi Tachikawa3

Abstract
Background: This study statistically explores the relationship between information provision and peoples’ attitudes
towards the application of gene-editing technology to food, by contrasting cases of gene-edited livestock andvegetables in Japan. Japanese food producers and researchers are optimistic about the application of the clusteredregularly interspaced short palindromic repeats (CRISPR) approach to food. Due to the strict regulations regarding
genetically modified (GM) food, GM crops are not commercially cultivated in Japan. Consumers worldwide haveconcerns about application of this technology to food. Further examination of this issue for Japanese consumers withlower acceptance towards GM food should provide essential information for global agricultural communities.
Methods: Using a web survey, split-ballot experimental design was used to randomly assign the respondents into
two groups: (1) the animal group, for which information on breeding technologies, including gene editing, was pro‑vided using pig illustrations. (2) The plant group, for which information was provided using tomato illustrations. Multi‑
variate analysis of variance and post-hoc t-tests were applied to examine the statistical differences between the plant
and animal groups for attitudes towards gene-edited livestock and vegetables. Statistical analyses were conducted to
examine if scientific knowledge influences these attitudes.
Results: Respondents found gene-edited vegetables more beneficial than gene-edited livestock. Their agreement wasstronger for vegetables than for livestock. Respondents’ attitudes towards gene-edited livestock differed depending onwhether they were shown pig illustrations or tomato illustrations. The plant group scored significantly lower regarding
gene-edited livestock compared to the animal group. No statistical difference was observed between the two groups
in the case of gene-edited vegetables. Furthermore, the higher science literacy group always scored higher regarding
improvements in vegetable breeding, but this was not concordant regarding improvements in livestock breeding.
Conclusions: People were more concerned about gene-edited livestock than gene-edited vegetables. The respondents
who were provided information with tomato illustrations in advance demonstrated lower acceptance towards geneedited livestock than those who were provided information with pig illustrations. Applying the technology to livestock,such as size enlargement for improvements, might be considered as risky by the public, in contrast with vegetables.
Keywords: Gene-edited foods, Information provision, Gene-edited animals, Science literacy, Breeding, Geneticallymodified food*Correspondence: naokokn@ism.ac.jp
1
Joint Support‑Center for Data Science Research, Research Organizationof Information and Systems, 10‑3 Midori‑cho, Tachikawa, Tokyo 190‑8562,
Japan
Full list of author information is available at the end of the article© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) andthe source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material
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Kato‑Nitta et al. CABI Agric Biosci(2021) 2:14

Background
Producing larger, tastier, and more nutritious food has
always been a prime agricultural concern. Recent progress in genetic technologies, such as gene editing or
a clustered regularly interspaced short palindromic
repeats (CRISPR) approach has made breeding processes
remarkably easier and faster. Japan, our study location,
had experienced strong opposition to genetically modified (GM) crops by consumer groups in the 1990s. The
Japanese government responded by setting strict regulations regarding GM crops, including the introduction oflabelling regulations in 2001. Presently, there is no commercial cultivation of GM crops in Japan (ISAAA 2018).
Given this situation, nowadays, the Japanese industry,
government, and academia are optimistic about theapplication of this new gene-editing technology to food.
A recent report by the Japanese Consumer Affairs
Agency (2017) stated that about 83% of Japanese survey
respondents answered ‘avoid’ regarding GM food. Previous research statistically investigating Japanese people’s
attitudinal differences regarding applying conventional
breeding technologies, genetic modification, and gene
editing to food showed that the public perceptions
regarding gene-edited crops tended to be more positive
than perceptions regarding GM crops (Kato-Nitta et al.
2019). The study, however, also noted that this difference was exceedingly small compared to the difference
between GM crops and conventional breeding. That is,
the public in Japan tended to consider gene editing closer
to genetic modification than conventional hybridisation.
A report investigating US consumers indicated the same
trend (FMI Foundation 2020). Furthermore, most Norwegian consumers were worried that gene-edited foodis risky in relation to health and environmental issues(NBAB 2020). Because gene-editing technology has
great potential for food production, further explorationof the factors associated with peoples’ attitudes towards
the application of this emerging technology is urgently
required.
To better understand this issue, this study statistically
explores the relationship between information provisionand peoples’ attitudes towards the application of geneediting technology to food. This exploration is done bycontrasting cases of gene-edited livestock and vegetables
in Japan, where the production of GM food is strictly
regulated and not generally accepted. Unfortunately, we
did not find studies in the available literature that explore
this topic. However, a recent research has claimed thatthe application of biotechnology in animal agriculture is‘controversial’. The research in question found that people’s personality traits affect their opinions regarding GM
animal products (Lin et al. 2019; Machado-Oliveira et al.
2020; Ardebili and Rickertsen 2020). Therefore, to shed

Page 2 of 12

light on public perception of gene-edited food, it is necessary to examine, with adequate control of individual
factors, the influence of different information provisions
on peoples’ attitudes towards gene-edited animals andvegetables.
A Norwegian report (NBAB 2020) indicated that Norwegian people were apprehensive about changing theappearance of both plant and animal products by utilising gene editing. Based on this report, Japanese people
would also be apprehensive to accept them. However, thequestion remains as to which of gene-edited animals or
gene-edited plants do people feel higher apprehensiveness towards. Given that humans are animals, people
generally differentiate animals from plants. Humans consider animals, including livestock, closer to humans andare more sympathetic to them than they are to plants,
including vegetables. Therefore, the former holds a
notion of animal welfare, which is one of the differentiations between these two species made by humans (Yunes
et al. 2019). In fact, some countries including the US haveset different regulations for gene-edited plants and animals (e.g. FDA 2017).
The discussion above leads to our assumption that
there would be a difference in people’s attitudes toward
gene-edited livestock and vegetables. Hence, we measurepeople’s assessments of the following food development
goals by utilising a gene-editing technique: increasing
the size of livestock/vegetables; making livestock/vegetables more resistant to disease and increasing the nutritional value of livestock/vegetables. We then examine if
the type of information provision affects peoples’ attitudes towards the above food development goals. The
results of this research would provide empirical evidence
regarding the impact of information provision on bothgene-edited livestock and vegetables. This would provide
valuable information to a large section of the agri-foodcommunity.
According to the contrast effect proposed in psychological literature (Sherif et al. 1958), when people arepresented something having a relatively lower emotional
hurdle in advance, they tend to show stronger rejection
towards something that has relatively higher emotional
hurdle afterwards. In this study, we presumed that when
the target of gene editing was livestock or vegetables, theformer would be more likely to lead people’s intuitive dislike, or prove to be a higher emotional hurdle than thelatter. Based on these assumptions, we derived the following hypothesis: the people who received explanation
about breeding technologies with vegetable pictures inadvance show lower acceptance concerning the subsequent questions on gene-edited livestock, compared tothose who received explanation about breeding technologies with livestock pictures in advance. By statisticallyKato‑Nitta et al. CABI Agric Biosci(2021) 2:14

examining the above hypothesis, this study could deepen
the understanding of the public perceptions of the role ofinnovation in food production.
The secondary purpose of this study is to statisticallyexamine if people’s scientific knowledge has any impact on
the relationships between information provision and theirattitudes towards gene-edited livestock/vegetables. This
will deepen our understanding of the relationship between
information provision and public perception toward
emerging technologies. The impact of the amount of scientific knowledge people possess (science literacy) on people’s risk perceptions towards emerging science, including
gene editing, has been examined in vast amounts of literature (e.g. Bucchi and Neresini 2002; Kato-Nitta et al.
2019; Slovic 1999) and is still controversial (Ahteensuu
2012; Kato-Nitta 2013). Therefore, we further examine theeffects of science literacy on peoples’ attitudes towards theabove food development goals, utilising gene-editing technology. This study’s observations from the statistical analyses on information provision, science literacy, and people’s
attitudes towards gene-edited livestock/vegetables should
further promote our understanding of public perceptions
and the role of innovation in society, and thus add value toexisting literature.

Methods
Data

A web-based survey was conducted in March 2018. The
survey was entrusted to a survey company which holdsa large opt-in panel of approximately six million people
in Japan. The target population in this study is Japanesegeneral population aged 20–69 years. The survey used aquasi-representative sample by initially screening among
61340 volunteers. A total of 6000 respondents aged
between 20 and 69 years were chosen on a first-come,first-serve basis. Using a sampling frame based on an optin panel means having a larger coverage error compared
to using a voter’s list or a residential register, both ofwhich are known to cover the entire population very wellin Japanese case. To mitigate such bias in demographic
distributions, the sample allocation was made in proportion to regional size, gender, and age based on the 2015
Japan national population census. Furthermore, to ensure
the reliability of the data, ‘satisficers’ were excluded.
‘Satisficers’ are respondents who make decisions without applying enough cognitive effort when providing
answers in a survey (Krosnick 1991; Maniaci and Rogge
2014; Tourangeau et al. 2013). Specifically, we excluded
respondents who gave wrong answers to all the three
directed questions scale items (DQS; Maniaci and Rogge
2014), marked multiple logically inconsistent answers,
or had an extremely short total response time which wasless than 1/10 of the median value. The selected final

Page 3 of 12

sample size had 4514 respondents. The specialised terms
in the survey design and sampling described above arefurther explained in Appendix 1. Table 1 shows the survey demographics.
Information provision

The survey employed the split-ballot experimental design,
which randomly assigns the respondents into two groups.
The observed and unobserved factors of the individual
respondents are adequately controlled with this randomisation procedure. The first group is the animal group,
where pig illustrations were used to provide informationon conventional breeding, genetic modification, and gene
editing. The second is the plant group, where tomato illustrations were used to provide the same information.
All the other conditions between the two groups were
the same and the same two-stage-information-provision
procedures were implemented. In the first stage, therespondents were given basic textual information on
the genome itself and on the agricultural applicationsof genome research. This stage adopted the information used in Kato-Nitta et al. (2019). In the second stage,illustrations with text were used to explain three existing
breeding technologies: conventional breeding, genetic
modification, and gene editing. In this stage, the respondents were shown either pig (animal group) or tomato
illustrations (plant group). As such, the respondents
received information on gene editing by contrasting withthe other existing technologies of conventional breeding
and genetic modification. The two types of illustration in
Fig. 1 were shown to the respondents. The illustrationsin Fig. 1 were shown respectively several times as the

Table 1 Demographic distributions
Variable

Number

Percentage (%)

Male

2131

47.2

Female

2383

52.8

20–29

574

12.7

30–39

790

17.5

40–49

994

22.0

50–59

936

20.7

60–69

1220

27.0

High school, vocational
school or less

1976

43.8

Junior college, College, or
above

2523

55.9

15

0.3

GenderAge (years)

EducationOthern = 4514

Kato‑Nitta et al. CABI Agric Biosci(2021) 2:14

respondents answered questions related to the application of genome research to food.
Prior to statistical analyses, we used chi-squared
tests to confirm that both the samples from the animal(n = 2288) and plant groups (n = 2226) represent the target population to the same degree. That is, we statisticallycontrasted the distribution of the demographics (gender,
age, and education), and there was no statistically significant difference between the two groups. Furthermore, we
confirmed with chi-squared tests that there was no statistical difference between the attitudes of the two groups
in terms of items located before the second stage of theprovision of information. That is, there were no statistical attitudinal differences between the two groups forthe questions related to genome research on agricultural
products before the information provision with illustrations. The detail of such questions and their distributions
are shown in Table 2. Hence, as the result of randomisation, the homogeneity of the two groups before thePage 4 of 12

assignment of the experimental condition was successfully achieved; thus, the internal validity of the study was
secured.
Measures and analyses

Ten items were used as dependent variables to assess various facets of perceptions regarding applying gene editing to agriculture. These items were originally developed
for this survey, based on the current application of geneediting technology to plants and non-human animals,
as mentioned in previous studies (Zhang et al. 2018; deGraeff et al. 2019; Menchaca 2020). The details of theitems are shown in Fig. 2. As shown in Fig. 2, these items
are interpreted as the advantages of applying genome
research to food.
In Fig. 2, items L1 to L5 were set as one block (Livestock block; L block), and items V1 to V5 were set as
another (Vegetable block; V block). To avoid ordereffects, the provision of L and V blocks was randomised.

Fig. 1 Information provision with illustrations: animal group (pig illustrations) and plant group (tomato illustrations). The animal and plant groups
were provided identical information. The only difference was whether they were shown pig or tomato illustrations. The text within the illustrations
was also identical. The tomato illustrations were the same as the ones used by Kato-Nitta et al. (2019), and the pig illustrations were developed
specifically for this survey.

Kato‑Nitta et al. CABI Agric Biosci(2021) 2:14

Page 5 of 12

Table 2 Percentages for the questions related to genome research applied to agricultural products
QuestionsCategories

Before taking this survey, did you know that genomic
research has been applied to agricultural products?

I didn’t know this at all

I’d heard of it

I knew a lot about it

Total

47.5%

49.4%

3.1%

100.0%(Animal group %, plant group %)(48.8%, 46.1%)(48.0%, 50.8%)(3.2%, 3.1%)(100%, 100%)

Are you interested in genomic research applied toagricultural products?

Yes, I’m interested

No, I’m not interested

Cannot say either way Total

35.8%

29.5%

34.7%

100.0%(Animal group %, plant group %)(35.7%, 35.9%)(30.2%, 28.8%)(34.2%, 35.3%)(100%, 100%)

Would you like to know the current state of genomic
research applied to agricultural products?

Yes, I’d like to know this No, I don’t want to know this Cannot say either way Total
47.9%

15.3%

36.9%

100.0%(Animal group %, plant group %)(47.8%, 47.9%)(15.2%, 15.3%)(37.0%, 36.8%)(100%, 100%)

Are you in favor of making progress in genomic
research applied to agricultural products?

I am in favor of thisI am opposed to thisCannot say either way Total

33.4%

7.8%

58.8%

100.0%(Animal group %, plant group %)(33.6%, 33.2%)(8.3%, 7.3%)(58.1%, 59.6%)(100%, 100%)n = 4514 (animal group n = 2288, plant group n = 2226)

Also, items within the blocks were randomised. As shownin Fig. 2, the same item-number from L and V blocks arecorrespondent but has only one difference depending onwhether the technology application is for livestock orvegetables. All respondents answered all the ten items(L1 to V5). The respondents were asked to mark on oneof the five-point scales (1 = I oppose this, 2 = I oppose
this to some extent, 3 = Cannot say either way, 4 = I’m infavour to some extent, 5 = I’m in favour of this). With theabove ten items, we examine if there are statistical attitudinal differences due to different information provisionsof animal and plant illustrations on the application ofgene editing to agriculture.
Second, we examine if science literacy influences theabove ten items. To assess respondents’ science literacy,

11 items with enough reliability and validity in previous
studies (European Union 2001; National Science Board
2016) as well as in Japan (Ministry of Education, Culture,
Sports, Science and Technology 2004; Kato-Nitta et al.
2019) were used (Appendix 2). The science literacy scaleconsists of items such as, ‘The oxygen we breathe comesfrom plants: true or false’. Either 1 or 0 point for 11 items
were added to yield the number-right scores. Thus, thepossible lowest and highest score for this scale were 0
point and 11 points, respectively. The median value andthe mean value of number-right scores in this study were
6.00 and 5.96, respectively. The correct answer rate ofthis scale for this study was 54.2%, just about the samewith the correct answer rate of 53.6% and 54% reported
in the previous surveys using the same scale in JapanThe use of gene editing technology increases the speed of breeding improvements compared to conventional
technologies. In addition, the following four improvements can be made to agricultural products. First,
increasing the size increases fruit flesh and meat yield. Secondly, resistance to disease can be increased.
Thirdly, nutritional value can be increased. Finally, taste can be improved. With this in mind, what do you
think about using gene editing technology to advance breeding improvements such as the following?
L1
L2
L3
L4
L5
V1
V2
V3
V4
V5

Increasing size of livestock by 20% (increase by a factor of 1.2)
Increasing size of livestock by 50% (increase by a factor of 1.5)
Making livestock more resistant to disease
Improving nutritional value of livestock
Improving the taste of livestock
Increasing size of vegetables by 20% (increase by a factor of 1.2)
Increasing size of vegetables by 50% (increase by a factor of 1.5)
Making vegetables more resistant to disease
Improving the nutritional value of vegetables
Improving the taste of vegetables

Fig. 2 Items to assess people’s attitudes regarding applying gene-editing techniques to agricultureKato‑Nitta et al. CABI Agric Biosci(2021) 2:14

(Kato-Nitta et al. 2019; Ministry of Education, Culture,
Sports, Science and Technology 2004). Respondents whoscored seven and above were categorised as having a
relatively higher level of science literacy (higher science
literacy group), and others were categorised as having
a relatively lower level of science literacy (lower science
literacy group). We further confirmed that no statistical difference was observed in both the animal and plantgroups for the level of science literacy with chi-squared
tests. Therefore, the samples from the animal and plantgroups represent the target population as to science literacy to the same degree.
As such, we examine if any differences were observed
between the animal and plant groups, as well as between
higher and lower science literacy groups, based on theabove ten items. The working hypotheses for the statistical tests are as follows.
Differences in information provision

H1 There is a statistical difference between the animal and plant groups regarding respondents’ attitudes
towards the application of gene editing to livestock.
H2 There is a statistical difference between the animal and plant groups regarding respondents’ attitudes
towards the application of gene editing to vegetables.
Differences in science literacy

H3 General science literacy influences respondents’attitudes towards the application of gene editing tolivestock.
H4 General science literacy influences respondents’attitudes towards the application of gene editing tovegetables.
To test hypotheses H1 to H4, we first apply a two-way
multivariate analysis of variance (MANOVA), simultaneously using all the dependent variables of the above
ten items to examine the main effects of the two factors(information provision and science literacy). We thenstatistically examine the effects of information provision
as well as science literacy on the respective ten dependent
variables, using post-hoc between-subjects’ t-tests (seealso NOTE for more information on the model specification). Adoption of this two-step procedure adequately
controls the experiment-wise Type-I error rate in thefirst step rather than requiring an adjustment of the alphalevel for each comparison in the second step.

Page 6 of 12

Results
Testing the main effects of information provisionand science literacy with a MANOVA

A two-way MANOVA was performed with the ten items(L1 to V5) and the result showed that no interaction
effects between information provision and science literacy: Wilks’s Λ = 0.997, F (df1 = 10, df2 = 4501) = 1.245,
p = 0.256. The main effects of information provision(group difference between the animal group and theplant group) and the science literacy were then examined
and found statistically significant; results for information
provision are as follows: Wilks’s Λ = 0.982, F (df1 = 10,
df2 = 4501) = 8.433, p < 0.000; science literacy: Wilks’sΛ = 0.984, F (df1 = 10, df2 = 4501) = 7.186, p < 0.000.
Because no interactions between two factors of information provision and science literacy were observed but themain effects of each factor were both observed, the posthoc t-tests were performed to confirm the effects of eachfactor.
Post‑hoc analyses of the attitudinal differences caused
by variations in information provision

The post-hoc t-tests were performed to examine H1 andH2 on each of the ten items. Table 3 shows the results.
For items L1 to L5, all the mean values of the plant groupwere lower than those of the animal group and were statistically significant (p < 0.01). That is, respondents who
were shown tomato illustrations in advance found lower
benefits of the application of gene-editing techniques
to livestock, compared to respondents who were shown
pig illustrations. The animal group scored significantly
higher points than the plant group regarding gene-edited
livestock. That is, respondents who were shown pig illustrations in advance found a higher level of benefits of
the application of gene-editing techniques to livestock
as compared to respondents who were shown tomato
illustrations. On the other hand, there were no statisticaldifferences between the two groups regarding the vegetable items of V1 to V5. The effect sizes of Cohen’s d forlivestock items of L1 to L5 were 0.104 to 0.130 and wererelatively larger than those of vegetable items (V1 to V5,
0.002 to 0.039).
Post‑hoc analyses of the attitudinal differences related
to variations in the amount of science literacy

The post-hoc t-tests were performed to examine H3 andH4. Table 4 shows the results. Among items L1 to L5,
there were no statistical differences between the higher
science literacy group and the lower science literacy
group except for one item (making livestock more resistant to disease) (p < 0.01). The average value of this item
was higher in the higher science literacy group. People

Kato‑Nitta et al. CABI Agric Biosci(2021) 2:14

Page 7 of 12

Table 3 T-tests on the differences in the mean values of the attitudes between the plant group and the animal group towards geneedited foods

L

Item

Group

mean sd

1. Increasing size of livestock by
20% (increase by a factor of 1.2)

Plant

2.86

Animal 2.97

2. Increasing size of livestock by
50% (increase by a factor of 1.5)

Animal 2.82

3. Making livestock more resistant
to disease

Animal 3.72

4. Improving nutritional value of
livestock

Animal 3.50

5. Improving the taste of livestock

Plant
Plant
Plant
Plant

2.70
3.59
3.38
3.40

Animal 3.53
V

1. Increasing size of vegetables by
20% (increase by a factor of 1.2)

Animal 3.20

Plant

2. Increasing size of vegetables by
50% (increase by a factor of 1.5)

Animal 3.04

3. Making vegetables more resist‑
ant to disease

Animal 3.73

4. Improving the nutritional valueof vegetables

Animal 3.70

5. Improving the taste of vegeta‑
bles

Animal 3.65

Plant
Plant
Plant
Plant

3.18
3.00
3.71
3.69
3.65

df

t (Welch’s test)

1.09 4495.68 3.50

p-valueGroup mean difference [95% CI]

Cohen’s d

0.000

0.112 [− 0.175, − 0.049]

0.104

0.000

0.129 [− 0.192, − 0.065]

0.119

0.000

0.130 [− 0.188, − 0.071]

0.130

0.000

0.121 [− 0.182, − 0.060]

0.115

0.000

0.128 [− 0.190, − 0.067]

0.122

0.371

0.028 [− 0.088, 0.033]

0.027

0.187

0.042 [− 0.103, 0.020]

0.039

0.458

0.042 [− 0.078, 0.035]

0.022

0.782

0.008 [− 0.067, 0.050]

0.008

0.947

0.002 [− 0.061, 0.057]

0.002

1.06
1.10 4499.06 3.98
1.07
1.01 4493.40 4.38
0.98
1.05 4504.60 3.88
1.04
1.06 4504.88 4.08
1.05
1.04 4504.82 0.89
1.03
1.07 4503.74 1.32
1.05
0.96 4511.56 0.74
0.98
1.01 4505.56 0.28
1.00
1.02 4508.52 0.74
1.02

Sample size for each group: n = 2226 (plant group), n = 2288 (animal group)with relatively higher scientific knowledge found more
benefits of this aspect of gene-edited livestock. As for V1
to V5, all the items were statistically significant (p < 0.05),
and all the average values were higher in the higher
science literacy group. People with relatively higher
scientific knowledge found more benefits regarding geneedited vegetables.
Summary of the statistical tests for H1 to H4

The results of statistical tests for the hypotheses are summarised as follows. There was a statistical difference
between the animal group and the plant group regarding respondents’ attitudes towards all livestock items (L1to L5). Therefore, H1 was supported. The plant group
scored significantly lower regarding gene-edited livestockcompared to the animal group. H2 was not supported
because there was no statistical difference between the
animal group and the plant group regarding respondents’attitudes regarding all the items regarding vegetables (V1
to V5). H3 was not supported except for one item (making livestock more resistant to disease). Hence, this item
may be different from the other items assessing the application of gene editing to livestock in terms of the relation
to scientific knowledge. H4 was supported regarding all
the five vegetable items (V1 to V5).

Examination of mean differences of livestock items
and their counterpart vegetable items

As is shown in Table 3, the mean values for all livestock
items were lower than their counterpart vegetable items
for both the animal and the plant groups. The lowest
mean value observed in both groups was for the item of
increasing the size of livestock by 50%. We therefore further examined if the differences in mean values of livestock items against their counterpart vegetable items
were statistically significant with paired t-tests. That is,with respect to each group of the plant group and theanimal group, we compared the within-subject difference between pairs of items such as L1 and V1, L2 andV2, and so on. For the plant group (n = 2226), the meandifferences of all the paired items were statistically significant (p < 0.000). The effect sizes (Cohen’s d) ranged
from 0.181 to 0.431. For the animal group (n = 2288), themean differences of all the paired items were statisticallysignificant (p < 0.000; Cohen’s d: 0.213 to 0.326) with the
exception of one pair: making livestock/vegetables moreresistant to disease (p = 0.463, Cohen’s d = 0.015).

Discussion and conclusions
The results of the examination of mean differences of
livestock items and their counterpart vegetable items
could be interpreted to imply a tendency for people toKato‑Nitta et al. CABI Agric Biosci(2021) 2:14

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Table 4 T-tests on the differences in the mean values of the attitudes between the higher science literacy group and the lower
science literacy group towards gene-edited foods

L

Item

Group

1. Increasing size of livestock by 20% (increaseby a factor of 1.2)

High

2.95

1.15 4112.31 1.60

Low

2.90

1.01

2. Increasing size of livestock by 50% (increaseby a factor of 1.5)

High

2.76

1.17 4107.94 0.02

Low

2.76

1.02

3. Making livestock more resistant to disease
4. Improving nutritional value of livestock
5. Improving the taste of livestock
V

mean

sd

Df

t (Welch’s test)

High

3.72

1.03 4218.48 4.02

Low

3.60

0.96

High

3.46

1.12 4118.04 0.92

Low

3.43

0.98

High

3.50

1.12 4141.29 1.60

Low

3.45

1.00

1. Increasing size of vegetables by 20% (increaseby a factor of 1.2)

High

3.26

1.10 4123.92 4.26

Low

3.13

0.97

2. Increasing size of vegetables by 50% (increaseby a factor of 1.5)

High

3.07

1.14 4099.44 2.85

Low

2.98

0.99

3. Making vegetables more resistant to disease
4. Improving the nutritional value of vegetables
5. Improving the taste of vegetables

High

3.81

1.01 4240.20 5.90

Low

3.64

0.94

High

3.75

1.01 4198.22 3.09

Low

3.66

0.96

High

3.70

1.08 4147.82 2.59

Low

3.62

0.96

p-valueGroup meandifference [95% CI]

Cohen’s d

0.109

0.052 [− 0.012, 0.116] 0.048

0.985

0.001 [− 0.064, 0.065] 0.001

0.000

0.120 [0.062, 0.179]

0.356

0.029 [− 0.033, 0.091] 0.028

0.110

0.051 [− 0.012, 0.114] 0.048

0.000

0.133 [0.072, 0.194]

0.129

0.004

0.091 [0.028, 0.154]

0.086

0.000

0.172 [0.115, 0.229]

0.178

0.002

0.094 [0.034, 0.153]

0.093

0.010

0.079 [0.019, 0.140]

0.078

0.121

Sample size for each group: n = 2051 (higher science literacy group), n = 2463 (lower science literacy group)

have more negative concerns regarding the application ofgene-editing technology to food in the cases of livestock
than vegetables, especially regarding size enlargement.
Such results could imply that our assumption was valid inthat, when the target of gene editing was livestock or vegetables, the former would have a higher emotional hurdle
than the latter.
The approach of experimental split-ballot design bycontrasting two groups of animal-illustrated and plantillustrated explanations conducted under the above
assumption revealed that public attitudes towards food
may change due to just one piece of information provision. The respondents who were shown tomato illustrations during the explanation of genome technologies
had lower benefits or a stronger tendency for rejection
towards gene-edited livestock than did the respondents
who were shown pig illustrations. This result may beinterpreted using the contrast effect proposed by Sherif
et al. (1958), where the first stimulus has a relatively low
emotional hurdle, and it provokes a stronger attitudinal
response to the second stimulus with a higher emotional
hurdle. In this study, the illustrations of pigs (higher hurdle) or tomatoes (lower hurdle) could be interpreted as
the first stimulus, and the questionnaire items of geneedited livestock (higher hurdle) or gene-edited vegetables(lower hurdle) could be interpreted as the second
stimulus.
Even when the attitudinal differences due to variationsin information provision were statistically significant,the effect sizes of Cohen’s d were small for general criteria (Cohen 1977). A previous study investigated people’s attitudinal differences for the food application of
the three technologies of conventional breeding, genetic
modification, and gene editing (Kato-Nitta et al. 2019).
This research might be practically helpful in the interpretation of the results of this study. Their results revealed
approximately 0.1 point mean differences in public attitudes towards gene-edited crops and GM crops by utilising a 5-point scale. The degree of attitudinal differences
due to information provision was almost as large as
observed differences in benefit perceptions between GM
crops and gene-edited crops. To fully address the practical implications of the effect sizes presented in this study,
we need to compare our results with the results fromother studies. However, we could not find relevant previous research that exactly matched our study’s structure
to use as a reference. Thus, we anticipate future studies
will utilise a different method of information provisionsuch as more realistic illustrations of pigs and tomatoes
or illustrations of different livestock or vegetables. SuchKato‑Nitta et al. CABI Agric Biosci(2021) 2:14

an accumulation of empirical results could lead to a real
understanding of the practical value of the findings of thisstudy. The differences (effect sizes) observed in this study
could serve as reference points for such future studies
that examine the mean differences in attitudinal variablescaused by information provision.
As for the examination of the influence of science literacy on gene-edited vegetables, the higher science literacy
group always scored higher regarding improvements invegetable breeding. These results were concordant with aprevious study examining the effects of general scientificknowledge on people’s benefit perceptions towards geneedited crops (Kato-Nitta et al. 2019). The higher science
literacy group scored higher regarding improvement inlivestock breeding, but only for making livestock moreresistant to disease. The results of the previous study
mentioned above also showed that the influence of science literacy on reducing risk perceptions was not valid
in the case of emerging technologies such as gene editing.
Therefore, in the case of livestock, improvements suchas changing its size or nutrition value might be considered risky rather than beneficial by the public. According
to a study reviewing major national surveys carried out
in Ireland, the Irish public tended to show more positive attitudes towards medical applications of biotechnology than agri-food applications (Morris and Adley
2001). Their results may add another interpretation ofthis study’s results regarding science literacy and item
L3 (making livestock resistant to disease). The effects ofscience literacy were statistically significant to all vegetable items (V1 to V5). This may be because in the caseof gene-edited vegetables, improvements in nutritional
values as well as an increase in size may also be considered as beneficial by the public, just like by producers or
scientists.
The ten dependent variables used in this study suchas ‘increasing size of livestock by 20%’ could mean both
livestock itself and meat made from livestock. Hence,
they are incapable of differentiating between those twokinds of concept. Thus, further studies should investigate
this issue by developing items capable of distinguishingthose two concepts. In addition, as we utilised a web survey with a non-probability sample, we must be cautious
as it is not free from a self-selection bias. The external
validity was not secured in this sense, even though sample allocation was made to mitigate the coverage error in
the demographics. For overcoming the above limitationsof this study and for further generalisation of the results,
the following measures could be proposed as futuresuggestions:Page 9 of 12

1. Using a sampling frame which is not an online-optin panel, but which secures the representativeness ofthe population.
2. Using a different method for measurement for the
dependent variables.
3. Utilising illustrations of livestock or vegetables otherthan pigs or tomatoes.
4. Conducting the same survey multiple times or conducting the same survey in different countries.
Our approach using the experimental split-ballot
design based on a web-survey with randomisation principle was valid in terms of quantitatively examining public
perceptions of the role of innovation in food production,
as it allows valid reasoning utilising the adequate manipulation of experimental variables. The results of this study
provided valuable information for the agri-food community as they help us gain insights into how information
provision affects the response of consumers. The resultsof this study revealed that the public attitude may change
due to just one piece of information provided. Experts,
including food producers and researchers in related
fields, must be aware and pay attention to the impact of
informational context, before disseminating their information. It can also be used to provide useful information
to the public so that people would understand how ourperceptions are influenced by the provision of information. In other words, it might increase the ‘literacy’ of the
public in the broader sense, including that of scientists
outside the domain-specific field of genome research.

Appendix 1
This paper includes some technical terms specialisedin quantitative survey methodology. The terms aredescribed below.
• Opt-in panel
Survey respondent group consisting of voluntary registrants operated by a survey company.
• Quasi-representative sample
The representative sample is drawn from the population by some procedures of probability sampling from a sampling frame that adequately covers
the population. On the other hand, using opt-in panel
as a sampling frame in a survey does not guaranteesuch representativeness, since the frame itself does
not necessarily represent the population properlyKato‑Nitta et al. CABI Agric Biosci

•

•

•

•

•

(2021) 2:14

because of its inherent nature of volunteered registrants. To mitigate possible bias, measures are taken
to make sample allocation proportionate to population distribution for some demographics. The sample
obtained via this operation is often called a quasirepresentative sample. See also sample allocation.
Initial screening
For collecting quasi-representative sample, initial
screening that precedes the main body of the survey
usually identifies and allocates the target conditions,such as region, gender, and age.
Sample allocation
In a web survey, proportionate sample allocation isoften made with regard to the combination of a few
demographic variables to approximate the demographic distribution of the target population. In thecurrent study, demographic groups were allocatedto a total of 470 groups according to gender, age,and place of residence. The 470 groups are based on
the following numbers of the groups: 5 age groups(20–29, 30–39, 40–49, 50–59, and 60–69), 2 genders(male and female), and 47 regions (number of prefectures in Japan). This kind of allocation or its variations are the general operation when using an opt-inpanel administrated by a survey company.
Recruitment of the sample on first-come first-servebasis
In this study, survey respondents, who first received
a solicitation email, were recruited from panel members that were about ten times the required sample
size, considering the population ratio of gender, age,and region (47 prefectures). The respondents wereselected on a first-come-first-serve basis until thenumber matched the allocated sample size. This typeof first-come-first-serve principle is also a common
practice in web-based surveys using an opt-in panel.
Directed Questions Scale, DQS
DQS (Maniaci and Rogge 2014) is an item instructedby a researcher to select a specific response option
regardless of whether the answer really applies to the
respondents or not. It is used to judge if the respondent has adequately examined the contents of survey
questions, depending on whether the respondent
complied with the instruction. In this study, three
DQS are provided, and respondents who gave wrong
answers to all the three DQS were excluded.
Split-Ballot Survey
This is a between-subject-one-factor experimental
design with multiple dependent variables often usedin social surveys. This study has two groups (conditions), the animal and plant group, and respondents
were randomly assigned to one of those two conditions. The treatment of such randomisation enabled

Page 10 of 12

us to control both observable and unobservable variables of the respondents. The analytical model generally used for this experimental design is MANOVA(Hotelling’s T-Squared test). After confirming astatistically significant result with this test, we conducted post-hoc t-tests that are shown in Table 3.

Appendix 2
Science literacy scale:
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.

The center of the Earth is very hot.
All radioactivity is man-made.
The oxygen we breathe comes from plants.
It is the mother’s genes that decide whether thebaby is a boy or a girlLasers work by focusing sound waves.
Electrons are smaller than atoms.
Antibiotics kill viruses as well as bacteria.
The continents on which we live have been moving
for millions of years and will.
Human beings, as we know them today, developed
from earlier species of animals.
The earliest humans lived at the same time as thedinosaurs.
Radioactive milk can be made safe by boiling it.

In the survey, the presentation order of the above 11
items were randomised to avoid order effects. Some of
the items in this scale may be ambiguous for experts insome disciplines; however, the scale was developed tomeasure science literacy for the general public and has
been repeatedly and internationally used in previous
studies. See Sjøberg (2015).

Note
For the group mean comparison, the multivariate analysis of covariance (MANCOVA) is an alternative analysis.
However, we chose the MANOVA in this study for thefollowing reasons: the survey respondents were randomly
assigned into two groups; thus, the covariate distributions of both the animal and plant groups were almost
identical. In such cases, we do not need to adjust themeans of dependent variables for the differences in distributions of covariates (science literacy) among the twogroups. In this study, the difference in the means of thedependent variable among the two groups adjusted by
MANCOVA did not essentially differ from the differencein the unadjusted means. We also conducted a MANCOVA for a robustness check, utilising science literacy
as a continuous variable, but found essentially the same
result as MANOVA that are presented in this paper.

Kato‑Nitta et al. CABI Agric Biosci(2021) 2:14

Abbreviations
GM: Genetically modified; JSPS: Japan Society for the Promotion of Sci‑
ence; CRISPR: Clustered regularly interspaced short palindromic repeats;
MANOVA: Multivariate analysis of variance; MANCOVA: Multivariate analysis ofcovariance.
Acknowledgements
Part of this study was presented at the Gene Editing in Agriculture and Food:
Social Concerns, Public Engagement and Governance virtual conference heldon October 20, 2020. The conference was organized by Iowa State University
and Keystone Policy Center.
Authors’ contributions
NKN designed the current research, designed and organised the experimental
questionnaire survey, conducted the statistical analyses, and wrote the paper;
YI and TM designed the experimental questionnaire survey; MT organised theresearch project. All authors read and approved the final manuscript.
Funding
This work is supported by Japan Society for the Promotion of Science (JSPS)
KAKENHI (JP16H04992; JP17K01015; 17K13866), and JST Program on Open
Innovation Platform with Enterprises, Research Institute and Academia(OPERA) “Gene editing” Consortium grant.
Availability of data and materials
Some part of the data in this study was used in previous study (Tachikawa
et al. 2019). The datasets generated and/or analysed during the current study
are not publicly available due to the regulation from Japanese privacy protec‑
tion law, but are available from the corresponding author on reasonable
request.
Ethics approval and consent to participate
This survey was conducted under Japanese Privacy Information Protection
Law and was approved by the Institute of Statistical Mathematics Ethical
Review Board beforehand (No. R17-06). Participation was completely volun‑tary, and participants could withdraw at any time. Informed consent of allparticipants was obtained by the survey company.
Consent for publication
All authors reviewed the paper and consented for publication.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Joint Support‑Center for Data Science Research, Research Organizationof Information and Systems, 10‑3 Midori‑cho, Tachikawa, Tokyo 190‑8562,
Japan. 2 The Institute of Statistical Mathematics, 10‑3 Midori‑cho, Tachi‑
kawa, Tokyo 190‑8562, Japan. 3 Nagoya University, Furo‑cho, Chikusa‑ku,
Nagoya 464‑8601, Japan. 4 Statistics Bureau, Ministry of Internal Affairs
and Communications, 19‑1 Wakamatsu‑cho, Shinjuku‑ku, Tokyo 162‑8668,
Japan.
Received: 1 July 2020 Accepted: 4 February 2021

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