Data

Two web-based surveys were conducted from December 2016 to February 2017. The surveys were entrusted with a survey company, Nippon Information Incorporated. Survey 1 used a quasi-representative sample from the company’s large opt-in panel of approximately 1,100,000 volunteers from the online population in Japan. An initial screening was made to mitigate the potential bias in demographic distributions, and allocation was made in proportion to the size according to region, gender, and age based on the 2015 Japan national population census, and drew 3350 respondents aged between 20 and 69 years on a first-come, first-serve-basis. Then, the final participants of 3000 were selected by excluding respondents with extreme-shorter response time (from the shorter side of time to be about less than 1/10 of median total response time). This treatment was made to enhance reliability of the data based on the idea of ‘satisficing’ in quantitative survey methodology (Krosnick, 1991; Maniaci and Rogge, 2014; Tourangeau et al., 2013). This idea is based on behaviour which respondents do not pay enough amount of cognitive effort to provide the suitable answers in a survey.

The participants for Survey 2 were scientists with and without expertise in molecular biology. We adopted an opportunistic sampling method in addition to recruiting volunteers using academic societies’ websites, such as the Molecular Biology Society of Japan and the Physical Society of Japan. We also utilised electronic mailing lists for recruiting participants, including TENNET (operated by the Japan Astronomical Society) and Jeconet (a Japanese academic mailing list related to ecology). Survey 2 participants’ fields of specialty were distributed as follows: Micro-biology, such as molecular biology, 56.3%; macro-biology, such as ecology, 13.7%; physical sciences, informatics, chemistry, or geology, 11.6%; social sciences and humanities, 6.1%; medicine, nursing, or health, 4.6%; other disciplines, 7.6%. The final sample size for the second survey was 197. Table 1 shows the survey demographics.

Table 1 Demographic distributions of survey 1 and survey 2 Full size table

The data in this study were used in previous studies (Kato-Nitta et al., 2017; Tachikawa et al., 2017), and the surveys utilised were conducted under Japanese Privacy Information Protection Law. Participation was completely voluntary, and participants could withdraw at any time. Informed consent of all participants was obtained by Nippon Information Incorporated.

The surveys provided fundamental information, starting with the definition of genome and to textual explanation of the basic genome research, as well as health and agricultural applications of genome research. At this point, the initial attitudes, as a baseline of within-subject experimental design, were measured with 11 items on benefit, risk, and value perceptions toward genome research applied to agricultural crops. Details of the fundamental information provided are shown in Fig. 1.

Fig. 1 Provided fundamental Information on genome research. Fundamental textual information provision about definition of genome, basic genome research, and health and agricultural applications of genome research Full size image

Then, figures with texts (Figs. 2 and 3) were used when explaining the differences among the three existing breeding technologies of conventional breeding, genetic modification, and gene editing. The tomato is considered as a fine model plant by biologists (Busch et al., 1991), so we used figures depicting tomatoes to explain the technological differences. Next, three measurements—one of perceptions of applying conventional breeding, one of perceptions of applying genetic modification, and one of perceptions of applying gene editing—were made, each using 11 items (33 total items) spanning the oldest to the newest technologies. In order to answer these items, participants were allowed to refer to the figures (Fig. 2) and access the provided URL links to the website of the Ministry of Agriculture, Forestry, and Fisheries during the survey.

Fig. 2 Information on technological differences. Information provision by illustrations with text to explain the differences among the three existing breeding technologies of conventional breeding, genetic modification, and gene editing Full size image

Fig. 3 Information on technological differences in text. Information provision in text to explain the differences among the three existing breeding technologies of conventional breeding, genetic modification, and gene editing. This information is provided with the information described in Fig. 2 Full size image

Measures and analyses

Relationship between domain-specific knowledge and attitudinal change

To answer the first research question, we assessed various facets of perceptions on applying genome research to agricultural crops with the 11-item scale. Nine of the eleven items were adopted from the JSPS KAKENHI (17019024) research project ‘Public attitudes toward genomic researches in Japan (g-elsi)’, and two extra items were added for the current study. Four measurements were made using this scale: before the explanation of technical differences (1st, baseline) and after presentation of technical differences among the three breeding technologies (2nd, 3rd, and 4th). The details of the 11 items are provided below, as well as in Fig. 4 and Table 4. The participants were asked to answer using a five-point scale (1 = disagree to 5 = agree).

Table 2 Cronbach’s alpha for aggregated variables for calculation on dependent variables Full size table

What do you think of a genome research application for breeding agricultural crops using the technology of (conventional breeding/genetic modification/gene editing)?

1. Beneficial to stable food supply 2. Beneficial to human health care 3. Beneficial to economic development 4. Impacts plant and insect ecology 5. Insufficient safety confirmation 6. Fear of unexpected adverse effects 7. Possibility of misusing this technology 8. Bioethically questionable 9. Cannot understand well and feel somewhat fearful 10. Universally favoured to be promoted 11. Research considered insignificant

On the above 11 items, we statistically explored the differences in mean values of the four conditions of (1) before information on technologies, (2) conventional breeding, (3) genetic modification, and finally, (4) gene editing, using a single-factor repeated measures ANOVA. As the result of the statistical test depends on the sample size, we further calculated effect sizes to determine the extent of attitudinal changes and also conducted power analyses. We categorised participants into three groups by level of domain-specific knowledge of molecular biology: (A) lay public (n = 3000), (B) experts in the other fields (n = 86), (C) experts in molecular biology (n = 111). We also observed differences in attitudinal changes of the three groups from the data including the above three groups.

Influence of science literacy on attitudinal change

To answer the second research question, we tested the following hypothesis with regression analyses: people’s scientific knowledge influences their attitudinal change on agricultural crops owing to information provided that explains the differences among applied technologies. For these analyses, we exclusively used the data from Survey 1 (the lay public).

Dependent variables

We constructed two composite variables using nine items out of the above 11 items: benefit perception comprised three items and risk perception comprised six items. A Cronbach’s alpha coefficient for each of these constructs was calculated to confirm the internal consistency and reliability for each item. These values are shown in Table 2.

There were two items related to value perception: ‘Universally favoured to be promoted’ and ‘Research considered insignificant’. Since the former is assessing individual perception of other individuals’ value perception and the latter is assessing individual perception per se, the two items were considered to be unsuitable for aggregation; therefore, we used only the latter item to be included in regression models for the purpose of analysis in assessing individual attitude change on value perceptions.

After the above operationalisation, we calculated the sizes of the respective changes (difference scores) described as follows (Y1 to Y9) and then used them as dependent variable in regression analysis.

[Benefit perceptions]

Y1 = Gene editing minus before information

Y2 = Genetic modification minus before information

Y3 = Conventional breeding minus before information

[Risk perceptions]

Y4 = Gene editing minus before information

Y5 = Genetic modification minus before information

Y6 = Conventional breeding minus before information

[Value perceptions]

Y7 = Gene editing minus before information

Y8 = Genetic modification minus before information

Y9 = Conventional breeding minus before information

We decided to use the difference score (gain score) after weighing both its pros (Cronbach and Furby, 1970) and cons (Edwards, 1970) and its intuitive appeal on interpretability of results. Our dependent variables represented the perceived superiority or inferiority of each technology to the baseline of ‘before the information on technological differences’. R-squares of regression equations are generally lower compared with the analysis not using such operation, as is characteristic when the major source of variations (baseline) is subtracted from the dependent variable. When the size of Y1 to Y3 and Y7 to Y9 are positive, the change can be interpreted as positive, and the benefit or value perception is higher compared with the perception before information provision. When the size of Y4 to Y6 is positive, the change can be interpreted as positive, and the risk perception is higher compared with the perception before information provision.

Independent variables

Science literacy: We measured participants’ general scientific knowledge with 11 items that have been repeatedly used in international comparative studies (European Union, 2001; National Science Board, 2016), as well as by the Japanese government (Ministry of Education, Culture, Sports, Science and Technology, 2004). The scale consists of items such as ‘Antibiotics kill viruses, as well as bacteria: True or false’. We calculated the total sum of the correct answers to the 11 questions. The current study’s participants (the lay public) in Survey 1 recorded a correct answer rate of 53.6%, which was about a same correct answer rate of 54% reported in the previous survey conducted by the Japanese government (Ministry of Education, Culture, Sports, Science and Technology, 2004).

Individual relevance to the information (benefit of general genome research, risk of general genome research, and value of general genome research): We measured participants’ individual relevance to the information by measuring participants’ benefit, risk, and value perceptions on general genome research. As this research focuses on people’s perceptions of genome research application on agricultural crops, we defined perceptions on general genome research as ‘perceptions on basic genome science and genome research as applied to medicine’. We adopted benefit of general genome research as control variables for models Y1 to Y3, risk of general genome research for Y4 to Y6, and value of general genome research for Y7 to Y9. Items for construct variables of individual relevance were measured before provision of information on technological differences (Figs. 2, 3) and the respective Cronbach’s alpha values for each of the aggregated independent variables are provided in Table 3. As is shown in Table 3, Cronbach’s alphas for the aggregated independent variables yielded adequate internal consistency and reliability.

Table 3 Cronbach’s alpha for aggregated independent variables Full size table

Trust in food governance: Previous studies noted that trust has been considered the essential variable to understanding people’s risk perceptions (Lobb, 2005; Slovic, 1999; Slovic et al., 1981). Recent empirical studies have also shown that trust was an important factor for both risk and benefit perceptions on the application of biotechnology to agricultural crops (Rodríguez-Entrena, M. and Salazar-Ordóñez, 2013; Slovic, 1999). We introduced this concept as controls to test the hypothesis associated with the second research question. Trust in Japanese food governance was assessed with items used in previous Japanese studies (Kato-Nitta et al., 2017). This scale consists of four items evaluating participants’ trust in governmental food safety policy, as well as safety measures of food business companies. The participants were asked to answer using a seven-point scale (1 = completely disagree to 7 = completely agree). The Cronbach’s alpha value for this scale is shown in Table 3.

Risk-avoidance orientation: We utilised this concept for controlling the level of original disposition of the participants with respect to risk. The part of this scale was used in the previous studies (and two extra items were added for the purpose of the current study (Kato-Nitta et al., 2017). The scale consists of six items and was developed to assess if the participants have a ‘zero-risk’ orientation or whether they favourably evaluate products described as ‘additive-free’ or ‘pesticide-free’. The answers to this scale was made with a five-point scale (1 = Do not praise at all to 5 = Praise highly). The Cronbach’s alpha value for this scale is shown in Table 3.