Understanding Thai Consumers' Intentions to Purchase Genetically Modified Foods

Bing Zhu

Department of Marketing, Assumption University, Bangkok 10240, Thailand

Ananya Phunthasaen

Department of Marketing, Assumption University, Bangkok 10240, Thailand

Chainarong Rungruengarporn

Department of Marketing, Assumption University, Bangkok 10240, Thailand

Salila Pinpak

Department of Marketing, Assumption University, Bangkok 10240, Thailand

DOI: https://doi.org/10.36956/rwae.v5i4.1298

Received: 3 September 2024 | Revised: 13 September 2024 | Accepted: 20 September 2024 | Published Online: 18 October 2024

Copyright © 2024 Bing Zhu, Ananya Phunthasaen, Chainarong Rungruengarporn, Salila Pinpak. Published by Nan Yang Academy of Sciences Pte. Ltd.

Creative Commons LicenseThis is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.


Abstract

This research explored the various factors affecting the purchase intention of Thai consumers towards genetically modified foods. To ensure the reliability of our findings, we conducted an extensive online survey in Bangkok over the course of three months, from June to August. The data analysis was mainly based on partial least squares structural equation modelling (PLS-SEM), a robust method for analyzing complex relationships. The results of the analysis confirmed the validity of all the hypotheses, with the exception of the direct relationship between food labels and purchase intention. Additionally, we found that all the mediation effects were statistically significant. In the study, we delve into a comprehensive discussion of the results and their corresponding implications, providing a thorough understanding of the factors influencing consumer behavior in the context of biotechnology and food choices.

Keywords: Genetically modified foods; Consumer knowledge; Food labels; PLS-SEM; Risk; Benefits


References

[1] Hwang, H., Nam, S.J., 2021 The influence of consumers' knowledge on their responses to genetically modified foods. GM Crops Food. 2(1), 146–157. DOI: https://doi: 10.1080/21645698.2020.1840911

[2] Chow, S., Norris, J.F., Bilder, B.G., 2016. Insight into the genetically modified foods: from the concerns of safety to food development (Part I). Science Insights. DOI: https://doi.org/10.15354/si.16.vi010

[3] Costa-Font, M., Gil, J.M., Traill, W.B., 2008. Consumer acceptance, valuation of and attitudes towards genetically modified food: review and implications for food policy. Food Policy. 33(2), 99–111. DOI: https://doi.org/10.1016/j.foodpol.2007.07.002

[4] Yeung, R.M.W., Morris, J., 2001. Food safety risk: consumer perception and purchase behavior. British Food Journal. 103 (3), 170–187. DOI: https://doi.org/10.1108/00070700110386728

[5] Stanton, J., Rezai, G., Baglione, S., 2021. The effect of persuasive/possessing information regarding GMOs on consumer attitudes. Future Foods. 4, 100076. DOI: https://doi.org/10.1016/j.fufo.2021.100076

[6] Zhou, Y., Chen, S., Wang, T., et al., 2022. Does education affect consumers' attitudes toward genetically modified foods? Evidence from China's two rounds of education reforms. China Agricultural Economic Review. 14(3), 631–645. DOI: https://doi.org/10.1108/CAER-01-2021-0024

[7] Gaskell, G., Bauer, M.W., Durant, J., et al., 1999. Worlds apart? The reception of genetically modified foods in Europe and the U.S. Science. 285 (5426), 384–387.

[8] Zheng, Z., Gao, Y., Zhang, Y., et al., 2017. Changing attitudes toward genetically modified foods in urban China. China Agricultural Economic Review. 9(3), 397–414. DOI: https://doi.org/10.1108/CAER-04-2017-0061

[9] World Health Organization, 2014. Food, genetically modified. Available from: https://www.who.int/news-room/questions-and-answers/item/food-genetically-modified (cited 9 August 2024).

[10] ISAAA, 2023. ISAAA2023 Accomplishment Report. Available from: https://www.isaaa.org/ (cited 11 August 2024).

[11] Roberts, R.J., 2018. The Nobel Laureates’ campaign supporting GMOs. Journal of Innovation & Knowledge. 3(2), 61–65. DOI: https://doi.org/10.1016/j.jik.2017.12.006

[12] IOWA State University, 2019. The Nobel Laureates' Campaign to Support GMOs. Available online: https://www.lectures.iastate.edu/lectures/nobel-laureates-campaign-support-gmos (cited 11 August 2024).

[13] Bánáti, D., 2011. Consumer response to food scandals and scares. Trends in Food Science & Technology. 22(2–3), 56–60. DOI: https://doi.org/10.1016/j.tifs.2010.12.007

[14] Smith, P.J., Jamiyansuren, B., Kitsuki, A., et al., 2018. Determinants of Comparative Advantage in GMO Intensive Industries. World Trade Review. 17(3), 427–49. DOI: https://doi.org/10.1017/S1474745617000180

[15] Hunt, K.P., Wald, D.M., 2020. The role of scientific source credibility and goodwill in public skepticism toward GM foods. Environmental Communication. 14, 971–986.

[16] Siddiqui, S.A., Asif, Z., Murid, M., et al., 2022. Consumer Social and Psychological Factors Influencing the Use of Genetically Modified Foods—A Review. Sustainability. 14, 15884. DOI: https://doi.org/10.3390/su142315884

[17] Future Market Insight, 2023. Genetically modified food market. Available from: https://www.futuremarketinsights.com/reports/genetically-modified-foods-market (cited 11 August 2024).

[18] Market Research Intellect, 2024. Genetically Modified Food Report. Available from : https://www.marketresearchintellect.com/download-sample/?rid=381243&utm_source=Pulse&utm_medium=014 (cited 11 August 2024).

[19] AgbioInvestor GM Monitor, 2024. Global GM Crop Area Review. Available from: https://gm.agbioinvestor.com/downloads/9 (cited 11 August 2024).

[20] Data Bridge Market Research, 2022. Global Genetically Modified (GMO) Seeds Market - Industry Trends and Forecast to 2029. Available from: https://www.databridgemarketresearch.com/reports/global-genetically-modified-gmo-seeds-market (cited 9 August 2024).

[21] Researchnester, 2023. Genetically Modified (GMO) Food Market Size & Share | Forecast Report 2029. Available from: https://www.researchnester.com/reports/genetically-modified-gmo-food-market/117 (cited 27 August 2024).

[22] Food and Agriculture Organization of the United Nations, 2022. The State of Food and Agriculture 2022. Leveraging automation in agriculture for transforming agrifood systems. Rome, FAO. DOI: https://doi.org/10.4060/cb9479en

[23] Akpan, J.I., Udoh, P., Adebisi, B., 2020. Small business awareness and adoption of state-of-the-art technologies in emerging and developing markets, and lessons from the COVID-19 pandemic. Journal of Small Business & Entrepreneurship. 34(3), 18. DOI: https://doi.org/10.1080/08276331.2020.1820185

[24] Breeman, G., Giest, S., Rimkutė, D., 2017. Food Security and the Sustainability of GMOs in the United States and the European Union. In Advances in Food Security and Sustainability. 2, 165–193. DOI: https://doi.org/10.1016/bs.af2s.2017.09.005

[25] Hemphill, T.A., Banerjee, S., 2015. Genetically Modified Organisms and the US Retail Food Labeling Controversy: Consumer Perceptions, Regulation, and Public Policy. Business and Society Review. 120, 435–464. DOI: https://doi.org/10.1111/basr.12062

[26] Kennedy, B., Thigpen, L. C., 2020. Many publics around world doubt safety of genetically modified foods. Pew Research Center. Available from: https://www.pewresearch.org/short-reads/2020/11/11/many-publics-around-world-doubt-safety-of-genetically-modified-foods/ (cited 27 August 2024).

[27] Larsson, T., 2016. Who catches the biotech train? Understanding diverging political responses to GMOs in Southeast Asia. The Journal of Peasant Studies. 43(5), 1068–1094. DOI: https://doi.org/10.1080/03066150.2016.1176561

[28] Chiaravutthi Y, Pongjit C., 2022. Thai Consumer Willingness to Pay for Differing GM Labeling Policies: Comparisons across Time. 40(3), 69-86.

[29] Bovay, J., Alston, J.M., 2018. GMO food labels in the United States: Economic implications of the new law. Food Policy. 78, 14–25.

[30] Davis, V., 2018. GMO Labeling Makes Public More Likely to Trust Food Companies. Science. Available from: https://www.science.org/content/article/gmo-labeling-makes-public-more-likely-trust-food-companies (cited 9 August 2024).

[31] Kerr, J.R., Wilson, M.S., 2018. Changes in perceived scientific consensus shift beliefs about climate change and GM food safety. PLoS ONE. 13(7), e0200295. DOI: https://doi.org/10.1371/journal.pone.0200295

[32] Overton, H.K., Yang, F., 2023. Do information disputes work: the effect of perceived risk, news disputes and credibility on consumer attitudes and trust toward biotechnology companies. Journal of Communication Management. DOI: https://doi.org/10.1108/JCOM-04-2023-0043

[33] Zafar, M.Z., Shi, X., Yang, H., et al., 2023. The Impact of Interpretive Packaged Food Labels on Consumer Purchase Intention: The Comparative Analysis of Efficacy and Inefficiency of Food Labels. International Journal of Environmental Research and Public Health. 19(22), 15098. DOI: https// doi.org/10.3390/ijerph192215098

[34] Medani, R.K., Neill, A., Garrod, G., et al., 2024. Societal perceptions and attitudes towards genetically modified (GM) crops, feed, and food products in the Middle East, North Africa, and Turkey (MENAT) region: A systematic literature review. Food Quality and Preference. 117, 105148. DOI: https://doi.org/10.1016/j.foodqual.2024.105148

[35] 6WResearch, 2023. Thailand GMO Testing Market (2024-2030) Outlook | Analysis, Trends, Industry, Size, Revenue, Value, Forecast, Share, Growth & Companies. Available from: https://www.6wresearch.com/industry-report/thailand-gmo-testing-market (cited 27 August 2024).

[36] Napasintuwong, O., 2019. Current Status of Agricultural Biotechnology in Thailand. FFTC Journal of Agricultural Policy. Available from: https://ap.fftc.org.tw/author/1828 (cited 27 August 2024).

[37] Nawaz, M.A., Ali, M.A., Golokhvast, K., et al., 2023. GMOs: History, Economic Status, Risks, and Socio-Economic Regulatory Frameworks. GMOs and Political Stance. pp. 1–13. DOI: https://doi.org/10.1016/B978-0-12-823903-2.00013-5

[38] USDA Foreign Agricultural Service., 2023. Thailand Updates Its Implementation on GM Foods Regulations. United States Department of Agriculture Foreign Agricultural Service GAIN. Available from: https://apps.fas.usda.gov/newgainapi/api/Report/DownloadReportByFileName?fileName=Thailand%20Updates%20Its%20Implementation%20on%20GM%20Foods%20Regulations%20_Bangkok_Thailand_TH2023-0006.pdf (cited 27 August 2024).

[39] Ajzen, I., 2011. The theory of planned behavior: Reactions and reflections. Psychol. Health. 26, 1113–1127.

[40] Ajzen, I., Fishbein, M.,1970. The Prediction of Behavior from Attitudinal and Normative Variables. J. Exp. Soc. Psychol. 6, 466–487.

[41] Akyüz, C. N., Theuvsen, L.,2020. The Impact of Behavioral Drivers on Adoption of Sustainable Agricultural Practices: The Case of Organic Farming in Turkey. Sustainability. 12(17), 6875. DOI: https://doi.org/10.3390/su12176875

[42] Lobb, A.E., Mazzocchi, M., Traill, W.B., 2007. Modeling risk perception and trust in food safety information within the theory of planned behavior. Food Quality and Preference. 18(2), 384–395.

[43] Choi, J., Lee, A., Ok, C., 2013. The Effects of Consumers' Perceived Risk and Benefit on Attitude and Behavioral Intention: A Study of Street Food. Journal of Travel & Tourism Marketing. 30(3), 222–237. DOI: https://doi.org/10.1080/10548408.2013.774916

[44] Kwon, H.J., Ahn, M., Kang,J., 2021. The Effects of Knowledge Types on Consumer Decision Making for Non-Toxic Housing Materials and Products. Sustainability. 13, 11024. DOI: https://doi.org/10.3390/su131911024

[45] Tajdini, S., 2021. The effects of the subjective-experiential knowledge gap on consumers’ information search behavior and perceptions of consumption risk. Journal of Business Research. 135, 66–77. DOI: https://doi.org/10.1016/j.jbusres.2021.06.025

[46] Ajzen, I., Fishbein, M., 1980. Understanding Attitudes and Predicting Social Behavior. Prentice-Hall, Englewood Cliffs.

[47] Fishbein, M., Ajzen, I., 1975. Belief, attitude, intention and behavior: An intro duction to theory and research. Reading, Addison-Wesley Publishing: Boston.

[48] Alhamad, H., Donyai, P., 2021. The Validity of the Theory of Planned Behavior for Understanding People's Beliefs and Intentions toward Reusing Medicines. Pharmacy. 9(1), 58. DOI: https://doi.org/10.3390/pharmacy9010058

[49] Conner, M., Norman, P., 2022. Understanding the intention-behavior gap: The role of intention strength. Frontier Psychology. 13(13), 923464. DOI: https://doi.org/10.3389/fpsyg.2022.923464

[50] Webster, J., Linda K.T., Lisa R., 1994. The dimensionality and correlates of flow in human-computer interactions. Computers in Human Behavior. 9(4), 411–426.

[51] Yang, M., Gao, J., Yang, Q., et al., 2024. Modeling the intention to consume and willingness to pay premium price for 3D-printed food in an emerging economy. Humanities and Social Sciences Communications volume. 11, 274. DOI: https://doi.org/10.1057/s41599-024-02776-1

[52] Ajzen, I., 2020. The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies. 2, 314–324.

[53] Bray, H.J., Ankeny, R.A., 2017. Not just about “the science”: science education and attitudes to genetically modified foods among women in Australia. New Genetics and Society. 36(1), 1–21. DOI: https://doi.org/10.1080/14636778.2017.1287561

[54] Xu, R., Wu, Y., Luan, J., 2020. Consumer-perceived risks of genetically modified food in China. Appetite. 147, 104520. DOI: https://doi.org/10.1016/j.appet.2019.104520

[55] Vindigni, G., Peri, L., Consentino, F., et al., 2022. Exploring Consumers’ Attitudes towards Food Products Derived by New Plant Breeding Techniques. Sustainability. 14(10), 5995. DOI: https://doi.org/10.3390/su14105995

[56] Evans, L.M., Petty, R.E., See, Y.H.M., 2009. The Impact of Perceived Message Complexity and Need for Cognition on Information Processing and Attitudes. Journal of Research in Personality. 43(5), 880–889.

[57] Bašinskienė, L., Šeinauskienė, B., 2021. Gene Editing versus Gene Modification: Awareness, Attitudes and Behavioral Intentions of Lithuanian Consumers, Producers and Farmers. Chemical Engineering Transactions. 87, 433–438.

[58] Li, L., Bautista, R.J., 2019. Examining Personal and Media Factors Associated with Attitude towards Genetically Modified Foods among University Students in Kunming, China. International Journal of Environmental Research and Public Health. 16(23), 4613. DOI: https://doi.org/10.3390/ijerph16234613

[59] Lefebvre, S., Cook, L.A., Griffiths, M.A., 2019. Consumer perceptions of genetically modified foods: a mixed-method approach. Journal of Consumer Marketing. 36(1), 113–123. DOI: https://doi.org/10.1108/JCM-12-2016-2043

[60] Silk, K.J., Weiner, J., Parrott, R.L., 2005. Gene Cuisine or Frankenfood? The Theory of Reasoned Action as an Audience Segmentation Strategy for Messages about Genetically Modified Foods. Journal of Health Communication. 10(8), 751–767. DOI: https://doi.org/10.1080/10810730500326740

[61] Bilkey, W.J., 1953. A psychological approach to consumer behavior analysis. J. Market. 18(1), 18–25. DOI: https://doi.org/10.2307/1246865

[62] Dhir, A., Malodia, S., Awan, U., et al., 2021. Extended valence theory perspective on consumers’ e-waste recycling intentions in Japan. Journal of Cleaner Production. 312, 127443. DOI: https://doi.org/10.1016/j.jclepro.2021.127443

[63] Mascarenhas, B.A., Perpétuo, K.C., Barrote, B.E., et al., 2021. The Influence of Perceptions of Risks and Benefits on the Continuity of Use of Fintech Services. Brazilian Business Review. 18(1), 1–21. DOI: https://doi.org/10.15728/bbr.2021.18.1.1

[64] Wang, Y., Hazen, B.T., 2016. Consumer product knowledge and intention to purchase remanufactured products. Int. J. Prod. Econ. 181, 460–469. DOI: https://doi.org/10.1016/j.ijpe.2015.08.031

[65] Kim, D.J., Ferrin, D.L., Rao, H.R., 2009. Trust and satisfaction, two stepping stones for successful e-commerce relationships: A longitudinal exploration. Information Systems Research, 20(2), 237–257.

[66] Mou, J., Cohen, J., Dou, Y., et al., 2017. PREDICTING BUYERS’ REPURCHASE INTENTIONS IN CROSS-BORDER E-COMMERCE: A VALENCE FRAMEWORK PERSPECTIVE. In Proceedings of the 25th European Conference on Information Systems (ECIS), Guimarães: Portugal. pp. 2382–2394. Available from: http://aisel.aisnet.org/ecis2017_rp/151 (cited 16 September 2024)

[67] Al-Debei, M.M., Hujran, O., Al-Adwan, S.A., 2024. Net valence analysis of iris recognition technology‑based FinTech. Financial Innovation. 10, 59. DOI: https://doi.org/10.1186/s40854-023-00509-y

[68] Mou, J., Shin, D.H., Cohen, J., 2016. The role of trust and health belief in the acceptance of online health services. Information Technology & People. 29(4), 876–900. DOI: https://doi.org/10.1108/ITP-06-2015-0140

[69] Zhang, M., Liu, G.-L., 2015. The effects of consumer’s subjective and objective knowledge on perceptions and attitude towards genetically modified foods: objective knowledge as a determinant. International Journal of Food Science & Technology. 50(5), 1198–205. DOI: https://doi.org/10.1111/ijfs.12753

[70] Boccia, F., Sarnacchiaro, P., 2015. Genetically modified foods and consumer perspective. Recent Pat Food Nutr Agric. 7 (1), 28–34. DOI: https://doi.org/10.2174/2212798407666150401105044

[71] Zhou, F., Tian, W., 2003. Consumer perceptions and attitudes toward GM food and their determinants: a case study in Beijing. China Agricultural Economic Review. 1(3), 266–293.

[72] Huang, J., Qiu, H., Bai, J., et al., 2006. Awareness, acceptance of and willingness to buy genetically modified foods in urban China. Appetite. 46(2), 144–151.

[73] Macer, D., Ng, M., 2000. Changing attitudes to biotechnology in Japan. Nature Biotechnology. 18 (9), 945–947.

[74] López, M.O.A., Pérez, E.F., Fuentes, E.E.S., et al., 2016. Perceptions and attitudes of the Mexican urban population towards genetically modified organisms. British Food Journal. 18(12), 2873–2892. DOI: https://doi.org/10.1108/BFJ-06-2016-0247

[75] Hossain, F., Onyango, B., 2004. Product attributes and consumer acceptance of nutritionally enhanced genetically modified foods. International Journal of Consumer Studies. 28(3), 255–267. DOI: https://doi.org/10.1111/j.1470-6431.2004.00352.x

[76] Moseley, B.E., 1999. The safety and social acceptance of novel foods. International Journal of Food Microbiology. 50(1–2), 25–31. DOI: https://doi.org/10.1016/S0168-1605(99)00074-4

[77] Goodman, R.E., 2014. Biosafety: evaluation and regulation of genetically modified (GM) crops in the United States. Journal of Hauzhong Agricultural University. 33(6), 85–114.

[78] Charlebois, S., Somogyi, S., Music, J., et al., 2019. Biotechnology in food: Canadian attitudes towards genetic engineering in both plant- and animal-based foods. British Food Journal. 121(12), 3181–3192. DOI: https://doi.org/10.1108/BFJ-07-2018-0471

[79] Bauer, R.A., 1960. Consumer behavior as risk raking. In Proceedings of the 43rd Conference of the Dynamic Marketing for a Changing World, ed. R. S. Hancock: Chicago. pp. 389–398.

[80] Jacobs, L., Worthley, R., 1999. A comparative study of risk appraisal: a new look at risk assessment in different countries. Environmental Monitoring and Assessment. 59(2), 225–247. DOI: https://doi.org/10.1023/A:1006163606270

[81] Leiserowitz, A., 2006. Climate change risk perception and policy preferences: the role of affect, imagery, and values. Climate Change. 77(1–2), 45–72, DOI: https://doi.org/10.1007/s10584-006-9059-9

[82] Pappas, M., 2016. Marketing strategies, perceived risks, and consumer trust in online buying behavior. Journal of Retailing and Consumer Services. 29, 92-103. DOI: https://doi.org/10.1016/j.jretconser.2015.11.007

[83] Kim, D.J., Ferrin, D.L., Rao, H.R., 2008. A trust-based consumer decision making model in electronic commerce: the role of trust, perceived risk, and their antecedents. Decision Support System. 44(2), 544–564. DOI: https://doi.org/10.1016/j.dss.2007.07.001

[84] Liu, T.M., Brock, J.L., Shi, C.G., et al., 2013. Perceived benefits, perceived risk, and trust: Influences on consumers' group buying behavior. Asia Pacific Journal of Marketing and Logistics. 25 (2), 225–248. DOI: https://doi.org/10.1108/13555851311314031

[85] Chandon, P., Wansink, B., Laurent, G., 2000. A benefit congruency framework of sales promotion effectiveness. Journal of Marketing. 64 (4), 65–81. DOI: https://doi.org/10.1509/jmkg.64.4.65.18

[86] Loh, Z., Hassan, S.H., 2022. Consumers’ attitudes, perceived risks and perceived benefits towards repurchase intention of food truck products. British Food Journal. 12(4), 1314–1332. DOI: https://doi.org/10.1108/BFJ-03-2021-0216

[87] Moodie, A., 2016. GMO food labels are coming to more of us grocery shelves- are consumers ready? The Guardian. Available from: https://www.theguardian.com/sustainable-business/2016/mar/24/gmo-food-labels-general-mills-kellog-mars (cited 28 August 2024).

[88] Şanlıer, N., Sezgin, C.A., 2020. Consumers’ knowledge level, attitudes, behaviors and acceptance of GM foods. Journal of Human Sciences. 17(4), 1235–1249. DOI: https://doi.org/10.14687/jhs.v17i4.6016

[89] Official Journal of the European Union, 2011. Regulation (EU) No 1169/2011 of The European Parliament and of the Council of 25 October 2011. Available from: https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2011:304:0018:0063:en:PDF (cited 28 August 2024).

[90] Moreira, M.J., García-Díez, J., de Almeida, J.M.M.M., et al., 2021. Consumer Knowledge about Food Labeling and Fraud. Foods. 10(5), 1095. DOI: https://doi.org/10.3390/foods10051095

[91] Luomala, H., Jokitalo, M., Karhu, H., et al., 2015. Perceived health and taste ambivalence in food consumption. Journal of Consumer Marketing. 32(4), 290–301.

[92] Escandon-Barbosa, D., Rialp-Criado, J., 2019.The Impact of the Content of the Label on the Buying Intention of a Wine Consumer. Frontier Psychology. DOI: https://doi.org/10.3389/fpsyg.2018.02761

[93] Jiang, D., Zhang, G., 2021. Marketing Clues on the Label Raise the Purchase Intention of Genetically Modified Food. Sustainability. 13, 9970. DOI: https://doi.org/10.3390/su13179970

[94] Costanigro, M., Lusk, J.L., 2014. The signaling effect of mandatory labels on genetically engineered food. Food Policy. 49, 259–267.

[95] Liaukonyte, J., Streletskaya, N.A., Kaiser, H.M., et al., 2013. Consumer response to ‘contains’ and ‘free of’ labeling: evidence from lab experiments. Applied Economic Perspectives and Policy. 5(3), 476–507.

[96] Eagly, A.H., Chaiken, S., 1993. The Psychology of Attitudes. Harcourt Brace Jovanovich College Publishers: Texas.

[97] Pieniak, Z., Aertsens, J., Verbeke, W., 2010. Subjective and objective knowledge as determinants of organic vegetables consumption, Food quality and preference. 21(6), 581–588.

[98] Altamore, L., Bacarella, S., Columba, P., et al., 2017. The Italian consumers’ preferences for pasta: does environment matter? Chemical Engineering Transactions. 58, 859–864.

[99] Ajzen, I., 1985. From intentions to actions: A theory of planned behavior. In: Action control. Springer: Heidelberg. pp. 11–39.

[100] Ajzen, I., Fishbein, M., 2000. Attitudes and the attitude-behavior relation: Reasoned and automatic processes, European review of social psychology. 11(1), 1–33.

[101] Lusk, J., Jamal, M., Kurlander, L., et al., 2005. A meta-analysis of genetically modified food valuation studies. Journal of Agricultural and Resource Economics. 30(1), 28–44.

[102] McCluskey, J., Swinnen, J., 2004. Political economy of the media and consumer perceptions of biotechnology. American Journal of Agricultural Economics. 86(5), 1230–1237.

[103] Akbari, M., Ardekani, Z. F., Pino, G., et al., 2023. Consumer Attitude towards Genetically Modified Foods in Iran: Application of Three-Dimensional Model of Corporate Social Responsibility. Foods. 12, 1553. DOI: https://doi.org/10.3390/foods12071553

[104] Muzhinji, N., Ntuli, V., 2021. Genetically modified organisms and food security in Southern Africa: Conundrum and discourse. GM Crop. 2, 25–35.

[105] Macall, D.M., Williams, C., Gleim, S., et al., 2021. Canadian consumer opinions regarding food purchase decisions. Journal of Agriculture and Food Research. 3, 100098. DOI: https://doi.org/10.1016/j.jafr.2020.100098

[106] Sendhil, R., Nyika, J., Yadav, S., et al., 2022. Genetically modified foods: Bibliometric analysis on consumer perception and preference. GM Crops Food. 13(1), 65–85. DOI: https://doi.org/10.1080/21645698.2022.2038525

[107] Sörqvist, P., Marsh, J.E., Holmgren, M., et al., 2016. Effects of labeling a product eco-friendly and genetically modified: A cross-cultural comparison for estimates of taste, willingness to pay and health consequences. Food Quality and Preference. 50, 65–70. DOI: https://doi.org/10.1016/j.foodqual.2016.01.007

[108] Bredahl, L., 1999. Consumers’ cognitions with regard to genetically modified foods: results of a qualitative study in four countries. Appetite. 33(3), 343–360. DOI: https://doi.org/10.1006/appe.1999.0267

[109] Curtis, K.R., McCluskey, J.J., Wahl, T.I., 2004. Consumer acceptance of genetically modified food products in the developing world. AgBioForum. 7(1&2),70–75.

[110] Bredahl, L., Grunert, G., Frewer, L.J., 1998. Consumer attitudes and decision making with regard to genetically engineered food products – a review of the literature and a presentation of models for future research. Journal of Consumer Policy. 21(3), 251–277. DOI: https://doi.org/10.1023/A:1006940724167

[111] Hallman, W.K., Hebden, W.C., Auino, H.L., et al., 2003. Public Perceptions of Genetically Modified Foods: National Study of Americans’ Knowledge and Opinion, Food Policy Institute, Cook College, Rutgers, the State University of New Jersey, New Brunswick, NJ. Available from: www.foodpolicyinstitute.org (cited 28 August 2024).

[112] Chen, M., 2011. The gender gap in food choice motives as determinants of consumers' attitudes toward GM foods in Taiwan. British Food Journal. 113(6), 697–709. DOI: https://doi.org/10.1108/00070701111140052

[113] Ajzen I., 1991. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50, 179–211. DOI: 10.1016/0749-5978(91)90020-T

[114] Ajzen I., Madden T. J., 1986. Prediction of goal-directed behavior: attitudes, intentions, and perceived behavioral control. Journal of Experimental Social Psychology. 22, 453–474. DOI: https://doi.org/10.1016/0022-1031(86)90045-4

[115] Dodds, W.B., Monroe, K.B., Grewal, D., 1991. Effects of price, brand, and store information on buyers’ product evaluations. J. Mark. Res. 28, 307–319. DOI: https://doi.org/10.1177/002224379102800305

[116] Li, J., Guo, F., Xu, J., et al., 2022. What Influences Consumers' Intention to Purchase Innovative Products: Evidence from China. Frontier Psychology. 13, 838244. DOI: https://doi.org/10.3389/fpsyg.2022.838244

[117] Spears, N., Singh, S.N., 2004. Measuring Attitude toward the Brand and Purchase Intentions. Journal of Current Issues & Research in Advertising. 26(2), 53–66. DOI: https://doi.org/10.1080/10641734.2004.10505164

[118] Immonen, A.-M., Luomala, H.T., 2017. Different shades of displeasure: When fear and anger lead to opposite consumer responses to GM foods. British Food Journal. 119(12), 2740–2752. DOI: https://doi.org/10.1108/BFJ-08-2016-0374

[119] Latiff, A.A.Z., Rezai, G., Mohamed, Z., et al., 2015. Food Labels’ Impact Assessment on Consumer Purchasing Behavior in Malaysia, Journal of Food Products Marketing. 1–15. DOI: https://doi.org/10.1080/10454446.2013.856053

[120] Chang, H.H., Huang, C.Y., Fu, C.S., et al., 2017. The effects of innovative, consumer and social characteristics on willingness to try nano-foods: Product uncertainty as a moderator. Information Technology & People. 30(3), 653–690. DOI: https://doi.org/10.1108/ITP-10-2015-0266

[121] Assumption University Announcement, 2022. Reporting the Research/ Academic Works for the IRB Approval. Available from: https://iras.au.edu/wp-content/uploads/2023/01/1-AU-Announcement-No.6_2022-Reporting-the-Research-Academic-Works-for-the-IRB-Approval.pdf (cited 16 September 2024).

[122] Manti, S., Licari, A., 2018. How to obtain informed consent for research. Breathe (Sheff). 14(2),145–152. DOI: https://doi.org/10.1183/20734735.001918

[123] Taber, K.S., 2018. The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Research in Science Education. 48, 1273–1296. DOI: https://doi.org/10.1007/s11165-016-9602-2

[124] Latkin, C.A., Edwards, C., Davey-Rothwell, M.A., et al., 2017. The relationship between social desirability bias and self-reports of health, substance use, and social network factors among urban substance users in Baltimore, Maryland. Addictive Behaviors. 73, 133–136. DOI: https://doi.org/10.1016/j.addbeh.2017.05.005

[125] Joinson A.,1999. Social desirability, anonymity, and Internet-based questionnaires. Behavior Research Methods, Instruments, & Computers. 31(3), 433–438. DOI: https://doi.org/10.3758/bf03200723

[126] Xu, P., Zhu, B., Saenghiran, T., 2024. Young Consumers’ Discontinuance Intention to Use Smartphone Fitness Applications – A Study of Generation Z Consumers in Bangkok. In: Wei, J., Margetis, G. (eds) Human-Centered Design, Operation and Evaluation of Mobile Communications. HCII 2024. Lecture Notes in Computer Science, vol 14737. Springer, Cham. DOI: https://doi.org/10.1007/978-3-031-60458-4_9

[127] Preacher, J.K., Hayes, F.A., 2008. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods. 40(3), 879–891. Available from: https://link.springer.com/content/pdf/10.3758/BRM.40.3.879.pdf (cited 28 August 2024).

[128] Ringle, C.M., Wende, S., Becker, J.M., 2024. SmartPLS 4. Bönningstedt: SmartPLS. Available from: https://www.smartpls.com (cited 28 August 2024).

[129] Kotler, P., Kartajaya, H., Setiawan, I., 2021. Marketing 5.0: Technology for Humanity. John Willey & Sons. Inc., Hoboken: New Jersey.

[130] dos Santos, P.M., Cirillo, M.Â., 2021. Construction of the average variance extracted index for construct validation in structural equation models with adaptive regressions. Communications in Statistics - Simulation and Computation. 52(4), 1639–1650. DOI: https://doi.org/10.1080/03610918.2021.1888122

[131] Ramayah, T., Cheah, J., Chuah, F., et al., 2018. Partial Least Squares Structural Equation Modeling (PLS-SEM) Using SmartPLS 3.0: An Updated and Practical Guide to Statistical Analysis, Pearson: Malaysia.

[132] Byrne, M.B., 2016. Structural Equation Modeling with Amos: Basic Concepts, Applications, and Programming, Routledge: New York.

[133] Henseler, J., Ringle, M.C., Sarstedt, M., 2015. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science. 43(1), 115–135. DOI: https://doi.org/10.1007/s11747-014-0403-8

[134] Hair, J.F., Risher, J.J., Sarstedt, M., et al., 2019. When to use and how to report the results of PLS-SEM. European Business Review. 30(1), 2–24.

[135] Bagozzi, R.P., Yi, Y., 1988. On the evaluation of structural equation models. Journal of Academy of Marketing Science. 16(1),74–94.

[136] Zhu, B., Charoennan, W., Embalzado, H., 2021. The influence of perceived risks on millennials' intention to use m-payment for mobile shopping in Bangkok. International Journal of Retail & Distribution Management. 50(4), 479–497. DOI: https://doi.org/10.1108/IJRDM-05-2020-0174

[137] Nunnally, J.C., Bernstein, I.H., 1994. Psychometric theory (3rd ed.). McGraw-Hill Publisher: New York.

[138] Rigdon, E.E., 2012. Rethinking partial least squares path modeling: in praise of simple methods. Long Range Planning. 45(5/6), 341–358.

[139] Henseler, J., Ringle, C.M., Sinkovics, R.R., 2009. The use of partial least squares path modeling in international marketing. In: Sinkovics, R.R., Ghauri, P.N. (eds.) New Challenges to International Marketing (Advances in International Marketing, Vol. 20). Emerald Group Publishing Limited: Leeds. pp. 277–319. DOI: https://doi.org/10.1108/S1474-7979(2009)0000020014

[140] Hair, J.F., Ringle, C.M., Sarstedt, M., 2011. PLS-SEM: indeed a silver bullet. Journal of Marketing Theory and Practice. 19(2), 139–151.

[141] Homburg, C., Schwemmle, M., Kuehnl, C., 2015. New Product Design: Concept, Measurement, and Consequences. Journal of Marketing.79(3), 41–56. Available from: http://www.jstor.org/stable/43784405

[142] Reibstein, D.J., Day, G.,Wind, J., 2009. Guest Editorial: Is Marketing Academia Losing Its Way? Journal of Marketing. 73(4), 1–3. DOI: https://doi.org/10.1509/jmkg.73.4.001

[143] Lehmann, D.R., McAlister, L., Staelin, R., 2011. Sophistication in research in marketing. Journal of Marketing. 75(4), 155–165.

[144] Shmueli, G., Sarstedt, M., Hair, J.F., et al., 2019. Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing. 53(11), 2322–2347. DOI: https://doi.org/10.1108/EJM-02-2019-0189

[145] Shmueli, G., Ray, S., Velasquez Estrada, J.M., et al., 2016. The elephant in the room: evaluating the predictive performance of PLS models. Journal of Business Research. 69(10), 4552–4564.

[146] Rasoolimanesh, M.S., Ali, F., 2018. Guest editorial. Journal of Hospitality and Tourism Technology. 9(3), 238–248. DOI: https://doi.org/10.1108/JHTT-10-2018-142

[147] Tibbe, T.D., Montoya, A.K., 2022. Correcting the Bias Correction for the Bootstrap Confidence Interval in Mediation Analysis. Frontier Psychology. 13, 810258. DOI: https://doi.org/10.3389/fpsyg.2022.810258

[148] Rahman, A.A.N.P., Harun, R., Johari, R.N., 2020. The Effect of Packaging Design Elements on Youth Purchase Intention of Junk Food. Jurnal Bisnis Manajemen dan Perbankan. 6(1), 25–35. DOI: https://doi.org/10.21070/jbmp.v6i1.442

[149] Alsini, N., Kutbi, H.A., Hakim, N., et al., 2023. Factors influencing grocery shopping choices and the prevalence of food label use among Saudi mothers: a cross-sectional pilot study. Nutrition & Food Science. 53(2), 432–444. DOI: https://doi.org/10.1108/NFS-11-2021-0345

[150] Guo, Q.Z., Yao, N.Z., Zhu, W.W., 2020. How consumers’ perception and information processing affect their acceptance of genetically modified foods in China: A risk communication perspective. Food Research International. 137, 109518. DOI: https://doi.org/10.1016/j.foodres.2020.109518

[151] Bauer, R.A., 1967. Consumer behavior as risk taking. In Cox, D.F. (ed.), Risk Taking & Information Handling in Consumer Behavior. Graduate School of Business Administration, Harvard University: Boston.

[152] Andrade, C., 2021. The Inconvenient Truth About Convenience and Purposive Samples. Indian Journal of Psychological Medine. 43(1), 86–88. DOI: https://doi.org/10.1177/0253717620977000

[153] Acharya, A.S., Prakash, A., Saxena, P., et al., 2013. Sampling: Why and how of it. Indian Journal of Medical Specialties. 4(2), 330–333. DOI: https://doi.org/10.7713/ijms.2013.0032

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