Artificial Intelligence and Consumer’s Perception: A Research on Environmentally Conscious Consumer

Year 2024, Volume: 4 Issue: 2, 105 - 115

Apoorva Bhatnagar , Megha Sharma

https://doi.org/10.57019/jmv.1488022

Abstract

The purpose of this study is to explore the limited exploration of the simultaneous influence of beneficial artificial intelligence, destructive artificial intelligence, and risky artificial intelligence on green purchase intention and green purchase behaviour using the Technology Acceptance Model (TAM) and Innovation Resistance Theory (IRT). Further, it also checks the impact of green purchase intention on green purchase behaviour. Data was collected using a well-structured questionnaire from 124 consumers through online mode and analyzed using Confirmatory Factor Analysis (CFA) for reliability and validity concerns and Structural Equation Modelling (SEM) for interaction among the variables. The study's results exhibit the positive impact of beneficial artificial intelligence on green purchase intention and green purchase behaviour. Also, it reveals that destructive artificial intelligence has a positive impact on green purchase intention but a negative impact on green purchase behaviour. In addition, green purchase intention is found to be the predictor of green purchase behaviour. The extant literature is found on the impact of artificial intelligence on purchase behaviour. However, no research has been done on consumer perception of artificial intelligence and its impact on green purchase intention and green purchase behaviour as per the author’s knowledge. This study contributes to the literature of artificial intelligence as well as green consumer behaviour.

Keywords

Innovation resistance theory (IRT), Artificial intelligence, Green purchase intention, Green purchase behaviour, Technology acceptance model (TAM), Structure equation modelling (SEM)

References

• Adwan, A., & Aladwan, R. (2022). Use of artificial intelligence system to predict consumers’ behaviours. International Journal of Data and Network Science, 6(4), 1223-1232.
• Ajzen, I. and Fishbein, M. (1980) Understanding Attitudes and Predicting Social Behaviour. Englewood Cliffs, NJ: Prentice-Hall.
• Alagarsamy, S., Mehrolia, S., & Singh, B. (2021). Mediating effect of brand relationship quality on relational bonds and online grocery retailer loyalty. Journal of Internet Commerce, 20(2), 246-272.
• Ali, M., Ullah, S., Ahmad, M. S., Cheok, M. Y., & Alenezi, H. (2023). Assessing the impact of green consumption behaviour and green purchase intention among millennials toward sustainable environment. Environmental Science and Pollution Research, 30(9), 23335-23347.
• Ameen, N., Tarhini, A., Reppel, A., & Anand, A. (2021). Customer experiences in the age of artificial intelligence. Computers in Human Behaviour, 114, 106548.
• Anderson, J. C., & Gerbing, D. W. (1984). The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis. Psychometrika, 49, 155-173.
• André, Q., Carmon, Z., Wertenbroch, K., Crum, A., Frank, D., Goldstein, W., ... & Yang, H. (2018). Consumer choice and autonomy in the age of artificial intelligence and big data. Customer needs and solutions, 5, 28-37.
• Arsenijevic, U. and Jovic, M. (2019) ‘Artificial intelligence marketing: Chatbots.’ 2019 International Conference on Artificial Intelligence: Applications and Innovations. Retrieved from: IEEE Xplore Database [Accessed on 7 January 2021].
• Arsenijevic, U., & Jovic, M. (2019, September). Artificial intelligence marketing: chatbots. In 2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI) (pp. 19-193). IEEE.
• Aytekin, P., Virlanuta, F. O., Guven, H., Stanciu, S., & Bolakca, I. (2021). Consumers' perception of risk towards artificial intelligence technologies used in trade: a scale development study. Amfiteatru Economic, 23(56), 65-86.
• Bhagat, R., Chauhan, V., & Bhagat, P. (2023). Investigating the impact of artificial intelligence on consumer’s purchase intention in e-retailing. Foresight, 25(2), 249-263.
• Bhatnagar, A., & Singh, V. K. (2021). Conscientiousness and social entrepreneurial vision: testing the moderating effect of family influence. International Journal of Business and Globalisation, 28(4), 435-449.
• Bhatnagar, A., Verma, S., Singh, V. K., & Dasgupta, A. (2020). Openness to Experience and Green Purchase Behaviour: A Multiple Mediation Analysis. Manag Econ Res J, 6(3).
• Biswas, S. S. (2023). Role of ChatGPT in public health. Annals of Biomedical Engineering, 51(5), 868–869. https://doi.org/10.1007/s10439-023-03172-7
• Bray, J., Johns, N., & Kilburn, D. (2011). An exploratory study into the factors impeding ethical consumption. Journal of Business Ethics, 98, 597-608.
• Brown, T. A. (2015). Confirmatory factor analysis for applied research. Guilford publications.
• Campbell, C., Sands, S., Ferraro, C., Tsao, H. Y. J., & Mavrommatis, A. (2020). From data to action: How marketers can leverage AI. Business horizons, 63(2), 227-243.
• Chan-Olmsted, S. M. (2019). A review of artificial intelligence adoptions in the media industry. International Journal on Media Management, 21(3-4), 193-215.
• Choi, S., Ng, A., 2011. Environmental and economic dimensions of sustainability and price effects on consumer responses. J. Bus. Ethics 104 (2), 269e282.
• Cooper, G. (2023). Examining science education in ChatGPT: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 32(3), 444–452. https://doi.org/10.1007/s10956-023-10039-y
• Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24-42.
• Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation, Massachusetts Institute of Technology).
• Dhir, A., Sadiq, M., Talwar, S., Sakashita, M., & Kaur, P. (2021). Why do retail consumers buy green apparel? A knowledge-attitude-behaviour-context perspective. Journal of Retailing and Consumer Services, 59, 102398.
• Do Paco, A., Shiel, C., & Alves, H. (2019). A new model for testing green consumer behaviour. Journal of Cleaner Production, 207, 998-1006.
• Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024). Generative ai. Business & Information Systems Engineering, 66(1), 111-126.
• Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics.
• Frank, B. (2021). Artificial intelligence-enabled environmental sustainability of products: Marketing benefits and their variation by consumer, location, and product types. Journal of Cleaner Production, 285, 125242.
• Frank, D. A., Jacobsen, L. F., Søndergaard, H. A., & Otterbring, T. (2023). In companies we trust: consumer adoption of artificial intelligence services and the role of trust in companies and AI autonomy. Information Technology & People, 36(8), 155-173.
• Fui-Hoon Nah, F., Zheng, R., Cai, J., Siau, K., & Chen, L. (2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Journal of Information Technology Case and Application Research, 25(3), 277-304.
• Gkikas, D., Theodoridis, P. (2022). AI in Consumer Behaviour. In: Virvou, M., Tsihrintzis, G.A., Tsoukalas, L.H., Jain, L.C. (eds) Advances in Artificial Intelligence-based Technologies. Learning and Analytics in Intelligent Systems, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-030-80571-5_10
• Grewal, D., Hulland, J., Kopalle, P. K., and Karahanna, E. (2020). The future of technology and marketing: A multidisciplinary perspective. Journal of the Academy of Marketing Science, 48(1), 1–8.
• Gupta, D. G., & Jain, V. (2023). Use of Artificial Intelligence with ethics and privacy for personalized customer services. In Artificial Intelligence in customer service: The next frontier for personalized engagement (pp. 231-257). Cham: Springer International Publishing.
• Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012). The use of partial least squares structural equation modeling in strategic management research: a review of past practices and recommendations for future applications. Long Range Planning, 45(5-6), 320-340.
• Halloran, L. J., Mhanna, S., & Brunner, P. (2023). AI tools such as ChatGPT will disrupt hydrology, too. Hydrological Processes, 37(3), 1–3. https://doi.org/10.1002/hyp.14843
• Hew, J. J., Leong, L. Y., Tan, G. W. H., Ooi, K. B., & Lee, V. H. (2019). The age of mobile social commerce: An Artificial Neural Network analysis on its resistances. Technological Forecasting and Social Change, 144, 311-324.
• Hooper, D., Coughlan, J., & Mullen, M. (2008, September). Evaluating model fit: a synthesis of the structural equation modelling literature. In 7th European Conference on Research Methodology for Business and Management Studies (Vol. 2008, pp. 195-200).
• Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
• Huang, M. H., and Rust, R. T. (2021). A strategic framework for artifcial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50.
• Jaiswal, D., & Kant, R. (2018). Green purchasing behaviour: A conceptual framework and empirical investigation of Indian consumers. Journal of retailing and consumer services, 41, 60-69.
• Jo, A. (2023). The promise and peril of generative AI. Nature, 614(1), 214-216.
• Joo, Y., Seok, H., & Nam, Y. (2020). The moderating effect of social media use on sustainable rural tourism: a theory of planned behaviour model. Sustainability, 12(10), 4095.
• Joshi, Y., & Rahman, Z. (2016). Predictors of young consumer’s green purchase behaviour. Management of Environmental Quality: An International Journal, 27(4), 452-472.
• Joshi, Y., & Rahman, Z. (2018). Determinants of sustainable consumption behaviour: review and conceptual framework. Green Initiatives for Business Sustainability and Value Creation, 239-262.
• Karulkar, Y., Bandorawalla, P., Banerjee, A., Vasudevan, A., & Parihar, M. (2024). Innovation Through Computing Green Marketing and Its Effect on Consumer Purchase Intention. In Artificial Intelligence-Augmented Digital Twins: Transforming Industrial Operations for Innovation and Sustainability (pp. 69-84). Cham: Springer Nature Switzerland.
• Khan, S., Tomar, S., Fatima, M., & Khan, M. Z. (2022). Impact of artificial intelligent and industry 4.0 based products on consumer behaviour characteristics: A meta-analysis-based review. Sustainable Operations and Computers, 3, 218-225.
• Koller, M., Floh, A., and Zauner, A. (2011). Further insights into perceived value and consumer loyalty: A “green” perspective. Psychology and Marketing, 28(12), 1154-1176.
• Korzeniowski, L., & Goczyla, K. (2019). Artificial intelligence for software development: the present and the challenges for the future. Biuletyn Wojskowej Akademii Technicznej, 68(1).
• Krafft, M., Kumar, V., Harmeling, C., Singh, S., Zhu, T., Chen, J., ... & Rosa, E. (2021). Insight is power: Understanding the terms of the consumer-firm data exchange. Journal of Retailing, 97(1), 133-149.
• Kumar, A., & Smith, S. (2018). Understanding local food consumers: Theory of planned behaviour and segmentation approach. Journal of Food Products Marketing, 24(2), 196-215.
• Kumar, V., Rajan, B., Venkatesan, R., and Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135–155.
• Larva, D. (2021). Consumer perceptions on the use of artificial intelligence in marketing.
• Liang, Y., Lee, S.H., Workman, J.E. (2020) ‘Implementation of Artificial Intelligence in Fashion: Are Consumers Ready?’. Clothing and Textiles Research Journal. 38(1): 3- 18. Retrieved from: Sage Journals Database [Accessed on 25 March 2021].
• Libai, B., Bart, Y., Gensler, S., Hofacker, C., Kaplan, A., Kötterheinrich, K., et al. (2020). Brave new world? On AI and the management of customer relationships. Journal of Interactive Marketing, 51, 44–56.
• Lin, S. W., Huang, E. Y., & Cheng, K. T. (2023). A binding tie: why do customers stick to omnichannel retailers?. Information Technology & People, 36(3), 1126-1159.
• Liu, T., Wang, W., Xu, J., Ding, D., & Deng, H. (2021). Interactive effects of advising strength and brand familiarity on users' trust and distrust in online recommendation agents. Information Technology & People, 34(7), 1920-1948.
• Lupo, G. (2023). Risky artificial intelligence: The role of incidents in the path to AI regulation. Law, Technology and Humans, 5(1), 133-152.
• Ma, L., and Sun, B. (2020). Machine learning and AI in marketing— connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481–504.
• Malhotra, N.K. & Dash, S. (2010). Marketing Research: An Applied Orientation, Pearson Education India. Edition 6th.
• Malik, C., & Singhal, N. (2017). Consumer environmental attitude and willingness to purchase environmentally friendly products: An SEM approach. Vision, 21(2), 152-161.
• Marangunic, N., & Granic, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal access in the information society, 14, 81-95.
• Meagher, K. (2017) ‘Clicks for Cars: How AI and Machine Learning Are Revolutionizing Automotive Marketing.’ Digital Dealer [Online]. New York. Retrieved from: ProQuest Database [Accessed on 7 January 2021].
• Moser, A. K. (2015). Thinking green, buying green? Drivers of pro-environmental purchasing behaviour. Journal of consumer marketing, 32(3), 167-175.
• Moslehpour, M., Yin Chau, K., Du, L., Qiu, R., Lin, C. Y., & Batbayar, B. (2023). Predictors of green purchase intention toward eco-innovation and green products: Evidence from Taiwan. Economic research-Ekonomska istrazivanja, 36(2).
• Nguyen, T. N., Lobo, A., & Greenland, S. (2017). Energy efficient household appliances in emerging markets: the influence of consumers' values and knowledge on their attitudes and purchase behaviour. International Journal of Consumer Studies, 41(2), 167-177.
• Nunnally, Jum C. (1978), Psychometric Theory, 2d ed., New York: McGraw-Hill.
• Nyilasy, G., Gangadharbatla, H., and Paladino, A. (2014). Perceived greenwashing: The interactive effects of green advertising and corporate environmental performance on consumer reactions. Journal of Business Ethics, 125, 693-707.
• Ottman, J.A. (2011). The New Rules of Green Marketing: Strategies, Tools, and Inspiration for Sustainable Branding. Berrett-Koehler Publishers, San Francisco, CA.
• Poole, D.L., Mackworth, A.K., Goebel, R., 1998. Computational Intelligence: A Logical Approach, vol. 1. Oxford University Press, New York.
• Puntoni, S., Reczek, R.W., Giesler, M., Botti, S. (2020) ‘Consumers and artificial intelligence: an experiential perspective.’ American Marketing Association, 85 (1): 131-151. Retrieved from: Sage Journals Database [Accessed on 25 March 2021].
• Puntoni, S., Walker Reczek, R., Giesler, M., and Botti, S. (2021). Consumers and artifcial intelligence: An experiential perspective. Journal of Marketing, 85(1), 131–151.
• Ram, S. and Sheth, J.N. (1989), Consumer Resistance to Innovations: The Marketing Problem and its solutions. Journal of Consumer Marketing, 6(2), 5-14. https://doi.org/10.1108/EUM0000000002542
• Roberts, J. A. (1996). Green consumers in the 1990s: Profile and implications for advertising. Journal of Business Research, 36(3), 217-231.
• Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach ((3rd ed.)). Upper Saddle River, NJ: Pearson Education Limited.
• Russell, S.J., Norvig, P., 2009. Artificial Intelligence: A Modern Approach, third ed. Prentice Hall, Upper Saddle River, NJ.
• Ryzhkova, M., Soboleva, E., Sazonova, A., & Chikov, M. (2020). Consumers’ perception of artificial intelligence in banking sector. In SHS Web of Conferences (Vol. 80, p. 01019). EDP Sciences.
• Sallam, M. (2023). ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. Healthcare, 11 (6), 887.
• Sadriwala, M. F., & Sadriwala, K. F. (2022). Perceived usefulness and ease of use of artificial intelligence on marketing innovation. International Journal of Innovation in the Digital Economy (IJIDE), 13(1), 1-10.
• Shaik, M. (2023). Impact of artificial intelligence on marketing. East Asian Journal of Multidisciplinary Research, 2(3), 993-1004.
• Sharma, N., Paço, A., & Kautish, P. (2022). The impact of eco-innovation on green buying behaviour: the moderating effect of emotional loyalty and generation. Management of Environmental Quality: An International Journal, 33(4), 1026-1045.
• Shih, H. P. (2004). Extended technology acceptance model of Internet utilization behaviour. Information & management, 41(6), 719-729.
• Shishan, F., Hmoud, H., Zaidan, H., & Qasem, Z. (2022). The innovation resistance theory: the case of cryptocurrencies. In Artificial Intelligence for Sustainable Finance and Sustainable Technology: Proceedings of ICGER 2021 1 (pp. 459-465). Springer International Publishing.
• Singh, S. (2023). Destructive Artificial Intelligence: Impact and Challenges. Indian J. Integrated Rsch. L., 3, 1.
• Sinha, R., & Annamdevula, S. (2022). The antecedents of green purchase behaviour of Indian households. Electronic Green Journal, 1(47).
• Smith, S., & Paladino, A. (2010). Eating clean and green? Investigating consumer motivations towards the purchase of organic food. Australasian Marketing Journal, 18(2), 93-104.
• Sotala, K. (2018). Disjunctive scenarios of catastrophic AI risk. In Artificial intelligence safety and security (pp. 315-337). Chapman and Hall/CRC.
• Stone, M., Aravopoulou, E., Ekinci, Y., Evans, G., Hobbs, M., Labib, A., ... & Machtynger, L. (2020). Artificial intelligence (AI) in strategic marketing decision-making: a research agenda. The Bottom Line, 33(2), 183-200.
• Taddeo, M., and Floridi, L. (2018). How AI can be a force for good. Science, 361(6404), 751–752.
• Taeihagh, A. (2021). Governance of artificial intelligence. Policy and society, 40(2), 137-157.
• Tan, C. N. L., Ojo, A. O., & Thurasamy, R. (2019). Determinants of green product buying decision among young consumers in Malaysia. Young Consumers, 20(2).
• Taufique, K. M. R., & Vaithianathan, S. (2018). A fresh look at understanding Green consumer behaviour among young urban Indian consumers through the lens of Theory of Planned Behaviour. Journal of Cleaner Production, 183, 46-55.
• Tewari, A., Mathur, S., Srivastava, S., & Gangwar, D. (2022). Examining the role of receptivity to green communication, altruism and openness to change on young consumers’ intention to purchase green apparel: A multi-analytical approach. Journal of Retailing and Consumer Services, 66, 102938.
• Tromp, N., Hekkert, P., & Verbeek, P.-P. (2011). Design for socially responsible behaviour: A classification of influence based on intended user experience. Design Issues, 27(3), 3–19. https://doi.org/10.1162/DESI_ a_00087
• Tussyadiah, I., and Miller, G. (2019). Perceived impacts of artificial intelligence and responses to positive behaviour change intervention. In Information and Communication Technologies in Tourism 2019: Proceedings of the International Conference in Nicosia, Cyprus, January 30–February 1, 2019 (pp. 359-370). Springer International Publishing.
• Tussyadiah, I., Li, S., & Miller, G. (2019). Privacy protection in tourism: Where we are and where we should be heading for. In Information and Communication Technologies in Tourism 2019: Proceedings of the International Conference in Nicosia, Cyprus, January 30–February 1, 2019 (pp. 278-290). Springer International Publishing.
• Verma, A., Chandra, P., & Joshi, A. (2021). AI chatbots, it’s feasibility and reliability in modern world. Int. Res. J. Eng. Technol. (IRJET), 8(7), 735-739.
• Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), 100002.
• Vermeir, I., & Verbeke, W. (2006). Sustainable food consumption: Exploring the consumer “attitude–behavioural intention” gap. Journal of Agricultural and Environmental Ethics, 19, 169-194.
• Wirth, N. (2018). Hello marketing, what can artificial intelligence help you with?. International Journal of Market Research, 60(5), 435-438.
• Woo, H., Kim, K. H., Kim, S. J., & Wang, H. (2019). Service innovations’ roles in long-term relationships with business customers. Journal of Global Scholars of Marketing Science, 29(4), 457-469.
• Yadav, R., & Pathak, G. S. (2016). Young consumers' intention towards buying green products in a developing nation: Extending the theory of planned behaviour. Journal of Cleaner Production, 135, 732-739.
• Yan, L., Keh, H. T., & Wang, X. (2021). Powering sustainable consumption: The roles of green consumption values and power distance belief. Journal of Business Ethics, 169, 499-516.
• Yang, Y. C., & Zhao, X. (2019). Exploring the relationship of green packaging design with consumers' green trust, and green brand attachment. Social Behaviour and Personality: An International Journal, 47(8), 1-10.
• Yin, Y., Siau, K., Wen, X., & Yan, S. (2022). Smart health: Intelligent healthcare systems in the metaverse, artificial intelligence, and data science era. Journal of Organizational and End User Computing, 34(1), 1–14.
• Zarifhonarvar, A. (2024). Economics of chatgpt: A labor market view on the occupational impact of artificial intelligence. Journal of Electronic Business & Digital Economics, 3(2), 100-116.
• Zafar, A. U., Shen, J., Shahzad, M., & Islam, T. (2021). Relation of impulsive urges and sustainable purchase decisions in the personalized environment of social media. Sustainable Production and Consumption, 25, 591-603.