Artificial Intelligence and Consumer’s Perception: A Research on Environmentally Conscious Consumer
Yıl 2024,
Cilt: 4 Sayı: 2, 105 - 115
Apoorva Bhatnagar
,
Megha Sharma
Öz
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.
Kaynakça
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Yıl 2024,
Cilt: 4 Sayı: 2, 105 - 115
Apoorva Bhatnagar
,
Megha Sharma
Kaynakça
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- 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.
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- 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
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- 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.
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- 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.
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