Determinants of Artificial Intelligence (AI) Adoption in Green Entrepreneurship: An Integrated Framework of UTAUT and VBN Theory

Yiteng Zhang; Xinmin Yang

Authors

Yiteng Zhang
Xinmin Yang

Keywords:

Artificial Intelligence (AI), Green Entrepreneurship, Technology Acceptance, Value-Belief-Norm (VBN) Theory, Unified Theory of Acceptance and Use of Technology (UTAUT)

Abstract

With the rapid development of artificial intelligence (AI) technologies, their potential in green entrepreneurship has become increasingly evident, particularly in achieving sustainable development goals. Although AI can support green entrepreneurship by optimizing resource management, reducing energy consumption, and lowering emissions, its adoption faces numerous challenges. This paper aims to explore the application of AI in green entrepreneurship, focusing on the factors influencing the formation of Green Entrepreneurial Intention (GEI). By integrating the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Value-Belief-Norm (VBN) theory, we construct an integrated framework to examine how performance expectancy, effort expectancy, social influence, facilitating conditions, and pro-environmental personal norms collectively influence entrepreneurs' decisions to adopt AI. Using survey data from 260 green entrepreneurs in Beijing, we applied Partial Least Squares Structural Equation Modeling (PLS-SEM) for analysis. The results show that all five factors significantly positively affect GEI, with pro-environmental personal norms having the strongest influence. This study offers a new theoretical perspective on green entrepreneurship and provides practical recommendations for policymakers, entrepreneurs, and AI technology providers in promoting the adoption of sustainable technologies.

References

Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021). Artificial intelligence in sustainable energy industry: Status quo, challenges and opportunities. Journal of Cleaner Production, 289, 125834.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

Bao, W., & Xie, X. (2022). Artificial intelligence in sustainable farming: A systematic literature review of AI applications in agriculture and animal husbandry. Journal of Agricultural Science and Technology, 24(3), 45–59.

Bockarjova, M., & Steg, L. (2014). Can Protection Motivation Theory predict pro-environmental behavior? Explaining the adoption of electric vehicles in the Netherlands. Global Environmental Change, 28, 276–288.

Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3), 185–216.

Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Lawrence Erlbaum.

Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., … Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994.

Dwivedi, Y. K., Rana, N. P., & Kumar, V. (2020). A meta-analytic review of UTAUT: Toward a unified research model of technology adoption. Journal of Global Information Management, 28(4), 1–32.

Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719–734.

Floyd, D. L., Prentice-Dunn, S., & Rogers, R. W. (2000). A meta-analysis of research on Protection Motivation Theory. Journal of Applied Social Psychology, 30(2), 407–429.

Frank, A. G. (2021). Data-enabled redesign of business models for sustainability: The role of AI-driven innovation. Sustainability, 13(12), 6584.

Gast, J., Gundolf, K., & Cesinger, B. (2017). Doing business in a green way: A systematic review of the ecological sustainability entrepreneurship literature. Journal of Cleaner Production, 147, 44–56.

Gkargkavouzi, A., Halkos, G., & Matsiori, S. (2019). Environmental behavior in a private-sphere context: Integrating theories of planned behavior and value-belief-norm theory. Energy Policy, 135, 111009.

Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433.

Hair Jr, J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123.

Harland, P., Staats, H., & Wilke, H. A. M. (1999). Explaining pro-environmental intention and behavior by personal norms and the theory of planned behavior. Journal of Applied Social Psychology, 29(12), 2505–2528.

Jöhnk, J., Weißert, M., & Wyrtki, K. (2021). Ready or not, AI comes—An interview study of organizational AI readiness and the challenges of AI adoption. Business & Information Systems Engineering, 63(5), 423–431.

Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10.

Klein, N., Mohamed, A., & Di Salvo, C. (2021). Applications of artificial intelligence in climate change mitigation: A review. Climate Policy, 21(5), 585–597.

Maddux, J. E., & Rogers, R. W. (1983). Protection motivation and self-efficacy: A revised theory of fear appeals and attitude change. Journal of Experimental Social Psychology, 19(5), 469–479.

Maruping, L. M., Bala, H., Venkatesh, V., & Brown, S. A. (2017). Going beyond intention: Integrating behavioral expectation into the unified theory of acceptance and use of technology. Journal of the Association for Information Systems, 18(4), 1–44.

Matthews, L. (2017). Applying multigroup analysis in PLS-SEM: A step-by-step process. In H. Latan & R. Noonan (Eds.), Partial least squares path modeling (pp. 219–243). Springer.

Milne, S., Sheeran, P., & Orbell, S. (2000). Prediction and intervention in health-related behavior: A meta-analytic review of Protection Motivation Theory. Journal of Applied Social Psychology, 30(1), 106–143.

Murugan, G., Singh, R., & Chandrasekaran, K. (2022). Artificial intelligence in sustainable agriculture: A review of applications and challenges. Artificial Intelligence in Agriculture, 6, 10–23.

Zhao, X., Chen, F., & Hu, D. (2020). Artificial intelligence in transportation: Opportunities for reducing emissions. Transportation Research Part D: Transport and Environment, 85, 102390.