Empirical Analysis of Digital Innovation’s Impact on Corporate ESG Performance: The Mediating Role of GAI Technology.
DOI:
https://doi.org/10.71204/dapv6405Keywords:
Digital Innovation, ESG Performance, Generative Artificial Intelligence, Technology Adoption, Corporate SustainabilityAbstract
This study investigates the relationship between corporate digital innovation and Environmental, Social, and Governance (ESG) performance, with a specific focus on the mediating role of Generative Artificial Intelligence (GAI) technology adoption. Using a comprehensive panel dataset of 8,000 firm-year observations from the CMARS and WIND database spanning from 2015 to 2023, we employ multiple econometric techniques to examine this relationship. Our findings reveal that digital innovation significantly enhances corporate ESG performance, with GAI technology adoption serving as a crucial mediating mechanism. Specifically, digital innovation positively influences GAI technology adoption, which subsequently improves ESG performance. Furthermore, our heterogeneity analysis indicates that this relationship varies across firm size, industry type, and ownership structure. The results remain robust after addressing potential endogeneity concerns through instrumental variable estimation, propensity score matching, and difference-in-differences approaches. This research contributes to the growing literature on technology-driven sustainability transformations and offers practical implications for corporate strategy and policy development in promoting sustainable business practices through technological advancement.
References
Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3-30. DOI: https://doi.org/10.1257/jep.33.2.3
Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Review Press.
Bai, C., Quayson, M., & Sarkis, J. (2022). Digital business transformation and sustainable supply chain management: A systematic literature review. International Journal of Production Economics, 244, 108381. DOI: https://doi.org/10.1016/j.ijpe.2021.108381
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. DOI: https://doi.org/10.1177/014920639101700108
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173-1182. DOI: https://doi.org/10.1037//0022-3514.51.6.1173
Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2013). Digital business strategy: Toward a next generation of insights. MIS Quarterly, 37(2), 471-482. DOI: https://doi.org/10.25300/MISQ/2013/37:2.3
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901.
Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review, 95(4), 3-11.
Brynjolfsson, E., Rock, D., & Syverson, C. (2019). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. In The Economics of Artificial Intelligence: An Agenda (pp. 23-57). University of Chicago Press. DOI: https://doi.org/10.7208/chicago/9780226613475.003.0001
Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., Allas, T., Dahlström, P., ... & Trench, M. (2018). Notes from the AI frontier: Modeling the impact of AI on the world economy. McKinsey Global Institute.
Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128-152. DOI: https://doi.org/10.2307/2393553
Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2021). The AI gambit: Leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations. AI & Society, 36, 1-25. DOI: https://doi.org/10.2139/ssrn.3804983
Cui, J. (2025). The Explore of Knowledge Management Dynamic Capabilities, AI-Driven Knowledge Sharing, Knowledge-Based Organizational Support, and Organizational Learning on Job Performance: Evidence from Chinese Technological Companies. arXiv preprint arXiv:2501.02468. DOI: https://doi.org/10.2139/ssrn.5083169
Cui, J. (2025). The Impact of Absorptive Capacity, Organizational Creativity, Organizational Agility, and Organizational Resilience on Organizational Performance: Mediating Role of Digital Transformation. Organizational Creativity, Organizational Agility, and Organizational Resilience on Organizational Performance: Mediating Role of Digital Transformation (January 05, 2025). DOI: https://doi.org/10.2139/ssrn.5083189
Cui, J., Wan, Q., Chen, W., & Gan, Z. (2024). Application and Analysis of the Constructive Potential of China's Digital Public Sphere Education. The Educational Review, USA, 8(3), 350-354. DOI: https://doi.org/10.26855/er.2024.03.002
Damanpour, F., & Schneider, M. (2006). Phases of the adoption of innovation in organizations: Effects of environment, organization and top managers. British Journal of Management, 17(3), 215-236. DOI: https://doi.org/10.1111/j.1467-8551.2006.00498.x
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. DOI: https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Fichman, R. G., & Kemerer, C. F. (1997). The assimilation of software process innovations: An organizational learning perspective. Management Science, 43(10), 1345-1363. DOI: https://doi.org/10.1287/mnsc.43.10.1345
Fiksel, J., Lambert, J. H., Artman, K. B., Harris, J. L., & Phifer, H. E. (2014). Environmental excellence: The new supply chain edge. Supply Chain Management Review, 18(1), 70-82.
George, G., Merrill, R. K., & Schillebeeckx, S. J. (2020). Digital sustainability and entrepreneurship: How digital innovations are helping tackle climate change and sustainable development. Entrepreneurship Theory and Practice, 44(6), 990-1000.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27.
Hanelt, A., Bohnsack, R., Marz, D., & Antunes Marante, C. (2021). A systematic review of the literature on digital transformation: Insights and implications for strategy and organizational change. Journal of Management Studies, 58(5), 1159-1197. DOI: https://doi.org/10.1111/joms.12639
Hart, S. L. (1995). A natural-resource-based view of the firm. Academy of Management Review, 20(4), 986-1014. DOI: https://doi.org/10.2307/258963
Hekkert, M. P., Suurs, R. A., Negro, S. O., Kuhlmann, S., & Smits, R. E. (2007). Functions of innovation systems: A new approach for analysing technological change. Technological Forecasting and Social Change, 74(4), 413-432. DOI: https://doi.org/10.1016/j.techfore.2006.03.002
Korinek, A., & Stiglitz, J. E. (2021). Artificial intelligence, globalization, and strategies for economic development. NBER Working Paper, (w28453). DOI: https://doi.org/10.3386/w28453
Li, Y., Gong, M., Zhang, X. Y., & Koh, L. (2018). The impact of environmental, social, and governance disclosure on firm value: The role of CEO power. The British Accounting Review, 50(1), 60-75. DOI: https://doi.org/10.1016/j.bar.2017.09.007
Liang, H., & Renneboog, L. (2017). On the foundations of corporate social responsibility. The Journal of Finance, 72(2), 853-910. DOI: https://doi.org/10.1111/jofi.12487
Lindgreen, A., Vallaster, C., Yousofzai, S., & Hirsch, B. (Eds.). (2019). Measuring and controlling sustainability: Spanning theory and practice. Routledge. DOI: https://doi.org/10.4324/9781315401904
Nambisan, S., Lyytinen, K., Majchrzak, A., & Song, M. (2017). Digital innovation management: Reinventing innovation management research in a digital world. MIS Quarterly, 41(1), 223-238. DOI: https://doi.org/10.25300/MISQ/2017/41:1.03
Nambisan, S., Wright, M., & Feldman, M. (2019). The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes. Research Policy, 48(8), 103773. DOI: https://doi.org/10.1016/j.respol.2019.03.018
Porter, M. E., & Kramer, M. R. (2011). Creating shared value. Harvard Business Review, 89(1/2), 62-77.
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879-891. DOI: https://doi.org/10.3758/BRM.40.3.879
Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J. F., Breazeal, C., ... & Wellman, M. (2019). Machine behaviour. Nature, 568(7753), 477-486. DOI: https://doi.org/10.1038/s41586-019-1138-y
Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2017). Reshaping business with artificial intelligence: Closing the gap between ambition and action. MIT Sloan Management Review, 59(1).
Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free Press.
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., ... & Bengio, Y. (2022). Tackling climate change with machine learning. ACM Computing Surveys, 55(2), 1-96. DOI: https://doi.org/10.1145/3485128
Russo, M. V., & Fouts, P. A. (1997). A resource-based perspective on corporate environmental performance and profitability. Academy of Management Journal, 40(3), 534-559. DOI: https://doi.org/10.2307/257052
Sarkis, J. (2021). Supply chain sustainability: Learning from the COVID-19 pandemic. International Journal of Operations & Production Management, 41(1), 63-73. DOI: https://doi.org/10.1108/IJOPM-08-2020-0568
Schretzen, H., Wamba, S. F., Guillemette, M. G., & Omrani, H. (2021). The impact of artificial intelligence capabilities on corporate environmental, social, and governance (ESG) performance. Journal of Cleaner Production, 328, 129506.
Shrivastava, P. (1995). Environmental technologies and competitive advantage. Strategic Management Journal, 16(S1), 183-200. DOI: https://doi.org/10.1002/smj.4250160923
Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290-312. DOI: https://doi.org/10.2307/270723
Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319-1350. DOI: https://doi.org/10.1002/smj.640
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509-533. DOI: https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118-144. DOI: https://doi.org/10.1016/j.jsis.2019.01.003
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., ... & Nerini, F. F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1), 1-10. DOI: https://doi.org/10.1038/s41467-019-14108-y
Wan, Q., & Cui, J. (2024). Dynamic Evolutionary Game Analysis of How Fintech in Banking Mitigates Risks in Agricultural Supply Chain Finance. arXiv preprint arXiv:2411.07604.
Whelan, T., & Fink, C. (2016). The comprehensive business case for sustainability. Harvard Business Review, 21(1), 1-12.
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Philip, S. Y. (2019). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1), 4-24. DOI: https://doi.org/10.1109/TNNLS.2020.2978386
Zhou, L., & Cui, J. (2025). Dynamic Connectedness of Green Bond Markets in China and America: A R2 Decomposed Connectedness Approach. International Journal of Global Economics and Management, 6(2), 144-158. DOI: https://doi.org/10.62051/ijgem.v6n2.14
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Jun Cui (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in this journal are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are properly credited. Authors retain copyright of their work, and readers are free to copy, share, adapt, and build upon the material for any purpose, including commercial use, as long as appropriate attribution is given.
How to Cite
