Evaluating the Role of Artificial Intelligence on ESG Reporting : Evidence from India
DOI:
https://doi.org/10.17010/pijom/2024/v17i11/174020Keywords:
artificial intelligence
, ESG reporting, ESG disclosure, sustainability, India.JEL Classification Codes
, G32, M14, O33, Q56Paper Submission Date
, October 5, 2023, Paper sent back for Revision, August 3, 2024, Paper Acceptance Date, September 5, Paper Published Online, November 15, 2024Abstract
Purpose : This paper investigated how artificial intelligence (AI) technologies influence ESG reporting by examining 253 selected publicly listed companies in India from 2016 to 2023.
Methodology : This study applied multiple regression analysis to measure the influence of artificial intelligence on ESG (Environmental, Social, and Governance) performance of the companies toward sustainability. The combined ESG scores and individual ESG pillar scores were derived from the Refinitiv database, and content analysis was employed to measure the artificial intelligence (AI) score.
Findings : The results indicated that AI positively and significantly influenced the overall ESG, environmental, and governance scores. However, results showed a negative relationship between AI and social score. Further, the results indicated that AI was positively influenced by board characteristics, such as the board of director’s size, frequency of meetings, and board independence in strategic initiatives. In recent years, companies have become more informed about AI’s adoption, benefits, and implications within business. Also, the low AI scores suggest that some companies are still in the early stages of adoption of AI.
Practical Implications : This study extended the existing literature and widened the scope of stakeholders’ theory. By addressing stakeholder concerns through AI, companies can enhance trust, reputation, and long-term viability, ultimately reducing the likelihood and impact of adverse events.
Originality : This study extended the present-day literature by empirically testing how AI can influence the ESG performance of companies, including its potential impact on the individual dimensions or pillars of the overall ESG frameworks.
Downloads
Published
How to Cite
Issue
Section
References
Abbott, L. J., Park, Y., & Parker, S. (2000). The effects of audit committee activity and independence on corporate fraud. Managerial Finance, 26(11), 55–68. https://doi.org/10.1108/03074350010766990
Allegrini, M., & Greco, G. (2013). Corporate boards, audit committees and voluntary disclosure: Evidence from Italian listed companies. Journal of Management & Governance, 17(1), 187–216. https://doi.org/10.1007/s10997-011-9168-3
Anastasi, S., Madonna, M., & Monica, L. (2021). Implications of embedded artificial intelligence - machine learning on safety of machinery. Procedia Computer Science, 180, 338–343. https://doi.org/10.1016/j.procs.2021.01.171
Bronson, S. N., Carcello, J. V., Hollingsworth, C. W., & Neal, T. L. (2009). Are fully independent audit committees really necessary? Journal of Accounting and Public Policy, 28(4), 265–280. https://doi.org/10.1016/j.jaccpubpol.2009.06.001
Burgess, A. (2018). The executive guide to artificial intelligence: How to identify and implement applications for AI in your organization (1st ed.). Springer. https://doi.org/10.1007/978-3-319-63820-1
Burstrom, T., Parida, V., Lahti, T., & Wincent, J. (2021). AI-enabled business-model innovation and transformation in industrial ecosystems: A framework, model and outline for further research. Journal of Business Research, 127, 85–95. https://doi.org/10.1016/j.jbusres.2021.01.016
Cockburn, I. M., Henderson, R., & Stern, S. (2023). The impact of artificial intelligence on innovation: An exploratory analysis. In A. Agrawal, J. Gans & A. Goldfarb (eds.), The economics of artificial intelligence: An agenda (pp. 115–148). University of Chicago Press. https://doi.org/10.7208/9780226613475-006
Couchoro, M. K., Sodokin, K., & Koriko, M. (2021). Information and communication technologies, artificial intelligence, and the fight against money laundering in Africa. Strategic Change, 30(3), 281–291. https://doi.org/10.1002/jsc.2410
Dorfleitner, G., Halbritter, G., & Nguyen, M. (2015). Measuring the level and risk of corporate responsibility - An empirical comparison of different ESG rating approaches. Journal of Asset Management, 16(7), 450–466. https://doi.org/10.1057/jam.2015.31
Fariha, R., Hossain, M. M., & Ghosh, R. (2022). Board characteristics, audit committee attributes and firm performance: Empirical evidence from emerging economy. Asian Journal of Accounting Research, 7(1), 84–96. https://doi.org/10.1108/AJAR-11-2020-0115
Fasihi, S., & Barshad, R. (2023). Risk disclosure: The effect of audit committee characteristics. Journal of Empirical Research in Accounting, 13(1), 141–160. https://doi.org/10.22051/jera.2022.37494.2924
Hackston, D., & Milne, M. J. (1996). Some determinants of social and environmental disclosures in New Zealand companies. Accounting, Auditing & Accountability Journal, 9(1), 77–108. https://doi.org/10.1108/09513579610109987
Han, J., Huang, Y., Liu, S., & Towey, K. (2020). Artificial intelligence for anti-money laundering: A review and extension. Digital Finance, 2(3–4), 211–239. https://doi.org/10.1007/s42521-020-00023-1
Hwang, S., & Kim, J. (2021). Toward a chatbot for financial sustainability. Sustainability, 13(6), 3173. https://doi.org/10.3390/su13063173
Jin, M., & Kim, B. (2022). The effects of ESG activity recognition of corporate employees on job performance: The case of South Korea. Journal of Risk and Financial Management, 15(7), 316. https://doi.org/10.3390/jrfm15070316
Joshi, B., & Joshi, H. (2024). Do corporate cash holdings matter for ESG performance? Empirical evidence from India. Prabandhan: Indian Journal of Management, 17(10), 8–24. https://doi.org/10.17010/pijom/2024/v17i10/173993
Kumar, N., & Sudesh. (2019). Does corporate governance affect bank performance? Empirical evidence from India. Prabandhan: Indian Journal of Management, 12(3), 7–23. https://doi.org/10.17010/pijom/2019/v12i3/142337
Kumar, N., Kumar, P., & Nigam, D. (2021). A study of interaction effect of financial performance on the relationship of board gender diversity and corporate social responsibility. Prabandhan: Indian Journal of Management, 14(8), 8–24. https://doi.org/10.17010/pijom/2021/v14i8/165676
Kurucz, E. C., Colbert, B. A., & Wheeler, D. (2009). Chapter 4: The business case for corporate social responsibility. In A. Crane (ed.), The Oxford handbook of corporate social responsibility (pp. 83–112). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199211593.003.0004
Kute, D. V., Pradhan, B., Shukla, N., & Alamri, A. (2021). Deep learning and explainable artificial intelligence techniques applied for detecting money laundering–A critical review. IEEE Access, 9, 82300–82317. https://doi.org/10.1109/ACCESS.2021.3086230
Lichtenthaler, U. (2020). Beyond artificial intelligence: Why companies need to go the extra step. Journal of Business Strategy, 41(1), 19–26. https://doi.org/10.1108/JBS-05-2018-0086
Matta, R., Kochhar, K., Mohapatra, A. K., & Mohanty, D. (2022). Board characteristics and risk disclosure quality by integrated reporters: Evidence from Indian banks. Prabandhan: Indian Journal of Management, 15(5), 27–42. https://doi.org/10.17010/pijom/2022/v15i5/169579
Matta, R., & Mohapatra, A. K. (2021). Do content elements and capitals of integrated reporting framework require rethinking amid COVID-19? Empirical Economics Letters, 20(1), 13–25.
Meiryani, M., Tandyopranoto, C. D., Emanuel, J., Lindawati, A. S., Fahlevi, M., Aljuaid, M., & Hasan, F. (2022). The effect of global price movements on the energy sector commodity on bitcoin price movement during the COVID-19 pandemic. Heliyon, 8(10), Article ID e10820. https://doi.org/10.1016/j.heliyon.2022.e10820
Mohapatra, A. K., Matta, R., & Gupta, N. (2024). Evaluating the trend of the research in sustainability reporting: A bibliometric review. International Journal of Sustainable Economy, 16(2), 208–230. https://doi.org/10.1504/IJSE.2024.137617
Narula, R., Rao, P., Kumar, S., & Matta, R. (2024). ESG scores and firm performance : Evidence from emerging market. International Review of Economics & Finance, 89(Part A), 1170–1184. https://doi.org/10.1016/j.iref.2023.08.024
Nguyen, Q. K., & Dang, V. C. (2022). The effect of FinTech development on financial stability in an emerging market: The role of market discipline. Research in Globalization, 5, Article ID 100105. https://doi.org/10.1016/j.resglo.2022.100105
Nguyen, Q. K., & Dang, V. C. (2023). The impact of FinTech development on stock price crash risk and the role of corporate social responsibility: Evidence from Vietnam. Business Strategy and Development, 6(4), 557–570. https://doi.org/10.1002/bsd2.262
Nguyen, T. H., Elmagrhi, M. H., Ntim, C. G., & Wu, Y. (2021). Environmental performance, sustainability, governance and financial performance: Evidence from heavily polluting industries in China. Business Strategy and the Environment, 30(5), 2313–2331. https://doi.org/10.1002/bse.2748
Nikitas, A., Michalakopoulou, K., Njoya, E. T., & Karampatzakis, D. (2020). Artificial intelligence, transport and the smart city: Definitions and dimensions of a new mobility era. Sustainability, 12(7), 2789. https://doi.org/10.3390/su12072789
Oliveira, J., Rodrigues, L. L., & Craig, R. (2011). Voluntary risk reporting to enhance institutional and organizational legitimacy: Evidence from Portuguese banks. Journal of Financial Regulation and Compliance, 19(3), 271–289. https://doi.org/10.1108/13581981111147892
Patil, K., & Kulkarni, M. S. (2019). Artificial intelligence in financial services: Customer chatbot advisor adoption. International Journal of Innovative Technology and Exploring Engineering, 9(1), 4296–4303. https://doi.org/10.35940/ijitee.A4928.119119
Plastino, E., & Purdy, M. (2018). Game changing value from artificial intelligence: Eight strategies. Strategy & Leadership, 46(1), 16–22. https://doi.org/10.1108/SL-11-2017-0106
Raar, J. (2002). Environmental initiatives: Towards triple-bottom line reporting. Corporate Communications: An International Journal, 7(3), 169–183. https://doi.org/10.1108/13563280210436781
Raimo, N., Vitolla, F., Marrone, A., & Rubino, M. (2021). Do audit committee attributes influence integrated reporting quality? An agency theory viewpoint. Business Strategy and the Environment, 30(1), 522–534. https://doi.org/10.1002/bse.2635
Rao, K., & Tilt, C. (2016). Board diversity and CSR reporting: An Australian study. Meditar Accountancy Research, 24(2), 182–210. https://doi.org/10.1108/MEDAR-08-2015-0052
Ratia, M., Myllarniemi, J., & Helander, N. (2018). Robotic process automation - Creating value by digitalizing work in the private healthcare? Proceedings of the 22nd International Academic Mindtrek Conference (pp. 222–227). ACM Digital Library. https://doi.org/10.1145/3275116.3275129
Saetra, H. S. (2021). A framework for evaluating and disclosing the ESG related impacts of AI with the SDGs. Sustainability, 13(15), 8503. https://doi.org/10.3390/su13158503
Saha, R., & Kabra, K. C. (2022). Corporate governance and voluntary disclosure: Evidence from India. Journal of Financial Reporting and Accounting, 20(1), 127–160. https://doi.org/10.1108/JFRA-03-2020-0079
Shiyyab, F. S., Alzoubi, A. B., Obidat, Q. M., & Alshurafat, H. (2023). The impact of artificial intelligence disclosure on financial performance. International Journal of Financial Studies, 11(3), 115. https://doi.org/10.3390/ijfs11030115
Singh, A. K., Sardana, V., Singhania, S., Vikram, A., & Attree, A. K. (2022). Impact of board composition on bank performance: Evidence from the Indian banking sector. Prabandhan: Indian Journal of Management, 15(11), 8–23. https://doi.org/10.17010/pijom/2022/v15i11/172520
Vijai, C., Suriyalakshmi, S. M., & Elayaraja, M. (2020). The future of robotic process automation (RPA) in the banking sector for better customer experience. Shanlax International Journal of Commerce, 8(2), 61–65. https://doi.org/10.34293/commerce.v8i2.1709