New PIJM now accepts submissions exclusively via the Editorial Scholar portal — a dedicated submission & review system.
Go to Editorial Scholar →
Associated Management Consultants Pvt. Ltd. | COPE Member
Scopus Q2 UGC-CARE |
Prabandhan: Indian Journal of Management logo

Prabandhan: Indian Journal of Management

ISSN: 0975-2854 Frequency: Monthly Peer Review: Double-blind Published since: 2008 Language: English
A publication of AMCPL
amcon.co.in
New Delhi, India
Indexed in: Scopus Q2 UGC-CARE Group II Google Scholar J-Gate NISCAIR Crossref Cabell's Journalytics

Evaluating the Role of Artificial Intelligence on ESG Reporting : Evidence from India

Open Access Original Article

Evaluating the Role of Artificial Intelligence on ESG Reporting : Evidence from India

Amiya Kumar Mohapatra1,  Rahul Matta2,  Rashmi Soni3,  Nandeesh V. Hiremath4

1 Professor and Dean (Research) , Jaipuria Institute of Management, Dakachaya, Indore - 453 771, Madhya Pradesh

2 FPM Scholar, Indian Institute of Management, Rohtak, City Southern Bypass, Rohtak - 124 010, Haryana

3 Professor, K. J. Somaiya Institute of Management, Somaiya Vidyavihar University, Mumbai - 400 077, Maharashtra

4 Professor and Director, Kirloskar Institute of Management, Yantrapur, Harihar - 577 601, Karnataka

Volume 17
Issue 11
Pages 8–22
Year 2024
Received: Oct. 5, 2023 Accepted: Sept. 5, 2024 Published: Nov. 15, 2024
Abstract

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.

Keywords artificial intelligence ESG reporting ESG disclosure sustainability India
How to Cite

Amiya Kumar Mohapatra, Rahul Matta, Rashmi Soni, Nandeesh V. Hiremath (2024). Evaluating the Role of Artificial Intelligence on ESG Reporting : Evidence from India. Prabandhan: Indian Journal of Management, 17(11), 8–22. https://doi.org/10.17010/pijom/2024/v17i11/174020

References
  1. 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
  2. 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
  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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. Hwang, S., & Kim, J. (2021). Toward a chatbot for financial sustainability. Sustainability, 13(6), 3173. https://doi.org/10.3390/su13063173
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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.
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
Editorial Scholar

Submit your Research to PIJM

Submit your Manuscript → Author Guidelines