The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Does CSR Reduce Idiosyncratic Risk? Roles of Operational Efficiency and AI Innovation
Abstract
In the context of Industry 4.0, corporate social responsibility (CSR) plays an increasingly important role in firm risk management. To mitigate risk and improve operational productivity, artificial intelligence (AI), a key disruptive technology, has been increasingly employed in many industries. In this study, we examine the relationship between CSR and idiosyncratic risk (IR) as moderated by AI innovation and operational efficiency based on data from 1614 firms in China during the period from 2010 to 2017. The empirical results show that CSR can negatively affect IR, while beyond a certain level, it acts in the opposite direction, exhibiting a U‐shaped relationship. In particular, operational efficiency shifts the turning point of the U‐shaped curve to the right, implying that firms with high operational efficiency can obtain a higher optimal benefit by improving their CSR. Furthermore, AI innovation flattens the U‐shaped curve, thereby weakening CSR's positive impact on IR. This result indicates that AI innovation is not always beneficial. As AI innovation can have some substitution effect on CSR, enterprises should focus on either CSR or AI innovation as their main differentiation strategy to maximize the utilization of resources. We further instrument CSR using the social expenditure ratio of the province where the firm is headquartered to address a potential endogeneity concern. Finally, we provide several prominent managerial implications for enterprises, governments, and investors.
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Liu, Y., Dong, J., Mei, L., & Shen, R. (2023) · Technovation · doi:10.1016/j.technovation.2022.102458 · Cited by 363
Camilleri, M. A. (2024) · Expert Systems · doi:10.1111/exsy.13406 · Cited by 331
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Metadata
- Authors
- Li, G., Li, N., & Sethi, S. P.
- Venue
- Production and Operations Management
- Publisher
- SAGE Publications
- Published
- 2021-07
- Volume
- 30
- Issue
- 7
- Pages
- 2027-2045
- ISSN
- 1059-1478 (ISSN-L)
- 1937-5956
- OpenAlex
- W3035456428
- Cited by
- 265
- References
- 60
- FWCI
- 29.02
- Type
- journal article
- Language
- en
- Open access
- closed
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