Re-Imagining CSR: The Role of Semi-Supervised Machine Learning for CSR Programs to Achieve Objective Key Results (OKRs)

Andrew Ellestad () and Darrell Coleman ()
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Andrew Ellestad: The College of Idaho
Darrell Coleman: The College of Idaho

A chapter in Global Corporate Governance, 2025, pp 27-42 from Springer

Abstract: Abstract Corporate social responsibility (CSR) research has evolved in a somewhat organized fashion over the last 60 years. Research began with whether to respond, then how to respond, then to the modeling of frameworks and measurement. However, lately, CSR researchers are in a soul-searching mode, calling for a stronger focus on purpose and today’s emerging technologies (Carroll in Busin Soc 60:1258–1278, 2021; Frederick in Busin Soc 37:40, 1998; Munro in CSR for purpose, shared value and deep transformation: the new responsibility, Emerald Publishing Limited, 2020). We suggest that soul searching will lead to greater focus on the purpose of CSR programs via objective key results (OKRs) and clarifying the role of human versus machine in accomplishing those objectives. With OKRs, we develop what we call socio-corporate OKRs that integrate societal and firm objectives to make clear the purpose of CSR programs. To clarify the role of humans and machines, we integrate today’s artificial intelligence (AI) technology, in particular semi-supervised machine learning, which gives a role for both humans and machines. We subsequently propose that managers set socio-corporate OKRs and that machines, via semi-supervised machine learning, create the CSR program. In doing so it will reduce the level of bias in decision-making, human or machine, and will allow the true purpose of CSR programs to emerge and prosper. We contribute by showing how today's emerging concepts of OKRs and AI can fulfill the call to “re-imagine the future” of CSR (Carroll in Busin Soc 60:1258–1278, 2021, p. 1274). We conclude with recommendations for future research of ML and CSR theory.

Date: 2025
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DOI: 10.1007/978-3-031-86330-1_3

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