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- W4307851099 startingPage "113402" @default.
- W4307851099 abstract "Firms are increasingly using forms of AI to serve stakeholders across various business functions, resulting in both positive and negative outcomes. Stakeholder theory explains how firms create and destroy value via their stakeholder encounters, making it an ideal foundation for understanding AI deployment on firm-level performance. As AI continues to evolve, both when it comes to the activities and roles it takes and the stakeholders it affects, the AI-stakeholder framework developed herein identifies and situates key managerial decisions related to the adoption and deployment of AI that drive the firm’s likelihood of creating or destroying value through stakeholder encounters. The AI–stakeholder framework focuses on stakeholder justice and is supported by testable propositions about the conditions most likely to affect the outcomes of incorporating AI into business processes. The framework also supports future research and practical managerial guidance by articulating the challenges and potential of AI for managing stakeholder encounters." @default.
- W4307851099 created "2022-11-06" @default.
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- W4307851099 date "2023-01-01" @default.
- W4307851099 modified "2023-09-24" @default.
- W4307851099 title "In consilium apparatus: Artificial intelligence, stakeholder reciprocity, and firm performance" @default.
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- W4307851099 doi "https://doi.org/10.1016/j.jbusres.2022.113402" @default.
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