Accelerating AI Adoption with Responsible AI Signals and Employee Engagement Mechanisms in Health Care

Weisha Wang (), Long Chen (), Mengran Xiong () and Yichuan Wang ()
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Weisha Wang: University of Southampton Highfield
Long Chen: University of Southampton Highfield
Mengran Xiong: Sheffield University Management School, University of Sheffield
Yichuan Wang: Sheffield University Management School, University of Sheffield

Information Systems Frontiers, 2023, vol. 25, issue 6, No 8, 2239-2256

Abstract: Abstract Artificial Intelligence (AI) technology is transforming the healthcare sector. However, despite this, the associated ethical implications remain open to debate. This research investigates how signals of AI responsibility impact healthcare practitioners’ attitudes toward AI, satisfaction with AI, AI usage intentions, including the underlying mechanisms. Our research outlines autonomy, beneficence, explainability, justice, and non-maleficence as the five key signals of AI responsibility for healthcare practitioners. The findings reveal that these five signals significantly increase healthcare practitioners’ engagement, which subsequently leads to more favourable attitudes, greater satisfaction, and higher usage intentions with AI technology. Moreover, ‘techno-overload’ as a primary ‘techno-stressor’ moderates the mediating effect of engagement on the relationship between AI justice and behavioural and attitudinal outcomes. When healthcare practitioners perceive AI technology as adding extra workload, such techno-overload will undermine the importance of the justice signal and subsequently affect their attitudes, satisfaction, and usage intentions with AI technology.

Keywords: Artificial Intelligence (AI); Responsible AI; Employee engagement; Attitudes; Satisfaction; Usage intentions (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10796-021-10154-4

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