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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Weisha Wang & Long Chen & Mengran Xiong & Yichuan Wang, 2023. "Accelerating AI Adoption with Responsible AI Signals and Employee Engagement Mechanisms in Health Care," Information Systems Frontiers, Springer, vol. 25(6), pages 2239-2256, December.
    • Handle: RePEc:spr:infosf:v:25:y:2023:i:6:d:10.1007_s10796-021-10154-4
    DOI: 10.1007/s10796-021-10154-4
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