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

Lookup NU author(s): Dr Weisha Wang

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

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.

Publication metadata

Author(s): Wang W, Chen L, Xiong M, Wang Y

Publication type: Article

Publication status: Published

Journal: Information Systems Frontiers

Year: 2023

Volume: 25

Pages: 2239-2256

Print publication date: 01/12/2023

Online publication date: 29/06/2021

Acceptance date: 01/06/2021

Date deposited: 14/01/2022

ISSN (print): 1387-3326

ISSN (electronic): 1572-9419

Publisher: Springer Nature

URL: https://doi.org/10.1007/s10796-021-10154-4

DOI: 10.1007/s10796-021-10154-4

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