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Accelerating AI adoption with responsible AI signals and employee engagement mechanisms in health care

Accelerating AI adoption with responsible AI signals and employee engagement mechanisms in health care
Accelerating AI adoption with responsible AI signals and employee engagement mechanisms in health care
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.
1572-9419
Wang, Weisha
3b06920a-f578-41b8-a356-7e2da53d3bf6
Chen, Long
de6da7d4-cf22-4422-9595-c5d109193076
Xiong, Mengran
fafdbc2b-80dc-41a6-b6aa-0035b19eb380
Wang, Yichuan
8b5a22f0-2723-42d2-bf36-9e82221f92fc
Wang, Weisha
3b06920a-f578-41b8-a356-7e2da53d3bf6
Chen, Long
de6da7d4-cf22-4422-9595-c5d109193076
Xiong, Mengran
fafdbc2b-80dc-41a6-b6aa-0035b19eb380
Wang, Yichuan
8b5a22f0-2723-42d2-bf36-9e82221f92fc

Wang, Weisha, Chen, Long, Xiong, Mengran and Wang, Yichuan (2021) Accelerating AI adoption with responsible AI signals and employee engagement mechanisms in health care. Information Systems Frontiers. (In Press)

Record type: Article

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.

Text
AI in healthcare_Published version - Accepted Manuscript
Restricted to Repository staff only until 31 May 2022.
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More information

Accepted/In Press date: 30 May 2021

Identifiers

Local EPrints ID: 449624
URI: http://eprints.soton.ac.uk/id/eprint/449624
ISSN: 1572-9419
PURE UUID: 7d26caf6-d6dd-4a8b-a1f6-b10fda09e7d2
ORCID for Weisha Wang: ORCID iD orcid.org/0000-0002-2985-3416

Catalogue record

Date deposited: 09 Jun 2021 16:31
Last modified: 10 Jun 2021 01:44

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Contributors

Author: Weisha Wang ORCID iD
Author: Long Chen
Author: Mengran Xiong
Author: Yichuan Wang

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