Good, bad, or both? Measurement of physician's ambivalent attitudes towards AI

Maier, Sophia Bettina ; Jussupow, Ekaterina ; Heinzl, Armin

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Document Type: Conference or workshop publication
Year of publication: 2019
Book title: 27th European Conference on Information Systems - Information Systems for a Sharing Society, ECIS 2019, Stockholm and Uppsala, Sweden, June 8-14, 2019
Page range: Paper 115
Conference title: ECIS 2019
Location of the conference venue: Stockholm & Uppsala, Sweden
Date of the conference: 08.-14.06.2019
Publisher: Johannesson, Paul
Place of publication: Atlanta, GA
Publishing house: AISeL
ISBN: 978-1-7336325-0-8
Publication language: English
Institution: Business School > ABWL u. Wirtschaftsinformatik I (Heinzl 2002-)
Subject: 004 Computer science, internet
Abstract: Artificial intelligence is currently one of the most controversial discussed technologies across various work domains. In healthcare, AI fosters widespread positive beliefs of substantially increasing the quality of care, yet evoking physicians’ fears of being marginalized or replaced. The described controversy leads to ambivalent attitudes, as physicians hold both strong positive and negative evaluations at the same time. However, current research in information systems has not been able to measure ambivalence because uni-polar attitude scales cannot assess this construct. Additionally, it is unclear whether ambivalence has positive or negative consequences and how it is related to resistance to change. In the context of AI in healthcare, we conducted a survey study (n=74) to measure context-specific attitudes and attitude ambivalence of physicians. We distinguish between two states of ambivalence and show that physicians who experience an inner conflict (Felt Ambivalence) from conflicting attitudes (Potential Ambivalence) develop resistance to change. Moreover, including ambivalence into a regression model explains more variance than uni-polar attitudes alone. With this research, we show that ambivalent attitudes can be measured in the context of technological change. Additionally, we explore how context-specific attitudes towards AI in healthcare drive physicians’ ambivalence towards it.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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