What drives managers towards algorithm aversion and how to overcome it? Mitigating the impact of innovation resistance through technology readiness

Hasan Mahmud, A.K.M. Najmul Islam and Ranjan Kumar Mitra

Technological Forecasting and Social Change, 2023, vol. 193, issue C

Abstract: Artificial intelligence has been increasingly used in many managerial decision-making contexts for its superior performance. However, many managers are still averse to using AI algorithms in decision-making. The benefits of algorithms cannot be realized optimally if managers are reluctant to use them. Although there are many studies investigating the factors that make an individual averse towards algorithms, studies examining such factors in the managerial decision-making context are scarce. This study, drawing on the innovation resistance theory and the technology readiness concept, proposes a structural model depicting relationships between managers' perceived barriers and algorithm aversion. The model also incorporates the role of managers' technology readiness in influencing these relationships. To test the model, data were collected from 179 managers of 31 banks/financial institutions in Bangladesh through an online survey and were analyzed using partial least squares structural equation modeling. The results indicate that barriers related to value, tradition, and image are significantly associated with algorithm aversion, whereas barriers related to usage and risk have a non-significant impact. Moderation analysis suggests that motivating factors of technology readiness mitigate the impact of value barriers on algorithm aversion.

Keywords: Algorithm aversion; Innovation resistance theory; Technology readiness; Algorithmic decision-making; Artificial intelligence (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:193:y:2023:i:c:s0040162523003268

DOI: 10.1016/j.techfore.2023.122641

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