Algorithm aversion during disruptions: The case of safety stock

Wang, Xun ORCID: https://orcid.org/0000-0001-7800-726X, Sanchez Rodrigues, Vasco ORCID: https://orcid.org/0000-0003-3375-3079, Demir, Emrah ORCID: https://orcid.org/0000-0002-4726-2556 and Sarkis, Joseph 2024. Algorithm aversion during disruptions: The case of safety stock. International Journal of Production Economics 278 , 109442. 10.1016/j.ijpe.2024.109442

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Abstract

Algorithm aversion occurs when organizations or individuals reject optimal analytical decision support in favour of informal, subjective decisions. This phenomenon has been observed in many practical decision-making scenarios and is generally believed to negatively impact decision quality. However, its existence and effect in volatile supply chain environments has not been empirically tested in the literature. Safety stock buffering demand volatility is an important decision in supply chain management, making it an ideal lens to observe algorithm aversion. In this paper, we empirically investigate algorithm aversion behaviour in the context of safety stock settings. We collect data from a case retail company across a range of stockkeeping units (SKUs), encompassing both pre-disruption and post-disruption time stages with varying levels of volatility. We introduce a simulation model to determine whether algorithm aversion exists for safety stock decisions and to assess how algorithm adoption and adaptation affects performance. Our findings indicate that algorithm aversion occurs during supply chain disruptions, with algorithmic decisions significantly outperforming human judgment. Based on interview results and theories of information systems, we propose a theory to explain and generalise the above findings. This theory attributes algorithm aversion behaviour to reduced sense of fitness among algorithm users and lack of slack resources for both users and developers. It also offers insights into how the adoption and adaptation of algorithms influence decision performance during disruptive events.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Publisher:	Elsevier
ISSN:	0925-5273
Date of First Compliant Deposit:	21 October 2024
Date of Acceptance:	15 October 2024
Last Modified:	22 Oct 2024 09:48
URI:	https://orca.cardiff.ac.uk/id/eprint/173169

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