Unveiling the impact of the congruence between artificial intelligence and explorative learning on supply chain resilience

Dai, Jing, Geng, Ruoqi ORCID: https://orcid.org/0000-0001-8183-7379, Xu, Dong, Shangguan, Wuyue and Shao, Jinan 2024. Unveiling the impact of the congruence between artificial intelligence and explorative learning on supply chain resilience. International Journal of Operations & Production Management 10.1108/IJOPM-12-2023-0990

Preview

PDF - Accepted Post-Print Version Available under License Creative Commons Attribution Non-commercial. Download (1MB) | Preview

Abstract

Purpose – Drawing upon socio-technical system theory, this study intends to investigate the effects of the congruence and incongruence between artificial intelligence (AI) and explorative learning on supply chain resilience as well as the moderating role of organizational inertia. Design/methodology/approach – Using survey data collected from 170 Chinese manufacturing firms, we performed polynomial regression and response surface analyses to test our hypotheses. Findings – We find that the congruence between AI and explorative learning enhances firms’ supply chain resilience, while the incongruence between these two factors impairs their supply chain resilience. In addition, compared with low–low congruence, high–high congruence between AI and explorative learning improves supply chain resilience to a greater extent. Moreover, organizational inertia attenuates the positive influence of the congruence between AI and explorative learning on supply chain resilience, while it aggravates the negative influence of the incongruence between these two factors on supply chain resilience. Originality/value – Our study expands the literature on supply chain resilience by demonstrating that the congruence between a firm’s AI (i.e. technical aspect) and explorative learning (i.e. social aspect) boosts its supply chain resilience. More importantly, our study sheds new light on the role of organizational inertia in moderating the congruent effect of AI and explorative learning, thereby extending the boundary condition for socio-technical system theory in the supply chain resilience literature. Keywords Supply chain resilience, Artificial intelligence, Explorative learning, Organizational inertia, Socio-technical system

Item Type:	Article
Status:	In Press
Schools:	Business (Including Economics)
Publisher:	Emerald
ISSN:	0144-3577
Date of First Compliant Deposit:	6 August 2024
Date of Acceptance:	4 July 2024
Last Modified:	08 Nov 2024 20:15
URI:	https://orca.cardiff.ac.uk/id/eprint/171154

Actions (repository staff only)

Edit Item