Exploring the impact of human-centred AI on firms’ social and operational performance: A large language model approach

Liu, Zhen-yuan Ralph, Dong, Shuqi Kyra, Zeng, Wenjuan, Wang, Yu-ting and Niu, Dong-fang 2025. Exploring the impact of human-centred AI on firms’ social and operational performance: A large language model approach. Transportation Research Part E: Logistics and Transportation Review 203 , 104381. 10.1016/j.tre.2025.104381

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Abstract

To transform a technology-driven orientation into one focuses on social value, Industry 5.0 places human beings at the centre of the manufacturing process, facilitating a human-centric, sustainable, and resilient industrial transition. While Human-Centred AI (HCAI) is widely acknowledged as a key enabler of this transition, research on its impact on firms’ social and operational performance and the relevant boundary conditions remains scarce. Drawing upon situated AI theory, we propose the novel HER framework to systematically identify HCAI through three core pillars including human-centric design, ethical management, and responsible implications. We argue that HCAI, as a form of situated AI innovation, empowers firms to balance operational efficiency with social value creation. Utilising a unique dataset of 4,693 listed firms with 37,365 observations in the Shanghai and Shenzhen A-share markets between 2009 and 2023, we first employ the methodology of large language model to quantify HCAI from 2.837 million textual patent data according to the HER framework. Then, we examine the impact of HCAI on firms’ social and operational performance as well as the potential moderators. The analysis results confirm that HCAI enhances both social and operational performance, especially with workforce diversity as a crucial mechanism for generating social value. Furthermore, we identify the CSR committee and industrial AI exposure as significant contingent factors that moderate the social and operational enhancing effects of HCAI. By extending situated AI theory through a social perspective, this study offers theoretical insights into HCAI influences and then provides managerial guidance for optimising AI strategies in the transition to Industry 5.0.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Business (Including Economics)
Publisher:	Elsevier
ISSN:	1366-5545
Date of Acceptance:	14 August 2025
Last Modified:	12 Dec 2025 16:15
URI:	https://orca.cardiff.ac.uk/id/eprint/183203

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