Title: Exploring human factors as barriers to artificial intelligence-human collaboration among shop floor workers in Bangladesh's garment factories

Authors: Surajit Bag; Gautam Srivastava

Addresses: Department of Transport and Supply Chain Management, University of Johannesburg, Johannesburg, South Africa ' School of Management, IILM University, Greater Noida, India

Abstract: Garment factories in developing countries like Bangladesh are moving towards digitalised and automated systems by enabling artificial intelligence (AI)-human collaboration. In Bangladesh, garment factories are facing a lot of challenges in implementing AI. This study aims to identify the important human factors as barriers to AI-human collaboration in the garment factories of Bangladesh. An extensive literature review was done to identify the human factors as barriers to AI-human collaboration in garment factories. Interpretive structural modelling (ISM) is used to understand the complex relationship between the identified barriers and determine the sequence of the barriers in the form of a framework. Further, MICMAC analysis is used to provide a comprehensive analysis of the proposed framework by classifying the barriers into driving forces and dependence forces. Findings show that the bottom-level human factors as barriers are the lack of strong leadership support (B2) that finally impacts the top-level barriers, i.e., lack of employees' psychological safety (B7), lack of employee engagement (B10), lack of employee commitment (B11), and lack of empowerment of shop floor workers (B12). Enabling strong leadership support can eliminate bottom-level barriers, facilitating AI-human collaboration on the shop floor in the garment industry in Bangladesh.

Keywords: human factors; barriers; AI-human collaboration; garment factory; shop floor; operations; interpretive structural modelling; ISM; Bangladesh.

DOI: 10.1504/IJSOM.2026.155444

International Journal of Services and Operations Management, 2026 Vol.54 No.3, pp.401 - 421

Received: 15 Jan 2024
Accepted: 25 Jan 2024
Published online: 03 Aug 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article