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Reinforcing the significance of human factor in achieving quality performance in data-driven supply chain management

Sanjana Mondal (HBS, GITAM University, Hyderabad, India)
Kaushik Samaddar (Symbiosis International University, Pune, India)

The TQM Journal

ISSN: 1754-2731

Article publication date: 16 August 2021

Issue publication date: 16 January 2023




The paper aims to explore the various dimensions of human factor relevant for integrating data-driven supply chain quality management practices (DDSCQMPs) with organizational performance. Keeping the transition phase from “Industry 4.0” to “Industry 5.0” in mind, the paper reinforces the role of the human factor and critically discusses the issues and challenges in the present organizational setup.


Following the grounded theory approach, the study arranged in-depth interviews and focus group sessions with industry experts from various service-oriented firms in India. Dimensions of human factor identified from there were grouped together through a morphological analysis (MA), and interlinkages between them were explored through a cross-consistency matrix.


This research work identified 20 critical dimensions of human factor and have grouped them under five important categories, namely, cohesive force, motivating force, regulating force, supporting force and functional force that drive quality performance in the supply chain domain.


In line with the requirements of the present “Industry 4.0” and the forthcoming “Industry 5.0”, where the need to collaborate human factor with smart system gets priority, the paper made a novel attempt in presenting the critical human factors and categorizing them under important driving forces. The research also contributed in linking DDSCQMPs with organizational performance. The proposed framework can guide the future researchers in expanding the theoretical constructs through initiating further cross-cultural studies across industries.



Mondal, S. and Samaddar, K. (2023), "Reinforcing the significance of human factor in achieving quality performance in data-driven supply chain management", The TQM Journal, Vol. 35 No. 1, pp. 183-209.



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