Bag, S, Rahman, MS, Srivastava, G, Shore, A and Ram, P (2022) Examining the role of virtue ethics and big data in enhancing viable, sustainable, and digital supply chain performance. Technological Forecasting and Social Change, 186. ISSN 0040-1625

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Abstract

In the real world of practice, data-driven supply chains have gained huge popularity in recent years. This has led operations and supply management researchers to the focus on the role of advanced information and technology, including big data. Literature highlights that the use of big data can enhance business performance. Nonetheless, big data is analyzed by humans and a lack of virtue ethics could lead to disastrous consequences (erroneous decision-making can stem from bad data analysis resulting in not only huge business losses but also deterioration of relationships with suppliers and customers in the supply chain). To address the calls of previous researchers, this study utilizes the Ethical Theory of Organizing framework and Stakeholder theory to develop the theoretical model and further examine the relationships. The samples are drawn from the manufacturing industry. Hypothesis testing is executed through covariance-based structural equation modeling and finally, the conclusions are drawn. The findings of this work provide a more nuanced understanding of virtue ethics and big data implications, thereby answering the important questions of “why” and “how” data-driven green and lean practices increase the stakeholders' trust and enhance viable, sustainable, and digital supply chain performance.

Item Type:	Article
Uncontrolled Keywords:	10 Technology; 14 Economics; 15 Commerce, Management, Tourism and Services; Science Studies
Subjects:	H Social Sciences > HF Commerce > HF5001 Business H Social Sciences > HF Commerce H Social Sciences > HF Commerce > HF5001 Business > HF5410 Marketing. Distribution of Products
Divisions:	Business & Management (from Sep 19)
Publisher:	Elsevier
SWORD Depositor:	A Symplectic
Date Deposited:	02 Feb 2023 12:06
Last Modified:	02 Feb 2023 12:15
DOI or ID number:	10.1016/j.techfore.2022.122154
URI:	https://researchonline.ljmu.ac.uk/id/eprint/18365

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