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Article type: Research Article
Authors: Movahedipour, Mahmooda; b; * | Zeng, Jianqiua; 1 | Yang, Mengkec; 1 | Wu, Xiankangd; 1
Affiliations: [a] School of Economic and Management, Beijing University Posts and Telecommunications, Beijing, China | [b] Academic Center for Education, Culture and Research, (ACECR), Tehran, Iran | [c] School of Automation, Beijing University of Posts and Telecommunications, Beijing, China | [d] School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China
Correspondence: [*] Corresponding author: Mahmood Movahedipour, School of Economic and Management, Beijing University Posts and Telecommunications, 100876 Beijing, China. Tel.: +86 133 91837996; E-mails: mahmood.movahedipour@bupt.edu.cn; mahmood.movahedipour@yahoo.com.
Note: [1] Academic Editors: Jianqiu Zeng, Mengke Yang and Xiankang Wu.
Abstract: Sustainability draws increased supply-chain management (SCM) attention. This article analyzes critical barriers to the assessment, evaluation and attainment of sustainable supply-chain management (SSCM), assessed through critical-barrier identification and qualitative data analysis. Namely a literature review of 188 articles, published between the years 2010 and 2016, helps identify the most influential barriers. The qualitative data analysis pertains to fifteen such barriers, identified in the literature review and through our collaboration with other academic researchers and industrial specialists. Notably, the study’s qualitative data analysis, interpretive structural modeling (ISM), unconceals the mutual impact among the most prominent SSCM barriers. The inadequacy of information-technology implementation is recognized as the most significant barrier, which could prevent business enterprises and other organizations from implementing a SSCM framework, with intentionality the sustainability in their business. The article concludes with suggestions for future research directions.
Keywords: MICMAC, ISM, sustainability, supply-chain network
DOI: 10.3233/HSM-17101
Journal: Human Systems Management, vol. 37, no. 1, pp. 27-43, 2018
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