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Barriers to Reverse Logistics in the Computer Supply Chain Using Interpretive Structural Model

Author

Listed:
  • Syed Mithun Ali

    (Bangladesh University of Engineering and Technology)

  • Asraf Arafin

    (Bangladesh University of Engineering and Technology)

  • Md. Abdul Moktadir

    (University of Dhaka)

  • Towfique Rahman

    (Bangladesh University of Engineering and Technology)

  • Nuzhat Zahan

    (Bangladesh University of Engineering and Technology)

Abstract

Researchers and practitioners are paying attention to reverse logistics (RL) issues due to growing environmental concerns, competitive advantage, promising financial potential, legislative reasons and social responsibility. This study aims to examine the contextual relationship and interactions among barriers to implement RL practices in the computer supply chain of Bangladesh. We applied Interpretive Structural Modeling (ISM) technique to diagnose significant barriers and proposed a hierarchical framework for investigating the relationships among them. We also used MICMAC (Matriced’ Impacts Croisés Multiplication Appliquée á unClassement) analysis to classify the barriers based on the driving power and dependence among them. Seven barriers were finalized in the Bangladesh context based on the previous literature and professional feedback. The findings reveal that financial constraints along with the lack of interest from top management are the most influential barriers to RL for the computer supply chains of Bangladesh. The ISM-based analysis can provide managers with insights for developing strategies for implementing RL practices in the computer supply chain of Bangladesh.

Suggested Citation

  • Syed Mithun Ali & Asraf Arafin & Md. Abdul Moktadir & Towfique Rahman & Nuzhat Zahan, 2018. "Barriers to Reverse Logistics in the Computer Supply Chain Using Interpretive Structural Model," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 19(1), pages 53-68, March.
    • Handle: RePEc:spr:gjofsm:v:19:y:2018:i:1:d:10.1007_s40171-017-0176-2
    DOI: 10.1007/s40171-017-0176-2
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    Cited by:

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    3. Zhaojun Yang & Xiaoting Guo & Jun Sun & Yali Zhang, 2021. "Contextual and organizational factors in sustainable supply chain decision making: grey relational analysis and interpretative structural modeling," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(8), pages 12056-12076, August.
    4. Shelly Gupta & Sanjay Dhingra, 2022. "Modeling the key factors influencing the adoption of mobile financial services: an interpretive structural modeling approach," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 27(2), pages 96-110, June.
    5. Josip Marić & Marco Opazo-Basáez, 2019. "Green Servitization for Flexible and Sustainable Supply Chain Operations: A Review of Reverse Logistics Services in Manufacturing," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 20(1), pages 65-80, December.
    6. Chowdhury, Nighat Afroz & Ali, Syed Mithun & Mahtab, Zuhayer & Rahman, Towfique & Kabir, Golam & Paul, Sanjoy Kumar, 2019. "A structural model for investigating the driving and dependence power of supply chain risks in the readymade garment industry," Journal of Retailing and Consumer Services, Elsevier, vol. 51(C), pages 102-113.
    7. Snigdha Malhotra & Vernika Agarwal & P. K. Kapur, 2022. "Hierarchical framework for analysing the challenges of implementing industrial Internet of Things in manufacturing industries using ISM approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2356-2370, October.

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