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Transforming Supply Chains: Powering Circular Economy with Analytics, Integration and Flexibility Using Dual Theory and Deep Learning with PLS-SEM-ANN Analysis

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  • Muhammad Noman Shafique

    (CESAM—Centre for Environmental and Marine Studies, Department of Environment and Planning, University of Aveiro, 3810-193 Aveiro, Portugal
    Department of Management Sciences, University of Buner, Buner 19290, Pakistan)

  • Ammar Rashid

    (College of Engineering and IT, Ajman University, Ajman P.O. Box 346, United Arab Emirates)

  • Sook Fern Yeo

    (Faculty of Business, Multimedia University, Melaka 75450, Malaysia
    Department of Business Administration, Daffodil International University, Dhaka 1207, Bangladesh)

  • Umar Adeel

    (Department of Computer Science and Engineering, American University of Ras Al Khaimah, Ras Al-Khaimah P.O. Box 10021, United Arab Emirates)

Abstract

The Sustainable Development Goals and circular economy are two critical aspects of the 2030 Agenda for Sustainable Development. They both seek to reduce the waste of natural resources and enhance society’s social, economic, and environmental goals. This study aims to identify, develop, test, and verify the significant antecedents that affect the adoption of supply chain analytics and its consequences for achieving the circular economy. We have divided the conceptual framework into two parts. In the first part, the relationship among data integration and scalability, organizational readiness, and policies and regulations as Technological–Organizational–Environmental factors as antecedents in adopting supply chain analytics. In the second part, the dynamic capabilities view grounded the relationship among supply chain analytics, supply chain integration, and sustainable supply chain flexibility effect directly and indirectly on the circular economy. Data have been collected using the survey method from 231 respondents from the manufacturing industry in Pakistan. Data have been analyzed using (i) partial least square structure equation modeling (ii) and artificial neural network approaches. The empirical findings proved that antecedents (data integrity and scalability, organizational readiness, and policy and regulation) and consequences (supply chain integration and sustainable supply chain flexibility) of supply chain analytics adoption would improve the circular economy performance. Additionally, artificial neural networks have supported these relationships. The adoption of supply chain analytics will enable organizations to supply chain integration. Additionally, organizations with more integration and analytics in their operations tend to have more flexibility and a circular economy. Moreover, organizations and society will obtain social, economic, and environmental benefits and reduce wastage and negative environmental impacts.

Suggested Citation

  • Muhammad Noman Shafique & Ammar Rashid & Sook Fern Yeo & Umar Adeel, 2023. "Transforming Supply Chains: Powering Circular Economy with Analytics, Integration and Flexibility Using Dual Theory and Deep Learning with PLS-SEM-ANN Analysis," Sustainability, MDPI, vol. 15(15), pages 1-23, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:11979-:d:1210215
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    References listed on IDEAS

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    1. Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
    2. Stavros Kalogiannidis & Dimitrios Kalfas & Fotios Chatzitheodoridis & Stamatis Kontsas, 2022. "The Impact of Digitalization in Supporting the Performance of Circular Economy: A Case Study of Greece," JRFM, MDPI, vol. 15(8), pages 1-18, August.
    3. Gunasekaran, Angappa & Papadopoulos, Thanos & Dubey, Rameshwar & Wamba, Samuel Fosso & Childe, Stephen J. & Hazen, Benjamin & Akter, Shahriar, 2017. "Big data and predictive analytics for supply chain and organizational performance," Journal of Business Research, Elsevier, vol. 70(C), pages 308-317.
    4. Kalaitzi, Dimitra & Tsolakis, Naoum, 2022. "Supply chain analytics adoption: Determinants and impacts on organisational performance and competitive advantage," International Journal of Production Economics, Elsevier, vol. 248(C).
    5. Fosso Wamba, Samuel & Akter, Shahriar & Edwards, Andrew & Chopin, Geoffrey & Gnanzou, Denis, 2015. "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, Elsevier, vol. 165(C), pages 234-246.
    6. Marcelo Werneck Barbosa & Marcelo Bronzo Ladeira & Alberto Calle Vicente, 2017. "An analysis of international coauthorship networks in the supply chain analytics research area," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1703-1731, June.
    7. K. Gnana Sheela & S. N. Deepa, 2013. "Review on Methods to Fix Number of Hidden Neurons in Neural Networks," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-11, June.
    8. Adnan Khan & Meng Tao & Hassan Ahmad & Muhammad Nouman Shafique & Muhammad Zahid Nawaz, 2020. "Revisiting Green Supply Chain Management Practices: The Mediating Role of Emotional Intelligence," SAGE Open, , vol. 10(1), pages 21582440209, March.
    9. Kathleen M. Eisenhardt & Jeffrey A. Martin, 2000. "Dynamic capabilities: what are they?," Strategic Management Journal, Wiley Blackwell, vol. 21(10‐11), pages 1105-1121, October.
    10. Fehrer, Julia A. & Wieland, Heiko, 2021. "A systemic logic for circular business models," Journal of Business Research, Elsevier, vol. 125(C), pages 609-620.
    11. Ravi Srinivasan & Morgan Swink, 2018. "An Investigation of Visibility and Flexibility as Complements to Supply Chain Analytics: An Organizational Information Processing Theory Perspective," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1849-1867, October.
    12. Saleem Malik & Mehmood Chadhar & Savanid Vatanasakdakul & Madhu Chetty, 2021. "Factors Affecting the Organizational Adoption of Blockchain Technology: Extending the Technology–Organization–Environment (TOE) Framework in the Australian Context," Sustainability, MDPI, vol. 13(16), pages 1-33, August.
    13. Yee-Loong Chong, Alain & Liu, Martin J. & Luo, Jun & Keng-Boon, Ooi, 2015. "Predicting RFID adoption in healthcare supply chain from the perspectives of users," International Journal of Production Economics, Elsevier, vol. 159(C), pages 66-75.
    14. David J. Teece, 2007. "Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance," Strategic Management Journal, Wiley Blackwell, vol. 28(13), pages 1319-1350, December.
    15. Mobashar Mubarik & Raja Zuraidah binti Raja Mohd Rasi, 2019. "Triad of Big Data Supply Chain Analytics, Supply Chain Integration and Supply Chain Performance: Evidences from Oil and Gas Sector," Humanities and Social Sciences Letters, Conscientia Beam, vol. 7(4), pages 209-224.
    16. Dominik Eckstein & Matthias Goellner & Constantin Blome & Michael Henke, 2015. "The performance impact of supply chain agility and supply chain adaptability: the moderating effect of product complexity," International Journal of Production Research, Taylor & Francis Journals, vol. 53(10), pages 3028-3046, May.
    17. Gunjan Sood & Rajesh Kumar Jain, 2022. "Organisational enablers of advanced analytics adoption for supply chain flexibility and agility," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 41(3), pages 379-407.
    18. Stav Fainshmidt & Amir Pezeshkan & M. Lance Frazier & Anil Nair & Edward Markowski, 2016. "Dynamic Capabilities and Organizational Performance: A Meta-Analytic Evaluation and Extension," Journal of Management Studies, Wiley Blackwell, vol. 53(8), pages 1348-1380, December.
    19. Benzidia, Smail & Makaoui, Naouel & Bentahar, Omar, 2021. "The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    20. David J. Teece & Gary Pisano & Amy Shuen, 1997. "Dynamic capabilities and strategic management," Strategic Management Journal, Wiley Blackwell, vol. 18(7), pages 509-533, August.
    21. Ghazal Rezaei & Seyed Mohammad Hassan Hosseini & Shib Sankar Sana, 2022. "Exploring the Relationship between Data Analytics Capability and Competitive Advantage: The Mediating Roles of Supply Chain Resilience and Organization Flexibility," Sustainability, MDPI, vol. 14(16), pages 1-23, August.
    22. Tino T. Herden & Benjamin Nitsche & Benno Gerlach, 2020. "Overcoming Barriers in Supply Chain Analytics—Investigating Measures in LSCM Organizations," Logistics, MDPI, vol. 4(1), pages 1-27, February.
    23. Smaïl Benzidia & Naouel Makaoui & Omar Bentahar, 2021. "The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance," Post-Print hal-03028127, HAL.
    24. Piera Centobelli & Roberto Cerchione & Davide Chiaroni & Pasquale Del Vecchio & Andrea Urbinati, 2020. "Designing business models in circular economy: A systematic literature review and research agenda," Business Strategy and the Environment, Wiley Blackwell, vol. 29(4), pages 1734-1749, May.
    25. Constance E. Helfat & Margaret A. Peteraf, 2003. "The dynamic resource‐based view: capability lifecycles," Strategic Management Journal, Wiley Blackwell, vol. 24(10), pages 997-1010, October.
    26. Abed, Salma S., 2020. "Social commerce adoption using TOE framework: An empirical investigation of Saudi Arabian SMEs," International Journal of Information Management, Elsevier, vol. 53(C).
    27. Bongsug Kevin Chae & David L. Olson, 2013. "Business Analytics For Supply Chain: A Dynamic-Capabilities Framework," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 9-26.
    28. Hotlan Siagian & Zeplin Jiwa Husada Tarigan & Ferry Jie, 2021. "Supply Chain Integration Enables Resilience, Flexibility, and Innovation to Improve Business Performance in COVID-19 Era," Sustainability, MDPI, vol. 13(9), pages 1-19, April.
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