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Supply chain analytics implementation: A TOE perspective

In: Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 31

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  • Lodemann, Sebastian
  • Kersten, Wolfgang

Abstract

Purpose: Increasing quantity and sources of data and their potential to fuel value-adding applications in Supply Chain Management (SCM) are of considerable importance for corporations. Methodology: We utilize a qualitative research design of semi-structured expert interviews. Findings: Utilizing the Technology-Organization-Environment framework, we establish an integrated perspective: We propose that CSF possess a varying relevance to the success of the SCA (Supply Chain Analytics) project, depending on the initial drivers. Originality: While the benefit and potential value of SCA is established, the implementation of the technology remains a challenge for companies. This paper combines the concept of 'drivers' for adoption with Critical Success Factors (CSF) during the initial implementation.

Suggested Citation

  • Lodemann, Sebastian & Kersten, Wolfgang, 2021. "Supply chain analytics implementation: A TOE perspective," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 411-434, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    • Handle: RePEc:zbw:hiclch:249624
    DOI: 10.15480/882.3976
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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. Nam, Dalwoo & Lee, Junyeong & Lee, Heeseok, 2019. "Business analytics adoption process: An innovation diffusion perspective," International Journal of Information Management, Elsevier, vol. 49(C), pages 411-423.
    3. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    4. Bin Shen & Hau-Ling Chan, 2017. "Forecast Information Sharing for Managing Supply Chains in the Big Data Era: Recent Development and Future Research," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(01), pages 1-26, February.
    5. Queiroz, Maciel M. & Fosso Wamba, Samuel, 2019. "Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA," International Journal of Information Management, Elsevier, vol. 46(C), pages 70-82.
    6. 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.
    7. Kwon, Ohbyung & Lee, Namyeon & Shin, Bongsik, 2014. "Data quality management, data usage experience and acquisition intention of big data analytics," International Journal of Information Management, Elsevier, vol. 34(3), pages 387-394.
    8. Akter, Shahriar & Wamba, Samuel Fosso & Gunasekaran, Angappa & Dubey, Rameshwar & Childe, Stephen J., 2016. "How to improve firm performance using big data analytics capability and business strategy alignment?," International Journal of Production Economics, Elsevier, vol. 182(C), pages 113-131.
    9. Denolf, Janne M. & Trienekens, Jacques H. & Wognum, P.M. (Nel) & Schütz, Verena & van der Vorst, Jack G.A.J. & Omta, S.W.F. (Onno), 2018. "“Actionable” critical success factors for supply chain information system implementations," International Journal on Food System Dynamics, International Center for Management, Communication, and Research, vol. 9(1), January.
    10. Kim, Sanghyun & Garrison, Gary, 2010. "Understanding users’ behaviors regarding supply chain technology: Determinants impacting the adoption and implementation of RFID technology in South Korea," International Journal of Information Management, Elsevier, vol. 30(5), pages 388-398.
    11. Matta, Vic & Koonce, David & Jeyaraj, Anand, 2012. "Initiation, Experimentation, Implementation of innovations: The case for Radio Frequency Identification Systems," International Journal of Information Management, Elsevier, vol. 32(2), pages 164-174.
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