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Sustainable Diffusion of Inter-Organizational Technology in Supply Chains: An Approach to Heterogeneous Levels of Risk Aversion

Author

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  • Daeheon Choi

    (College of Business Administration, Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul 02707, Korea)

  • Chune Young Chung

    (School of Business Administration, College of Business and Economics, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Korea)

  • Kaun Y. Lee

    (School of Business Administration, College of Business and Economics, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Korea)

Abstract

This paper develops a model to analyze inter-organizational technology adoption in a supply chain. While the basic model is general, this study is motivated by several cases of inter-organizational technology adoption in supply chains. The proposed model in this study considers firms on both levels of the supply chain, namely, supplier firms and buyer firms. These individual firms’ thresholds for adoption should be considered by other firms’ decisions within a network, together with their own organizational attributes. The heterogeneity across the population should be allowed. That is, individual firms make a decision for adopting the technology at different times due to their different network sizes, prior beliefs, and amounts of information observed. The main finding is that this uncertainty decreases as other suppliers adopt the technology, and information about their experiences becomes available. In addition, at any given time, an estimate of the benefit to a supplier depends on the number of supplier firms and on the number of buyer firms that have already adopted the technology. Thus, we seek to capture this dependence and analyze its effect on the adoption of a new inter-organizational technology. The next step is to embed the firm-level adoption model into a population model. The model includes various types of heterogeneity in the population model to capture the factors affecting the speed of diffusion. This allows us to derive an adoption curve that is specified by the accumulated fraction of firms that have adopted the technology in or before any given period. The population model allows us to consider the effect of several strategies observed in practice and numerical experiments yielding many managerial implications in this area.

Suggested Citation

  • Daeheon Choi & Chune Young Chung & Kaun Y. Lee, 2018. "Sustainable Diffusion of Inter-Organizational Technology in Supply Chains: An Approach to Heterogeneous Levels of Risk Aversion," Sustainability, MDPI, vol. 10(6), pages 1-15, June.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:6:p:2108-:d:153528
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    References listed on IDEAS

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    1. Kevin F. McCardle, 1985. "Information Acquisition and the Adoption of New Technology," Management Science, INFORMS, vol. 31(11), pages 1372-1389, November.
    2. Min-Ren Yan & Kuo-Ming Chien & Tai-Ning Yang, 2016. "Green Component Procurement Collaboration for Improving Supply Chain Management in the High Technology Industries: A Case Study from the Systems Perspective," Sustainability, MDPI, vol. 8(2), pages 1-16, January.
    3. Rabik Ar Chatterjee & Jehoshua Eliashberg, 1990. "The Innovation Diffusion Process in a Heterogeneous Population: A Micromodeling Approach," Management Science, INFORMS, vol. 36(9), pages 1057-1079, September.
    4. Corey M. Angst & Ritu Agarwal & V. Sambamurthy & Ken Kelley, 2010. "Social Contagion and Information Technology Diffusion: The Adoption of Electronic Medical Records in U.S. Hospitals," Management Science, INFORMS, vol. 56(8), pages 1219-1241, August.
    5. Jiguang Wang & Bing Ran, 2018. "Sustainable Collaborative Governance in Supply Chain," Sustainability, MDPI, vol. 10(1), pages 1-17, January.
    6. Seungjin Whang, 2010. "Timing of RFID Adoption in a Supply Chain," Management Science, INFORMS, vol. 56(2), pages 343-355, February.
    7. Tsur, Yacov & Sternberg, Menachem & Hochman, Eithan, 1990. "Dynamic Modelling of Innovation Process Adoption with Risk Aversion and Learning," Oxford Economic Papers, Oxford University Press, vol. 42(2), pages 336-355, April.
    8. Juan Manuel Ramon-Jeronimo & Raquel Florez-Lopez & Maria Angeles Ramon-Jeronimo, 2017. "Understanding the Generation of Value along Supply Chains: Balancing Control Information and Relational Governance Mechanisms in Downstream and Upstream Relationships," Sustainability, MDPI, vol. 9(8), pages 1-31, August.
    9. Canan Ulu & James E. Smith, 2009. "Uncertainty, Information Acquisition, and Technology Adoption," Operations Research, INFORMS, vol. 57(3), pages 740-752, June.
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    Cited by:

    1. Daeheon Choi & Chune Young Chung & Thou Seyha & Jason Young, 2020. "Factors Affecting Organizations’ Resistance to the Adoption of Blockchain Technology in Supply Networks," Sustainability, MDPI, vol. 12(21), pages 1-37, October.

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