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Key Drivers and Performances of Smart Manufacturing Adoption: A Meta-Analysis

Author

Listed:
  • Juil Kim

    (Center for Growth Engine R&D Coordination, KISTEP, Eumseong 27740, Republic of Korea)

  • Hye-ryun Jeong

    (Department of Management of Technology, Konkuk University, Seoul 05029, Republic of Korea)

  • Hyesu Park

    (Department of Management of Technology, Konkuk University, Seoul 05029, Republic of Korea)

Abstract

This study focused on the smart factory, one of the critical paradigms in the digital transformation in manufacturing, and attempted a meta-analysis to systematically integrate statistical results from existing empirical analysis studies. An integration model, key factors—smart manufacturing adoption—performances, was established from collecting 42 Korean examples of literature. To compare effect sizes between domestic and foreign empirical study results, 11 foreign articles were added, and the moderating effect verification was conducted. As a result of the analysis, (1) the key factors of the adoption and continuous use of smart manufacturing were the network effect, social influences, finances, performance expectancy, facilitating condition, technological capabilities, and entrepreneurship. (2) The adoption and continuous use of smart manufacturing had a significant impact on business performances, especially the financial performance. (3) The impacts of entrepreneurship and the network effect as factors influencing the decision making of smart manufacturing adoption in Korea can be seen to be significantly higher than those of foreign countries. (4) The impact of smart manufacturing adoption on performances in Korea was higher than other countries. The findings of this study will provide practical implications for practitioners optimizing digital transformation manufacturing policies and supporting the adoption of smart manufacturing systems.

Suggested Citation

  • Juil Kim & Hye-ryun Jeong & Hyesu Park, 2023. "Key Drivers and Performances of Smart Manufacturing Adoption: A Meta-Analysis," Sustainability, MDPI, vol. 15(8), pages 1-19, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6496-:d:1120997
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    References listed on IDEAS

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    1. Ahmed Ghaithan & Mohammed Khan & Awsan Mohammed & Laith Hadidi, 2021. "Impact of Industry 4.0 and Lean Manufacturing on the Sustainability Performance of Plastic and Petrochemical Organizations in Saudi Arabia," Sustainability, MDPI, vol. 13(20), pages 1-20, October.
    2. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
    3. Paul Chwelos & Izak Benbasat & Albert S. Dexter, 2001. "Research Report: Empirical Test of an EDI Adoption Model," Information Systems Research, INFORMS, vol. 12(3), pages 304-321, September.
    4. Dixit, Aasheesh & Jakhar, Suresh Kumar & Kumar, Patanjal, 2022. "Does lean and sustainable manufacturing lead to Industry 4.0 adoption: The mediating role of ambidextrous innovation capabilities," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    5. Sarbu, Miruna, 2022. "The impact of industry 4.0 on innovation performance: Insights from German manufacturing and service firms," Technovation, Elsevier, vol. 113(C).
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