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Correlation mechanism between smart technology and smart supply chain innovation performance: A multi-case study from China's companies with Physical Internet

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  • Liu, Weihua
  • Long, Shangsong
  • Wei, Shuang

Abstract

In recent years, the development of smart supply chain has become more and more important for global companies with Physical Internet to enhance their competitiveness. Meanwhile, smart supply chain innovation is increasingly dependent on the development of smart technologies. However, in the research on smart supply chain, there is no theoretical framework for the correlation mechanism between smart technology level (STL) and smart supply chain innovation performance (SSCIP), thus according to resource-based view, this study conducts a multi-case study and takes China's companies with Physical Internet as examples to propose this theoretical framework. This study obtains several important findings. First, we find that STL promotes smart supply chain innovation (SSCI) practices, and companies focus on those practices distinctively. Meanwhile, we find that SSCI practices promote SSCIP. Second, government support policies have positively moderated the role of STL in promoting SSCI practices. Third, the quick response strategy has positively moderated the role of SSCI practices in promoting SSCIP.

Suggested Citation

  • Liu, Weihua & Long, Shangsong & Wei, Shuang, 2022. "Correlation mechanism between smart technology and smart supply chain innovation performance: A multi-case study from China's companies with Physical Internet," International Journal of Production Economics, Elsevier, vol. 245(C).
    • Handle: RePEc:eee:proeco:v:245:y:2022:i:c:s0925527321003704
    DOI: 10.1016/j.ijpe.2021.108394
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    as
    1. Sajjad Rahmanzadeh & Mir Saman Pishvaee & Mohammad Reza Rasouli, 2020. "Integrated innovative product design and supply chain tactical planning within a blockchain platform," International Journal of Production Research, Taylor & Francis Journals, vol. 58(7), pages 2242-2262, April.
    2. Sakun Boon-itt, 2009. "The effect of internal and external supply chain integration on product quality and innovation: evidence from Thai automotive industry," International Journal of Integrated Supply Management, Inderscience Enterprises Ltd, vol. 5(2), pages 97-112.
    3. Latan, Hengky & Chiappetta Jabbour, Charbel Jose & Lopes de Sousa Jabbour, Ana Beatriz & de Camargo Fiorini, Paula & Foropon, Cyril, 2020. "Innovative efforts of ISO 9001-certified manufacturing firms: Evidence of links between determinants of innovation, continuous innovation and firm performance," International Journal of Production Economics, Elsevier, vol. 223(C).
    4. Liu, Weihua & Yan, Xiaoyu & Li, Xiang & Wei, Wanying, 2020. "The impacts of market size and data-driven marketing on the sales mode selection in an Internet platform based supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    5. Liu, Weihua & Wang, Siyu & Lin, Yong & Xie, Dong & Zhang, Jiahui, 2020. "Effect of intelligent logistics policy on shareholder value: Evidence from Chinese logistics companies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 137(C).
    6. Sundarakani, Balan & Ajaykumar, Aneesh & Gunasekaran, Angappa, 2021. "Big data driven supply chain design and applications for blockchain: An action research using case study approach," Omega, Elsevier, vol. 102(C).
    7. Chen, Lujie & Olhager, Jan & Tang, Ou, 2014. "Manufacturing facility location and sustainability: A literature review and research agenda," International Journal of Production Economics, Elsevier, vol. 149(C), pages 154-163.
    8. Lucas, Marilyn T. & Noordewier, Thomas G., 2016. "Environmental management practices and firm financial performance: The moderating effect of industry pollution-related factors," International Journal of Production Economics, Elsevier, vol. 175(C), pages 24-34.
    9. Fazıl Paç, M. & Savin, Sergei & Velu, Chander, 2018. "When to adopt a service innovation: Nash equilibria in a competitive diffusion framework," European Journal of Operational Research, Elsevier, vol. 271(3), pages 968-984.
    10. Hau L. Lee, 2018. "Big Data and the Innovation Cycle," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1642-1646, September.
    11. Banales-Lopez, Santiago & Norberg-Bohm, Vicki, 2002. "Public policy for energy technology innovation: A historical analysis of fluidized bed combustion development in the USA," Energy Policy, Elsevier, vol. 30(13), pages 1173-1180, October.
    12. Aydin, Ayhan & Parker, Rodney P., 2018. "Innovation and technology diffusion in competitive supply chains," European Journal of Operational Research, Elsevier, vol. 265(3), pages 1102-1114.
    13. Yeung, Kwong & Zhou, Honggeng & Yeung, Andy C.L. & Cheng, T.C.E., 2012. "The impact of third-party logistics providers' capabilities on exporters' performance," International Journal of Production Economics, Elsevier, vol. 135(2), pages 741-753.
    14. Jin, Yan & Vonderembse, Mark & Ragu-Nathan, T.S. & Smith, Joy Turnheim, 2014. "Exploring relationships among IT-enabled sharing capability, supply chain flexibility, and competitive performance," International Journal of Production Economics, Elsevier, vol. 153(C), pages 24-34.
    15. Sun-A Kang & Sang-Min Cho, 2020. "Management Overconfidence and CSR Activities in Korea with a Big Data Approach," Sustainability, MDPI, vol. 12(11), pages 1-15, May.
    16. Hau-Ling Chan & Bin Shen & Yajun Cai, 2018. "Quick response strategy with cleaner technology in a supply chain: coordination and win-win situation analysis," International Journal of Production Research, Taylor & Francis Journals, vol. 56(10), pages 3397-3408, May.
    17. Gunasekaran, A., 1999. "Agile manufacturing: A framework for research and development," International Journal of Production Economics, Elsevier, vol. 62(1-2), pages 87-105, May.
    18. Tsan‐Ming Choi & Stein W. Wallace & Yulan Wang, 2018. "Big Data Analytics in Operations Management," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1868-1883, October.
    19. Benoît Chevalier‐Roignant & Christoph M. Flath & Lenos Trigeorgis, 2019. "Disruptive Innovation, Market Entry and Production Flexibility in Heterogeneous Oligopoly," Production and Operations Management, Production and Operations Management Society, vol. 28(7), pages 1641-1657, July.
    20. Yu, Wantao & Chavez, Roberto & Jacobs, Mark A. & Feng, Mengying, 2018. "Data-driven supply chain capabilities and performance: A resource-based view," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 371-385.
    21. Barenji, Ali Vatankhah & Wang, W.M. & Li, Zhi & Guerra-Zubiaga, David A., 2019. "Intelligent E-commerce logistics platform using hybrid agent based approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 126(C), pages 15-31.
    22. Shenle Pan & Eric Ballot & George Q. Huang & Benoit Montreuil, 2017. "Physical Internet and Interconnected Logistics Services: Research and Applications," Post-Print hal-01482909, HAL.
    23. Chung, Sai-Ho, 2021. "Applications of smart technologies in logistics and transport: A review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    24. Chahal, Hardeep & Gupta, Mahesh & Bhan, Namrita & Cheng, T.C.E., 2020. "Operations management research grounded in the resource-based view: A meta-analysis," International Journal of Production Economics, Elsevier, vol. 230(C).
    25. Nasiri, Mina & Ukko, Juhani & Saunila, Minna & Rantala, Tero, 2020. "Managing the digital supply chain: The role of smart technologies," Technovation, Elsevier, vol. 96.
    26. Ziping Wang & Dong‐Qing Yao & Xiaohang Yue & John J. Liu, 2018. "Impact of IT Capability on the Performance of Port Operation," Production and Operations Management, Production and Operations Management Society, vol. 27(11), pages 1996-2009, November.
    27. Brofman Epelbaum, Freddy Moises & Garcia Martinez, Marian, 2014. "The technological evolution of food traceability systems and their impact on firm sustainable performance: A RBV approach," International Journal of Production Economics, Elsevier, vol. 150(C), pages 215-224.
    28. Jesse Bockstedt & Cheryl Druehl & Anant Mishra, 2016. "Heterogeneous Submission Behavior and its Implications for Success in Innovation Contests with Public Submissions," Production and Operations Management, Production and Operations Management Society, vol. 25(7), pages 1157-1176, July.
    29. Roh, James & Hong, Paul & Min, Hokey, 2014. "Implementation of a responsive supply chain strategy in global complexity: The case of manufacturing firms," International Journal of Production Economics, Elsevier, vol. 147(PB), pages 198-210.
    30. Wang, Yi & Chen, Yang & Benitez-Amado, Jose, 2015. "How information technology influences environmental performance: Empirical evidence from China," International Journal of Information Management, Elsevier, vol. 35(2), pages 160-170.
    31. Adriana Barbeito‐Caamaño & Ricardo Chalmeta, 2020. "Using big data to evaluate corporate social responsibility and sustainable development practices," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 27(6), pages 2831-2848, November.
    32. Liu, Weihua & Wang, Di & Shen, Xinran & Yan, Xiaoyu & Wei, Wanying, 2018. "The impacts of distributional and peer-induced fairness concerns on the decision-making of order allocation in logistics service supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 102-122.
    33. Birger Wernerfelt, 1984. "A resource‐based view of the firm," Strategic Management Journal, Wiley Blackwell, vol. 5(2), pages 171-180, April.
    34. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Papadopoulos, Thanos & Luo, Zongwei & Wamba, Samuel Fosso & Roubaud, David, 2019. "Can big data and predictive analytics improve social and environmental sustainability?," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 534-545.
    35. Hao Luo & Siyu Tian & Xiang T. R. Kong, 2021. "Physical Internet-enabled customised furniture delivery in the metropolitan areas: digitalisation, optimisation and case study," International Journal of Production Research, Taylor & Francis Journals, vol. 59(7), pages 2193-2217, April.
    36. Shenle Pan & Eric Ballot & George Q. Huang & Benoit Montreuil, 2017. "Physical Internet and interconnected logistics services: research and applications," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2603-2609, May.
    37. Zhu, You & Zhou, Li & Xie, Chi & Wang, Gang-Jin & Nguyen, Truong V., 2019. "Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach," International Journal of Production Economics, Elsevier, vol. 211(C), pages 22-33.
    38. García, Francisco & Avella, Lucía & Fernández, Esteban, 2012. "Learning from exporting: The moderating effect of technological capabilities," International Business Review, Elsevier, vol. 21(6), pages 1099-1111.
    39. Kumar, Maneesh & Rodrigues, Vasco Sanchez, 2020. "Synergetic effect of lean and green on innovation: A resource-based perspective," International Journal of Production Economics, Elsevier, vol. 219(C), pages 469-479.
    40. Ray Y. Zhong & Chen Xu & Chao Chen & George Q. Huang, 2017. "Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2610-2621, May.
    41. Choi, Tsan-Ming & Wen, Xin & Sun, Xuting & Chung, Sai-Ho, 2019. "The mean-variance approach for global supply chain risk analysis with air logistics in the blockchain technology era," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 178-191.
    42. Yingyuan Guo & Xingneng Xia & Sheng Zhang & Danping Zhang, 2018. "Environmental Regulation, Government R&D Funding and Green Technology Innovation: Evidence from China Provincial Data," Sustainability, MDPI, vol. 10(4), pages 1-21, March.
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