Technological, Organizational, and Environmental Drivers of Supply Chain Resilience in a Regional Brown Sugar Industry: Evidence from Qiaojia, China
DOI:
https://doi.org/10.62177/apemr.v3i4.1549Keywords:
Supply Chain Resilience, Agri-food Supply Chain, Brown Sugar Industry, Technology-Organization-Environment Framework, Digital Technology, Structural Equation Modeling, Qiaojia, ChinaAbstract
Regional specialty agri-food supply chains face growing risks from production shocks, logistics disruptions, market volatility, and institutional uncertainty, yet evidence on resilience in traditional smallholder-based systems remains limited. Drawing on the technology-organization-environment framework, this study investigates resilience drivers in the Qiaojia brown sugar industry in Yunnan, China. A questionnaire survey of sugarcane growers, processors, distributors, retailers, government-related stakeholders, and other stakeholders yielded 230 valid responses from 248 returns, with an effective response rate of 92.74%. Structural equation modeling examined the effects of digital technology, information sharing, collaboration capability, risk control capability, response capability, and policy and institutional support. The results show acceptable internal consistency, convergent validity, and model fit, with Cronbach’s alpha values ranging from 0.696 to 0.803 and the main fit indices meeting conventional thresholds. All six hypothesized paths were positive and statistically significant. Policy and institutional support had the strongest standardized effect on supply chain resilience (β = 0.303), followed by response capability (β = 0.287), risk control capability (β = 0.266), collaboration capability (β = 0.262), digital technology (β = 0.230), and information sharing (β = 0.187). The findings suggest that resilience depends on the combined development of digital tools, organizational capabilities, and stable institutional support.
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References
Christopher, M., & Peck, H. (2004). Building the resilient supply chain. The International Journal of Logistics Management, 15(2), 1-14. https://doi.org/10.1108/09574090410700275 DOI: https://doi.org/10.1108/09574090410700275
Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain resilience. The International Journal of Logistics Management, 20(1), 124-143. https://doi.org/10.1108/09574090910954873 DOI: https://doi.org/10.1108/09574090910954873
Tukamuhabwa, B. R., Stevenson, M., Busby, J., & Zorzini, M. (2015). Supply chain resilience: Definition, review and theoretical foundations for further study. International Journal of Production Research, 53(18), 5592-5623. https://doi.org/10.1080/00207543.2015.1037934 DOI: https://doi.org/10.1080/00207543.2015.1037934
Stone, J., & Rahimifard, S. (2018). Resilience in agri-food supply chains: A critical analysis of the literature and synthesis of a novel framework. Supply Chain Management: An International Journal, 23(3), 207-238. https://doi.org/10.1108/SCM-06-2017-0201 DOI: https://doi.org/10.1108/SCM-06-2017-0201
Tendall, D. M., Joerin, J., Kopainsky, B., Edwards, P., Shreck, A., Le, Q. B., Kruetli, P., Grant, M., & Six, J. (2015). Food system resilience: Defining the concept. Global Food Security, 6, 17-23. https://doi.org/10.1016/j.gfs.2015.08.001 DOI: https://doi.org/10.1016/j.gfs.2015.08.001
Davis, K. F., Downs, S., & Gephart, J. A. (2021). Towards food supply chain resilience to environmental shocks. Nature Food, 2(1), 54-65. https://doi.org/10.1038/s43016-020-00196-3 DOI: https://doi.org/10.1038/s43016-020-00196-3
Xinhua News Agency. (2025, January 23). Qiaojia brown sugar and the making of a sweet industry. Xinhua Net. https://www.yn.xinhua.org/20250123/a724f98b32df423889e71e4305f56ff1/c.html
Zhang, S. (2026, April 22). Qiaojia’s 14,000 mu of sugarcane paves a sweet road for rural revitalization. Zhaotong News. https://www.ztnews.net/article/show-485545.html
Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.
Zhu, K., Kraemer, K. L., & Xu, S. (2006). The process of innovation assimilation by firms in different countries: A technology diffusion perspective on e-business. Management Science, 52(10), 1557-1576. https://doi.org/10.1287/mnsc.1050.0487 DOI: https://doi.org/10.1287/mnsc.1050.0487
Hastig, G. M., & Sodhi, M. S. (2020). Blockchain for supply chain traceability: Business requirements and critical success factors. Production and Operations Management, 29(4), 935-954. https://doi.org/10.1111/poms.13147 DOI: https://doi.org/10.1111/poms.13147
Kamble, S. S., Gunasekaran, A., & Sharma, R. (2020). Modeling the blockchain enabled traceability in agriculture supply chain. International Journal of Information Management, 52, Article 101967. https://doi.org/10.1016/j.ijinfomgt.2019.05.023 DOI: https://doi.org/10.1016/j.ijinfomgt.2019.05.023
Li, S., & Lin, B. (2006). Accessing information sharing and information quality in supply chain management. Decision Support Systems, 42(3), 1641-1656. https://doi.org/10.1016/j.dss.2006.02.011 DOI: https://doi.org/10.1016/j.dss.2006.02.011
Cao, M., & Zhang, Q. (2011). Supply chain collaboration: Impact on collaborative advantage and firm performance. Journal of Operations Management, 29(3), 163-180. https://doi.org/10.1016/j.jom.2010.12.008 DOI: https://doi.org/10.1016/j.jom.2010.12.008
Pettit, T. J., Croxton, K. L., & Fiksel, J. (2013). Ensuring supply chain resilience: Development and implementation of an assessment tool. Journal of Business Logistics, 34(1), 46-76. https://doi.org/10.1111/jbl.12009 DOI: https://doi.org/10.1111/jbl.12009
Tang, C. S. (2006). Robust strategies for mitigating supply chain disruptions. International Journal of Logistics Research and Applications, 9(1), 33-45. https://doi.org/10.1080/13675560500405584 DOI: https://doi.org/10.1080/13675560500405584
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423. https://doi.org/10.1037/0033-2909.103.3.411 DOI: https://doi.org/10.1037/0033-2909.103.3.411
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104 DOI: https://doi.org/10.1177/002224378101800104
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. https://doi.org/10.1037/0021-9010.88.5.879 DOI: https://doi.org/10.1037/0021-9010.88.5.879
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Copyright (c) 2026 Xin Yang, Xue Zhang

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DATE
Accepted: 2026-07-15
Published: 2026-07-31








