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Tytuł artykułu

The moderating effects of perceived cost on blockchain adoption intention in agricultural supply chains

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Background: The unique characteristics of blockchain technology, such as decentralization, transparency, traceability, verifiability, and immutability, have been proven to address challenges in agricultural supply chains such as low transparency, inefficiency, high dependency, financing difficulties, and information asymmetry. In reality, though, the adoption of blockchain technology in agricultural development has not met expectations. Most blockchain projects in agriculture are still in the pilot phase, with few actual applications implemented. Therefore, it is necessary to thoroughly investigate the factors influencing the adoption of blockchain technology in agricultural supply chains to promote its widespread application in this field. Methods: Based on the Technology-Organization-Environment (TOE) framework and the Diffusion of Innovations Theory (DOI), this study collected primary data through a questionnaire survey and employed Confirmatory Composite Analysis (CCA) using SmartPLS 4 to test the integrated model. The study examines the impact of technological, organizational, and environmental contexts on blockchain adoption intention in agricultural supply chains. In addition, the moderating role of perceived cost on these relationships is also explored. Results: The results indicate that technological, organizational, and environmental contexts significantly influence blockchain adoption intention, but the direct impact of perceived cost on adoption intention was non-significant. Notably, perceived cost had a crucial moderating effect on the relationship between organizational context and blockchain adoption intention. Conclusion: This study aims to explore the factors influencing blockchain adoption intentions in agricultural supply chains from an organizational perspective, innovatively applies CCA analysis within the TOE-DOI framework and adopts perceived cost as the moderating variable. These findings underscore the importance of internal evaluations of cost-effectiveness and strategic cost management for practitioners, highlighting the necessity of internal advocacy to enhance technology acceptance. Future research should diversify the sample or conduct longitudinal studies to further validate the moderating effects of perceived cost.
Czasopismo
Rocznik
Strony
585--599
Opis fizyczny
Bibliogr. 45 poz., rys., tab.
Twórcy
autor
  • Faculty of Economics and Management, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
  • Faculty of Economics and Management, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
  • Faculty of Economics and Management, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
  • Faculty of Economics and Management, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
Bibliografia
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  • 4. Bhat, S. A., Huang, N.-F., Sofi, I. B., & Sultan, M. (2021). Agriculture-food supply chain management based on blockchain and IoT: a narrative on enterprise blockchain interoperability. Agriculture, 12(1), 40. https://doi.org/10.3390/agriculture12010040
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  • 8. Cheah, J.-H., Sarstedt, M., Ringle, C. M., Ramayah, T., & Ting, H. (2018). Convergent validity assessment of formatively measured constructs in PLS-SEM: On using single-item versus multi-item measures in redundancy analyses. International Journal of Contemporary Hospitality Management, 30(11), 3192-3210. https://doi.org/10.1108/IJCHM-10-2017-0649
  • 9. Chiaraluce, G., Bentivoglio, D., Finco, A., Fiore, M., Contò, F., & Galati, A. (2024). Exploring the role of blockchain technology in modern high-value food supply chains: Global trends and future research directions. Agricultural and Food Economics, 12(1), 6. https://doi.org/10.1186/s40100-024-00301-1
  • 10. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research/Lawrence Erlbaum Associates.
  • 11. Chittipaka, V., Kumar, S., Sivarajah, U., Bowden, J. L.-H., & Baral, M. M. (2023). Blockchain Technology for Supply Chains operating in emerging markets: an empirical examination of technology-organization-environment (TOE) framework. Annals of Operations Research, 327(1), 465-492. https://doi.org/https://doi.org/10.1007/s10479-022-04801-5
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  • 13. Dong, X., Hao, X., Cai, C., Li, X., Wang, K., & Zhang, J. (2024). The impact of blockchain adoption in supply chain systems on corporate performance: a technology–organization–environment framework. Production planning & control, 1-22. https://doi.org/10.1080/09537287.2024.2364814
  • 14. Ghosh, D., & Dash, S. (2023). Barriers and facilitators of B2B degree of digital use and brand engagement: an integration of technology and behavioral perspectives. Journal of Business & Industrial Marketing, 38(12), 2793-2810. https://doi.org/10.1108/JBIM-09-2022-0406
  • 15. Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442-458. https://doi.org/https://doi.org/10.1108/IMDS-04-2016-0130
  • 16. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European business review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203
  • 17. Hair Jr, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101-110. https://doi.org/10.1016/j.jbusres.2019.11.069
  • 18. Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer Nature.
  • 19. Henseler, J. (2017). Bridging design and behavioral research with variance-based structural equation modeling. Journal of advertising, 46(1), 178-192. https://doi.org/10.1080/00913367.2017.1281780
  • 20. Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems, 116(1), 2-20.
  • 21. Kamble, S. S., Gunasekaran, A., & Sharma, R. (2020). Modeling the blockchain enabled traceability in agriculture supply chain. International journal of information management, 52, 101967.
  • 22. Khan, S., Kaushik, M. K., Kumar, R., & Khan, W. (2023). Investigating the barriers of blockchain technology integrated food supply chain: a BWM approach. Benchmarking: An International Journal, 30(3), 713-735. https://doi.org/10.1108/BIJ-08-2021-0489
  • 23. Leong, L.-Y., Hew, J.-J., Lee, V.-H., Tan, G. W.-H., Ooi, K.-B., & Rana, N. P. (2023). An SEM-ANN analysis of the impacts of Blockchain on competitive advantage. Industrial Management & Data Systems, 123(3), 967-1004. https://doi.org/10.1108/IMDS-11-2021-0671
  • 24. Liu, P., Cui, X., & Li, Y. (2023). Subsidy policies of a fresh supply chain considering the inputs of blockchain traceability service system. Science and Public Policy, 50(1), 72-86. https://doi.org/10.1093/scipol/scac044
  • 25. Lu, Y., & Chen, Y. (2021). Is China's agricultural enterprise growing steadily? Evidence from listed agricultural companies. Chinese Journal of Population, Resources and Environment, 19(2), 203-212. https://doi.org/10.1016/j.cjpre.2021.12.022
  • 26. Malisic, B., Misic, N., Krco, S., Martinovic, A., Tinaj, S., & Popovic, T. (2023). Blockchain adoption in the wine supply chain: a systematic literature review. Sustainability, 15(19), 14408. https://doi.org/10.3390/su151914408
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  • 29. Niu, B., Dong, J., Dai, Z., & Jin, J. Y. (2022). Market expansion vs. intensified competition: Overseas supplier’s adoption of blockchain in a cross-border agricultural supply chain. Electronic Commerce Research and Applications, 51, 101113. https://doi.org/10.1016/j.elerap.2021.101113
  • 30. Quayson, M., Bai, C., Sarkis, J., & Hossin, M. A. (2024). Evaluating barriers to blockchain technology for sustainable agricultural supply chain: A fuzzy hierarchical group DEMATEL approach. Operations Management Research, 1-26. https://doi.org/10.1007/s12063-024-00443-x
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  • 32. Rijanto, A. (2021). Business financing and blockchain technology adoption in agroindustry. Journal of Science and Technology Policy Management, 12(2), 215-235. https://doi.org/10.1108/JSTPM-03-2020-0065
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  • 44. Wu, H., Zhong, W., Zhong, B., Li, H., Guo, J., & Mehmood, I. (2023). Barrier identification, analysis and solutions of blockchain adoption in construction: a fuzzy DEMATEL and TOE integrated method. Engineering, Construction and Architectural Management. https://doi.org/10.1108/ECAM-02-2023-0168
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Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-76a1e21a-864e-4642-8c44-82e202790a47
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