Blockchain adoption factors in agricultural supply chains: A PLS-SEM study
Vol 8, Issue 11, 2024
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Abstract
The purpose of this study is to explore factors influencing the blockchain adoption in agricultural supply chains, to make a particular focus on how security and privacy considerations, policy support, and management support impact the blockchain adoption intention. it further investigates perceived usefulness as a mediating variable that potentially amplifies the effects of these factors on blockchain adoption intention, and sets perceived cost as a moderating variable to test its influence on the strength and direction of the relationship between perceived usefulness and adoption intention. through embedding the cost-benefit theory into the integrated tam-toe framework and utilizing the partial least squares structural equation modeling (PLS-SEM) method, this study identifies the pivotal factors that drive or impede blockchain adoption in the agricultural supply chains, which fills the gap of the relatively insufficient research on the blockchain adoption in agriculture field. the results further provide empirical evidence and strategic insights that can guide practical implementations, to equip stakeholders or practitioners with the necessary knowledge to navigate the complexities of integrating cutting-edge technologies into traditional agricultural operations, thereby promoting more efficient, transparent, and resilient agricultural supply chains.
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Abdulsalam, Y. S., & Hedabou, M. (2021). Security and privacy in cloud computing: technical review. Future Internet, 14(1), 11. https://doi.org/10.3390/fi14010011
AbuAkel, S. A., & Ibrahim, M. (2023). The Effect of Relative Advantage, Top Management Support and IT Infrastructure on E-Filing Adoption. Journal of Risk and Financial Management, 16(6), 295. https://doi.org/10.3390/jrfm16060295
Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS quarterly, 24(4), 665–694. https://doi.org/10.2307/3250951
Alobid, M., Abujudeh, S., & Szűcs, I. (2022). The role of blockchain in revolutionizing the agricultural sector. Sustainability, 14(7), 4313. https://doi.org/10.3390/su14074313
Astuti, R., & Hidayati, L. (2023). How might blockchain technology be used in the food supply chain? A systematic literature review. Cogent Business & Management, 10(2), 2246739. https://doi.org/10.1080/23311975.2023.2246739
Badghish, S., & Soomro, Y. A. (2024). Artificial Intelligence Adoption by SMEs to Achieve Sustainable Business Performance: Application of Technology-Organization-Environment Framework. Sustainability, 16(5), 1864. https://doi.org/10.3390/su16051864
Bawono, H. T., Winarno, W., & Karyono, K. (2022). Effect Of Technology, Organization, And External Environment on business performance mediated by the adoption of technology 4.0 in SMEs. Jurnal Manajerial, 9(02), 228–248. http://dx.doi.org/10.30587/jurnalmanajerial.v9i02.3854
Becker, J.-M., Cheah, J.-H., Gholamzade, R., et al. (2023). PLS-SEM’s most wanted guidance. International Journal of Contemporary Hospitality Management, 35(1), 321–346. https://doi.org/10.1108/IJCHM-04-2022-0474
Bhat, S. A., Huang, N.-F., Sofi, I. B., et al. (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
Bialas, C., Bechtsis, D., Aivazidou, E., et al. (2023). A holistic view on the adoption and cost-effectiveness of technology-driven supply chain management practices in healthcare. Sustainability, 15(6), 5541. https://doi.org/10.3390/su15065541
Biernacki, P., & Waldorf, D. (1981). Snowball sampling: Problems and techniques of chain referral sampling. Sociological methods & research, 10(2), 141–163. https://doi.org/10.1177/004912418101000205
Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of cross-cultural psychology, 1(3), 185–216. https://doi.org/10.1177/135910457000100301
Cao, Y., Li, H., & Su, L. (2024). Blockchain-driven incentive mechanism for agricultural water-saving: A tripartite game model. Journal of Cleaner Production, 434(2024), 140197. https://doi.org/10.1016/j.jclepro.2023.140197
Catalini, C. (2017). How blockchain applications will move beyond finance. Harvard Business Review, 2.
Chiaraluce, G., Bentivoglio, D., Finco, A., et al. (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), 1–22. https://doi.org/10.1186/s40100 024 00301 1
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295–336.
Chin, W. W. (2009). Bootstrap cross-validation indices for PLS path model assessment. In: Handbook of partial least squares: Concepts, methods and applications. Springer Link Publishing. pp. 83–97.
Chin, W. W., & Newsted, P. R. (1999). Structural equation modeling analysis with small samples using partial least squares. Statistical strategies for small sample research, 1(1), 307–341.
Chittipaka, V., Kumar, S., Sivarajah, U., et al. (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/10.1007/s10479-022-04801-5
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. psychometrika, 16(3), 297–334. https://doi.org/10.1007/BF02310555
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Dehghani, M., Kennedy, R. W., Mashatan, A., et al. (2022). High interest, low adoption. A mixed-method investigation into the factors influencing organisational adoption of blockchain technology. Journal of Business Research, 149(2022), 393–411. https://doi.org/10.1016/j.jbusres.2022.05.015
Eisenhardt, K. M., & Graebner, M. E. (2007). Theory building from cases: Opportunities and challenges. Academy of management journal, 50(1), 25–32. https://doi.org/10.5465/amj.2007.24160888
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
Forrest, J. F. (1991). Practitioners’ forum: Models of the process technological innovation. Technology Analysis & Strategic Management, 3(4), 439–453. https://doi.org/10.1080/09537329108524070
Ganguly, K. K. (2024). Understanding the challenges of the adoption of blockchain technology in the logistics sector: the TOE framework. Technology Analysis & Strategic Management, 36(3), 457–471. https://doi.org/10.1080/09537325.2022.2036333
Goyal, A., Kanyal, H., & Sharma, B. (2023). Analysis of IoT and blockchain technology for agricultural food supply chain transactions. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 234–241. https://doi.org/10.17762/ijritcc.v11i3.6342
Haber, S., & Stornetta, W. S. (1991). How to time-stamp a digital document. Journal of Cryptology, 3, 99–111.
Hair, J., Hollingsworth, C. L., Randolph, A. B., et al. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442–458. https://doi.org/10.1108/IMDS-04-2016-0130
Hair, J. F. (2009). Multivariate data analysis. Pearson Publishing.
Hair, J. F., Sarstedt, M., Ringle, C. M., et al. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the academy of marketing science, 40(2012), 414–433. https://doi.org/10.1007/s11747-011-0261-6
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43(2015), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Jahanbin, P., Wingreen, S. C., Sharma, R., et al. (2023). Enabling affordances of blockchain in agri-food supply chains: A value-driver framework using Q-methodology. International Journal of Innovation Studies, 7(4), 307–325. https://doi.org/10.1016/j.ijis.2023.08.001
Kabir, M. R. (2021). Behavioural intention to adopt blockchain for a transparent and effective taxing system. Journal of Global Operations and Strategic Sourcing, 14(1), 170–201. https://doi.org/10.1108/JGOSS-08-2020-0050
Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS‐SEM: The inverse square root and gamma‐exponential methods. Information systems journal, 28(1), 227–261. https://doi.org/10.1111/isj.12131
Kock, N., & Lynn, G. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for information Systems, 13(7).
Layard, R., & Glaister, S. (1994). Cost-benefit analysis. Cambridge University Press.
Lee, J., Lee, J., & Feick, L. (2001). The impact of switching costs on the customer satisfaction‐loyalty link: mobile phone service in France. Journal of services marketing, 15(1), 35–48. https://doi.org/10.1108/08876040110381463
Lin, H.-F. (2014). Understanding the determinants of electronic supply chain management system adoption: Using the technology-organization-environment framework. Technological Forecasting and Social Change, 86(2014), 80–92. https://doi.org/10.1016/j.techfore.2013.09.001
Luo, J., Ji, C., Qiu, C., et al. (2018). Agri-food supply chain management: Bibliometric and content analyses. Sustainability, 10(5), 1573. https://doi.org/10.3390/su10051573
Ma, M., & Agarwal, R. (2007). Through a glass darkly: Information technology design, identity verification, and knowledge contribution in online communities. Information systems research, 18(1), 42–67. https://doi.org/10.1287/isre.1070.0113
Markus, S., & Buijs, P. (2022). Beyond the hype: how blockchain affects supply chain performance. Supply Chain Management: An International Journal, 27(7), 177–193. http://dx.doi.org/10.1108/SCM-03-2022-0109
Mishra, N. K., Raj, A., Jeyaraj, A., et al. (2024). Antecedents and outcomes of blockchain technology adoption: meta-analysis. Journal of Computer Information Systems, 64(3), 342–359. https://doi.org/10.1080/08874417.2023.2205370
Nunnally, J. C., & Bernstein, I. H. (1994). The theory of measurement error. Psychometric theory, 3(1), 209–247.
Ozili, P. K. (2023). The acceptable R-square in empirical modelling for social science research. In: Social research methodology and publishing results: A guide to non-native English speakers. IGI global. pp. 134–143. https://10.4018/978-1-6684-6859-3.ch009
Panwar, A., Khari, M., Misra, S., et al. (2023). Blockchain in Agriculture to Ensure Trust, Effectiveness, and Traceability from Farm Fields to Groceries. Future Internet, 15(12), 404. https://doi.org/10.3390/fi15120404
Perera, S., Nanayakkara, S., Rodrigo, M., et al. (2020). Blockchain technology: Is it hype or real in the construction industry? Journal of industrial information integration, 17(2020), 100125. https://doi.org/10.1016/j.jii.2020.100125
Pugh, D. S. (1966). Modern organization theory: A psychological and sociological study. Psychological Bulletin, 66(4), 235. https://doi.org/10.1037/h0023853
Rupeika-Apoga, R., & Petrovska, K. (2022). Barriers to sustainable digital transformation in micro-, small-, and medium-sized enterprises. Sustainability, 14(20), 13558. https://doi.org/10.3390/su142013558
Saha, A., Raut, R. D., Kumar, M., et al. (2024). The intention of adopting blockchain technology in agri-food supply chains: evidence from an Indian economy. Journal of Modelling in Management. https://doi.org/10.1108/JM2-10-2023-0238
Shen, Z., Wang, S., Boussemart, J.-P., et al. (2022). Digital transition and green growth in Chinese agriculture. Technological Forecasting and Social Change, 181, 121742. https://doi.org/10.1016/j.techfore.2022.121742
Skalkos, D. (2023). Prospects, challenges and sustainability of the agri-food supply chain in the new global economy ii. Sustainability, 15(16), 12558. https://doi.org/10.3390/su151612558
Stone, M. (1974). Cross‐validatory choice and assessment of statistical predictions. Journal of the royal statistical society: Series B (Methodological), 36(2), 111-133. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
Su, C.-W., Xie, Y., Shahab, S., et al. (2021). Towards achieving sustainable development: role of technology innovation, technology adoption and CO2 emission for BRICS. International journal of environmental research and public health, 18(1), 277. https://doi.org/10.3390/ijerph18010277
Swetha, S., & JoePrathap, P. (2022). A Study on a Decentralized Network Secured Data Sharing using Blockchain. In: Proceedings of the International Conference on Computational Science and Technology (ICCST); 27–28 August 2022; Johor bahru, Malaysia. p. 52.
Treiblmaier, H., & Sillaber, C. (2021). The impact of blockchain on e-commerce: a framework for salient research topics. Electronic Commerce Research and Applications, 48, 101054. https://doi.org/10.1016/j.elerap.2021.101054
Tsai, J.-F., Tran, D.-H., Nguyen, P.-H., et al. (2023). Interval-Valued Hesitant Fuzzy DEMATEL-Based Blockchain Technology Adoption Barriers Evaluation Methodology in Agricultural Supply Chain Management. Sustainability, 15(5), 4686. https://doi.org/10.3390/su15054686
Varma, J. R. (2019). Blockchain in finance. Vikalpa, 44(1), 1–11. https://doi.org/10.1177/0256090919839897
Wang, X., Liu, L., Liu, J., et al. (2022). Understanding the determinants of blockchain technology adoption in the construction industry. Buildings, 12(10), 1709. https://doi.org/10.3390/buildings12101709
Werts, C. E., Linn, R. L., & Jöreskog, K. G. (1974). Intraclass reliability estimates: Testing structural assumptions. Educational and Psychological measurement, 34(1), 25–33. https://doi.org/10.1177/001316447403400104
Wong, K. K.-K. (2013). Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Marketing bulletin, 24(1), 1–32.
Xiong, H., Dalhaus, T., Wang, P., et al. (2020). Blockchain technology for agriculture: applications and rationale. frontiers in Blockchain, 3(2020), 7. https://doi.org/10.3389/fbloc.2020.00007
Yap, T. L., Nayak, R., Vu, N. T., et al. (2023). Adopting blockchain-based traceability in the fruit supply chain in a developing economy: facilitators and barriers. Information Technology and People. https://doi.org/10.1108/ITP-02-2023-0168
Zhan, S., & Wan, Z. (2024). Research of blockchain-embedded agricultural quality credit regulation influencing factors. Industrial Management & Data Systems. https://doi.org/10.1108/IMDS-11-2023-0879
Zheng, Y., Xu, Y., & Qiu, Z. (2023). Blockchain traceability adoption in agricultural supply chain coordination: An evolutionary game analysis. Agriculture, 13(1), 184. https://doi.org/10.3390/agriculture13010184
Zhong, J., Cheng, H., Chen, X., et al. (2023). A systematic analysis of quality management in agri-food supply chains: a hierarchy of capabilities perspective. Supply Chain Management: An International Journal, 28(3), 619–637. https://doi.org/10.1108/SCM-12-2021-0547
DOI: https://doi.org/10.24294/jipd.v8i11.8411
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