Sistematik Derlemeler ve Meta Analiz
BibTex RIS Kaynak Göster

VERIFYING THE DETERMINANTS OF BLOCKCHAIN ADOPTION INTENTION: A META-ANALYSIS ON SUPPLY CHAIN STUDIES

Yıl 2024, Cilt: 25 Sayı: 1, 384 - 408, 25.03.2024
https://doi.org/10.53443/anadoluibfd.1322124

Öz

Numerous significant variables for the adoption of Blockchain technology in supply chains have been identified empirically. These variables, which influence adoption behavior in a variety of contexts, are discussed theoretically using technology acceptance theories and various other theories and methodological approaches. Given that research have been undertaken in many contexts, it is necessary to validate the previously proposed relationships between factors that facilitate blockchain adoption and the intention to utilize blockchain technology. Therefore, the purpose of this study is to investigate and validate the critical variables that stand out in related studies by using meta-analysis. 38 studies published in SSCI and SCI-E-indexed journals were used after searching WoS, Scopus, and Google Scholar databases and employing various filtering criteria. In addition to the variables considered in the most widely accepted technological, environmental, and organizational classifications, the research results disclose newly emerging or relatively less interesting variables. While the study's empirical findings have managerial implications, this study also provides suggestions for future research agendas.

Kaynakça

  • Alazab, M., Alhyari, S., Awajan, A., & Abdallah, A. B. (2021). Blockchain technology in supply chain management: an empirical study of the factors affecting user adoption/acceptance. Cluster Computing, 24, 83-101. doi: 10.1007/s10586-020-03200-4.
  • AlShamsi, M., Al-Emran, M., & Shaalan, K. (2022). A systematic review on blockchain adoption. Applied Sciences, 12(9), 1-18. doi: 10.3390/app12094245.
  • Barari, M., Ross, M., Thaichon, S., & Surachartkumtonkun, J. (2021). A meta‐analysis of customer engagement behaviour. International Journal of Consumer Studies, 45(4), 457-477. doi: 10.1111/ijcs.12609.
  • Behl, A., Sampat, B., Pereira, V., Jayawardena, N. S., & Laker, B. (2023). Investigating the role of data-driven innovation and information quality on the adoption of blockchain technology on crowdfunding platforms. Annals of Operations Research, 1-30. doi: 10.1007/s10479-023-05290-w.
  • Benabdellah, C., A. Zekhnini, K. Cherrafi, A. Garza-Reyes, J. A. Kumar, A. & El Baz, J. (2023). Blockchain technology for viable circular digital supply chains: An integrated approach for evaluating the implementation barriers, Benchmarking: An International Journal. ahead-of-print. doi: 10.1108/BIJ-04-2022-0240.
  • Bhardwaj, K. A., Garg, A., & Gajpal, Y. (2021). Determinants of blockchain technology adoption in supply chains by small and medium enterprises (SMEs) in India. Mathematical Problems in Engineering, 2021, 1-14. doi: 10.1155/2021/5537395.
  • Birkel, H. S. & Hartmann, E. (2020). Internet of Things–the future of managing supply chain risks, Supply Chain Management: An International Journal, 25(5), 535-548. doi: 10.1108/SCM-09-2019-0356.
  • Chau, P. Y. (1996). An empirical assessment of a modified technology acceptance model. Journal of management information systems, 13(2), 185-204. http://www.jstor.com/stable/40398221.
  • Chen, L., & Holsapple, C. W. (2013). E-business adoption research: State of the art. Journal of Electronic Commerce Research, 14(3), 261.
  • Chengyue, Y., Prabhu, M., Goli, M., & Sahu, A. K. (2021). Factors affecting the adoption of blockchain technology in the complex industrial systems: data modeling. Complexity, 2021, 1-10. doi: 10.1155/2021/8329487.
  • Chittipaka, V., Kumar, S., Sivarajah, U., Bowden, J. L. H., & Baral, M. M. (2022). Blockchain Technology for Supply Chains operating in emerging markets: an empirical examination of technology-organization-environment (TOE) framework. Annals of Operations Research, 1-28. doi: 10.1007/s10479-022-04801-5.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340. https://www.jstor.org/stable/249008.
  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management science, 35(8), 982-1003. https://www.jstor.org/stable/2632151.
  • Dubey, R. Bryde, D. J. Dwivedi, Y. K. Graham, G. Foropon, C. & Papadopoulos, T. (2023). Dynamic digital capabilities and supply chain resilience: The role of government effectiveness, International Journal of Production Economics, 258, 1-16. doi: 10.1016/j.ijpe.2023.108790.
  • Gaitán, A., J., Peral Peral, B., & Ramón Jerónimo, M. (2015). Elderly and internet banking: An application of UTAUT2. Journal of Internet Banking and Commerce, 20 (1), 1-23. http://www.arraydev.com/commerce/jibc/.
  • Geyskens, I., Krishnan, R., Steenkamp, J.‐B. E., & Cunha, P. V. (2009). A review and evaluation of meta‐analysis practices in management research. Journal of Management, 35(2), 393–419. doi: 10.1177/0149206308328501.
  • Giri, G., & Manohar, H. L. (2023). Factors influencing the acceptance of private and public blockchain-based collaboration among supply chain practitioners: a parallel mediation model. Supply Chain Management: An International Journal, 28(1), 1-24. doi: 10.1108/SCM-02-2021-0057.
  • Guan, W., Ding, W., Zhang, B., Verny, J., & Hao, R. (2023). Do supply chain related factors enhance the prediction accuracy of blockchain adoption? A machine learning approach. Technological Forecasting and Social Change, 192, 1-17. doi: 10.1016/j.techfore.2023.122552.
  • Hale, J. L., Householder, B. J., & Greene, K. L. (2002). The theory of reasoned action. The persuasion handbook: Developments in theory and practice, 14, 259-286.
  • Hamdan, I. K., Aziguli, W., Zhang, D., Sumarliah, E., & Usmanova, K. (2022). Forecasting blockchain adoption in supply chains based on machine learning: Evidence from Palestinian food SMEs. British Food Journal, 124(12), 4592-4609. doi: 10.1108/BFJ-05-2021-0535.
  • Hashimy, L., Jain, G., & Grifell-Tatjé, E. (2023). Determinants of blockchain adoption as decentralized business model by Spanish firms–an innovation theory perspective. Industrial Management & Data Systems, 123(1), 204-228. https://I10.1108/IMDS-01-2022-0030.
  • Hsu, C. H. Zeng, J. Y. Chang, A. Y. & Cai, S. Q. (2022). Deploying Industry 4.0 Enablers to Strengthen Supply Chain Resilience to Mitigate Ripple Effects: An Empirical Study of Top Relay Manufacturer in China, IEEE Access, 10, 114829-114855. doi: 10.1109/ACCESS.2022.3215620.
  • Hunter, J. E., & Schmidt, F. L. (2004). Methods of meta-analysis: Correcting error and bias in research findings, Thousand Oaks, CA: Sage Publications.
  • Iranmanesh, M., Maroufkhani, P., Asadi, S., Ghobakhloo, M., Dwivedi, Y. K., & Tseng, M. L. (2023). Effects of supply chain transparency, alignment, adaptability, and agility on blockchain adoption in supply chain among SMEs. Computers & industrial engineering, 176, 1-12. doi: 10.1016/j.cie.2022.108931.
  • Ismagilova, E., Slade, E., Rana, N. P., & Dwivedi, Y. K. (2020). The effect of characteristics of source credibility on consumer behaviour: A meta-analysis. Journal of Retailing and Consumer Services, 53, 1-10. doi: 10.1016/j.jretconser.2019.01.005.
  • Jain, G., Singh, H., Chaturvedi, K. R., & Rakesh, S. (2020). Blockchain in logistics industry: in fizz customer trust or not. Journal of Enterprise Information Management. 33(3), 541-558. doi: 10.1108/JEIM-06-2018-0142.
  • Kabir, M. R., & Islam, M. A. (2021). Application of blockchain for supply chain financing: explaining the drivers using SEM. Journal of Open Innovation: Technology, Market, and Complexity, 7(3), 1-30. doi: 10.3390/joitmc7030167.
  • Kamalahmadi, M. & Parast, M. M. (2016). A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research, International journal of production economics, 171, 116-133. doi: 10.1016/j.ijpe.2015.10.023.
  • Kamble, S. S., Gunasekaran, A., Kumar, V., Belhadi, A., & Foropon, C. (2021). A machine learning based approach for predicting blockchain adoption in supply Chain. Technological Forecasting and Social Change, 163, 1-18. doi: 10.1016/j.techfore.2020.120465.
  • Kamble, S., Gunasekaran, A., & Arha, H. (2019). Understanding the Blockchain technology adoption in supply chains-Indian context. International Journal of Production Research, 57(7), 2009-2033. doi: 10.1080/00207543.2018.1518610.
  • Kim, H. M. & Laskowski, M. (2018). Toward an ontology‐driven blockchain design for supply‐chain provenance, Intelligent Systems in Accounting, Finance and Management, 25(1), 18-27. doi: 10.1002/isaf.1424.
  • Kshetri, N. (2018). Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80-89. doi: 10.1016/j.ijinfomgt.2017.12.005.
  • Kumar, N., Upreti, K., & Mohan, D. (2022). Blockchain adoption for provenance and traceability in the retail food supply chain: a consumer perspective. International Journal of E-Business Research (IJEBR), 18(2), 1-17. doi: 10.4018/IJEBR.294110.
  • Kumar, N., Upreti, K., Upreti, S., Shabbir Alam, M., & Agrawal, M. (2021). Blockchain integrated flexible vaccine supply chain architecture: Excavate the determinants of adoption. Human Behavior and Emerging Technologies, 3(5), 1106-1117. doi: 10.1002/hbe2.302.
  • Kumari, A., & Devi, N. C. (2023). Blockchain technology acceptance by investment professionals: a decomposed TPB model. Journal of Financial Reporting and Accounting, 21(1), 45-59. doi: 10.1108/JFRA-12-2021-0466.
  • Li, X., Lai, P. L., Yang, C. C., & Yuen, K. F. (2021). Determinants of blockchain adoption in the aviation industry: Empirical evidence from Korea. Journal of Air Transport Management, 97, 1-11. doi: 10.1016/j.jairtraman.2021.102139.
  • Miraz, M. H., Hassan, M. G., & Mohd Sharif, K. I. (2020). Factors affecting implementation of blockchain in retail market in Malaysia. International Journal of Supply Chain Management (IJSCM), 9(1), 385-391. http://excelingtech.co.uk/.
  • Mishra, N. K., Raj, A., Jeyaraj, A., & Gupta, R. (2023). Antecedents and Outcomes of Blockchain Technology Adoption: Meta-Analysis. Journal of Computer Information Systems, 1-18. doi: 10.1080/08874417.2023.2205370.
  • Montano, D. E., & Kasprzyk, D. (2015). Theory of reasoned action, theory of planned behavior, and the integrated behavioral model. Health behavior: Theory, research and practice, 70(4), 95-124.
  • Mukherjee, S., Baral, M. M., Lavanya, B. L., Nagariya, R., Singh Patel, B., & Chittipaka, V. (2023). Intentions to adopt the blockchain: investigation of the retail supply chain. Management Decision, 61(5), 1320-1351. doi: 10.1108/MD-03-2022-0369.
  • Nath, S. D., Khayer, A., Majumder, J., & Barua, S. (2022). Factors affecting blockchain adoption in apparel supply chains: does sustainability-oriented supplier development play a moderating role?. Industrial Management & Data Systems, 122(5), 1183-1214. doi: 10.1108/IMDS-07-2021-0466.
  • Park, K. O. (2020). A study on sustainable usage intention of blockchain in the big data era: Logistics and supply chain management companies. Sustainability, 12(24), 1-15. doi: 10.3390/su122410670.
  • Paul, J., & Barari, M. (2022). Meta‐analysis and traditional systematic literature reviews—What, why, when, where, and how?. Psychology & Marketing, 39(6), 1099-1115. doi: 10.1002/mar.21657.
  • Pham, C. T., & Nguyet, T. T. T. (2023). Determinants of blockchain adoption in news media platforms: A perspective from the Vietnamese press industry. Heliyon, 9(1), 1-13. doi: 10.1016/j.heliyon.2022.e12747.
  • Pieters, J. J., Kokkinou, A., & van Kollenburg, T. (2022). Understanding blockchain technology adoption by non-experts: an application of the unified theory of acceptance and use of technology (UTAUT). In Operations Research Forum, 3, 1-19. doi: 10.1007/s43069-021-00113-9.
  • Pimenta, M. L. Cezarino, L. O. Piato, E. L. da Silva, C. H. P. Oliveira, B. G. & Liboni, L. B. (2022) Supply chain resilience in a Covid-19 scenario: Mapping capabilities in a systemic framework, Sustainable Production and Consumption, 29, 649-656. doi: 10.1016/j.spc.2021.10.012.
  • Prisco, A., Abdallah, Y. O., Morande, S., & Gheith, M. H. (2022). Factors affecting blockchain adoption in Italian companies: the moderating role of firm size. Technology Analysis & Strategic Management, 1-14. doi: 10.1080/09537325.2022.2155511.
  • Queiroz, M. M., & Wamba, S. F. (2019). Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA. International Journal of Information Management, 46, 70-82. doi: 10.1016/j.ijinfomgt.2018.11.021.
  • Queiroz, M. M., Fosso Wamba, S., De Bourmont, M., & Telles, R. (2021). Blockchain adoption in operations and supply chain management: empirical evidence from an emerging economy. International Journal of Production Research, 59(20), 6087-6103. doi: 10.1080/00207543.2020.1803511.
  • Rana, J., & Paul, J. (2020). Health motive and the purchase of organic food: A meta‐analytic review. International Journal of Consumer Studies, 44(2), 162-171. doi: 10.1111/ijcs.12556.
  • Rogers, E. M. (2004). A prospective and retrospective look at the diffusion model. Journal of health communication, 9(S1), 13-19. doi: 10.1080/10810730490271449.
  • Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019). Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57(7), 2117-2135. doi: 10.1080/00207543.2018.1533261.
  • Shahzad, K., Zhang, Q., Khan, M. K., Ashfaq, M., & Hafeez, M. (2022). The acceptance and continued use of blockchain technology in supply chain management: a unified model from supply chain professional's stance. International Journal of Emerging Markets, (ahead-of-print). doi: 10.1108/IJOEM-11-2021-1714.
  • Sheel, A., & Nath, V. (2020). Antecedents of blockchain technology adoption intentions in the supply chain. International Journal of Business Innovation and Research, 21(4), 564-584. doi: 10.1504/IJBIR.2020.106011.
  • Sun, Y., Shahzad, M., & Razzaq, A. (2022). Sustainable organizational performance through blockchain technology adoption and knowledge management in China. Journal of Innovation & Knowledge, 7(4), 1-11. doi: 10.1016/j.jik.2022.100247.
  • Taherdoost, H. (2022). A critical review of blockchain acceptance models—blockchain technology adoption frameworks and applications. Computers, 11(2), 1-31. doi: 10.3390/computers11020024.
  • Thornton, A. & Lee, P. (2000). Publication bias in meta-analysis: its causes and consequences. Journal of Clinical Epidemiology, 53(2), 207-216. doi: 10.1016/S0895-4356(99)00161-4.
  • Tran, L. T. T., & Nguyen, P. T. (2021). Co-creating blockchain adoption: theory, practice and impact on usage behavior. Asia Pacific Journal of Marketing and Logistics, 33(7), 1667-1684. doi: 10.1108/APJML-08-2020-0609.
  • Turan, P., C. (2021). Success drivers of co‐branding: A meta‐analysis. International Journal of Consumer Studies, 45(4), 911-936. doi: 10.1111/ijcs.12682.
  • Ullah, N., Mugahed Al-rahmi, W., & Alkhalifah, A. (2021). Predictors for distributed ledger technology adoption: Integrating three traditional adoption theories for manufacturing and service operations. Production & Manufacturing Research, 9(1), 178-205. doi: 10.1080/21693277.2021.1976963.
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204. https://www.jstor.org/stable/2634758.
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 27(3), 425-478. https://www.jstor.org/stable/30036540.
  • Venkatesh, V., Thong, J. Y. & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 36(1), 157-178. https://www.jstor.org/stable/41410412.
  • Wamba, S. F., & Queiroz, M. M. (2022). Industry 4.0 and the supply chain digitalisation: a blockchain diffusion perspective. Production Planning & Control, 33(2-3), 193-210. doi: 10.1080/09537287.2020.1810756.
  • Wamba, S. F., Queiroz, M. M., & Trinchera, L. (2020). Dynamics between blockchain adoption determinants and supply chain performance: An empirical investigation. International Journal of Production Economics, 229, 1-15. doi: 10.1016/j.ijpe.2020.107791.
  • Wong, L. W., Leong, L. Y., Hew, J. J., Tan, G. W. H., & Ooi, K. B. (2020a). Time to seize the digital evolution: Adoption of blockchain in operations and supply chain management among Malaysian SMEs. International Journal of Information Management, 52, 1-19. doi: 10.1016/j.ijinfomgt.2019.08.005.
  • Wong, L. W., Tan, G. W. H., Lee, V. H., Ooi, K. B., & Sohal, A. (2020b). Unearthing the determinants of Blockchain adoption in supply chain management. International Journal of Production Research, 58(7), 2100-2123. doi: 10.1080/00207543.2020.1730463.
  • Woo, C., & Yoo, J. (2022). Exploring the Determinants of Blockchain Acceptance for Research Data Management. Journal of Computer Information Systems, 1-12. doi: 10.1080/08874417.2022.2049019.
  • Yang, C. S. (2019). Maritime shipping digitalization: Blockchain-based technology applications, future improvements, and intention to use. Transportation Research Part E: Logistics and Transportation Review, 131, 108-117. doi: 10.1016/j.tre.2019.09.020.
  • Zhu, Q., Bai, C., & Sarkis, J. (2022). Blockchain technology and supply chains: The paradox of the atheoretical research discourse. Transportation Research Part E: Logistics and Transportation Review, 164, 1-26. doi: 10.1016/j.tre.2022.102824.

BLOCKCHAIN TEKNOLOJİLERİNİ BENİMSEME NİYETİNİN BELİRLEYİCİLERİNİN DOĞRULANMASI: TEDARİK ZİNCİRİ ÇALIŞMALARI ÜZERİNE BİR META-ANALİZ

Yıl 2024, Cilt: 25 Sayı: 1, 384 - 408, 25.03.2024
https://doi.org/10.53443/anadoluibfd.1322124

Öz

Tedarik zincirlerinde Blockchain teknolojisinin benimsenmesine yönelik öne çıkan birçok faktör ampirik olarak belirlenmiştir. Çeşitli bağlamlarda benimseme davranışı üzerindeki etkili olan bu faktörler teorik açıdan teknoloji kabul teorileri ve diğer farklı teoriler ve metodolojik yaklaşımlarla ele alınmıştır. Çalışmaların birçok farklı bağlamda yapıldığı göz önüne alındığında, blockchain teknolojisinin benimsenmesini kolaylaştıran faktörler ile blockchain teknolojisini kullanma niyeti arasında daha önceden önerilen ilişkilerin doğrulanması gerekmektedir. Bu yüzden, bu çalışmanın amacı meta-analizi yardımıyla ilişkili çalışmalarda öne çıkan kritik faktörlerin araştırılması ve doğrulanmasıdır. WoS, Scopus ve Google scholar gibi veri tabanlarının taranması ve çeşitli eleme kriterlerinin uygulanması sonucu SSCI ve SCI-E indeksli dergilerde yayınlanmış 38 çalışma analizde kullanılmıştır. Araştırma bulguları, literatürde en çok kabul gören teknolojik, çevresel ve organizasyonel sınıflandırma dahilinde ele alınan faktörlere ek olarak, yeni ortaya çıkan ya da nispeten daha az ilgi gören değişkenleri de ortaya koymaktadır. Araştırma ampirik bulgularıyla yönetimsel çıkarımlara katkı sağlarken gelecek çalışmalar için gündem önerileri de sunmaktadır.

Kaynakça

  • Alazab, M., Alhyari, S., Awajan, A., & Abdallah, A. B. (2021). Blockchain technology in supply chain management: an empirical study of the factors affecting user adoption/acceptance. Cluster Computing, 24, 83-101. doi: 10.1007/s10586-020-03200-4.
  • AlShamsi, M., Al-Emran, M., & Shaalan, K. (2022). A systematic review on blockchain adoption. Applied Sciences, 12(9), 1-18. doi: 10.3390/app12094245.
  • Barari, M., Ross, M., Thaichon, S., & Surachartkumtonkun, J. (2021). A meta‐analysis of customer engagement behaviour. International Journal of Consumer Studies, 45(4), 457-477. doi: 10.1111/ijcs.12609.
  • Behl, A., Sampat, B., Pereira, V., Jayawardena, N. S., & Laker, B. (2023). Investigating the role of data-driven innovation and information quality on the adoption of blockchain technology on crowdfunding platforms. Annals of Operations Research, 1-30. doi: 10.1007/s10479-023-05290-w.
  • Benabdellah, C., A. Zekhnini, K. Cherrafi, A. Garza-Reyes, J. A. Kumar, A. & El Baz, J. (2023). Blockchain technology for viable circular digital supply chains: An integrated approach for evaluating the implementation barriers, Benchmarking: An International Journal. ahead-of-print. doi: 10.1108/BIJ-04-2022-0240.
  • Bhardwaj, K. A., Garg, A., & Gajpal, Y. (2021). Determinants of blockchain technology adoption in supply chains by small and medium enterprises (SMEs) in India. Mathematical Problems in Engineering, 2021, 1-14. doi: 10.1155/2021/5537395.
  • Birkel, H. S. & Hartmann, E. (2020). Internet of Things–the future of managing supply chain risks, Supply Chain Management: An International Journal, 25(5), 535-548. doi: 10.1108/SCM-09-2019-0356.
  • Chau, P. Y. (1996). An empirical assessment of a modified technology acceptance model. Journal of management information systems, 13(2), 185-204. http://www.jstor.com/stable/40398221.
  • Chen, L., & Holsapple, C. W. (2013). E-business adoption research: State of the art. Journal of Electronic Commerce Research, 14(3), 261.
  • Chengyue, Y., Prabhu, M., Goli, M., & Sahu, A. K. (2021). Factors affecting the adoption of blockchain technology in the complex industrial systems: data modeling. Complexity, 2021, 1-10. doi: 10.1155/2021/8329487.
  • Chittipaka, V., Kumar, S., Sivarajah, U., Bowden, J. L. H., & Baral, M. M. (2022). Blockchain Technology for Supply Chains operating in emerging markets: an empirical examination of technology-organization-environment (TOE) framework. Annals of Operations Research, 1-28. doi: 10.1007/s10479-022-04801-5.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340. https://www.jstor.org/stable/249008.
  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management science, 35(8), 982-1003. https://www.jstor.org/stable/2632151.
  • Dubey, R. Bryde, D. J. Dwivedi, Y. K. Graham, G. Foropon, C. & Papadopoulos, T. (2023). Dynamic digital capabilities and supply chain resilience: The role of government effectiveness, International Journal of Production Economics, 258, 1-16. doi: 10.1016/j.ijpe.2023.108790.
  • Gaitán, A., J., Peral Peral, B., & Ramón Jerónimo, M. (2015). Elderly and internet banking: An application of UTAUT2. Journal of Internet Banking and Commerce, 20 (1), 1-23. http://www.arraydev.com/commerce/jibc/.
  • Geyskens, I., Krishnan, R., Steenkamp, J.‐B. E., & Cunha, P. V. (2009). A review and evaluation of meta‐analysis practices in management research. Journal of Management, 35(2), 393–419. doi: 10.1177/0149206308328501.
  • Giri, G., & Manohar, H. L. (2023). Factors influencing the acceptance of private and public blockchain-based collaboration among supply chain practitioners: a parallel mediation model. Supply Chain Management: An International Journal, 28(1), 1-24. doi: 10.1108/SCM-02-2021-0057.
  • Guan, W., Ding, W., Zhang, B., Verny, J., & Hao, R. (2023). Do supply chain related factors enhance the prediction accuracy of blockchain adoption? A machine learning approach. Technological Forecasting and Social Change, 192, 1-17. doi: 10.1016/j.techfore.2023.122552.
  • Hale, J. L., Householder, B. J., & Greene, K. L. (2002). The theory of reasoned action. The persuasion handbook: Developments in theory and practice, 14, 259-286.
  • Hamdan, I. K., Aziguli, W., Zhang, D., Sumarliah, E., & Usmanova, K. (2022). Forecasting blockchain adoption in supply chains based on machine learning: Evidence from Palestinian food SMEs. British Food Journal, 124(12), 4592-4609. doi: 10.1108/BFJ-05-2021-0535.
  • Hashimy, L., Jain, G., & Grifell-Tatjé, E. (2023). Determinants of blockchain adoption as decentralized business model by Spanish firms–an innovation theory perspective. Industrial Management & Data Systems, 123(1), 204-228. https://I10.1108/IMDS-01-2022-0030.
  • Hsu, C. H. Zeng, J. Y. Chang, A. Y. & Cai, S. Q. (2022). Deploying Industry 4.0 Enablers to Strengthen Supply Chain Resilience to Mitigate Ripple Effects: An Empirical Study of Top Relay Manufacturer in China, IEEE Access, 10, 114829-114855. doi: 10.1109/ACCESS.2022.3215620.
  • Hunter, J. E., & Schmidt, F. L. (2004). Methods of meta-analysis: Correcting error and bias in research findings, Thousand Oaks, CA: Sage Publications.
  • Iranmanesh, M., Maroufkhani, P., Asadi, S., Ghobakhloo, M., Dwivedi, Y. K., & Tseng, M. L. (2023). Effects of supply chain transparency, alignment, adaptability, and agility on blockchain adoption in supply chain among SMEs. Computers & industrial engineering, 176, 1-12. doi: 10.1016/j.cie.2022.108931.
  • Ismagilova, E., Slade, E., Rana, N. P., & Dwivedi, Y. K. (2020). The effect of characteristics of source credibility on consumer behaviour: A meta-analysis. Journal of Retailing and Consumer Services, 53, 1-10. doi: 10.1016/j.jretconser.2019.01.005.
  • Jain, G., Singh, H., Chaturvedi, K. R., & Rakesh, S. (2020). Blockchain in logistics industry: in fizz customer trust or not. Journal of Enterprise Information Management. 33(3), 541-558. doi: 10.1108/JEIM-06-2018-0142.
  • Kabir, M. R., & Islam, M. A. (2021). Application of blockchain for supply chain financing: explaining the drivers using SEM. Journal of Open Innovation: Technology, Market, and Complexity, 7(3), 1-30. doi: 10.3390/joitmc7030167.
  • Kamalahmadi, M. & Parast, M. M. (2016). A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research, International journal of production economics, 171, 116-133. doi: 10.1016/j.ijpe.2015.10.023.
  • Kamble, S. S., Gunasekaran, A., Kumar, V., Belhadi, A., & Foropon, C. (2021). A machine learning based approach for predicting blockchain adoption in supply Chain. Technological Forecasting and Social Change, 163, 1-18. doi: 10.1016/j.techfore.2020.120465.
  • Kamble, S., Gunasekaran, A., & Arha, H. (2019). Understanding the Blockchain technology adoption in supply chains-Indian context. International Journal of Production Research, 57(7), 2009-2033. doi: 10.1080/00207543.2018.1518610.
  • Kim, H. M. & Laskowski, M. (2018). Toward an ontology‐driven blockchain design for supply‐chain provenance, Intelligent Systems in Accounting, Finance and Management, 25(1), 18-27. doi: 10.1002/isaf.1424.
  • Kshetri, N. (2018). Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80-89. doi: 10.1016/j.ijinfomgt.2017.12.005.
  • Kumar, N., Upreti, K., & Mohan, D. (2022). Blockchain adoption for provenance and traceability in the retail food supply chain: a consumer perspective. International Journal of E-Business Research (IJEBR), 18(2), 1-17. doi: 10.4018/IJEBR.294110.
  • Kumar, N., Upreti, K., Upreti, S., Shabbir Alam, M., & Agrawal, M. (2021). Blockchain integrated flexible vaccine supply chain architecture: Excavate the determinants of adoption. Human Behavior and Emerging Technologies, 3(5), 1106-1117. doi: 10.1002/hbe2.302.
  • Kumari, A., & Devi, N. C. (2023). Blockchain technology acceptance by investment professionals: a decomposed TPB model. Journal of Financial Reporting and Accounting, 21(1), 45-59. doi: 10.1108/JFRA-12-2021-0466.
  • Li, X., Lai, P. L., Yang, C. C., & Yuen, K. F. (2021). Determinants of blockchain adoption in the aviation industry: Empirical evidence from Korea. Journal of Air Transport Management, 97, 1-11. doi: 10.1016/j.jairtraman.2021.102139.
  • Miraz, M. H., Hassan, M. G., & Mohd Sharif, K. I. (2020). Factors affecting implementation of blockchain in retail market in Malaysia. International Journal of Supply Chain Management (IJSCM), 9(1), 385-391. http://excelingtech.co.uk/.
  • Mishra, N. K., Raj, A., Jeyaraj, A., & Gupta, R. (2023). Antecedents and Outcomes of Blockchain Technology Adoption: Meta-Analysis. Journal of Computer Information Systems, 1-18. doi: 10.1080/08874417.2023.2205370.
  • Montano, D. E., & Kasprzyk, D. (2015). Theory of reasoned action, theory of planned behavior, and the integrated behavioral model. Health behavior: Theory, research and practice, 70(4), 95-124.
  • Mukherjee, S., Baral, M. M., Lavanya, B. L., Nagariya, R., Singh Patel, B., & Chittipaka, V. (2023). Intentions to adopt the blockchain: investigation of the retail supply chain. Management Decision, 61(5), 1320-1351. doi: 10.1108/MD-03-2022-0369.
  • Nath, S. D., Khayer, A., Majumder, J., & Barua, S. (2022). Factors affecting blockchain adoption in apparel supply chains: does sustainability-oriented supplier development play a moderating role?. Industrial Management & Data Systems, 122(5), 1183-1214. doi: 10.1108/IMDS-07-2021-0466.
  • Park, K. O. (2020). A study on sustainable usage intention of blockchain in the big data era: Logistics and supply chain management companies. Sustainability, 12(24), 1-15. doi: 10.3390/su122410670.
  • Paul, J., & Barari, M. (2022). Meta‐analysis and traditional systematic literature reviews—What, why, when, where, and how?. Psychology & Marketing, 39(6), 1099-1115. doi: 10.1002/mar.21657.
  • Pham, C. T., & Nguyet, T. T. T. (2023). Determinants of blockchain adoption in news media platforms: A perspective from the Vietnamese press industry. Heliyon, 9(1), 1-13. doi: 10.1016/j.heliyon.2022.e12747.
  • Pieters, J. J., Kokkinou, A., & van Kollenburg, T. (2022). Understanding blockchain technology adoption by non-experts: an application of the unified theory of acceptance and use of technology (UTAUT). In Operations Research Forum, 3, 1-19. doi: 10.1007/s43069-021-00113-9.
  • Pimenta, M. L. Cezarino, L. O. Piato, E. L. da Silva, C. H. P. Oliveira, B. G. & Liboni, L. B. (2022) Supply chain resilience in a Covid-19 scenario: Mapping capabilities in a systemic framework, Sustainable Production and Consumption, 29, 649-656. doi: 10.1016/j.spc.2021.10.012.
  • Prisco, A., Abdallah, Y. O., Morande, S., & Gheith, M. H. (2022). Factors affecting blockchain adoption in Italian companies: the moderating role of firm size. Technology Analysis & Strategic Management, 1-14. doi: 10.1080/09537325.2022.2155511.
  • Queiroz, M. M., & Wamba, S. F. (2019). Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA. International Journal of Information Management, 46, 70-82. doi: 10.1016/j.ijinfomgt.2018.11.021.
  • Queiroz, M. M., Fosso Wamba, S., De Bourmont, M., & Telles, R. (2021). Blockchain adoption in operations and supply chain management: empirical evidence from an emerging economy. International Journal of Production Research, 59(20), 6087-6103. doi: 10.1080/00207543.2020.1803511.
  • Rana, J., & Paul, J. (2020). Health motive and the purchase of organic food: A meta‐analytic review. International Journal of Consumer Studies, 44(2), 162-171. doi: 10.1111/ijcs.12556.
  • Rogers, E. M. (2004). A prospective and retrospective look at the diffusion model. Journal of health communication, 9(S1), 13-19. doi: 10.1080/10810730490271449.
  • Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019). Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57(7), 2117-2135. doi: 10.1080/00207543.2018.1533261.
  • Shahzad, K., Zhang, Q., Khan, M. K., Ashfaq, M., & Hafeez, M. (2022). The acceptance and continued use of blockchain technology in supply chain management: a unified model from supply chain professional's stance. International Journal of Emerging Markets, (ahead-of-print). doi: 10.1108/IJOEM-11-2021-1714.
  • Sheel, A., & Nath, V. (2020). Antecedents of blockchain technology adoption intentions in the supply chain. International Journal of Business Innovation and Research, 21(4), 564-584. doi: 10.1504/IJBIR.2020.106011.
  • Sun, Y., Shahzad, M., & Razzaq, A. (2022). Sustainable organizational performance through blockchain technology adoption and knowledge management in China. Journal of Innovation & Knowledge, 7(4), 1-11. doi: 10.1016/j.jik.2022.100247.
  • Taherdoost, H. (2022). A critical review of blockchain acceptance models—blockchain technology adoption frameworks and applications. Computers, 11(2), 1-31. doi: 10.3390/computers11020024.
  • Thornton, A. & Lee, P. (2000). Publication bias in meta-analysis: its causes and consequences. Journal of Clinical Epidemiology, 53(2), 207-216. doi: 10.1016/S0895-4356(99)00161-4.
  • Tran, L. T. T., & Nguyen, P. T. (2021). Co-creating blockchain adoption: theory, practice and impact on usage behavior. Asia Pacific Journal of Marketing and Logistics, 33(7), 1667-1684. doi: 10.1108/APJML-08-2020-0609.
  • Turan, P., C. (2021). Success drivers of co‐branding: A meta‐analysis. International Journal of Consumer Studies, 45(4), 911-936. doi: 10.1111/ijcs.12682.
  • Ullah, N., Mugahed Al-rahmi, W., & Alkhalifah, A. (2021). Predictors for distributed ledger technology adoption: Integrating three traditional adoption theories for manufacturing and service operations. Production & Manufacturing Research, 9(1), 178-205. doi: 10.1080/21693277.2021.1976963.
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204. https://www.jstor.org/stable/2634758.
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 27(3), 425-478. https://www.jstor.org/stable/30036540.
  • Venkatesh, V., Thong, J. Y. & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 36(1), 157-178. https://www.jstor.org/stable/41410412.
  • Wamba, S. F., & Queiroz, M. M. (2022). Industry 4.0 and the supply chain digitalisation: a blockchain diffusion perspective. Production Planning & Control, 33(2-3), 193-210. doi: 10.1080/09537287.2020.1810756.
  • Wamba, S. F., Queiroz, M. M., & Trinchera, L. (2020). Dynamics between blockchain adoption determinants and supply chain performance: An empirical investigation. International Journal of Production Economics, 229, 1-15. doi: 10.1016/j.ijpe.2020.107791.
  • Wong, L. W., Leong, L. Y., Hew, J. J., Tan, G. W. H., & Ooi, K. B. (2020a). Time to seize the digital evolution: Adoption of blockchain in operations and supply chain management among Malaysian SMEs. International Journal of Information Management, 52, 1-19. doi: 10.1016/j.ijinfomgt.2019.08.005.
  • Wong, L. W., Tan, G. W. H., Lee, V. H., Ooi, K. B., & Sohal, A. (2020b). Unearthing the determinants of Blockchain adoption in supply chain management. International Journal of Production Research, 58(7), 2100-2123. doi: 10.1080/00207543.2020.1730463.
  • Woo, C., & Yoo, J. (2022). Exploring the Determinants of Blockchain Acceptance for Research Data Management. Journal of Computer Information Systems, 1-12. doi: 10.1080/08874417.2022.2049019.
  • Yang, C. S. (2019). Maritime shipping digitalization: Blockchain-based technology applications, future improvements, and intention to use. Transportation Research Part E: Logistics and Transportation Review, 131, 108-117. doi: 10.1016/j.tre.2019.09.020.
  • Zhu, Q., Bai, C., & Sarkis, J. (2022). Blockchain technology and supply chains: The paradox of the atheoretical research discourse. Transportation Research Part E: Logistics and Transportation Review, 164, 1-26. doi: 10.1016/j.tre.2022.102824.
Toplam 70 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Endüstriyel Pazarlama
Bölüm Araştırma Makalesi
Yazarlar

Haldun Çolak 0000-0003-4369-6063

Celal Hakan Kağnıcıoğlu 0000-0001-7164-3538

Yayımlanma Tarihi 25 Mart 2024
Gönderilme Tarihi 3 Temmuz 2023
Yayımlandığı Sayı Yıl 2024 Cilt: 25 Sayı: 1

Kaynak Göster

APA Çolak, H., & Kağnıcıoğlu, C. H. (2024). VERIFYING THE DETERMINANTS OF BLOCKCHAIN ADOPTION INTENTION: A META-ANALYSIS ON SUPPLY CHAIN STUDIES. Anadolu Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 25(1), 384-408. https://doi.org/10.53443/anadoluibfd.1322124

88x31.png
Bu eser 2023 yılından itibaren Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı ile lisanslanmıştır.