Systematic Reviews and Meta Analysis
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VERIFYING THE DETERMINANTS OF BLOCKCHAIN ADOPTION INTENTION: A META-ANALYSIS ON SUPPLY CHAIN STUDIES

Year 2024, Volume: 25 Issue: 1, 384 - 408, 25.03.2024
https://doi.org/10.53443/anadoluibfd.1322124

Abstract

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.

References

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

Year 2024, Volume: 25 Issue: 1, 384 - 408, 25.03.2024
https://doi.org/10.53443/anadoluibfd.1322124

Abstract

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.

References

  • 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.
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Details

Primary Language English
Subjects Industrial Marketing
Journal Section Araştırma Makalesileri
Authors

Haldun Çolak 0000-0003-4369-6063

Celal Hakan Kağnıcıoğlu 0000-0001-7164-3538

Publication Date March 25, 2024
Submission Date July 3, 2023
Published in Issue Year 2024 Volume: 25 Issue: 1

Cite

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


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