Predicting the impact of blockchain technology implementation in SMEs
In the last few years, blockchain technology, or BCT, has gained much traction. Small and medium-sized businesses (SMEs) struggle more than their larger counterparts when it comes to technological adaptation because they lack the technology infrastructure required to implement blockchain technologies. The major contribution of this paper is to predict the impact of blockchain technology implementation on the performance of SMEs. A multiple-output regres-sion model is utilized in this research to predict the impact of BCT on SMEs’ performance. The cost of implementing and maintaining blockchain technology, IT project management, compatibility, benefit over other available techno-logical options, and trialability are the independent variables that were consideredin the analysis. Software revision, sophistication level, innovation complexity, and observability are the dependent variables. Researchers and industry professionals can use the study to comprehend how implementing blockchain technology affects SMEs.

- Abubakar, A.M. & Al-zyoud, M.F. (2021). Problem-atic internet usage and safety behavior: does time autonomy matter, Telematics and Informatics, 56, 1-11.
- Chowdhury, S., Rodriguez-Espindola, O., Dey, P. & Budhwar, P. (2022). Blockchain technology adop-tion for managing risks in operations and supply chain management: evidence from the UK. Annals of Operations Research. https://doi.org/10.1007/s10479-021-04487-1
- de Oliveira F.C., Zanoni R., Dalla-Rosa R. & Verschoore J.R. (2021). Blockchain technology and relational gains in business interactions: a fuzzy set qualitative comparative analysis of sup-ply chain specialists’ perceptions, International Journal of Advanced Operations Management, 13 (4), 372 –390.
- Ferreira, A. I., Palazzo, G. & Carvalho, H. (2023). Im-plications of the blockchain technology adoption by additive symbiotic networks, Cleaner Logistics and Supply Chain, 6, 100095 https://doi.org/10.1016/j.clscn.2023.100095
- Giri, G. & Manohar, H.L. (2021). Factors influencing the acceptance of private and public blockchain-based collaboration among supply chain practi-tioners: a parallel mediation model, Supply Chain Management: An International Journal. 28 (1), 1-24.
- Kamble, S., Gunasekaran, A., Kumar, V., Belhadi, A. & Foropon, C. (2022). A machine learning based approach for predicting blockchain adoption in supply Chain, Technological Forecasting and So-cial Change, 163. 10.1016/j.techfore.2020.120465.
- Kaur, J., Kumar, S., & Narkhede, B.E. (2022). Barriers to blockchain adoption for supply chain finance: the case of Indian SMEs, Electronic Commerce Research. https://doi.org/10.1007/s10660-022-09566-4
- Koh, S. C. L., Morris, J., Ebrahimi, S. M., & Obayi, R. (2016). Integrated resource efficiency: Measure-ment and management, International Journal of Operations & Production Management, 36 (11), 1576–1600.
- Morkunas V.J., Paschen J., & Boon E. (2019). How blockchain technologies impact your business model, Business Horizons, 62 (3), 295 –306. 10.1016/j.bushor.2019.01.009
- Nasih S., Arezki S. & Gadi T. (2019). Blockchain tech-nology impact on the maritime supply chain. ACM International Conference Proceeding Series. DOI: 10.1145/3368756.3369104
- 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 Man-agement, 46, 70-82. https://doi.org/10.1016/j.ijinfo-mgt.2018.11.021.
- Rogers, E.M. (2010). Diffusion of Innovations.
- Simon and Schuster.Salim, T. A., Barachi, M. E., Alfatih D. A. Mohamed, Halstead, S. & Babreak, N. (2022). The mediator and moderator roles of perceived cost on the rela-tionship between organizational readiness and the intention to adopt blockchain technology, Technol-ogyin Society, 71. https://doi.org/10.1016/j.tech-soc.2022.102108
- Savolainen, R. (1993). The sense-making theory: Re-viewing the interests of a user-centered approach to information seeking and use, Information Processing & Management, 29 (1), 13-28, https://doi.org/10.1016/0306-4573(93)90020-E
- Schmid, L., Gerharz, A., Groll, A. & Pauly, M. (2023). Tree-based ensembles for multi-output regression: Comparing multivariate approaches with separate univariate ones, Computational Statistics & Data Analysis, 179. https://doi.org/10.1016/j.csda.2022.107628
- Seebacher, S. & Schuritz, R. (2017). Blockchain tech-nology as an enabler of service systems: a struc-tured literature review. Lecture Notes in Business Information Processing, Springer, Cham, 279.
- Stranieri S.,Riccardi F., Meuwissen M.P.M. & Soregaroli C. (2021). Exploring the impact of blockchain on the performance of agri-food supply chains, Food Control, 119. DOI: 10.1016/j.food-cont.2020.107495
- Wu C. & Jin S. (2022). Does Blockchain Technology Promote the Quality of Enterprise Accounting In-formation? Journal of Global Business and Trade, 18 (5). DOI: 10.20294/jgbt.2022.18.5.1
- Yu, W., Zhao, G., Liu, Q. & Song, Y. (2021). Role of big data analytics capability in developing inte-grated hospital supply chains and operational flex-ibility: An organizational information processing theory perspective, Technological Forecasting and Social Change, 163. https://doi.org/10.1016/j.tech-fore.2020.120417