UPSI Digital Repository (UDRep)
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Abstract : Universiti Pendidikan Sultan Idris |
Recently, blockchain-driven supply chain finance has been used in the scope of risk management to solve the financing problem of small and medium-sized enterprises. Among them, risk assessment is the core part of risk management. Therefore, this paper aims to review and analyze articles associated with risk assessment based on blockchain-driven supply chain finance mode. This research also aimed to provide the best practices and identify the academic challenges, motivations and recommendations related with the research. In addition, a methodological approach followed in previous research in this domain was discussed to give some insights for future comers with what to expect. Therefore, articles related to risk assessment based on blockchain-driven supply chain finance were searched systematically. The search was conducted on five major databases, namely, ScienceDirect, Scopus, Web of Science, Springer, Emerald from 2016 to 2022. These indices were considered sufficiently extensive and reliable to cover the scope of the literature. Articles were selected on the basis of the inclusion and exclusion criteria (n = 90). All these articles form a coherent taxonomy, describing the corresponding current views in the literature according to three main categories. The research is considered a major topic which warrants attention. This study emphasizes the current research positions and opportunities in this field and promotes the understanding of this research field. 2023 Elsevier Ltd |
References |
Abbasi, W. A., Wang, Z. R., Zhou, Y. J., & Hassan, S. (2019). Research on measurement of supply chain finance credit risk based on Internet of Things. International Journal of Distributed Sensor Networks, 15(9), 14. https://doi.org/10.1177/1550147719874002 Adams, R., Kewell, B., & Parry, G. (2018). Blockchain for good? Digital ledger technology and sustainable development goals. In Handbook of sustainability and social science research (pp. 127–140). Springer. Alamoodi, A., Zaidan, B., Zaidan, A. A., Samuri, S. M., Ismail, A. R., Zughoul, O., & Chyad, M. (2019). A review of data analysis for early-childhood period: Taxonomy, motivations, challenges, recommendation, and methodological aspects. IEEE Access, 7, 51069–51103. Alamoodi, A. H., Zaidan, B. B., Zaidan, A. A., Albahri, O. S., Mohammed, K. I., Malik, R. Q., & Alaa, M. (2021). Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review. Expert Systems with Applications, 167, Article 114155. https://doi.org/10.1016/j.eswa.2020.114155 Alora, A., & Barua, M. K. (2020). Development of a supply chain risk index for manufacturing supply chains. International Journal of Productivity and Performance Management, ahead-of-print.. https://doi.org/10.1108/ijppm-11-2018-0422 Altman, E. I., & Sabato, G. (2013). Modeling credit risk for SMEs: Evidence from the US market. In Managing and Measuring Risk: Emerging Global Standards and Regulations After the Financial Crisis, 251–279. Armstrong, S. J. W. U. (2016). Eight things you need to know about the future of retail. Wired UK. Aslam, J., Saleem, A., Khan, N. T., & Kim, Y. B. (2021). Factors influencing blockchain adoption in supply chain management practices: A study based on the oil industry. Journal of Innovation Knowledge, 6(2), 124–134. Baesens, B., Roesch, D., & Scheule, H. (2016). Credit risk analytics: Measurement techniques, applications, and examples in SAS. John Wiley & Sons. Bals, L., Schulze, H., Kelly, S., & Stek, K. (2019). Purchasing and supply management (PSM) competencies: Current and future requirements. Journal of Purchasing and Supply Management, 25(5), 100572. Bellotti, T., & Crook, J. (2009). Support vector machines for credit scoring and discovery of significant features. Expert Systems With Applications, 36(2), 3302–3308. Burgers, C., Brugman, B. C., & Boeynaems, A. (2019). Systematic literature reviews: Four applications for interdisciplinary research. Journal of Pragmatics, 145, 102–109. Calabrese, R., & Osmetti, S. A. (2013). Modelling small and medium enterprise loan defaults as rare events: The generalized extreme value regression model. Journal of Applied Statistics, 40(6), 1172–1188. Camerinelli, E. (2009). Supply chain finance. Journal of Payments Strategy Systems., 3(2), 114–128. Caniato, F., Gelsomino, L. M., Perego, A., & Ronchi, S. (2016). Does finance solve the supply chain financing problem? Supply Chain Management-an International Journal, 21(5), 534–549. https://doi.org/10.1108/scm-11-2015-0436 Rezende, C., de Carvalho Ferreira, M., Amorim Sobreiro, V., Kimura, H., de Moraes, L., & Barboza, F. (2016). A systematic review of literature about finance and sustainability. Journal of Sustainable Finance Investment, 6(2), 112–147. Choi, T. M. (2020). Supply chain financing using blockchain: Impacts on supply chains selling fashionable products. Annals of Operations Research, 23. https://doi.org/10.1007/s10479-020-03615-7 Cielen, A., Peeters, L., & Vanhoof, K. (2004). Bankruptcy prediction using a data envelopment analysis. European Journal of Operational Research, 154(2), 526–532. Chang, J. H., Hung, M. W., & Tsai, F. T. (2015). Credit contagion and competitive effects of bond rating downgrades along the supply chain. Finance Research Letters, 15, 232–238. Chen, H. R., Yan, Y. C., Ma, N. N., & Liu, J. (2020). Effects of risk attitudes and investment spillover on supplier encroachment. Soft Computing, 24(4), 2395–2416. https://doi.org/10.1007/s00500-018-03677-7 Chen, J. (2020). Risk assessment algorithm for agricultural value chain financing in coastal areas based on HMM model. Journal of Coastal Research, 42–46. https://doi.org/10.2112/si103-010.1 Chen, J. J., Cai, T. F., He, W. X., Chen, L., Zhao, G., Zou, W. W., & Guo, L. L. (2020). A Blockchain-driven supply chain finance application for auto retail industry. Entropy, 22(1), 16. https://doi.org/10.3390/e22010095 Chen, L. J., Chan, H. K., & Zhao, X. D. (2020). Supply chain finance: Latest research topics and research opportunities. International Journal of Production Economics, 229, 7. https://doi.org/10.1016/j.ijpe.2020.107766 Chen, X. F., Liu, C. J., & Li, S. T. (2019). The role of supply chain finance in improving the competitive advantage of online retailing enterprises. Electronic Commerce Research and Applications, 33, 11. https://doi.org/10.1016/j.elerap.2018.100821 Chen, Y. M. J., Tsai, H., & Liu, Y. F. (2018). Supply chain finance risk management: Payment default in tourism channels. Tourism Economics, 24(5), 593–614. https://doi.org/10.1177/1354816618762187 De Kruijff, J., & Weigand, H. (2017). Understanding the blockchain using enterprise ontology. International Conference on Advanced Information Systems Engineering, 29–43. Debajyoti, B., Hamed, J., Amir H. A., & Pietro De, G. (2022). Traceability vs. Sustainability in supply chains: The implications of blockchain. European Journal of Operation Research, 305, 128–147. Du, M. X., Chen, Q. J., Xiao, Yang, H. H., & Ma, X. F. (2020). Supply Chain Finance Innovation Using Blockchain. IEEE Transactions on Engineering Management, 67(4), 1045–1058. Doi: 10.1109/tem.2020.2971858. Elaish, M. M., Shuib, L., Ghani, N. A., Yadegaridehkordi, E., & Alaa, M. J. (2017). Mobile learning for English language acquisition: Taxonomy, challenges, and recommendations. IEEE Access, 5, 19033–19047. Fayyaz, M. R., Rasouli, M. R., & Amiri, B. (2021). A data-driven and network-aware approach for credit risk prediction in supply chain finance. Industrial Management & Data Systems, 121(4), 785–808. https://doi.org/10.1108/imds-01-2020-0052 Gu, J., Xia, X., He, Y., & Xu, Z. (2017). An approach to evaluating the spontaneous and contagious credit risk for supply chain enterprises based on fuzzy preference relations. Computers & Industrial Engineering, 106, 361–372. https://doi.org/10.1016/j.cie.2017.02.012 Guo, Y. H., Zhou, W. J., Luo, C. Y., Liu, C. R., & Xiong, H. (2016). Instance-based credit risk assessment for investment decisions in P2P lending. European Journal of Operational Research, 249(2), 417–426. https://doi.org/10.1016/j.ejor.2015.05.050 Huang, X., Liu, X., & Ren, Y. (2018). Enterprise credit risk evaluation based on neural network algorithm. Cognitive Systems Research, 52, 317–324. https://doi.org/10.1016/j.cogsys.2018.07.023 Hung, J. L., He, W., & Shen, J. (2020). Big data analytics for supply chain relationship in banking. Industrial Marketing Management, 86, 144–153. https://doi.org/10.1016/j.indmarman.2019.11.001 Hussain, M., Al-Haiqi, A., Zaidan, A. A., Zaidan, B. B., Kiah, M. L., Anuar, N. B., & Abdulnabi, M. (2015). The landscape of research on smartphone medical apps: Coherent taxonomy, motivations, open challenges and recommendations. Compute Methods Programs Biomed, 122(3), 393–408. https://doi.org/10.1016/j.cmpb.2015.08.015 Jiang, W., Carter, D. R., Fu, H., Jacobson, M. G., Zipp, K. Y., Jin, J., & Yang, L. J. (2019). The impact of the biomass crop assistance program on the United States forest products market: An application of the global forest products model. Forests, 10(3), 215. Roeder, J., Palmer, M., & Muntermann, J. (2022). Data-driven decision-making in credit risk management: The information value of analyst reports. Decision Support Systems, 1–12. Zhao, J., & Li, B.o. (2022). Credit risk assessment of small and medium-sized enterprises in supply chain finance based on SVM and BP neural network. Neural Computing and Applications, 1–15. |
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