Volume 23 , Issue 2 , PP: 174-185, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Sulima Ahmed M. Zubair 1 *
Doi: https://doi.org/10.54216/IJNS.230214
The goal of this study is to bring neutrosophic structured element theory into the assessment of the entrepreneurial orientation of online peer-to-peer lending platforms, as well as to simplify the complicated processes of conventional neutrosophic decision making. This study discusses several methods for assessing the entrepreneurial orientation of online P2P lending systems. Two strategies are offered to deal with the triangular single-valued neutrosophic number multiple attribute decision making issues with incomplete definite knowledge on criteria's weights using the neutrosophic structured element approach. In terms of computational efficiency and performance, the recommended techniques produced encouraging results. The proposed methodologies are easy in understanding and computation, have a great lot of practical utility, and provide new concepts for applying neutrosophic structured element theory to neutrosophic MADM issues and other fields.
P2P online lending platform , neutrosophic sets , multi-attribute decision making , neutrosophic structured elements , TOPSIS approach , peer to peer.
[1] Yum, H., Lee, B., & Chae, M. (2012). From the wisdom of crowds to my own judgment in microfinance through online peer-to-peer lending platforms. Electronic Commerce Research and Applications, 11(5), 469-483.
[2] Malekipirbazari, M., & Aksakalli, V. (2015). Risk assessment in social lending via random forests. Expert Systems with Applications, 42(10), 4621-4631.
[3] Moritz, A., & Block, J. H. (2016). Crowdfunding: A literature review and research directions. Crowdfunding in Europe, 25-53.
[4] Galloway, I. (2009). Peer-to-peer lending and community development finance. Community Investments, 21(3), 19-23.
[5] Herzenstein, M., Andrews, R. L., Dholakia, U. M., & Lyandres, E. (2008). The democratization of personal consumer loans? Determinants of success in online peer-to-peer loan auctions. Bulletin of the University of Delaware, 15(3), 274-277.
[6] Bachmann, A., Becker, A., Buerckner, D., Hilker, M., Kock, F., Lehmann, M., ... & Funk, B. (2011). Online peer-to-peer lending-a literature review. Journal of Internet Banking and Commerce, 16(2), 1.
[7] Basha, S. A., Elgammal, M. M., & Abuzayed, B. M. (2021). Online peer-to-peer lending: A review of the literature. Electronic Commerce Research and Applications, 48, 101069.
[8] Liu, Z., Shang, J., Wu, S. Y., & Chen, P. Y. (2020). Social collateral, soft information and online peer-to-peer lending: A theoretical model. European Journal of Operational Research, 281(2), 428-438.
[9] Wang, C., Zhang, W., Zhao, X., & Wang, J. (2019). Soft information in online peer-to-peer lending: Evidence from a leading platform in China. Electronic Commerce Research and Applications, 36, 100873.
[10] Wang, C., Chen, X., Jin, W., & Fan, X. (2022). Credit guarantee types for financing retailers through online peer-to-peer lending: Equilibrium and coordinating strategy. European Journal of Operational Research, 297(1), 380-392.
[11] Klafft, M. (2008, July). Online peer-to-peer lending: a lenders' perspective. In Proceedings of the international conference on E-learning, E-business, enterprise information systems, and E-government, EEE (pp. 371-375).
[12] Chen, D., Lai, F., & Lin, Z. (2014). A trust model for online peer-to-peer lending: a lender’s perspective. Information Technology and Management, 15(4), 239-254.
[13] Wei, Z., & Lin, M. (2017). Market mechanisms in online peer-to-peer lending. Management Science, 63(12), 4236-4257.
[14] Chen, X., Zhou, L., & Wan, D. (2016). Group social capital and lending outcomes in the financial credit market: An empirical study of online peer-to-peer lending. Electronic Commerce Research and Applications, 15, 1-13.
[15] Gao, G. X., Fan, Z. P., Fang, X., & Lim, Y. F. (2018). Optimal Stackelberg strategies for financing a supply chain through online peer-to-peer lending. European Journal of Operational Research, 267(2), 585-597.
[16] Liu, Z., Shang, J., Wu, S. Y., & Chen, P. Y. (2020). Social collateral, soft information and online peer-to-peer lending: A theoretical model. European Journal of Operational Research, 281(2), 428-438.
[17] Wang, C., Zhang, W., Zhao, X., & Wang, J. (2019). Soft information in online peer-to-peer lending: Evidence from a leading platform in China. Electronic Commerce Research and Applications, 36, 100873.
[18] Jiang, C., Wang, Z., Wang, R., & Ding, Y. (2018). Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending. Annals of Operations Research, 266(1), 511-529.
[19] Wang, H., Kou, G., & Peng, Y. (2021). Multi-class misclassification cost matrix for credit ratings in peer-to-peer lending. Journal of the Operational Research Society, 72(4), 923-934.
[20] Pierrakis, Y. (2019). Peer-to-peer lending to businesses: Investors’ characteristics, investment criteria and motivation. The International Journal of Entrepreneurship and Innovation, 20(4), 239-251.
[21] Omarini, A. E. (2018). Peer-to-peer lending: business model analysis and the platform dilemma.
[22] Lumpkin, G. T., & Dess, G. G. (2001). Linking two dimensions of entrepreneurial orientation to firm performance: The moderating role of environment and industry life cycle. Journal of business venturing, 16(5), 429-451.
[23] Wiklund, J., & Shepherd, D. (2003). Knowledge‐based resources, entrepreneurial orientation, and the performance of small and medium‐sized businesses. Strategic management journal, 24(13), 1307-1314.
[24] Zahra, S. A., & Covin, J. G. (1995). Contextual influences on the corporate entrepreneurship-performance relationship: A longitudinal analysis. Journal of business venturing, 10(1), 43-58.
[25] Chen, X., Yang, L., Wang, P., & Yue, W. (2013). An effective interval-valued intuitionistic fuzzy entropy to evaluate entrepreneurship orientation of online P2P lending platforms. Advances in Mathematical Physics, 2013.
[26] Ji, X., Yu, L., & Fu, J. (2019). Evaluating personal default risk in P2P lending platform: based on dual hesitant pythagorean fuzzy TODIM approach. Mathematics, 8(1), 8.
[27] Smarandache. F, A unifying field in logics. Neutrosophy: Neutrosophic probability, set and logic, American Research Press, Rehoboth 1999.
[28] Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
[29] Atanassov, K. T. (1999). Intuitionistic fuzzy sets. In Intuitionistic fuzzy sets (pp. 1-137). Physica, Heidelberg.
[30] Kutlu Gündoğdu, F., & Kahraman, C. (2019). Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of intelligent & fuzzy systems, 36(1), 337-352.
[31] Yager, R. R. (2013, June). Pythagorean fuzzy subsets. In 2013 joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS) (pp. 57-61). IEEE.
[32] Cuong, B. C., & Kreinovich, V. (2014). Picture fuzzy sets. Journal of Computer Science and Cybernetics, 30(4), 409-420.
[33] Ulucay, V., Deli, I., & Şahin, M. (2018). Similarity measures of bipolar neutrosophic sets and their application to multiple criteria decision making. Neural Computing and Applications, 29(3), 739-748.
[34] Akram, M., & Smarandache, F. (2018). Decision-making with bipolar neutrosophic TOPSIS and bipolar neutrosophic ELECTRE-I. Axioms, 7(2), 33.
[35] Wang, H., Smarandache, F., Zhang, Y., & Sunderraman, R. (2010). Single valued neutrosophic sets. Infinite study.
[36] Yang, W., Cai, L., Edalatpanah, S. A., & Smarandache, F. (2020). Triangular single valued neutrosophic data envelopment analysis: application to hospital performance measurement. Symmetry, 12(4), 588.
[37] Ihsan, M., Saeed, M, & Rahman, A. U. (2023). Optimizing Hard Disk Selection via a Fuzzy Parameterized Single-Valued Neutrosophic Soft Set Approach. J. Oper. Strateg Anal., 1(2), 62-69.
[38] Peng, J. J., Wang, J. Q., Zhang, H. Y., & Chen, X. H. (2014). An outranking approach for multi-criteria decision-making problems with simplified neutrosophic sets. Applied Soft Computing, 25, 336-346.
[39] Edalatpanah, S. A., & Smarandache, F. (2019). Data envelopment analysis for simplified neutrosophic sets. Infinite Study.
[40] Tian, Z. P., Wang, J., Zhang, H. Y., Chen, X. H., & Wang, J. Q. (2016). Simplified neutrosophic linguistic normalized weighted Bonferroni mean operator and its application to multi-criteria decision-making problems. Filomat, 30(12), 3339-3360.
[41] Broumi, S., & Smarandache, F. (2013). Correlation coefficient of interval neutrosophic set. In Applied Mechanics and Materials (Vol. 436, pp. 511-517). Trans Tech Publications Ltd.
[42] Zhang, D., Su, Y., Zhao, M., & Chen, X. (2022). CPT-TODIM method for interval neutrosophic MAGDM and its application to third-party logistics service providers selection. Technological and Economic Development of Economy, 28(1), 201-219.
[43] Khalil, S., Kousar, S., Freen, G., & Imran, M. (2022). Multi-Objective Interval-Valued Neutrosophic Optimization with Application. International Journal of Fuzzy Systems, 24(3), 1343-1355.
[44] Peng, J. J., Wang, J. Q., Wu, X. H., Wang, J., & Chen, X. H. (2015). Multi-valued neutrosophic sets and power aggregation operators with their applications in multi-criteria group decision-making problems. International Journal of Computational Intelligence Systems, 8(2), 345-363.
[45] Liu, P., Cheng, S., & Zhang, Y. (2019). An extended multi-criteria group decision-making PROMETHEE method based on probability multi-valued neutrosophic sets. International journal of fuzzy systems, 21(2), 388-406.
[46] Polymenis, A. (2021). A neutrosophic Student’s t –type of statistic for AR (1) random processes. Journal of Fuzzy Extension and Applications, 2(4), 388-393.
[47] Kumar, R., Edalatpanah, S. A., Jha, S., & Singh, R. (2019). A novel approach to solve gaussian valued neutrosophic shortest path problems. International Journal of Engineering and Advanced Technology, 8(3), pp. 347–353
[48] Kumari R, S., Kalayathankal, S., George, M., & Smarandache, F. (2023). On some related concepts n-cylindrical fuzzy neutrosophic topological spaces. Journal of Fuzzy Extension and Applications, 4(1), 40-51.
[49] R, S., Kalayathankal, S., George, M., & Smarandache, F. (2023). n-Cylindrical fuzzy neutrosophic topological spaces. Journal of Fuzzy Extension and Applications, 4(2), 141-147.
[50] Wang, Q., Huang, Y., Kong, S., Ma, X., Liu, Y., Das, S. K., & Edalatpanah, S. A. (2021). A Novel Method for Solving Multiobjective Linear Programming Problems with Triangular Neutrosophic Numbers. Journal of Mathematics, 2021.
[51] Adebisi, S., & Smarandache, F. (2023). On refined neutrosophic finite p-group. Journal of Fuzzy Extension and Applications, 4(2), 136-140.
[52] Mao, X., Guoxi, Z., Fallah, M., & Edalatpanah, S. A. (2020). A neutrosophic-based approach in data envelopment analysis with undesirable outputs. Mathematical problems in engineering, 2020.
[53] Debnath, S. (2021). Neutrosophication of statistical data in a study to assess the knowledge, attitude and symptoms on reproductive tract infection among women. Journal of Fuzzy Extension and Applications, 2(1), 33-40.
[54] Das, S. K., & Edalatpanah, S. A. (2020). A new ranking function of triangular neutrosophic number and its application in integer programming. International Journal of Neutrosophic Science, 4(2), 82-92.
[55] Ricardo, J. E., Rosado, Z. M. M., Pataron, E. K. C., & Vargas, V. Y. V. (2021). Measuring legal and socioeconomic effect of the declared debtors usign the ahp technique in a neutrosophic framework. Neutrosophic Sets and Systems, 44, 357-366.
[56] Zhang, C., Li, D., Kang, X., Song, D., Sangaiah, A. K., & Broumi, S. (2020). Neutrosophic fusion of rough set theory: an overview. Computers in Industry, 115, 103117.
[57] Kumar Mohanta, K. ., & Sharanappa, D. S. . (2023). Neutrosophic Data Envelopment Analysis: a Comprehensive Review and Current Trends. Optimality, 1(1), 10–22.
[58] Bhat, S. A. (2023). An Enhanced AHP Group Decision-Making Model Employing Neutrosophic Trapezoidal Numbers. J. Oper. Strateg Anal., 1(2), 81-89.
[59] Garg, H. (2022). SVNMPR: A new single‐valued neutrosophic multiplicative preference relation and their application to decision‐making process. International Journal of Intelligent Systems, 37(3), 2089-2130.
[60] Akram, M., & Nawaz, H. S. (2022). Implementation of single-valued neutrosophic soft hypergraphs on human nervous system. Artificial Intelligence Review, 1-39.
[61] Edalatpanah, S. A. (2020). Neutrosophic structured element. Expert systems, 37(5), e12542.
[62] Jaynes, E. T. (1982). On the rationale of maximum-entropy methods. Proceedings of the IEEE, 70(9), 939-952.
[63] Jaynes, E. T. (1988). The relation of Bayesian and maximum entropy methods. In Maximum-entropy and Bayesian methods in science and engineering (pp. 25-29). Springer, Dordrecht.
[64] Miller, D. (1983). The correlates of entrepreneurship in three types of firms. Management science, 29(7), 770-791.
[65] Covin, J. G., & Slevin, D. P. (1989). Strategic management of small firms in hostile and benign environments. Strategic management journal, 10(1), 75-87.