Application of a maximum classification consensus approach for construction of a group ordinal classification of applicants in employee recruitment

References

• Acikgoz, Y. (2019). Employee recruitment and job search: Towards a multi-level integration. Human Resource Management Review, 29(1), 1–13. https://doi.org/10.1016/j.hrmr.2018.02.009
   Web of Science ®Google Scholar
• Afshari, A. R., Nikolić, M., & Ćoćkalo, D. (2014). Applications of fuzzy decision making for personnel selection problem: A review. Journal of Engineering Management and Competitiveness, 4(2), 68–77. https://doi.org/10.5937/jemc1402068A
   Google Scholar
• Arrow, K. J. (1964). Social choice and individual values. Wiley.
   Google Scholar
• Carmeli, A., & Schaubroeck, J. (2005). How leveraging human resource capital with its competitive distinctiveness enhances the performance of commercial and public organizations. Human Resource Management, 44(4), 391–412. https://doi.org/10.1002/hrm.20081
   Web of Science ®Google Scholar
• Chao, X. R., Kou, G., Peng, Y., & Viedma, E. H. (2021). Large-scale group decision-making with non-cooperative behaviors and heterogeneous preferences: An application in financial inclusion. European Journal of Operational Research, 288(1), 271–293. https://doi.org/10.1016/j.ejor.2020.05.047
   Web of Science ®Google Scholar
• Chen, X., Ding, Z. G., Dong, Y. C., & Liang, H. M. (2021). Managing consensus with minimum adjustments in group decision making with opinions evolution. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(4), 2299–2311. https://doi.org/10.1109/TSMC.2019.2912231
   Web of Science ®Google Scholar
• Chen, X., Xu, W. J., Liang, H. M., & Dong, Y. C. (2020). The classification-based consensus in multi-attribute group decision-making. Journal of the Operational Research Society, 71(9), 1375–1389. https://doi.org/10.1080/01605682.2019.1609888
   Web of Science ®Google Scholar
• Cheng, D., Cheng, F. X., Zhou, Z. L., & Wu, Y. (2020). Reaching a minimum adjustment consensus in social network group decision-making. Information Fusion, 59, 30–43. https://doi.org/10.1016/j.inffus.2020.01.004
   Web of Science ®Google Scholar
• Chuang, Y. C., Hu, S. K., Liou, J. J. H., & Tzeng, G. H. (2020). A data-driven MADM model for personnel selection and improvement. Technological and Economic Development of Economy, 26(4), 751–784. https://doi.org/10.3846/tede.2020.12366
   Web of Science ®Google Scholar
• Dahooie, J. H., Abadi, E. B. J., Vanaki, A. S., & Firoozfar, H. R. (2018). Competency-based IT personnel selection using a hybrid SWARA and ARAS-G methodology. Human Factors and Ergonomics in Manufacturing & Service Industries, 28(1), 5–16. https://doi.org/10.1002/hfm.20713
   Web of Science ®Google Scholar
• Ding, R. X., Palomares, I., Wang, X. Q., Yang, G. R., Liu, B. S., Dong, Y. C., Herrera-Viedma, E., & Herrera, F. (2020). Large-scale decision-making: Characterization, taxonomy, challenges and future directions from an artificial intelligence and applications perspective. Information Fusion, 59, 84–102. https://doi.org/10.1016/j.inffus.2020.01.006
   Web of Science ®Google Scholar
• Dong, Y. C., Zha, Q. B., Zhang, H. J., Kou, G., Fujita, H., Chiclana, F., & Herrera-Viedma, E. (2018). Consensus reaching in social network group decision making: Research paradigms and challenges. Knowledge-Based Systems, 162, 3–13. https://doi.org/10.1016/j.knosys.2018.06.036
   Web of Science ®Google Scholar
• Dong, Y. C., Zhang, H. J., & Herrera-Viedma, E. (2016). Integrating experts’ weights generated dynamically into the consensus reaching process and its applications in managing non-cooperative behaviors. Decision Support Systems, 84, 1–15. https://doi.org/10.1016/j.dss.2016.01.002
   Web of Science ®Google Scholar
• Dwivedi, P., Chaturvedi, V., & Vashist, J. K. (2020). Efficient team formation from pool of talent: Comparing AHP-LP and TOPSIS-LP approach. Journal of Enterprise Information Management, 33(5), 1293–1318. https://doi.org/10.1108/JEIM-09-2019-0283
   Web of Science ®Google Scholar
• Fishburn, P. C. (1973). The theory of social choice. Princeton University Press.
   Google Scholar
• Gai, T. T., Cao, M. S., Chiclana, F., Zhang, Z., Dong, Y. C., Herrera-Viedma, E., & Wu, J. (2023). Consensus-trust driven bidirectional feedback mechanism for improving consensus in social network large-group decision making. Group Decision and Negotiation, 32(1), 45–74. https://doi.org/10.1007/s10726-022-09798-7
   Web of Science ®Google Scholar
• Goers, J., & Horton, G. (2023). Combinatorial multi-criteria acceptability analysis: A decision analysis and consensus-building approach for cooperative groups. European Journal of Operational Research, 308(1), 243–254. https://doi.org/10.1016/j.ejor.2022.12.002
   Web of Science ®Google Scholar
• Gong, Z. W., Guo, W. W., & Słowiński, R. (2021). Transaction and interaction behavior-based consensus model and its application to optimal carbon emission reduction. Omega, 104, 102491. https://doi.org/10.1016/j.omega.2021.102491
   Web of Science ®Google Scholar
• Graves, L. M., & Karren, R. J. (1996). The employee selection interview: A fresh look at an old problem. Human Resource Management, 35(2), 163–180. https://doi.org/10.1002/(SICI)1099-050X(199622)35:2<163::AID-HRM2>3.0.CO;2-W
   Web of Science ®Google Scholar
• Güngör, Z., Serhadlıoğlu, G., & Kesen, S. E. (2009). A fuzzy AHP approach to personnel selection problem. Applied Soft Computing, 9(2), 641–646. https://doi.org/10.1016/j.asoc.2008.09.003
   Web of Science ®Google Scholar
• Herrera-Viedma, E., Cabrerizo, F. J., Kacprzyk, J., & Pedrycz, W. (2014). A review of soft consensus models in a fuzzy environment. Information Fusion, 17, 4–13. https://doi.org/10.1016/j.inffus.2013.04.002
   Web of Science ®Google Scholar
• Ishizaka, A., Tasiou, M., & Martínez, L. (2020). Analytic hierarchy process-fuzzy sorting: An analytic hierarchy process–based method for fuzzy classification in sorting problems. Journal of the Operational Research Society, 71(6), 928–947. https://doi.org/10.1080/01605682.2019.1595188
   Web of Science ®Google Scholar
• Ji, P., Zhang, H. Y., & Wang, J. Q. (2018). A projection-based TODIM method under multi-valued neutrosophic environments and its application in personnel selection. Neural Computing and Applications, 29(1), 221–234. https://doi.org/10.1007/s00521-016-2436-z
   Web of Science ®Google Scholar
• Karabasevic, D., Zavadskas, E. K., Stanujkic, D., Popovic, G., & Brzakovic, M. (2018). An approach to personnel selection in the IT industry based on the EDAS method. Transformations in Business and Economics, 17, 54–65.
   Web of Science ®Google Scholar
• Karam, S., Nagahi, M., Dayarathna, V. L., Ma, J., Jaradat, R., & Hamilton, M. (2020). Integrating systems thinking skills with multi-criteria decision-making technology to recruit employee candidates. Expert Systems with Applications, 160, 113585. https://doi.org/10.1016/j.eswa.2020.113585
   Web of Science ®Google Scholar
• Kilic, H. S., Demirci, A. E., & Delen, D. (2020). An integrated decision analysis methodology based on IF-DEMATEL and IF-ELECTRE for personnel selection. Decision Support Systems, 137, 113360. https://doi.org/10.1016/j.dss.2020.113360
   Web of Science ®Google Scholar
• Kosareva, N., Kosareva, E. K., Krylovas, A., & Dadelo, S. (2015). Personnel ranking and selection problem solution by application of KEMIRA method. International Journal of Computers Communications & Control, 11(1), 51–66. https://doi.org/10.15837/ijccc.2016.1.2159
   Web of Science ®Google Scholar
• Labella, Á., Liu, H. B., Rodríguez, R. M., & Martínez, L. (2020). A cost consensus metric for consensus reaching processes based on a comprehensive minimum cost model. European Journal of Operational Research, 281(2), 316–331. https://doi.org/10.1016/j.ejor.2019.08.030
   Web of Science ®Google Scholar
• Li, J. T., He, R. J., & Wang, T. (2022). A data-driven decision-making framework for personnel selection based on LGBWM and IFNs. Applied Soft Computing, 126, 109227. https://doi.org/10.1016/j.asoc.2022.109227
   Web of Science ®Google Scholar
• Lin, H. T. (2010). Personnel selection using analytic network process and fuzzy data envelopment analysis approaches. Computers & Industrial Engineering, 59(4), 937–944. https://doi.org/10.1016/j.cie.2010.09.004
   Web of Science ®Google Scholar
• Liu, H. C., Qin, J. T., Mao, L. X., & Zhang, Z. Y. (2015). Personnel selection using interval 2-tuple linguistic VIKOR method. Human Factors and Ergonomics in Manufacturing & Service Industries, 25(3), 370–384. https://doi.org/10.1002/hfm.20553
   Web of Science ®Google Scholar
• Liu, J. P., Liao, X. W., Huang, W., & Liao, X. Z. (2019). Market segmentation: A multiple criteria approach combining preference analysis and segmentation decision. Omega, 83, 1–13. https://doi.org/10.1016/j.omega.2018.01.008
   Web of Science ®Google Scholar
• Luo, S. Z., & Xing, L. N. (2019). A hybrid decision making framework for personnel selection using BWM, MABAC and PROMETHEE. International Journal of Fuzzy Systems, 21(8), 2421–2434. https://doi.org/10.1007/s40815-019-00745-4
   Web of Science ®Google Scholar
• Manoharan, T. R., Muralidharan, C., & Deshmukh, S. G. (2011). An integrated fuzzy multi-attribute decision-making model for employees’ performance appraisal. The International Journal of Human Resource Management, 22(3), 722–745. https://doi.org/10.1080/09585192.2011.543763
   Web of Science ®Google Scholar
• Morente-Molinera, J. A., Wu, X., Morfeq, A., Al-Hmouz, R., & Herrera-Viedma, E. (2020). A novel multi-criteria group decision-making method for heterogeneous and dynamic contexts using multi-granular fuzzy linguistic modelling and consensus measures. Information Fusion, 53, 240–250. https://doi.org/10.1016/j.inffus.2019.06.028
   Web of Science ®Google Scholar
• Negahban, S., Oh, S., & Shah, D. (2017). Rank centrality: Ranking from pairwise comparisons. Operations Research, 65(1), 266–287. https://doi.org/10.1287/opre.2016.1534
   Web of Science ®Google Scholar
• Orlovsky, S. A. (1978). Decision-making with a fuzzy preference relation. Fuzzy Sets and Systems, 1(3), 155–167. https://doi.org/10.1016/0165-0114(78)90001-5
   Google Scholar
• Palomares, I., Estrella, F. J., Martínez, L., & Herrera, F. (2014a). Consensus under a fuzzy context: Taxonomy, analysis framework AFRYCA and experimental case of study. Information Fusion, 20, 252–271. https://doi.org/10.1016/j.inffus.2014.03.002
   Web of Science ®Google Scholar
• Palomares, I., Martínez, L., & Herrera, F. (2014b). A consensus model to detect and manage noncooperative behaviors in large-scale group decision making. IEEE Transactions on Fuzzy Systems, 22(3), 516–530. https://doi.org/10.1109/TFUZZ.2013.2262769
   Web of Science ®Google Scholar
• Pérez, I. J., Cabrerizo, F. J., Alonso, S., Dong, Y. C., Chiclana, F., & Herrera-Viedma, E. (2018). On dynamic consensus processes in group decision making problems. Information Sciences, 459, 20–35. https://doi.org/10.1016/j.ins.2018.05.017
   Web of Science ®Google Scholar
• Pérez, I. J., Cabrerizo, F. J., Alonso, S., & Herrera-Viedma, E. (2014). A new consensus model for group decision making problems with non-homogeneous experts. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(4), 494–498. https://doi.org/10.1109/TSMC.2013.2259155
   Web of Science ®Google Scholar
• Petridis, K., Drogalas, G., & Zografidou, E. (2021). Internal auditor selection using a TOPSIS/non-linear programming model. Annals of Operations Research, 296(1–2), 513–539. https://doi.org/10.1007/s10479-019-03307-x
   Web of Science ®Google Scholar
• Phillips, J. M., & Gully, S. M. (2015). Multilevel and strategic recruiting: Where have we been, where can we go from here? Journal of Management, 41(5), 1416–1445. https://doi.org/10.1177/0149206315582248
   Web of Science ®Google Scholar
• Saaty, T. L. (1980). The analytic hierarchy process. McGraw-Hill.
   Google Scholar
• Samanlioglu, F., Taskaya, Y. E., Gulen, U. C., & Cokcan, O. (2018). A fuzzy AHP–TOPSIS-based group decision-making approach to IT personnel selection. International Journal of Fuzzy Systems, 20(5), 1576–1591. https://doi.org/10.1007/s40815-018-0474-7
   Web of Science ®Google Scholar
• Shehu, M. A., & Saeed, F. (2016). An adaptive personnel selection model for recruitment using domaindriven data mining. Journal of Theoretical and Applied Information Technology, 91(1), 117–130.
   Google Scholar
• Susskind, L. E., Mckearnen, S., & Thomas-Lamar, J. (1999). The consensus building handbook: A comprehensive guide to reaching agreement. Sage Publications.
   Google Scholar
• Wan, S. P., Wang, F., & Dong, J. Y. (2018). A group decision-making method considering both the group consensus and multiplicative consistency of interval-valued intuitionistic fuzzy preference relations. Information Sciences, 466, 109–128. https://doi.org/10.1016/j.ins.2018.07.031
   Web of Science ®Google Scholar
• Wan, S. P., Yan, J., & Dong, J. Y. (2022a). Personalized individual semantics based consensus reaching process for large-scale group decision making with probabilistic linguistic preference relations and application to COVID-19 surveillance. Expert Systems with Applications, 191, 116328. https://doi.org/10.1016/j.eswa.2021.116328
   Web of Science ®Google Scholar
• Wan, S. P., Zou, W. C., Dong, J. Y., & Martínez, L. (2022b). A consensual method for multi-criteria group decision-making with linguistic intuitionistic information. Information Sciences, 582, 797–832. https://doi.org/10.1016/j.ins.2021.10.030
   Web of Science ®Google Scholar
• Wang, M. W., Liang, D. C., & Xu, Z. S. (2022a). Consensus achievement strategy of opinion dynamics based on deep reinforcement learning with time constraint. Journal of the Operational Research Society, 73(12), 2741–2755. https://doi.org/10.1080/01605682.2021.2015257
   Web of Science ®Google Scholar
• Wang, S., Wu, J., Chiclana, F., Sun, Q., & Herrera-Viedma, E. (2022b). Two-stage feedback mechanism with different power structures for consensus in large-scale group decision making. IEEE Transactions on Fuzzy Systems, 30(10), 4177–4189. https://doi.org/10.1109/TFUZZ.2022.3144536
   Web of Science ®Google Scholar
• Wu, J., Chiclana, F., Fujita, H., & Herrera-Viedma, E. (2017). A visual interaction consensus model for social network group decision making with trust propagation. Knowledge-Based Systems, 122, 39–50. https://doi.org/10.1016/j.knosys.2017.01.031
   Web of Science ®Google Scholar
• Wu, J., Dai, L., Chiclana, F., Fujita, H., & Herrera-Viedma, E. (2018). A minimum adjustment cost feedback mechanism based consensus model for group decision making under social network with distributed linguistic trust. Information Fusion, 41, 232–242. https://doi.org/10.1016/j.inffus.2017.09.012
   Web of Science ®Google Scholar
• Xiao, J., Wang, X. L., & Zhang, H. J. (2022). Exploring the ordinal classifications of failure modes in the reliability management: An optimization-based consensus model with bounded confidences. Group Decision and Negotiation, 31(1), 49–80. https://doi.org/10.1007/s10726-021-09756-9
   Web of Science ®Google Scholar
• Xu, G. L., Wan, S. P., Wang, F., Dong, J. Y., & Zeng, Y. F. (2016). Mathematical programming methods for consistency and consensus in group decision making with intuitionistic fuzzy preference relations. Knowledge-Based Systems, 98, 30–43. https://doi.org/10.1016/j.knosys.2015.12.007
   Web of Science ®Google Scholar
• Yu, W. Y., Zhang, Z., & Zhong, Q. Y. (2021). Consensus reaching for MAGDM with multi-granular hesitant fuzzy linguistic term sets: A minimum adjustment-based approach. Annals of Operations Research, 300(2), 443–466. https://doi.org/10.1007/s10479-019-03432-7
   Web of Science ®Google Scholar
• Zhang, B. W., Dong, Y. C., & Herrera-Viedma, E. (2019). Group decision making with heterogeneous preference structures: An automatic mechanism to support consensus reaching. Group Decision and Negotiation, 28(3), 585–617. https://doi.org/10.1007/s10726-018-09609-y
   Web of Science ®Google Scholar
• Zhang, B. W., Dong, Y. C., Zhang, H. J., & Pedrycz, W. (2020a). Consensus mechanism with maximum-return modifications and minimum-cost feedback: A perspective of game theory. European Journal of Operational Research, 287(2), 546–559. https://doi.org/10.1016/j.ejor.2020.04.014
   Web of Science ®Google Scholar
• Zhang, G. Q., Dong, Y. C., & Xu, Y. F. (2014). Consistency and consensus measures for linguistic preference relations based on distribution assessments. Information Fusion, 17, 46–55. https://doi.org/10.1016/j.inffus.2012.01.006
   Web of Science ®Google Scholar
• Zhang, H. J., Dong, Y. C., Xiao, J., Chiclana, F., & Herrera-Viedma, E. (2021a). Consensus and opinion evolution-based failure mode and effect analysis approach for reliability management in social network and uncertainty contexts. Reliability Engineering & System Safety, 208, 107425. https://doi.org/10.1016/j.ress.2020.107425
   Web of Science ®Google Scholar
• Zhang, H. J., Zhao, S. H., Kou, G., Li, C. C., Dong, Y. C., & Herrera, F. (2020b). An overview on feedback mechanisms with minimum adjustment or cost in consensus reaching in group decision making: Research paradigms and challenges. Information Fusion, 60, 65–79. https://doi.org/10.1016/j.inffus.2020.03.001
   Web of Science ®Google Scholar
• Zhang, S. F., & Liu, S. Y. (2011). A GRA-based intuitionistic fuzzy multi-criteria group decision making method for personnel selection. Expert Systems with Applications, 38(9), 11401–11405. https://doi.org/10.1016/j.eswa.2011.03.012
   Web of Science ®Google Scholar
• Zhang, Z., Kou, X. Y., Yu, W. Y., & Gao, Y. (2021b). Consistency improvement for fuzzy preference relations with self-confidence: An application in two-sided matching decision making. Journal of the Operational Research Society, 72(8), 1914–1927. https://doi.org/10.1080/01605682.2020.1748529
   Web of Science ®Google Scholar
• Zhang, Z., & Li, Z. L. (2022a). Personalized individual semantics-based consistency control and consensus reaching in linguistic group decision making. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(9), 5623–5635. https://doi.org/10.1109/TSMC.2021.3129510
   Web of Science ®Google Scholar
• Zhang, Z., & Li, Z. L. (2022b). Consensus-based TOPSIS-Sort-B for multi-criteria sorting in the context of group decision-making. Annals of Operations Research, https://doi.org/10.1007/s10479-022-04985-w
   Web of Science ®Google Scholar