A data-driven MADM model for personnel selection and improvement
Personnel selection and human resource improvement are characteristically multiple-attribute decision-making (MADM) problems. Previously developed MADM models have principally depended on experts’ judgements as input for the derivation of solutions. However, the subjectivity of the experts’ experience can have a negative influence on this type of decision-making process. With the arrival of today’s data-based decision-making environment, we develop a data-driven MADM model, which integrates machine learning and MADM methods, to help managers select personnel more objectively and to support their competency improvement. First, RST, a machining learning tool, is applied to obtain the initial influential significance-relation matrix from real assessment data. Subsequently, the DANP method is used to derive an influential significance-network relation map and influential weights from the initial matrix. Finally, the PROMETHEE-AS method is applied to assess the gap between the aspiration and current levels for every candidate. An example was carried out using performance data with evaluation attributes obtained from the human resource department of a Chinese food company. The results revealed that the data-driven MADM model could enable human resource managers to resolve the issues of personnel selection and improvement simultaneously, and can actually be applied in the era of big data analytics in the future.
First published online 15 May 2020
Keyword : human resource development, personnel selection and improvement, data-driven decision-making environment, data-driven multiple attribute decision-making (Data-driven MADM), rough set theory (RST), DEMATEL-based analytical network process (DANP), preference ranking organization method for enrichment evaluation with aspiration level (PROMETHEE-AS)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Afshari, A. R., Mojahed, M., Yusuff, R. M., Hong, T. S., & Ismail, M. Y. (2010). Personnel selection using ELECTRE. Journal of Applied Sciences, 10(23), 3068–3075. https://doi.org/10.3923/jas.2010.3068.3075
Afshari, A. R., Yusuff, R. M., & Derayatifar, A. R. (2013). Linguistic extension of fuzzy integral for group personnel selection problem. Arabian Journal for Science and Engineering, 38(10), 2901–2910. https://doi.org/10.1007/s13369-012-0491-z
Aksakal, E., & Dağdeviren, M. (2010). An integrated approach for personnel selection with DEMATEL and ANP methods. Journal of the Faculty of Engineering and Architecture of Gazi University, 25(4), 905–913.
Alguliyev, R. M., Aliguliyev, R. M., & Mahmudova, R. S. (2015). Multicriteria personnel selection by the modified fuzzy VIKOR method. Scientific World Journal, 2015, 612767. https://doi.org/10.1155/2015/612767
Bai, C., & Sarkis, J. (2010). Green supplier development: Analytical evaluation using rough set theory. Journal of Cleaner Production, 18(12), 1200–1210. https://doi.org/10.1016/j.jclepro.2010.01.016
Bal, M. (2013). Rough sets theory as symbolic data mining method: An application on complete decision table. Information Sciences Letters, 2(1), 111–116. https://doi.org/10.12785/isl/020105
Bates, R. (2014). Improving human resources for health planning in developing economies. Human Resource Development International, 17(1), 88–97. https://doi.org/10.1080/13678868.2013.857509
Bello, M., Bello, R., Nowé, A., & García-Lorenzo, M. M. (2018). A method for the team selection problem between two decision-makers using the ant colony optimization. In M. Collan & J. Kacprzyk (Eds.), Soft computing applications for group decision-making and consensus modeling. Studies in fuzziness and soft computing (pp. 391–410). Springer. https://doi.org/10.1007/978-3-319-60207-3_23
Bello, R., & Falcon, R. (2017). Rough sets in machine learning: A review. In G. Wang, A. Skowron, Y. Yao, D. Ślęzak, & L. Polkowski (Eds.), Thriving rough sets. Studies in computational intelligence (Vol. 708). Springer, Cham. https://doi.org/10.1007/978-3-319-54966-8_5
Bohlouli, M., Mittas, N., Kakarontzas, G., Theodosiou, T., Angelis, L., & Fathi, M. (2017). Competence assessment as an expert system for human resource management: A mathematical approach. Expert Systems with Applications, 70, 83–102. https://doi.org/10.1016/j.eswa.2016.10.046
Boran, S., Goztepe, K., & Yavuz, E. (2008). A study on election of personnel based on performance measurement by using analytic network process (ANP). International Journal of Computer Science and Network Security, 8(4), 333–338.
Brans, J. P., & Mareschal, B. (1995). The PROMETHEE VI procedure: How to differentiate hard from soft multicriteria problems. Journal of Decision Systems, 4(3), 213–223. https://doi.org/10.1080/12460125.1995.10511652
Brans, J. P., & Vincke, P. (1985). A preference ranking organization method (The PROMETHEE method for MCDM). Management Science, 31(6), 647–656. https://doi.org/10.1287/mnsc.31.6.647
Capaldo, G., & Zollo, G. (2001). Applying fuzzy logic to personnel assessment: A case study. Omega, 29(6), 585–597. https://doi.org/10.1016/S0305-0483(01)00047-0
Celik, M., Er, I. D., & Topcu, Y. I. (2009). Computer-based systematic execution model on human resources management in maritime transportation industry: The case of master selection for embarking on board merchant ships. Expert Systems with Applications, 36(2), 1048–1060. https://doi.org/10.1016/j.eswa.2007.11.004
Chen, Q., Tsai, S. B., Zhai, Y., Zhou, J., Yu, J., Chang, L. C., Li, G., Zheng, Y., & Wang, J. (2018). An empirical study on low-carbon: Human resources performance evaluation. International Journal of Environmental Research and Public Health, 15(1), 62. https://doi.org/10.3390/ijerph15010062
Chen, Y. S., & Cheng, C. H. (2010). A delphi-based rough sets fusion model for extracting payment rules of vehicle license tax in the government sector. Expert Systems with Applications, 37(3), 2161– 2174. https://doi.org/10.1016/j.eswa.2009.07.027
Chien, C. F., & Chen, L. F. (2008). Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with Applications, 34(1), 280–290. https://doi.org/10.1016/j.eswa.2006.09.003
Cho, J. C. (2018). Selecting candidates for pharmacy residencies: A national survey of residency program directors. Journal of Clinical Pharmacy and Therapeutics, 43(6), 844–848. https://doi.org/10.1111/jcpt.12723
Choobdari Namin, N., Baradaran Jamili, B., & Allah Kalvandi, H. (2013). Evaluation of staff efficiency using the combined model of Neuro/DEA (case study: Operational unit of Gilan province gas company). Life Science Journal, 10(S6), 185–191. https://doi.org/10.7537/marslsj1006s13.29
Ding, J. F., Kuo, J. F., Shyu, W. H., & Chou, C. C. (2019). Evaluating determinants of attractiveness and their cause-effect relationships for container ports in Taiwan: Users’ perspectives. Maritime Policy & Management, 46(4), 466–490. https://doi.org/10.1080/03088839.2018.1562245
Fontela, E., & Gabus, A. (1976). The DEMATEL observer. Battelle Geneva Research Center.
Fouladi, P., & Jafari Navimipour, N. (2017). Human resources ranking in a cloud-based knowledge sharing framework using the quality control criteria. Kybernetes, 46(5), 876–892. https://doi.org/10.1108/K-01-2017-0007
Gabus, A., & Fontela, E. (1972). World problems, an invitation to further thought within the framework of DEMATEL. Battelle Geneva Research Center, 1–8.
Gibney, R., & Shang, J. (2007). Decision making in academia: A case of the dean selection process. Mathematical and Computer Modelling, 46(7–8), 1030–1040. https://doi.org/10.1016/j.mcm.2007.03.024
Golec, A., & Kahya, E. (2007). A fuzzy model for competency-based employee evaluation and selection. Computers & Industrial Engineering, 52(1), 143–161. https://doi.org/10.1016/j.cie.2006.11.004
Govindan, K., Kannan, D., & Shankar, M. (2015). Evaluation of green manufacturing practices using a hybrid MCDM model combining DANP with PROMETHEE. International Journal of Production Research, 53(21), 6344–6371. https://doi.org/10.1080/00207543.2014.898865
Guest, D. E. (1997). Human resource management and performance: A review and research agenda. The International Journal of Human Resource Management, 8(3), 263–276. https://doi.org/10.1080/095851997341630
Gungor, Z., Serhadlioglu, 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
Heidary Dahooie, J., Beheshti Jazan Abadi, E., Vanaki, A. S., & Firoozfar, H. R. (2018). Competencybased 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
Hu, K., Lu, Y., & Shi, C. (2003). Feature ranking in rough sets. AI Communications, 16(1), 41–50.
Hu, S. K., & Tzeng, G. H. (2019). A hybrid multiple-attribute decision-making model with modified PROMETHEE for identifying optimal performance-improvement strategies for sustainable development of a better life. Social Indicators Research, 144, 1021–1053. https://doi.org/10.1007/s11205-018-2033-x
Ishizaka, A., & Pereira, V. E. (2016). Portraying an employee performance management system based on multi-criteria decision analysis and visual techniques. International Journal of Manpower, 37(4), 628–659. https://doi.org/10.1108/IJM-07-2014-0149
Ji, P., Zhang, H. Y., & Wang, J. Q. (2016). 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
Kabak, M. (2013). A fuzzy DEMATEL-ANP based multi criteria decision making approach for personnel selection. Journal of Multiple-Valued Logic and Soft Computing, 20(5), 571–593.
Kabak, M., Burmaoglu, S., & Kazancoglu, Y. (2012). A fuzzy hybrid MCDM approach for professional selection. Expert Systems with Applications, 39(3), 3516–3525. https://doi.org/10.1016/j.eswa.2011.09.042
Karabasevic, D., Zavadskas, E. K., Turskis, Z., & Stanujkic, D. (2016). The framework for the selection of personnel based on the SWARA and ARAS methods under uncertainties. Informatica, 27(1), 49–65. https://doi.org/10.15388/Informatica.2016.76
Kazancoglu, Y., & Ozkan-Ozen, Y. (2018). Analyzing Workforce 4.0 in the Fourth Industrial Revolution and proposing a road map from operations management perspective with fuzzy DEMATEL. Journal of Enterprise Information Management, 31(6), 891–907. https://doi.org/10.1108/JEIM-01-2017-0015
Kiani Mavi, R., & Standing, C. (2018). Cause and effect analysis of business intelligence (BI) benefits with fuzzy DEMATEL. Knowledge Management Research & Practice, 16(2), 245–257. https://doi.org/10.1080/14778238.2018.1451234
Koutra, G., Barbounaki, S., Kardaras, D., & Stalidis, G. (2017). A multicriteria model for personnel selection in maritime industry in Greece. In Proceedings of the 2017 IEEE 19th Conference on Business Informatics (pp. 287–294). https://doi.org/10.1109/CBI.2017.52
Krishankumar, R., Premaladha, J., Ravichandran, K. S., Sekar, K. R., Manikandan, R., & Gao, X. Z. (2019). A novel extension to VIKOR method under intuitionistic fuzzy context for solving personnel selection problem. Soft Computing, 24, 1063–1081. https://doi.org/10.1007/s00500-019-03943-2
Krishankumar, R., Ravichandran, K. S., & Bala, A. (2017). Evaluation of competing personnel in software company using expectation maximization approach. ARPN Journal of Engineering and Applied Sciences, 12(5), 1630–1636.
Lee, W. S., Tzeng, G. H., & Cheng, C. H. (2009). Using novel MCDM methods based on Fama-French three-factor model for probing the stock selection. In Proceedings of the 10th Asia-Pacific Industrial Engineering and Management Systems Conference (pp. 1460–1474).
Li, N., Kong, H., Ma, Y., Gong, G., & Huai, W. (2016). Human performance modeling for manufacturing based on an improved KNN algorithm. International Journal of Advanced Manufacturing Technology, 84(1–4), 473–483. https://doi.org/10.1007/s00170-016-8418-6
Li, Y. M., Lai, C. Y., & Kao, C. P. (2011). Building a qualitative recruitment system via SVM with MCDM approach. Applied Intelligence, 35(1), 75–88. https://doi.org/10.1007/s10489-009-0204-9
Liao, S. K., & Chang, K. L. (2009). Selecting public relations personnel of hospitals by analytic network process. Journal of Hospital Marketing & Public Relations, 19(1), 52–63. https://doi.org/10.1080/15390940802581713
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
Liou, J. J. H., Chuang, Y. C., & Hsu, C. C. (2016). Improving airline service quality based on rough set theory and flow graphs. Journal of Industrial and Production Engineering, 33(2), 123–133. https://doi.org/10.1080/21681015.2015.1113571
Liou, J. J. H., Chuang, Y. C., & Tzeng, G. H. (2014). A fuzzy integral-based model for supplier evaluation and improvement. Information Sciences, 266, 199–217. https://doi.org/10.1016/j.ins.2013.09.025
Liou, J. J. H., Lu, M. T., Hu, S. K., Cheng, C. H., & Chuang, Y. C. (2017). A Hybrid MCDM Model for improving the electronic health record to better serve client needs. Sustainability, 9(10), 1819. https://doi.org/10.3390/su9101819
Liou, J. J., Chuang, Y. C., Zavadskas, E. K., & Tzeng, G. H. (2019). Data-driven hybrid multiple attribute decision-making model for green supplier evaluation and performance improvement. Journal of Cleaner Production, 241, 118321. https://doi.org/10.1016/j.jclepro.2019.118321
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, 25(3), 370–381. https://doi.org/10.1002/hfm.20553
Mahajan, P., Kandwal, R., & Vijay, R. (2012). Rough set approach in machine learning: A review. International Journal of Computer Applications, 56(10), 1–13. https://doi.org/10.5120/8924-2996
Manoharan, T. R., Muralidharan, C., & Deshmukh, S. G. (2011). An integrated fuzzy multi-attribute decision-making model for employees’ performance appraisal. International Journal of Human Resource Management, 22(3), 722–745. https://doi.org/10.1080/09585192.2011.543763
Modrzejewski, M. (1993). Feature selection using rough sets theory. In P. B. Brazdil (Ed.), Lecture notes in computer science (Lecture notes in artificial intelligence): Vol. 667. Machine learning: ECML-93 (pp. 213–226). Springer. https://doi.org/10.1007/3-540-56602-3_138
Moshkov, M., & Zielosko, B. (2011). Combinatorial machine learning: A rough set approach (Vol. 360). Springer Science & Business Media. https://doi.org/10.1007/978-3-642-20995-6
Nabeeh, N. A., Smarandache, F., Abdel-Basset, M., El-Ghareeb, H. A., & Aboelfetouh, A. (2019). An integrated neutrosophic-topsis approach and its application to personnel selection: A new trend in brain processing and analysis. IEEE Access, 7, 29734–29744. https://doi.org/10.1109/ACCESS.2019.2899841
Nguyen, J., Sánchez-Hernández, G., Armisen, A., Agell, N., Rovira, X., & Angulo, C. (2018). A linguistic multi-criteria decision-aiding system to support university career services. Applied Soft Computing, 67, 933–940. https://doi.org/10.1016/j.asoc.2017.06.052
Ozkan, C., Keskin, G. A., & Omurca, S. I. (2014). A variant perspective to performance appraisal system: Fuzzy C - Means algorithm. International Journal of Industrial Engineering, 21(3), 168–178.
Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information Science, 11(5), 341– 356. https://doi.org/10.1007/BF01001956
Pawlak, Z. (1997). Rough set approach to knowledge-based decision support. European Journal of Operational Research, 99(1), 48–57. https://doi.org/10.1016/S0377-2217(96)00382-7
Pawlak, Z. (1998). Rough set theory and its applications to data analysis. Cybernetics and Systems: An International Journal, 29(7), 661–688. https://doi.org/10.1080/019697298125470
Pawlak, Z. (1991). Rough sets. Theoretical aspects of reasoning about data. Kluwer Academic Publishers.
Pawlak, Z., Grzymala-Busse, J., Slowinski, R., & Ziarko, W. (1995). Rough sets. Communications of the ACM, 38(11), 88–95. https://doi.org/10.1145/219717.219791
Pawlak, Z., Polkowski, L., & Skowron, A. (2005). Rough sets. In L. Rivero, J. Doorn, & V. Ferraggine (Eds.), Encyclopedia of database technologies and applications (pp. 575–580). IGI Global. https://doi.org/10.4018/978-1-59140-560-3.ch095
Petridis, K., Drogalas, G., & Zografidou, E. (2019). Internal auditor selection using a TOPSIS/non-linear programming model. Annals of Operations Research, 1–27. https://doi.org/10.1007/s10479-019-03307-x
Petrović, D., Puharić, M., & Kastratović, E. (2018). Defining of necessary number of employees in airline by using artificial intelligence tools. International Review, 3–4, 77–89. https://doi.org/10.5937/IntRev1804077P
Rossi, L., Slowinski, R., & Susmaga, R. (1999). Rough set approach to the evaluation of stormwater pollution. International Journal of Environment and Pollution, 12(2–3), 232–250. https://doi.org/10.1504/IJEP.1999.002294
Saaty, T. L. (1996). Decision making with dependence and feedback. The analytic network process (Vol. 4922). RWS publications.
Saaty, T. L. (2001). Decision making with dependence and feedback. The analytic network process: The organization and prioritization of complexity. RWS publications.
Sang, X. Z., Liu, X. W., & Qin, J. D. (2015). An analytical solution to fuzzy TOPSIS and its application in personnel selection for knowledge-intensive enterprise. Applied Soft Computing, 30, 190–204. https://doi.org/10.1016/j.asoc.2015.01.002
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.
Si, S. L., You, X. Y., Liu, H. C., & Zhang, P. (2018). DEMATEL technique: A systematic review of the state-of-the-art literature on methodologies and applications. Mathematical Problems in Engineering, 2018, 3696457, 1–33. https://doi.org/10.1155/2018/3696457
Swiniarski, R. W., & Skowron, A. (2003). Rough set methods in feature selection and recognition. Pattern Recognition Letters, 24(6), 833–849. https://doi.org/10.1016/S0167-8655(02)00196-4
Tseng, M. L. (2009). Using the extension of DEMATEL to integrate hotel service quality perceptions into a cause-effect model in uncertainty. Expert Systems with Applications, 36(5), 9015–9023. https://doi.org/10.1016/j.eswa.2008.12.052
Tsui, C. W., Tzeng, G. H., & Wen, U. P. (2015). A hybrid MCDM approach for improving the performance of green suppliers in the TFT-LCD industry. International Journal of Production Research, 53(21), 6436–6454. https://doi.org/10.1080/00207543.2014.935829
Tzeng, G. H., & Shen, K. Y. (2017). New concepts and trends of hybrid multiple criteria decision making. CRC Press, Taylor & Francis Group. https://doi.org/10.1201/9781315166650
Wan, S. P., Wang, Q. Y., & Dong, J. Y. (2013). The extended VIKOR method for multi-attribute group decision making with triangular intuitionistic fuzzy numbers. Knowledge-Based Systems, 52, 65–77. https://doi.org/10.1016/j.knosys.2013.06.019
Young, H. R., Glerum, D. R., Wang, W., & Joseph, D. L. (2018). Who are the most engaged at work? A meta‐analysis of personality and employee engagement. Journal of Organizational Behavior, 39(10), 1330–1346. https://doi.org/10.1002/job.2303
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
Zolfani, S. H., & Antucheviciene, J. (2012). Team member selecting based on AHP and TOPSIS grey. Inzinerine Ekonomika-Engineering Economics, 23(4), 425–434. https://doi.org/10.5755/j01.ee.23.4.2725