AN MCDM APPROACH FOR PERSONNEL SELECTION USING THE COCOSO METHOD
Abstract
Candidate selection has a direct impact on the quality of a company's human resources department, so it's an essential activity for public and private companies alike. Various ways have been created to assist and make it easier for organizations to select the best candidate, specifically the right people to do specific roles. As a result, the purpose of this study is to propose the use of multi-criteria decision-making techniques in personnel selection. Therefore, for the determination of the weights of the criteria will be employed the SWARA method, whereas, for the ranking of the alternatives, i.e. selection of the best candidate, a CoCoSo method will be applied.
References
Afshari, A., Mojahed, M., & Yusuff, R. M. (2010). Simple additive weighting approach to personnel selection problem. International Journal of Innovation, Management and Technology, 1(5), 511.
Albayrak, E., & Erensal, Y. C. (2004). Using analytic hierarchy process (AHP) to improve human performance: An application of multiple criteria decision making problem. Journal of Intelligent Manufacturing, 15(4), 491-503.
Avakumović, J. (2019). HRM activities grouped in AMO model in the system of higher education. Ekonomski izazovi, 8(16), 90-99.
Bagal, D. K., Giri, A., Pattanaik, A. K., Jeet, S., Barua, A., & Panda, S. N. (2021). MCDM Optimization of Characteristics in Resistance Spot Welding for Dissimilar Materials Utilizing Advanced Hybrid Taguchi Method-Coupled CoCoSo, EDAS and WASPAS Method. In Next Generation Materials and Processing Technologies (pp. 475-490). Springer, Singapore.
Cooper, D., & Robertson, I. T. (1995). The psychology of personnel selection: A quality approach. Burns & Oates.
Dağdeviren, M. (2010). A hybrid multi-criteria decision-making model for personnel selection in manufacturing systems. Journal of Intelligent manufacturing, 21(4), 451-460.
Deveci, M., Pamucar, D., & Gokasar, I. (2021). Fuzzy Power Heronian function based CoCoSo method for the advantage prioritization of autonomous vehicles in real-time traffic management. Sustainable Cities and Society, 69, 102846.
Dursun, M., & Karsak, E. E. (2010). Fuzzy MCDM approach for personnel selection. Expert Systems with Applications, 37, 4324–4330.
Ecer, F. (2021). A consolidated MCDM framework for performance assessment of battery electric vehicles based on ranking strategies. Renewable and Sustainable Energy Reviews, 143, 110916.
Gürbüz, T., & Albayrak, Y. E. (2014). An engineering approach to human resources performance evaluation: Hybrid MCDM application with interactions. Applied Soft Computing, 21, 365-375.
Karabašević, D., Stanujkić, D., & Urošević, S. (2015). The MCDM Model for Personnel Selection Based on SWARA and ARAS Methods. Management, 20(77).
Karsak, E. E. (2001). Personnel selection using a fuzzy MCDM approach based on ideal and anti-ideal solutions. In Multiple criteria decision making in the new millennium (pp. 393-402). Springer, Berlin, Heidelberg.
Krylovas, A., Dadelo, S., Kosareva, N., & Zavadskas, E. K. (2017). Entropy–KEMIRA approach for MCDM problem solution in human resources selection task. International journal of information technology & decision making, 16(05), 1183-1209.
Mousa, S. K., & Othman, M. (2020). The impact of green human resource management practices on sustainable performance in healthcare organisations: A conceptual framework. Journal of Cleaner Production, 243, 118595.
Nikolaou, I., & Oostrom, J. K. (Eds.). (2015). Employee recruitment, selection, and assessment: Contemporary issues for theory and practice. Psychology Press.
Peng, X., & Luo, Z. (2021). Decision-making model for China’s stock market bubble warning: the CoCoSo with picture fuzzy information. Artificial Intelligence Review, 1-23.
Peng, X., Krishankumar, R., & Ravichandran, K. S. (2021). A novel interval-valued fuzzy soft decision-making method based on CoCoSo and CRITIC for intelligent healthcare management evaluation. Soft Computing, 25(6), 4213-4241.
Robertson, I. T., & Cooper, C. (Eds.). (2015). Personnel psychology and human resources management: A reader for students and practitioners. John Wiley & Sons.
Štang-Šušnjar, G., Slavić, A., Berber, N. (2017) Menadžment ljudskih resursa. Ekonomski fakultet, Subotica (In Serbian).
Stanujkic, D., Popovic, G., Zavadskas, E. K., Karabasevic, D., & Binkyte-Veliene, A. (2020). Assessment of Progress towards Achieving Sustainable Development Goals of the “Agenda 2030” by Using the CoCoSo and the Shannon Entropy Methods: The Case of the EU Countries. Sustainability, 12(14), 5717.
Torkayesh, A. E., Ecer, F., Pamucar, D., & Karamaşa, Ç. (2021b). Comparative assessment of social sustainability performance: Integrated data-driven weighting system and CoCoSo model. Sustainable Cities and Society, 71, 102975.
Torkayesh, A. E., Pamucar, D., Ecer, F., & Chatterjee, P. (2021а). An integrated BWM-LBWA-CoCoSo framework for evaluation of healthcare sectors in Eastern Europe. Socio-Economic Planning Sciences, 101052.
Ulutaş, A., Popovic, G., Radanov, P., Stanujkic, D., & Karabasevic, D. (2021). A new hybrid fuzzy PSI-PIPRECIA-CoCoSo MCDM based approach to solving the transportation company selection problem. Technological and Economic Development of Economy, 27(5), 1227-1249.
Uslu, Y. D., Yılmaz, E., & Yiğit, P. (2021). Developing Qualified Personnel Selection Strategies Using MCDM Approach: A University Hospital Practice. In Strategic Outlook in Business and Finance Innovation: Multidimensional Policies for Emerging Economies. Emerald Publishing Limited.
Wen, Z., Liao, H., Kazimieras Zavadskas, E., & Al-Barakati, A. (2019). Selection third-party logistics service providers in supply chain finance by a hesitant fuzzy linguistic combined compromise solution method. Economic research-Ekonomska istraživanja, 32(1), 4033-4058.