Integrating TOPSIS and three-dimensional importance-performance analysis to formulate the career competitiveness evaluation model under the MAES
DOI:
https://doi.org/10.4025/actascitechnol.v47i1.71080Palavras-chave:
Cadets, Career competitiveness, TOPSIS, Three-dimensional matrixResumo
Taiwan's declining birth rate and the lack of personnel in the national army have worsened. In particular, the loss of human resources caused by the lack of career competitiveness among national army officers has been increasing daily. Effective career competitiveness not only aligns personal interests with job roles but also enhances self-confidence and increases workplace value. The professional competitiveness of military officers depends on the nurturing of the Military Academy Education System (MAES), which involves the comprehensive consideration and evaluation of individual performance such as university education (UE), sport and combat ability (SCA), and military skill (MS); it is a complex multiple attribute decision making (MADM) problem. However, the current method fails to effectively consider individual attributes, which may result in cadets being assigned to unsuitable units or positions, leading them to consider retirement and thereby weakening national defense capabilities. To solve this problem, this research integrated the technique for order preference by similarity to an ideal solution (TOPSIS) and three-dimensional importance-performance analysis method to propose a novel three-dimensional TOPSIS (3D-TOPSIS) model, aimed at improving the evaluation process of cadets’ career competitiveness. The results demonstrate that this model accurately assesses cadets' attributes and distribution, providing valuable insights for personnel assignments and the allocation of educational resources.
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Abdul, D., & Wenqi, J. (2022). Evaluating appropriate communication technology for smart grid by using a comprehensive decision-making approach fuzzy TOPSIS. Soft Computing, 26(17), 8521–8536. https://doi.org/10.1007/s00500-022-07251-0 DOI: https://doi.org/10.1007/s00500-022-07251-0
Awodi, N. J., Liu, Y. K., Ayo-Imoru, R. M., & Ayodeji, A. (2023). Fuzzy TOPSIS-based risk assessment model for effective nuclear decommissioning risk management. Progress in Nuclear Energy, 155, 104524. https://doi.org/10.1016/j.pnucene.2022.104524 DOI: https://doi.org/10.1016/j.pnucene.2022.104524
Burnette, J. L., Pollack, J. M., Forsyth, R. B., Hoyt, C. L., Babij, A. D., Thomas, F. N., & Coy, A. E. (2020). A growth mindset intervention: Enhancing students’ entrepreneurial self-efficacy and career development. Entrepreneurship Theory and Practice, 44(5), 878–908. https://doi.org/10.1177/1042258719864293 DOI: https://doi.org/10.1177/1042258719864293
Chang, K. H. (2023). Integrating subjective-objective weights consideration and a combined compromise solution method for handling supplier selection issues. Systems, 11(2), 74. https://doi.org/10.3390/systems11020074 DOI: https://doi.org/10.3390/systems11020074
Chang, K. H., Chen, Y. J., & Liao, C. C. (2024). A novel improved FMEA method using data envelopment analysis method and 2-tuple fuzzy linguistic model. Annals of Operations Research. Advance online publication. https://doi.org/10.1007/s10479-024-05998-3 DOI: https://doi.org/10.1007/s10479-024-05998-3
Chang, K. H., Chang, Y. C., & Chung, H. Y. (2015). A novel AHP-based benefit evaluation model of military simulation training systems. Mathematical Problems in Engineering, 2015, 956757. https://doi.org/10.1155/2015/956757 DOI: https://doi.org/10.1155/2015/956757
Chen, L. C., & Chang, K. H. (2024). An entropy-based corpus method for improving keyword extraction: An example of sustainability corpus. Engineering Applications of Artificial Intelligence, 133(B), 108049. https://doi.org/10.1016/j.engappai.2024.108049 DOI: https://doi.org/10.1016/j.engappai.2024.108049
Chen, L. H., Lin, K. Y., & Chen, C. W. (2015). Identifying the key factors of department selection for ROC Military Academy cadets under the system of retardant tracking. Educational Policy Forum, 18(4), 99–129. https://doi.org/10.3966/156082982015111804004
Chodha, V., Dubey, R., Kumar, R., Singh, S., & Kaur, S. (2022). Selection of industrial arc welding robot with TOPSIS and Entropy MCDM techniques. Materials Today: Proceedings, 50, 709–715. https://doi.org/10.1016/j.matpr.2021.04.487 DOI: https://doi.org/10.1016/j.matpr.2021.04.487
Chung, H. Y., Chang, K. H., & Yao, J. C. (2023). Addressing environmental protection supplier selection issues in a fuzzy information environment using a novel soft fuzzy AHP–TOPSIS method. Systems, 11(6), 293. https://doi.org/10.3390/systems11060293 DOI: https://doi.org/10.3390/systems11060293
Davies, M. J., Coleman, L., & Stellino, M. B. (2016). The relationship between basic psychological need satisfaction, behavioral regulation, and participation in CrossFit. Journal of Sport Behavior, 39(3), 239–254. https://scholarlycommons.pacific.edu/cop-facarticles/108
Hsieh, P. J., Chen, C. C., & Liu, W. (2019). Integrating talent cultivation tools to enact a knowledge-oriented culture and achieve organizational talent cultivation strategies. Knowledge Management Research & Practice, 17(1), 108–124. https://doi.org/10.1080/14778238.2019.1571872 DOI: https://doi.org/10.1080/14778238.2019.1571872
Huang, Z., Li, J., & Yue, H. (2022). Study on comprehensive evaluation based on AHP-MADM model for patent value of balanced vehicle. Axioms, 11(9), 481. https://doi.org/10.3390/axioms11090481 DOI: https://doi.org/10.3390/axioms11090481
Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. Springer-Verlag. https://doi.org/10.1007/978-3-642-48318-9 DOI: https://doi.org/10.1007/978-3-642-48318-9_3
Iqbal, M., Ma, J., Ahmad, N., Ullah, Z., & Ahmed, R. I. (2021). Uptake and adoption of sustainable energy technologies: Prioritizing strategies to overcome barriers in the construction industry by using an integrated AHP?TOPSIS approach. Advanced Sustainable Systems, 5(7), 2100026. https://doi.org/10.1002/adsu.202100026 DOI: https://doi.org/10.1002/adsu.202100026
Jiang, J., Mok, K. H., & Shen, W. (2020). Riding over the national and global disequilibria: International learning and academic career development of Chinese Ph.D. returnees. Higher Education Policy, 33, 531–554. https://doi.org/10.1057/s41307-019-00175-9 DOI: https://doi.org/10.1057/s41307-019-00175-9
Kano, N., Seraku, N., Takahashi, F., & Tsuji, S. (1984). Attractive quality and must-be quality. Journal of the Japanese Society for Quality Control, 14(2), 39–48.
Keikha, A. (2022). Generalized hesitant fuzzy numbers and their application in solving MADM problems based on TOPSIS method. Soft Computing, 26(10), 4673–4683. https://doi.org/10.1007/s00500-022-06995-z DOI: https://doi.org/10.1007/s00500-022-06995-z
Lai, I. K. W., & Hitchcock, M. (2016). A comparison of service quality attributes for stand-alone and resort-based luxury hotels in Macau: 3-dimensional importance-performance analysis. Tourism Management, 55, 139–159. https://doi.org/10.1016/j.tourman.2016.01.007 DOI: https://doi.org/10.1016/j.tourman.2016.01.007
Lin, K. Y., Chen, L. H., Chen, C. W., & Chang, K. H. (2020). Integrating social networks and cluster analysis to discuss the relationship between college students’ learning cliques and course selection decision-making. Education Journal, 48(2), 21–46.
Liu, J., Fang, M., Jin, F., Wu, C., & Chen, H. (2020). Multi-attribute decision making based on stochastic DEA cross-efficiency with ordinal variable and its application to evaluation of banks’ sustainable development. Sustainability, 12(6), 2375. https://doi.org/10.3390/su12062375 DOI: https://doi.org/10.3390/su12062375
Martilla, J. A., & James, J. C. (1977). Importance-performance analysis. Journal of Marketing, 41(1), 77–79. DOI: https://doi.org/10.1177/002224297704100112
Military Academy. (2022). School affairs plan. Military Academy.
Mishra, A. R., Chen, S. M., & Rani, P. (2022). Multiattribute decision making based on Fermatean hesitant fuzzy sets and modified VIKOR method. Information Sciences, 607, 1532–1549. https://doi.org/10.1016/j.ins.2022.06.037 DOI: https://doi.org/10.1016/j.ins.2022.06.037
Ministry of National Defense. (2021). Quadrennial defense review. https://www.mnd.gov.tw/
Mitchell, R., Helen, P., & Kelli, B. (2016). Academic motivation and information literacy self-efficacy: The importance of a simple desire to know. Library & Information Science Research, 38(1), 2–9. https://doi.org/10.1016/j.lisr.2016.01.002 DOI: https://doi.org/10.1016/j.lisr.2016.01.002
Mondal, K., Pramanik, S., & Giri, B. C. (2021). NN-TOPSIS strategy for MADM in neutrosophic number setting. Neutrosophic Sets and Systems, 47, 66–92.
Piotrowska, M. (2019). Facets of competitiveness in improving the professional skills. Journal of Competitiveness, 11(2), 95–112. https://doi.org/10.7441/joc.2019.02.07 DOI: https://doi.org/10.7441/joc.2019.02.07
Precious, D., & Lindsay, A. (2019). Mental resilience training. BMJ Military Health, 165(2), 106–108. https://doi.org/10.1136/jramc-2018-001047 DOI: https://doi.org/10.1136/jramc-2018-001047
Rafiei-Sardooi, E., Azareh, A., Choubin, B., Mosavi, A. H., & Clague, J. J. (2021). Evaluating urban flood risk using hybrid method of TOPSIS and machine learning. International Journal of Disaster Risk Reduction, 66, 102614. https://doi.org/10.1016/j.ijdrr.2021.102614 DOI: https://doi.org/10.1016/j.ijdrr.2021.102614
Soylu, Y., Siyez, D. M., & Ozeren, E. (2021). Gender perception, career optimism and career adaptability among university students: The mediating role of personal growth initiative. International Journal of Progressive Education, 17(1), 1–15. https://doi.org/10.29329/ijpe.2020.329.1 DOI: https://doi.org/10.29329/ijpe.2021.329.1
Wang, Y., Liu, P., & Yao, Y. (2022). BMW-TOPSIS: A generalized TOPSIS model based on three-way decision. Information Sciences, 607, 799–818. https://doi.org/10.1016/j.ins.2022.06.018 DOI: https://doi.org/10.1016/j.ins.2022.06.018
Wen, T. C., Chang, K. H., Lai, H. H., Liu, Y. Y., & Wang, J. C. (2021). A novel rugby team player selection method integrating the TOPSIS and IPA methods. International Journal of Sport Psychology, 52(2), 137–158. https://doi.org/10.7352/IJSP.2021.52.137 DOI: https://doi.org/10.1123/shr.2021-0003
Zhou, M., Liu, X. B., Chen, Y. W., Qian, X. F., Yang, J. B., & Wu, J. (2020). Assignment of attribute weights with belief distributions for MADM under uncertainties. Knowledge-Based Systems, 189, 105110. https://doi.org/10.1016/j.knosys.2019.105110 DOI: https://doi.org/10.1016/j.knosys.2019.105110
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