Research on Human Resource Performance Evaluation and Optimization Decision-Making Based on Entropy Weight - TOPSIS Method
Main Article Content
Abstract
In technology-intensive enterprises, particularly those engaged in electromagnetic engineering, wireless communication, and antenna-related research and development, performance evaluation plays a critical role in talent allocation, innovation sustainability, and long-term organizational competitiveness. However, the complexity of R&D activities and the nonlinear characteristics of engineering outputs render conventional qualitative assessment approaches inadequate. This study proposes a hybrid multi-criteria decision-making framework that integrates the Entropy Weight Method (EWM) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to achieve objective and reproducible performance evaluation. The EWM is employed to determine indicator weights through statistical dispersion analysis, thereby reducing subjective bias in weighting assignment, while TOPSIS ranks personnel by measuring their relative closeness to the positive and negative ideal solutions. An empirical analysis involving ten R&D engineers demonstrates that the proposed framework provides highly discriminative and stable performance rankings. The results indicate that innovation-oriented indicators contribute most significantly to personnel differentiation, whereas operational indicators mainly reflect baseline organizational requirements. Sensitivity analysis further confirms the robustness of the ranking under varying weighting scenarios. The proposed method provides a scientific and quantitative decision-support tool for human resource optimization and offers practical reference value for research team management and innovation capability assessment in advanced electromagnetic engineering and communication technology development.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
T. Garavan, M. Morley, P. Gunnigle, et al., “Human capital accumulation: The role of human resource development,” Journal of European Industrial Training, vol. 25, pp. 48–68, 2001, doi: 10.1108/EUM0000000005437.
A. Toner-Rodgers, “Artificial intelligence, scientific discovery, and product innovation,” arXiv preprint arXiv:241217866, 2024.
A. M. Knott, “R&D/Returns causality: Absorptive capacity or organizational IQ,” Management Science, vol. 54, no. 12, pp. 2054–2067, 2008, doi: 10.1287/mnsc.1080.0933.
J. M. Gonzalez-Varona, N. Martin-Cruz, F. Acebes, and J. Pajares, “How public funding affects complexity in R&D projects. An analysis of team project perceptions,” Journal of Business Research, vol. 158, Art. no. 113672, 2023, doi: 10.1016/j.jbusres.2023.113672.
T. Distelmans, M. Cools, I. Grabner, K. Stouthuysen, and A. Van den Abbeele, “Governing innovation: How technology maturity shapes R&D contracts,” Technovation, vol. 153, Art. no. 103521, 2026, doi: 10.1016/j.technovation.2026.103521.
D. W. Bracken, D. S. Rose, and A. H. Church, “The evolution and devolution of 360 Degree Feedback,” Industrial and Organizational Psychology, vol. 9, no. 4, pp. 761–794, 2016, doi: 10.1017/iop.2016.93.
M. Alipanah Dolatabad, P. N. Tehrani, H. Pourasghari, and N. Sharfafchizadeh, “Employee performance appraisal methods: A scoping review,” medRxiv, Art. no. 2025.07.02.25330701, 2025, doi: 10.1101/2025.07.02.25330701.
R. W. Saaty, “The analytic hierarchy process-what it is and how it is used,” Mathematical Modelling, vol. 9, no. 3, pp. 161–176, 1987, doi: 10.1016/0270-0255(87)90473-8.
A. Ishizaka and A. Labib, “Analytic hierarchy process and expert choice: Benefits and limitations,” OR Insight, vol. 22, no. 4, pp. 201–220, 2009, doi: 10.1057/ori.2009.10.
G.-H. Tzeng and J.-J. Huang, Multiple Attribute Decision Making: Methods and Applications. CRC Press, 2011, doi: 10.1201/b11032.
A. Shaout and M. K. Yousif, “Performance evaluation-Methods and techniques survey,” International Journal of Computer and Information Technology, vol. 3, no. 5, pp. 966–979, 2014.
S. Greco, J. Figueira, and M. Ehrgott, Multiple Criteria Decision Analysis. Springer, 2016, doi: 10.1007/978-1-4939-3094-4.
F. W. Taylor, The Principles of Scientific Management. NuVision Publications, LLC, 1911.
G. P. Latham and K. N. Wexley, “Behavioral observation scales for performance appraisal purposes,” Personnel Psychology, vol. 30, no. 2, pp. 255–268, 1977, doi: 10.1111/j.1744-6570.1977.tb02092.x.
L. Cao, “Data science: A comprehensive overview,” ACM Comput. Surv., vol. 50, no. 3, Art. no. 43, 2017, doi: 10.1145/3076253.
N. Pandey, S. P. Dash, and P. Kaur, “A systematic review of multi-criteria decision-making (MCDM) methods applied to personnel selection: Trends, models, and effectiveness,” OPSEARCH, 2026, doi: 10.1007/s12597-026-01136-8.
S. V. Ratner, A. M. Shaposhnikov, and A. V. Lychev, “Network DEA and its applications (2017-2022): A systematic literature review,” Mathematics, vol. 11, no. 9, Art. no. 2141, 2023, doi: 10.3390/math11092141.
J. Hu, H. Wang, and D. Ji, “Educational performance evaluation based on fuzzy evaluation model,” Discover Computing, vol. 28, no. 1, Art. no. 345, 2025, doi: 10.1007/s10791-025-09854-1.
Y. Liu, Y. Wang, T. Li, R. Ma, K. Xu, and W. Xu, “Evaluation of new power system based on entropy weight-TOPSIS method,” Mathematical Problems in Engineering, vol. 2022, no. 1, Art. no. 7669139, 2022, doi: 10.1155/2022/7669139.
C. E. Shannon, “A mathematical theory of communication,” The Bell System Technical Journal, vol. 27, no. 3, pp. 379–423, 1948, doi: 10.1002/j.1538-7305.1948.tb01338.x.