DECISION SUPPORT SYSTEM MODEL IN HUMAN RESOURCE MANAGEMENT FOR CHINESE UNIVERSITIES IN SICHUAN PROVINCE
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Abstract
Leveraging advanced digital technologies to enhance management efficiency and optimize resource allocation is an effective means to promote all the human resource management activities in universities. This study aims to propose an optimal HRM decision support system model tailored to the needs of human resource management in Sichuan universities. To achieve this goal, a questionnaire survey was conducted with 21 experts based on literature review to analyze the existing issues in HRM in Sichuan universities. Subsequently, the Delphi method was used to establish a graphical model based on the feedback from these experts. Finally, referring to the CIPP evaluation principles, nine experts evaluated the model constructed in this study. The research results indicate that HRM in Sichuan universities faces issues primarily in aspects such as institutions and policy data management standards, big data, and artificial intelligence technology application. This study has proposed a model encompassing six core aspects of HRM: personnel information, personnel recruitment, development, performance evaluation, internal promotion, and compensation and benefits. The model enhances the integrity of data storage, records accuracy, and sharing smoothness; strengthens recruitment strategies formulation, candidate screening, and intelligent recruitment process; the identification of training needs improvements, development of personalized training plans, and resource allocation; optimizes intelligent performance evaluation, in-depth analysis, and sound feedback mechanism; establishes unified employment criteria, clear promotion channels, and intelligent job matching; and strengthens intelligent analysis, formulation of personalized incentive measures, and strategic analysis.
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References
Agbo, S. I., & Ogai, A. N. (2013). The need for introducing decision support system (DSS) in Nigerian universities management and administration. Information and Knowledge Management, 3(7), 14-20.
Begicevic Redep, N., Klacmer Calopa, M., & Bockaj, J. (2015). Decision making on human resource capacity in higher education institutions. Journal of Applied Economics, 18(2), 123-134.
Cai, C., & Chen, C. (2021). Optimization of human resource file information decision support system based on cloud computing. Complexity, 2021(1), 8919625.
Fortich, M. S., & Marcial, D. E. (2015). Human resource e-leave management: A decision support system. Information, 18(10), 1015-1024. https://doi.org/10.21460/INF.2014.102.329
Frysak, J. (2017). Feedback Mechanisms for Decision Support Systems: A Literature Review. Recent Advances in Information Systems and Technologies, 2(5), 481-490. doi: 10.1007/978-3-319-56538-5_49
Grecu, V., Chiliban, B., Kifor, C. V., & Oprean, C. (2013). An ICT system for decisional support in university management. Procedia Technology, 9, 1004-1012. https://doi.org/10.1016/j.protcy.2013.12.111
Jantan, H., Hamdan, A. R., & Othman, Z. A. (2010). Intelligent Techniques for Decision Support System in Human Resource Management. Decision support systems, 78, 261-276. doi: 10.5772/39401
Karabtsev, S. N., Kotov, R. M., Davzit, I. P., & Gurov, E. S. (2024). and Andrey L. Chebotarev Kemerovo State University, Kemerovo, Russia skarab@ kemsu. ru. Data Analytics in System Engineering: Proceedings of 7th Computational Methods in Systems and Software 2023, 3(3), 245.
Kayande, U., De Bruyn, A., Lilien, G. L., Rangaswamy, A., & van Bruggen, G. (2009). How Incorporating Feedback Mechanisms in a DSS Affects DSS Evaluations. Information Systems Research, 20(4), 527-546. doi: 10.1287/ISRE.1080.0198
Li, J. (2022). Design of enterprise human resources decision support system based on data mining. Soft Computing, 26(20), 10571-10580. https://doi.org/10.1007/s00500-021-06659-4
Liang, R., Jiang, Z., & Wang, J. (2018). A linguistic eutrophic multi-criteria group decision-making method to university human resource management. Symmetry, 10(9), 364. https://doi.org/10.3390/SYM10090364
Liu, X. (2018). Existing problems and countermeasures in enterprise human resources management. International Conference on Computer, Civil Engineering and Management Science (ICCEMS 2018)
Ma, S., Liang, Y., & Jan, N. (2022). Human Resource Management Decision System University Based on Decision Model. Mathematical Problems in Engineering, 2022, 1-8.
Mishra, N., Sawarkar, J., & Dubey, S. (2020). Database Performance Management in Cloud. International Journal of Engineering and Management Research, 10(5), 46-51.
Naz, M. I., Rajper, S., Ali, A., Khan, M. F., Asif, R., Mehmood, M. H., ... & Kalra, D. (2023, March). Framework of decision support system for effective resource management. In 2023 International Conference on Business Analytics for Technology and Security (ICBATS) (pp. 1-7). IEEE. https://doi.org/10.1109/ICBATS57792.2023.10111307
Pascu, R. V., Ciora, R. A., & Simion, C. L. (2014). Developing a decision support system for project management in universities. Creative and Knowledge Society, 4(2), 15-22. https://doi.org/10.2478/CPLBU-2014-0078
Patton, M. Q. (2008). State of the art in measuring development assistance. Address to the World Bank Independent Evaluation Group, 10.
Peng, T., & Deng, H. (2021). Evaluating urban resource and environment carrying capacity by using an innovative indicator system based on eco-civilization—a case study of Guiyang. Environmental Science and Pollution Research, 28(6), 6941-6955.
Rajagopal, M., Hinge, P. S., Srinivas, K., Palav, R. M., Balaji, P., & Muda, I. (2022). Artificial intelligence & data warehouse regional human resource management decision support system. Proceedings of the 2022 International Conference on Cloud Computing and Intelligent Systems (IC3I). https://doi.org/10.1109/IC3I56241.2022.10073122
Scriven, M. (1991). Chapter II: Beyond formative and summative evaluation. Teachers College Record, 92(6), 19-64.
Stake, R. E. (1975). An interview with Robert Stake on responsive evaluation. Evaluating the arts in education: A responsive approach, 33-38.
Stufflebeam, D. L. (1971). The relevance of the CIPP evaluation model for educational accountability. Journal of Research and Development in Education, 1(1), 29-44.
Yang, H.-P. (2022). Human resource big data analysis and decision making of group enterprises based on cloud platform. Proceedings of the 2022 International Conference on Measurement Technology and Mechatronics Automation (ICMTMA), 1068-1071. https://doi.org/10.1109/icmtma54903.2022.00215
Zhu, Q. (2023, July). Design and Implementation of Human Resource Optimization and Decision System for Universities Based on Artificial Intelligence Algorithms. In 2023 International Conference on Data Science and Network Security (ICDSNS), IEEE, 1-6, https://doi.org/10.1109/icdsns58469.2023.10244842
Zhukova, I., Kultsova, M., Navrotsky, M., & Dvoryankin, A. (2014). Intelligent support of decision making in human resource management using case-based reasoning and ontology. In Knowledge-Based Software Engineering: 11th Joint Conference, JCKBSE 2014, Volgograd, Russia, September 17-20, 2014. Proceedings 11 (pp. 172-184). Springer International. doi: 10.1007/978-3-319-11854-3_16