Iranian Journal of Information Management

Design an Expert System to Help Make Decisions in Attracting Faculty

Document Type : Original Article

Authors

1 Ph D. Candidate, Department of Information Technology Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran

2 Associate Prof., Department of Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran

10.22034/aimj.2023.182053

Abstract

Due to the limited power of human perception of the outside world and also the power of comprehensive and deep reasoning, he faces uncertainty and uncertainty. Multi-criteria decision-making methods, including methods to increase confidence and reduce uncertainty due to judgment Decision makers and comprehensiveness of information. One of the indicators calculated in the decision matrix in conditions of uncertainty is the minimum regret index. The purpose of this study is to identify options with the least regret in selecting faculty members. Therefore, using the variables obtained from the previous literature and the opinion of experts in the field of study, an expert system was designed to assist in decision-making in attracting faculty members.The results of the analysis indicated that the options of academic record, ethics (communication skills), teaching ability (ability to express and convey content) and decision-making skills are indicators that have the least risk of being considered in the scientific selection of individuals. According to the results of this research and the designed expert system can be used to help decision-making in attracting faculty members of universities by reducing time, increasing the quality and accuracy of decision-making.

Keywords

Acikgoz, Y. (2019). Employee recruitment and job search: Towards a multi-level integration, Human Resource Management Review, 29(1), 1-13.
Ahmady, S., Tatari, F., Yazdani, S., & Hosseini, S. A. (2016). Human Resources Management models for recruitment of faculty members: A critical review. Biosciences Biotechnology Research Asia, 13(1), 417.‏
 
Bobillo, F., Delgado, M., Gómez-Romero, J., & López, E. (2009). A semantic fuzzy expert system for a fuzzy balanced scorecard. Expert Systems with Applications, 36(1), 423-433.
Casal-Guisande, M., Comesaña-Campos, A., Pereira, A., Bouza-Rodríguez, J. B., & Cerqueiro-Pequeño, J. (2022). A decision-making methodology based on expert systems applied to machining tools condition monitoring. Mathematics, 10(3), 520.
Chang, E., & Chin, H. (2018). Signaling or experiencing: Commitment HRM effects on recruitment and employees' online ratings. Journal of Business Research, 84:175-185.
Darlington, K. (2000). The Essence of Expert systems, England, Prentice Hall.
Higgs, G. (2006). Integrating multicriteria techniques with geographical information systems in waste facility location to enhance public participation, Journal of Waste Management & Research, 24: 105-117.
Liao, S.H. (2005). Expert System Methodologies and Applications-a Decade Review from 1995 to 2004. Expert Systems with Applications, 28, 93–103.
Saibene, A., Assale, M., & Giltri, M. (2021). Expert systems: Definitions, advantages and issues in medical field applications. Expert Systems with Applications, 177, 114900.
Oleshchuk, V., & Fensli, R. (2011). Remote patient monitoring within a future 5G infrastructure. Wireless Personal Communications, 57, 431-439.
Mirmozaffari, M. (2019). Developing an expert system for diagnosing liver diseases. European Journal of Engineering and Technology Research, 4(3), 1-5.
Iranian Journal of Information Management
Volume 8, Issue 2 - Serial Number 15
March 2023
Pages 1-20
  • Receive Date: 03 September 2022
  • Revise Date: 31 January 2023
  • Accept Date: 19 August 2023