A hybrid MCDM model for evaluation of e-learning platforms of the Municipal organization

Author

Graduate University of Advanced Technology

Abstract
Given the vast scope of responsibilities and activities in any organization, success in quantitative and qualitative aspects of human resources can only be achieved through continuous quantitative and qualitative training. For the effective execution of the missions and services of any organization, especially municipal organizations, human resource training must be implemented, particularly in the current competitive era. Training is one of the areas that has undergone a fundamental transformation in information technology. Considering the COVID-19 pandemic and quarantine, the primary role of information technology in various aspects of education, including virtual learning and optimizing various educational processes, should be taken into account. Therefore, organizations are using information technology to implement effective training processes to achieve their organizational goals. However, various criteria influence the feasibility of implementing educational methods, which may conflict with each other when selecting the most suitable e-learning platform for professional training. An efficient group multi-criteria decision-making (MCDM) method has been developed to select the most effective e-learning platform. Nonetheless, uncertainty in decision-makers' judgments is one of the challenges raised in group-based multi-criteria decision-making methods. Therefore, in this paper, a hybrid method based on the group Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Grey Theory has been developed to account for uncertainty in decision-makers' judgments, aiming to select the most suitable platform for utilizing information technology in the e-learning of municipal organizations. The results showed that the synchronous virtual learning platform is selected as the best platform for professional training in municipal organizations.

Keywords


  • Receive Date 29 July 2024
  • Revise Date 05 September 2024
  • Accept Date 10 September 2024
  • First Publish Date 18 September 2024
  • Publish Date 22 September 2024