Developing a Hybrid Fuzzy MCDM and MODM Model for Allocating Knowledge Management Tools and Practices to Organizational Knowledge Issues
محورهای موضوعی : Fuzzy Optimization and Modeling Journal
Ehsan Yavari
1
(Department of Industrial Management, Imam Hossein Comprehensive University, Tehran, Iran)
Alireza Mardani
2
(Department of Financial Management, Imam Hossein Comprehensive University, Tehran, Iran)
Mohammad Amin Mirarab Razi
3
(Department of Public Administration, Islamic Azad University, Sari Branch, Iran.)
Mohammad Reza Fathi
4
(Department of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran)
کلید واژه: Knowledge Management Systems, Fuzzy Delphi, Best-Worst Method, Fuzzy TOPSIS, Allocation Model,
چکیده مقاله :
Today, due to the lack of a systematic approach to selecting knowledge management (KM) tools and practices, various organizational units face different challenges, resulting in the organization's failure to achieve its goals. This study aims to propose a hybrid multi-attribute decision-making (MADM) and multi-objective decision-making (MODM) model to provide organizational managers with a systematic method for selecting KM tools and practices appropriate to the knowledge issues facing their organization. This study first identified the tools and practices in KM and then modified these tools and practices using the fuzzy Delphi method. After conducting a literature review, the criteria and sub-criteria used to evaluate these tools and practices were identified and weighted according to the Best Worst Method (BWM). A fuzzy TOPSIS method was then used to rank the tools and practices based on the weights derived from the criteria and sub-criteria. An allocation model using bi-objective mathematical programming was developed as a final step to allocate KM tools and practices to organizational knowledge issues. According to the analysis of the criteria and sub-criteria, "stakeholder satisfaction" ranks highest among the main criteria, while "capital costs," "knowledge transfer," and "customers" rank highest among the sub-criteria. Based on the evaluation of KM tools and practices, "social media" ranked first among tools, and "ideation sessions" ranked first among practices. Following the solution of the model, it was determined how each tool and practice should be allocated to knowledge issues. In general, 200 points were generated on the Pareto front as a result of solving the model.
Today, due to the lack of a systematic approach to selecting knowledge management (KM) tools and practices, various organizational units face different challenges, resulting in the organization's failure to achieve its goals. This study aims to propose a hybrid multi-attribute decision-making (MADM) and multi-objective decision-making (MODM) model to provide organizational managers with a systematic method for selecting KM tools and practices appropriate to the knowledge issues facing their organization. This study first identified the tools and practices in KM and then modified these tools and practices using the fuzzy Delphi method. After conducting a literature review, the criteria and sub-criteria used to evaluate these tools and practices were identified and weighted according to the Best Worst Method (BWM). A fuzzy TOPSIS method was then used to rank the tools and practices based on the weights derived from the criteria and sub-criteria. An allocation model using bi-objective mathematical programming was developed as a final step to allocate KM tools and practices to organizational knowledge issues. According to the analysis of the criteria and sub-criteria, "stakeholder satisfaction" ranks highest among the main criteria, while "capital costs," "knowledge transfer," and "customers" rank highest among the sub-criteria. Based on the evaluation of KM tools and practices, "social media" ranked first among tools, and "ideation sessions" ranked first among practices. Following the solution of the model, it was determined how each tool and practice should be allocated to knowledge issues. In general, 200 points were generated on the Pareto front as a result of solving the model.
