A Markov-based assessment of Knowledge Management Models at Universities

There is a growing interest in knowledge management (KM) within academia and universities. Despite the high level of understanding and appreciation for Knowledge Management Models (KMMs) and their relevance to knowledge at universities, the role of KM models and approaches is rarely investigated. This study examines and ranks factors associated with KMM at universities in Tehran, Iran.

This study employs a mixed method approach, combining qualitative techniques (a three-phase Delphi method) and quantitative methods (Best-Worst method [BWM] and MARKOV). MARKOV is applied here as an integrated Machine Learning-Markov approach to enhance the performance of KM implementation at universities. The relevant factors are processed through a Markov-based model, enabling the identification of future KMM factors. BWM is utilized to determine first-stage weights, and the Markovian weighted average of KMM factors is considered the optimal result. A questionnaire with open-ended questions is employed to collect accurate data, with thirty experts selected to participate in the survey.

Nine factors were extracted from the literature review and Delphi method. The Markov model is employed to trace the priorities of KMM factors, serving as a predictive tool for modeling KM factors. The final weights of the factors are closely ranked as follows: (1) Information Technology; (2) applying knowledge; (3) structure; (4) Measuring knowledge; (5) culture; (6) Sharing knowledge; (7) leadership style; (8) Maintaining knowledge; and (9) Collecting knowledge.

Originality: 

To the best of the authors' knowledge, this is the first assessment and prediction of KMM factor usage at universities in Iran, an important country in the Middle East. The authors introduce new and customized factors for KMM. Predicting behaviors over time assists managers and university decision-makers in recognizing the fundamental dimensions of KM success. This study contributes by identifying and predicting the behaviors of KM context factors at universities in Iran.