Monte Carlo Data Envelopment Analysis with Genetic Algorithm for Knowledge Management performance measurement

► A Monte Carlo Data Envelopment Analysis model is proposed to measure the stochastic performance of Knowledge Management. ► Genetic Algorithm is used to determine the appropriate data collection budget allocation for stochastic variables. ► The proposed model is better than a deterministic approach...

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Bibliographic Details
Published in:Expert systems with applications Vol. 39; no. 10; pp. 9348 - 9358
Main Authors: Kuah, Chuen Tse, Wong, Kuan Yew, Wong, Wai Peng
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.08.2012
Subjects:
Accuracy
Computer simulation
Data Envelopment Analysis (DEA)
Genetic Algorithm (GA)
Genetic algorithms
Knowledge management
Knowledge Management (KM)
Monte Carlo methods
Monte Carlo simulation
Operations research
Performance measurement
Strategy
Knowledge Management (KM)
Genetic Algorithm (GA)
Data Envelopment Analysis (DEA)
Performance measurement
Monte Carlo simulation
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:► A Monte Carlo Data Envelopment Analysis model is proposed to measure the stochastic performance of Knowledge Management. ► Genetic Algorithm is used to determine the appropriate data collection budget allocation for stochastic variables. ► The proposed model is better than a deterministic approach in evaluating the efficiency of Knowledge Management. ► With Genetic Algorithm, the accuracy of the proposed model has been greatly improved. The paper targets to devise a genuine Knowledge Management (KM) performance measurement model in a stochastic setting based on Data Envelopment Analysis (DEA), Monte Carlo simulation and Genetic Algorithm (GA). The proposed model evaluates KM using a set of proxy measures correlated with the major KM processes. Data Collection Budget Allocation (DCBA) that maximizes the model accuracy is determined using GA. Additional data are generated and analyzed using a Monte-Carlo-enhanced DEA model to obtain the overall KM efficiency and KM processes’ efficiency scores. An application of the model has been carried out to evaluate KM performance in higher educational institutions. It is found that with GA, the accuracy of the model has been greatly improved. Lastly, comparing with a conventional deterministic DEA model, the results from the proposed model would be more useful for managers to determine future strategies to improve their KM.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2012.02.140