Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 9348-9358 |
| Number of pages | 11 |
| Journal | Expert Systems with Applications |
| Volume | 39 |
| Issue number | 10 |
| DOIs | |
| Publication status | Published - Aug 2012 |
| Externally published | Yes |
Keywords
- Data Envelopment Analysis (DEA)
- Genetic Algorithm (GA)
- Knowledge Management (KM)
- Monte Carlo simulation
- Performance measurement
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