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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| Vydáno v: | Expert systems with applications Ročník 39; číslo 10; s. 9348 - 9358 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.08.2012
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | ► 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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| AbstractList | 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. ► 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. |
| Author | Kuah, Chuen Tse Wong, Kuan Yew Wong, Wai Peng |
| Author_xml | – sequence: 1 givenname: Chuen Tse surname: Kuah fullname: Kuah, Chuen Tse organization: Department of Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Malaysia – sequence: 2 givenname: Kuan Yew surname: Wong fullname: Wong, Kuan Yew email: wongky@mail.fkm.utm.my, kuanyewwong@yahoo.com organization: Department of Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Malaysia – sequence: 3 givenname: Wai Peng surname: Wong fullname: Wong, Wai Peng organization: School of Management, Universiti Sains Malaysia, 11800 Penang, Malaysia |
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| CitedBy_id | crossref_primary_10_1016_j_eswa_2014_12_033 crossref_primary_10_1002_pra2_272 crossref_primary_10_1177_0266382115587852 crossref_primary_10_1007_s12652_018_0814_3 crossref_primary_10_1016_j_eap_2021_01_013 crossref_primary_10_1007_s13132_021_00758_1 crossref_primary_10_1057_jors_2013_157 crossref_primary_10_1016_j_eswa_2020_113422 crossref_primary_10_1080_17509653_2016_1249425 crossref_primary_10_1108_JKM_10_2014_0431 crossref_primary_10_1108_JKM_01_2021_0077 crossref_primary_10_3390_info7020029 crossref_primary_10_1177_0266666913513278 crossref_primary_10_1007_s10799_018_0292_5 crossref_primary_10_1108_JKM_11_2014_0487 crossref_primary_10_1080_03610926_2021_1879859 crossref_primary_10_1080_00207543_2014_935828 crossref_primary_10_3390_su15010068 crossref_primary_10_1108_JM2_02_2014_0013 crossref_primary_10_1016_j_techsoc_2021_101691 crossref_primary_10_1108_JKM_08_2018_0497 |
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| Keywords | Knowledge Management (KM) Genetic Algorithm (GA) Data Envelopment Analysis (DEA) Performance measurement Monte Carlo simulation |
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| Snippet | ► A Monte Carlo Data Envelopment Analysis model is proposed to measure the stochastic performance of Knowledge Management. ► Genetic Algorithm is used to... The paper targets to devise a genuine Knowledge Management (KM) performance measurement model in a stochastic setting based on Data Envelopment Analysis (DEA),... |
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| SubjectTerms | 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 |
| Title | Monte Carlo Data Envelopment Analysis with Genetic Algorithm for Knowledge Management performance measurement |
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