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The training institution efficiency of the semiconductor institute programme in Taiwan / L-J. Kao in Journal of the operational research society (JORS), Vol. 62 N° 12 (Décembre 2011) 

Public ISBD [article]  in Journal of the operational research society (JORS) > Vol. 62 N° 12 (Décembre 2011) . - pp. 2162–2172 Titre : The training institution efficiency of the semiconductor institute programme in Taiwan : application of spatiotemporal ICA with DEA approach Type de document : texte imprimé Auteurs : L-J. Kao, Auteur ; C-C. Lu, Auteur ; C-C. Chiu, Auteur Année de publication : 2011 Article en page(s) : pp. 2162–2172 Note générale : Recherche opérationnelle Langues : Anglais (eng) Mots-clés : Data  envelopment  analysis  Spatiotemporal  independent  component  analysis  Efficiency  measurement  Training  institutions  Tobit–Bayes  regression Index. décimale : 001.424 Résumé : Despite the fact that Taiwan’s high-tech industry has gradually secured a leading position in the world, enterprises in Taiwan have striven to strengthen their technical advancement by providing employees with various internal or external training programmes. These institutional training programmes are designed to sustain competitive advantage, enhance the quality of manpower and improve operational efficiency. Much literature assesses the efficiency of an internal training programme that is initiated by a firm, but only a little literature studies the efficiency of an external training programme that is led by a government. Various efficiency measurement tools, such as conventional statistical methods and non-parametric methods, have been successfully developed in the literature. Among these tools, the data envelopment analysis (DEA) approach is one of the most widely discussed. However, the DEA's capability to discriminate efficient decision-making units from inefficient decision-making units requires much improvement (Adler and Yazhemsky). In this paper, a two-stage approach of integrating spatiotemporal independent component analysis (stICA) and DEA is developed for efficiency measurement. stICA is used to search for latent source signals where no relevant signal mixture mechanisms are available; and DEA is used to measure the relative efficiencies of decision-making units (DMUs). We suggest using stICA first to extract the input variables for generating independent components (IC), then selecting the ICs representing the independent sources of input variables, and finally inputting the selected ICs as new variables in the DEA model. To find the effects of environmental variables on the estimated efficiency scores, the Tobit–Bayes (censored) regression is applied. A simulated dataset and the training institution dataset provided by the Semiconductor Institute in Taiwan is used for analysis. The empirical result shows that the proposed method can not only separate performance differences between the training institutions but also improve the discriminatory capability of the DEA's efficiency measurement. The study results can serve as a reference for training institutions wishing to enhance their training efficiency. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v62/n12/abs/jors2010177a.html [article] The training institution efficiency of the semiconductor institute programme in Taiwan : application of spatiotemporal ICA with DEA approach [texte imprimé] / L-J. Kao, Auteur ; C-C. Lu, Auteur ; C-C. Chiu, Auteur . - 2011 . - pp. 2162–2172. Recherche opérationnelle Langues : Anglais (eng) in Journal of the operational research society (JORS) > Vol. 62 N° 12 (Décembre 2011) . - pp. 2162–2172 Mots-clés : Data  envelopment  analysis  Spatiotemporal  independent  component  analysis  Efficiency  measurement  Training  institutions  Tobit–Bayes  regression Index. décimale : 001.424 Résumé : Despite the fact that Taiwan’s high-tech industry has gradually secured a leading position in the world, enterprises in Taiwan have striven to strengthen their technical advancement by providing employees with various internal or external training programmes. These institutional training programmes are designed to sustain competitive advantage, enhance the quality of manpower and improve operational efficiency. Much literature assesses the efficiency of an internal training programme that is initiated by a firm, but only a little literature studies the efficiency of an external training programme that is led by a government. Various efficiency measurement tools, such as conventional statistical methods and non-parametric methods, have been successfully developed in the literature. Among these tools, the data envelopment analysis (DEA) approach is one of the most widely discussed. However, the DEA's capability to discriminate efficient decision-making units from inefficient decision-making units requires much improvement (Adler and Yazhemsky). In this paper, a two-stage approach of integrating spatiotemporal independent component analysis (stICA) and DEA is developed for efficiency measurement. stICA is used to search for latent source signals where no relevant signal mixture mechanisms are available; and DEA is used to measure the relative efficiencies of decision-making units (DMUs). We suggest using stICA first to extract the input variables for generating independent components (IC), then selecting the ICs representing the independent sources of input variables, and finally inputting the selected ICs as new variables in the DEA model. To find the effects of environmental variables on the estimated efficiency scores, the Tobit–Bayes (censored) regression is applied. A simulated dataset and the training institution dataset provided by the Semiconductor Institute in Taiwan is used for analysis. The empirical result shows that the proposed method can not only separate performance differences between the training institutions but also improve the discriminatory capability of the DEA's efficiency measurement. The study results can serve as a reference for training institutions wishing to enhance their training efficiency. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v62/n12/abs/jors2010177a.html