IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v62y2011i12d10.1057_jors.2010.177.html
   My bibliography  Save this article

The training institution efficiency of the semiconductor institute programme in Taiwan—application of spatiotemporal ICA with DEA approach

Author

Listed:
  • L-J Kao

    (National Taipei University of Technology)

  • C-C Lu

    (Graduate Institute of Industrial and Business Management, National Taipei University of Technology)

  • C-C Chiu

    (National Taipei University of Technology)

Abstract

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.

Suggested Citation

  • L-J Kao & C-C Lu & C-C Chiu, 2011. "The training institution efficiency of the semiconductor institute programme in Taiwan—application of spatiotemporal ICA with DEA approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(12), pages 2162-2172, December.
    • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:12:d:10.1057_jors.2010.177
    DOI: 10.1057/jors.2010.177
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/jors.2010.177
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/jors.2010.177?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    2. Ancarani, A. & Di Mauro, C. & Giammanco, M.D., 2009. "The impact of managerial and organizational aspects on hospital wards' efficiency: Evidence from a case study," European Journal of Operational Research, Elsevier, vol. 194(1), pages 280-293, April.
    3. N Adler & B Golany, 2002. "Including principal component weights to improve discrimination in data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(9), pages 985-991, September.
    4. Dyson, R. G. & Allen, R. & Camanho, A. S. & Podinovski, V. V. & Sarrico, C. S. & Shale, E. A., 2001. "Pitfalls and protocols in DEA," European Journal of Operational Research, Elsevier, vol. 132(2), pages 245-259, July.
    5. Kao, Chiang & Liu, Shiang-Tai, 2009. "Stochastic data envelopment analysis in measuring the efficiency of Taiwan commercial banks," European Journal of Operational Research, Elsevier, vol. 196(1), pages 312-322, July.
    6. Adler, Nicole & Yazhemsky, Ekaterina, 2010. "Improving discrimination in data envelopment analysis: PCA-DEA or variable reduction," European Journal of Operational Research, Elsevier, vol. 202(1), pages 273-284, April.
    7. Ray, Subhash C. & Jeon, Yongil, 2008. "Reputation and efficiency: A non-parametric assessment of America's top-rated MBA programs," European Journal of Operational Research, Elsevier, vol. 189(1), pages 245-268, August.
    8. Darryl Holden, 2004. "Testing the Normality Assumption in the Tobit Model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(5), pages 521-532.
    9. Adler, Nicole & Golany, Boaz, 2001. "Evaluation of deregulated airline networks using data envelopment analysis combined with principal component analysis with an application to Western Europe," European Journal of Operational Research, Elsevier, vol. 132(2), pages 260-273, July.
    10. W.W. Cooper & Timothy W. Ruefli & Honghui Deng & Jun Wu & Zhongyi Zhang, 2008. "Are state-owned banks less efficient? A long- vs. short-run Data Envelopment Analysis of Chinese banks," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 3(5), pages 533-556.
    11. Sahoo, Biresh K. & Tone, Kaoru, 2009. "Decomposing capacity utilization in data envelopment analysis: An application to banks in India," European Journal of Operational Research, Elsevier, vol. 195(2), pages 575-594, June.
    12. Chib, Siddhartha, 1992. "Bayes inference in the Tobit censored regression model," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 79-99.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kunsch, Pierre L. & Ishizaka, Alessio, 2018. "Multiple-criteria performance ranking based on profile distributions: An application to university research evaluations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 154(C), pages 48-64.
    2. Tien-Hsiang Chang & Ling-Jing Kao & Tsung-Yin Ou & Hsin-Pin Fu, 2018. "A Hybrid Method to Measure the Operational Performance of Fast Food Chain Stores," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(04), pages 1269-1298, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kao, Ling-Jing & Lu, Chi-Jie & Chiu, Chih-Chou, 2011. "Efficiency measurement using independent component analysis and data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 210(2), pages 310-317, April.
    2. Anna Łozowicka & Bartłomiej Lach, 2022. "CI-DEA: A Way to Improve the Discriminatory Power of DEA—Using the Example of the Efficiency Assessment of the Digitalization in the Life of the Generation 50+," Sustainability, MDPI, vol. 14(6), pages 1-22, March.
    3. Bojiang Yang & Youliang Zhang & Hongjun Zhang & Rui Zhang & Baoyu Xu, 2016. "Factor-specific Malmquist productivity index based on common weights DEA," Operational Research, Springer, vol. 16(1), pages 51-70, April.
    4. Nataraja, Niranjan R. & Johnson, Andrew L., 2011. "Guidelines for using variable selection techniques in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 215(3), pages 662-669, December.
    5. Qiwei Xie & Yuanyuan Li & Lizheng Wang & Chao Liu, 2018. "Improving discrimination in data envelopment analysis without losing information based on Renyi’s entropy," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(4), pages 1053-1068, December.
    6. Villanueva-Cantillo, Jeyms & Munoz-Marquez, Manuel, 2021. "Methodology for calculating critical values of relevance measures in variable selection methods in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 290(2), pages 657-670.
    7. Vincenzo Patrizii & Anna Pettini & Giuliano Resce, 2017. "The Cost of Well-Being," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 133(3), pages 985-1010, September.
    8. Eskelinen, Juha, 2017. "Comparison of variable selection techniques for data envelopment analysis in a retail bank," European Journal of Operational Research, Elsevier, vol. 259(2), pages 778-788.
    9. Adler, Nicole & Yazhemsky, Ekaterina, 2010. "Improving discrimination in data envelopment analysis: PCA-DEA or variable reduction," European Journal of Operational Research, Elsevier, vol. 202(1), pages 273-284, April.
    10. Bruno Ricca & Massimiliano Ferrara & Salvatore Loprevite, 2023. "Searching for an effective accounting-based score of firm performance: a comparative study between different synthesis techniques," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3575-3602, August.
    11. Lin, Tzu-Yu & Chiu, Sheng-Hsiung, 2013. "Using independent component analysis and network DEA to improve bank performance evaluation," Economic Modelling, Elsevier, vol. 32(C), pages 608-616.
    12. Wilson, Paul W., 2018. "Dimension reduction in nonparametric models of production," European Journal of Operational Research, Elsevier, vol. 267(1), pages 349-367.
    13. Andrejić, Milan & Bojović, Nebojša & Kilibarda, Milorad, 2016. "A framework for measuring transport efficiency in distribution centers," Transport Policy, Elsevier, vol. 45(C), pages 99-106.
    14. Zelenyuk, Valentin, 2020. "Aggregation of inputs and outputs prior to Data Envelopment Analysis under big data," European Journal of Operational Research, Elsevier, vol. 282(1), pages 172-187.
    15. Wolff, François-Charles, 2014. "Lift ticket prices and quality in French ski resorts: Insights from a non-parametric analysis," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1155-1164.
    16. François-Charles Wolff, 2014. "Lift ticket prices and quality in French ski resorts: Insights from a non-parametric analysis," Working Papers hal-00952999, HAL.
    17. Jamal Ouenniche & Skarleth Carrales, 2018. "Assessing efficiency profiles of UK commercial banks: a DEA analysis with regression-based feedback," Annals of Operations Research, Springer, vol. 266(1), pages 551-587, July.
    18. Adler, Nicole & Raveh, Adi, 2008. "Presenting DEA graphically," Omega, Elsevier, vol. 36(5), pages 715-729, October.
    19. Valentin Zelenyuk, 2019. "Data Envelopment Analysis and Business Analytics: The Big Data Challenges and Some Solutions," CEPA Working Papers Series WP072019, School of Economics, University of Queensland, Australia.
    20. Toloo, Mehdi & Tone, Kaoru & Izadikhah, Mohammad, 2023. "Selecting slacks-based data envelopment analysis models," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1302-1318.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:jorsoc:v:62:y:2011:i:12:d:10.1057_jors.2010.177. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.