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A partial adjustment approach to evaluating and measuring the business value of information technology

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  • Lin, Winston T.
  • Chuang, Chia-Hung
  • Choi, Jeong Hoon
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    Abstract

    In this paper we develop a partial adjustment approach, based on the theory of partial adjustment, to assess the value of information technology. We also propose a performance measure derived from the approach. The constrained (structural or implicit) and unconstrained (non-structural or explicit) models derived from the approach are fitted into a set of panel data at the country level, covering the period from 1993 to 2006 for a sample of 12 countries including the G7 nations; and are estimated by the seemingly unrelated regression estimation. The results produced by the models are robust with respect to the choice of the production function representing the true (desired or maximum) output and challenge the widely held belief that the productivity paradox has disappeared and the view that the productivity paradox exists in developing economies only. The proposed approach compares favorably with the time-varying stochastic production frontier approach since it is easier and simpler to apply. But, unlike the non-parametric data envelopment analysis approach, it is parametric.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Production Economics.

    Volume (Year): 127 (2010)
    Issue (Month): 1 (September)
    Pages: 158-172

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    Handle: RePEc:eee:proeco:v:127:y:2010:i:1:p:158-172

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    Web page: http://www.elsevier.com/locate/ijpe

    Related research

    Keywords: Theory of partial adjustment Speeds of adjustment Data envelopment analysis Productivity paradox Seemingly unrelated regressions Constrained and unconstrained models;

    References

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    1. Rajiv D. Banker & Richard C. Morey, 1986. "The Use of Categorical Variables in Data Envelopment Analysis," Management Science, INFORMS, vol. 32(12), pages 1613-1627, December.
    2. Winston T. Lin, 2005. "Currency forecasting based on an error components-seemingly unrelated nonlinear regression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(8), pages 593-605.
    3. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    4. Sengupta, Jati K., 1999. "A dynamic efficiency model using data envelopment analysis," International Journal of Production Economics, Elsevier, vol. 62(3), pages 209-218, September.
    5. Sanjeev Dewan & Kenneth L. Kraemer, 2000. "Information Technology and Productivity: Evidence from Country-Level Data," Management Science, INFORMS, vol. 46(4), pages 548-562, April.
    6. Lin, Winston T. & Chen, Yueh H. & Chatov, Robert, 1987. "The demand for natural gas, electricity and heating oil in the United States," Resources and Energy, Elsevier, vol. 9(3), pages 233-258, October.
    7. Winston Lin & Yueh Chen, 1998. "Forecasting foreign exchange rates with an intrinsically nonlinear dynamic speed of adjustment model," Applied Economics, Taylor & Francis Journals, vol. 30(3), pages 295-312.
    8. Lin, Winston T, 1986. "Analysis of Lumber and Pulpwood Production in a Partial Adjustment Model with Dynamic and Variable Speeds of Adjustment," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(3), pages 305-16, July.
    9. Brynjolfsson, Erik. & Hitt, Lorin M., 1995. "Paradox lost? : firm-level evidence on the returns to information systems spending," Working papers 3786-95., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    10. Banker, Rajiv D., 1984. "Estimating most productive scale size using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 17(1), pages 35-44, July.
    11. Lin, Winston T., 1988. "The dynamic behavior of production in the extractive industry," Resources and Energy, Elsevier, vol. 10(3), pages 225-246, September.
    12. Jati Sengupta, 1995. "Estimating efficiency by cost frontiers: a comparison of parametric and nonparametric methods," Applied Economics Letters, Taylor & Francis Journals, vol. 2(4), pages 86-90.
    13. Park, Soo-Uk & Lesourd, Jean-Baptiste, 2000. "The efficiency of conventional fuel power plants in South Korea: A comparison of parametric and non-parametric approaches," International Journal of Production Economics, Elsevier, vol. 63(1), pages 59-67, January.
    14. Bresnahan, Timothy F, 1986. "Measuring the Spillovers from Technical Advance: Mainframe Computers inFinancial Services," American Economic Review, American Economic Association, vol. 76(4), pages 742-55, September.
    15. Chen, Yueh H. & Lin, Winston T., 2009. "Analyzing the relationships between information technology, inputs substitution and national characteristics based on CES stochastic frontier production models," International Journal of Production Economics, Elsevier, vol. 120(2), pages 552-569, August.
    16. William W. Cooper & Kyung Sam Park & Gang Yu, 1999. "IDEA and AR-IDEA: Models for Dealing with Imprecise Data in DEA," Management Science, INFORMS, vol. 45(4), pages 597-607, April.
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    Cited by:
    1. Lin, Winston T. & Chuang, Chia-Hung, 2013. "Investigating and comparing the dynamic patterns of the business value of information technology over time," European Journal of Operational Research, Elsevier, vol. 228(1), pages 249-261.
    2. Lin, Winston T. & Kao, Ta-Wei (Daniel), 2014. "The partial adjustment valuation approach with dynamic and variable speeds of adjustment to evaluating and measuring the business value of information technology," European Journal of Operational Research, Elsevier, vol. 238(1), pages 208-220.
    3. Lin, Winston T. & Chiang, Chung-Yean, 2011. "The impacts of country characteristics upon the value of information technology as measured by productive efficiency," International Journal of Production Economics, Elsevier, vol. 132(1), pages 13-33, July.

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