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Measurement of multiperiod aggregative efficiency

  • Park, K. Sam
  • Park, Kwangtae
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    This article proposes a new method for measuring an aggregative efficiency of multiple period production systems. Every organization or firm generates a time series of data that represent its performances in the resource utilization and output production over multiple periods, and often desires an aggregated measure of efficiency for several periods of interest. Data envelopment analysis (DEA) has become an accepted and well-known approach to evaluating efficiency performance in a wide range of cases. However, most of the DEA studies have dealt primarily with ways to gauge the efficiency of production in only a single period so this efficiency reflects the insufficient or partial performance of multiple period productions. The new method is developed through extensions of the concept of Debreu-Farrell technical efficiency and is applied to evaluating the efficiency of cable TV service units with 3-year data.

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    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 193 (2009)
    Issue (Month): 2 (March)
    Pages: 567-580

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    Handle: RePEc:eee:ejores:v:193:y:2009:i:2:p:567-580
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    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. Peter Bogetoft & Joseph M. Tama & Jørgen Tind, 2000. "Convex Input and Output Projections of Nonconvex Production Possibility Sets," Management Science, INFORMS, vol. 46(6), pages 858-869, June.
    3. Thompson, Russell G. & Dharmapala, P. S. & Thrall, Robert M., 1995. "Linked-cone DEA profit ratios and technical efficiency with application to Illinois coal mines," International Journal of Production Economics, Elsevier, vol. 39(1-2), pages 99-115, April.
    4. Sueyoshi, Toshiyuki & Sekitani, Kazuyuki, 2007. "The measurement of returns to scale under a simultaneous occurrence of multiple solutions in a reference set and a supporting hyperplane," European Journal of Operational Research, Elsevier, vol. 181(2), pages 549-570, September.
    5. Sueyoshi, Toshiyuki & Sekitani, Kazuyuki, 2007. "Measurement of returns to scale using a non-radial DEA model: A range-adjusted measure approach," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1918-1946, February.
    6. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity," Econometrica, Econometric Society, vol. 50(6), pages 1393-1414, November.
    7. 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.
    8. Charnes, A. & Cooper, W. W. & Huang, Z. M. & Sun, D. B., 1990. "Polyhedral Cone-Ratio DEA Models with an illustrative application to large commercial banks," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 73-91.
    9. Sueyoshi, Toshiyuki & Sekitani, Kazuyuki, 2005. "Returns to scale in dynamic DEA," European Journal of Operational Research, Elsevier, vol. 161(2), pages 536-544, March.
    10. Maria Silva Portela & Pedro Borges & Emmanuel Thanassoulis, 2003. "Finding Closest Targets in Non-Oriented DEA Models: The Case of Convex and Non-Convex Technologies," Journal of Productivity Analysis, Springer, vol. 19(2), pages 251-269, April.
    11. Nemoto, Jiro & Goto, Mika, 1999. "Dynamic data envelopment analysis: modeling intertemporal behavior of a firm in the presence of productive inefficiencies," Economics Letters, Elsevier, vol. 64(1), pages 51-56, July.
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