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Empirische Produktionsfunktion betriebswirtschaftlicher Forschung: Eine Analyse der Daten des Centrums für Hochschulentwicklung

Author

Listed:
  • Harald Dyckhoff

    (RWTH Aachen University)

  • Sylvia Rassenhövel

    (RWTH Aachen University)

  • Kirsten Sandfort

    (RWTH Aachen University)

Abstract

Zusammenfassung Das Centrum für Hochschulentwicklung (CHE) erhebt umfassend Daten in Forschung und Lehre, um mehr Transparenz über die Leistungen der Hochschulen zu schaffen. Diese Daten repräsentieren aus produktionstheoretischer Sicht eine Technologie, deren effizienter Rand eine empirische Produktionsfunktion darstellt. Der Beitrag analysiert exemplarisch die Daten des CHE-Forschungsrankings 2005 für die Betriebswirtschaftslehre. Ein in seiner Deutlichkeit überraschender empirischer Befund besagt, dass im Bereich normaler Betriebsgrößen betriebswirtschaftlicher Fachbereiche von etwa 20 bis 50 Wissenschaftlern oder 8 bis 16 Professuren nahezu konstante Forschungsskalenerträge vorliegen, wenn diese gemäß CHE durch Publikationspunkte, Promotionshäufigkeiten und Drittmittelausgaben gemessen werden.

Suggested Citation

  • Harald Dyckhoff & Sylvia Rassenhövel & Kirsten Sandfort, 2009. "Empirische Produktionsfunktion betriebswirtschaftlicher Forschung: Eine Analyse der Daten des Centrums für Hochschulentwicklung," Schmalenbach Journal of Business Research, Springer, vol. 61(1), pages 22-56, February.
    • Handle: RePEc:spr:sjobre:v:61:y:2009:i:1:d:10.1007_bf03371737
    DOI: 10.1007/BF03371737
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    References listed on IDEAS

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    1. Stefan Hornbostel, 2001. "Third party funding of German universities. An indicator of research activity?," Scientometrics, Springer;Akadémiai Kiadó, vol. 50(3), pages 523-537, March.
    2. 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.
    3. John C. Panzar & Robert D. Willig, 1977. "Economies of Scale in Multi-Output Production," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 91(3), pages 481-493.
    4. 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.
    5. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2006. "Introduction to Data Envelopment Analysis and Its Uses," Springer Books, Springer, number 978-0-387-29122-2, June.
    6. Wade D. Cook & Joe Zhu, 2005. "Modeling Performance Measurement," Springer Books, Springer, number 978-0-387-24138-8, June.
    7. Harald Dyckhoff & Katrin Allen, 1999. "Theoretische Begründung einer Effizienzanalyse mittels Data Envelopment Analysis (DEA)," Schmalenbach Journal of Business Research, Springer, vol. 51(5), pages 411-436, May.
    8. 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.
    9. Fandel, Gunter, 2007. "On the performance of universities in North Rhine-Westphalia, Germany: Government's redistribution of funds judged using DEA efficiency measures," European Journal of Operational Research, Elsevier, vol. 176(1), pages 521-533, January.
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    Cited by:

    1. Zharova, Alona & Härdle, Wolfgang Karl & Lessmann, Stefan, 2023. "Data-driven support for policy and decision-making in university research management: A case study from Germany," European Journal of Operational Research, Elsevier, vol. 308(1), pages 353-368.
    2. Jost Sieweke & Johannes Muck & Stefan Süß & Justus Haucap, 2014. "Forschungsevaluation an Universitäten — Ergebnisse einer explorativen Studie rechtsund wirtschaftswissenschaftlicher Fakultäten," Schmalenbach Journal of Business Research, Springer, vol. 66(4), pages 274-305, June.

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    More about this item

    Keywords

    C61; D24; L25;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance

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