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Identifying the Most Research Intensive Faculties of Business in Australia: A Multidimensional Approach

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Abstract

There is a growing policy focus in Australian higher education on quantitative research performance assessment. However, most of the analysis has addressed aggregate performance at the institutional level, an approach inconsistent with recent policy emphasis on diversity among universities, and one that ignores performance variations across disciplines. Using averaged and all available data for 2000-2004, cluster analysis is used to classify Australian Commerce Faculties into groups that exhibit similar research performance, measured by publication, PhD completion and secured competitive research grant funding. We also use factor analysis to generate full-multidimensional rankings within the resulting two or three clusters. It is found that in terms of total research output, with the exception of Adelaide all the Go8 members plus UTS and Griffith always belong to “Clusters A”. However, when research performance is expressed in per academic staff terms, an additional eleven universities join this same cluster. Our results additionally show that eight Australian faculties of Commerce not only possess low total research output but their per capita performance is also poor.

Suggested Citation

  • Valadkhani, Abbas & Ville, Simon, 2008. "Identifying the Most Research Intensive Faculties of Business in Australia: A Multidimensional Approach," Economics Working Papers wp08-03, School of Economics, University of Wollongong, NSW, Australia.
  • Handle: RePEc:uow:depec1:wp08-03
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    File URL: http://www.uow.edu.au/content/groups/public/@web/@commerce/@econ/documents/doc/uow042312.pdf
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    References listed on IDEAS

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    1. Ross Williams, 1998. "Funding Australian Universities," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 31(2), pages 148-156.
    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. Abbott, M. & Doucouliagos, C., 2003. "The efficiency of Australian universities: a data envelopment analysis," Economics of Education Review, Elsevier, vol. 22(1), pages 89-97, February.
    4. J. C. Glass & D. G. McKillop & G. O'Rourke, 2002. "Evaluating the productive performance of UK universities as cost-constrained revenue maximizers: an empirical analysis," Applied Economics, Taylor & Francis Journals, vol. 34(9), pages 1097-1108.
    5. Jerry G. Thursby, 2000. "What Do We Say about Ourselves and What Does It Mean? Yet Another Look at Economics Department Research," Journal of Economic Literature, American Economic Association, vol. 38(2), pages 383-404, June.
    6. Johnes, Jill & Johnes, Geraint, 1995. "Research funding and performance in U.K. University Departments of Economics: A frontier analysis," Economics of Education Review, Elsevier, vol. 14(3), pages 301-314, September.
    7. Abbas Valadkhani & Simon Ville, 2009. "Discipline-specific forecasting of research output in Australian universities," Applied Economics Letters, Taylor & Francis Journals, vol. 16(18), pages 1875-1880.
    8. Joseph Macri & Dipendra Sinha, 2006. "Rankings Methodology for International Comparisons of Institutions and Individuals: an Application to Economics in Australia and New Zealand," Journal of Economic Surveys, Wiley Blackwell, vol. 20(1), pages 111-156, February.
    9. Joan R. Rodgers & Abbas Valadkhani, 2006. "A Multidimensional Ranking of Australian Economics Departments," The Economic Record, The Economic Society of Australia, vol. 82(256), pages 30-43, March.
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    More about this item

    Keywords

    Faculties of Business; Australian higher education; Cluster analysis; Factor analysis;

    JEL classification:

    • A11 - General Economics and Teaching - - General Economics - - - Role of Economics; Role of Economists
    • A19 - General Economics and Teaching - - General Economics - - - Other
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • I29 - Health, Education, and Welfare - - Education - - - Other

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