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2013 | 3 | 1 | 191-202

Article title

Efficiency Analysis of IT investment in Polish Banks (1998-2008): An Application of Malmquist Productivity Index

Content

Title variants

Languages of publication

EN

Abstracts

EN
This paper empirically researches the productivity changes of Polish banking industry during period of 1998-2008, by applying a non-parametric Malmquist Productivity Index (MPI) method. This methodology is well establishing approach in exploring performance measures, productivity growth, technological change and technical efficiency. In specifying the variables input-output, an asset/profit approach has been is chosen, which simplify the potential correlation between financial results of the bank with the investment level in IT. Results indicate that during the study period, over eleven years Polish banking industry experienced steady technological progress. All 17 biggest banks chosen for the study, which represent 85% of the asset base, have maintained overall productivity gain. Within this group analysis shows no significant difference linked to the scale/size of the banks. Local inefficiency observed does not seem to have any systematic pattern.

Year

Volume

3

Issue

1

Pages

191-202

Physical description

Contributors

  • M.Sc., Doctoral Student, Department of Business Informatics, Faculty of Management, University of Gdańsk, BGŻ Bank,
  • Prof., Ph.D., hab., Department of Business Informatics, Faculty of Management, University of Gdańsk, Poland,

References

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  • 3. Asmild M., Paradi J.C., Aggarwall V., Schaffnit C. (2004), Combining DEA window analysis with the Malmquist Index approach in a study of the Canadian banking industry. Journal of Productivity Analysis.
  • 4. Beccalli E. (2007), Does IT investments improve bank performance? Evidence from Europe, [in:] Journal of Banking and Finance, Vol. 31, p. 2205-2230.
  • 5. Berger A.N., Humphrey D.B. (1992), Measurement and efficiency issues in commercial banking, [in:] Z. Griliches (ed.), Output Measurement in the Service Sectors, Chicago.
  • 6. Chen Y. and Zhu J. (2004), Measuring information technology's indirect impact on firm performance, Information Technology and Management, Vol. 5, p. 9-22.
  • 7. Cooper W.W., Seiford L.M., Tone K. (2007), Data Envelopment Analysis. A Comprehensive Text with Models, Applications, New York.
  • 8. Cooper W.W., Seiford L.M., Zhu J. (2004), Handbook on Data Envelopment Analysis, Boston.
  • 9. Kisielewska M., Guzowska M., Nellis J., Zarzecki D. (2007), Polish banking industry efficiency: A DEA window analysis approach, Cranfield School of Management.
  • 10. Monitor IT. (2008), DiS No. 20 ISSN 1429-2785.Sustainable Development, BIJIT.
  • 12. Guzika B. (2009), Podstawowe modele DEA w badaniu efektywności gospodarczej i społecznej, WUE, Poznań.
  • 13. Farrell M.J. (1957), The Measurement of Productive Efficiency, The Journal of the Royal Statistical Society A, Vol. 120, No. 3.
  • 14. Kopczewski T. (2000), Efektywność technologiczna i kosztowa banków komercyjnych w Polsce w latach 1997-2000, cz. I, [in:] Materiały i Studia NBP 113.
  • 15. Kopczewski T. (2001), Efektywność technologiczna i kosztowa banków komercyjnych w Polsce w latach 1997-2000, cz. II, [in:] Materiały i Studia NBP 135.
  • 16. User’s Guide to DEA – Solver Pro, Ver. 7.0.
  • 17. Wang C. H., Gopal R., Zionts S. (1997), Use of data envelopment analysis in assessing information technology impact on firm performance, [in:] Annuals of Operations Research, Vol. 73, No. 1-4, p. 191-213.

Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.desklight-c306b983-9774-4aae-90f5-df26adb9b81c
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