An empirical study to measure the relative efficiency and strategic planning using BSC-DEA and DEMATEL

Abstract: Performance evaluation is one of manager & apos; s main concerns in today competitive world, which covers all aspects and dimensions of organization and it is adequately flexible and measurable. So, the necessity of performance evaluation application for organizations where their intangible assets are higher than tangible ones, such as educational institutions, is more obviously observed. Balanced scorecard (BSC) is discussed by the aim of promoting manager & apos; s decision making and directing their attention toward extensive operational vision of organization compared to traditional measurement systems, which only include the financial measures. However, BSC is a qualitative approach and has some disadvantages and its integration by other quantitative techniques such as data envelopment analysis makes it more efficient. The proposed model of this paper uses DEMATEL technique as part of BSC-DEA model to empower strategic planning. The proposed model of this paper is applied for 10 zone university branches of Islamic Azad universities to provide an appropriate road map.

How to cite this paper

 Hemati, M., Danaei, A & Shahhosseini, M. (2012). An empirical study to measure the relative efficiency and strategic planning using BSC-DEA and DEMATEL.Management Science Letters , 2(4), 1109-1122.

Refrences

Achterbergh, J., Beeres, R., & Vriens, D. (2003). Does the balanced scorecard support organizational viability?, Kybernetes, 32, 1387–1404.

Amado, C. A., Santos ,S.P., & Marques, P.M.(2012). Integrating the Data Envelopment Analysis and the Balanced Scorecard approaches for enhanced performance assessment. Omega, 40(3), 390–403.

Anderson, P., & Peterson, N. C. (1993). A procedure for ranking efficient unit in data envelopment analysis. Management Science, 39, 1261-1264.

Asosheh, A., Nalchigar, S., & Jamporazmey, M. (2010). Information technology project evaluation: An integrated data envelopment analysis and balanced scorecard approach. Expert Systems with Applications, 37 (8), 5931–5938.

Blokdijk, G. (2008). Balanced scorecard 100 success secrets, 100 most asked questions on approach, development, management, measures performance and strategy. Emereo Pty Ltd.

Charnes, A, Cooper, W.W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–44.

Chen,T.Y., Chen, C.B., & Peng, S.Y. (2008). Firm operation performance analysis using data envelopment analysis and balanced scorecard: A case study of a credit cooperative bank. International Journal of Productivity and Performance Management, 57 (7), 523 – 539.

Chen,T.Y., & Chen, L.H. (2007). DEA performance evaluation based on BSC indicators Incorporated The case of semiconductor industry. International Journal of Productivity and Performance Management, 56(4), 335 – 357.

Cook, W.D., & Seiford, L.M. (2009). Data Envelopment Analysis(DEA)-thirty years on European. Journal of Operational Research, 192(1), 1–17.

Cooper, W.W., Seiford, L.M., & Zhu, J. (2004). Handbook on data envelopment analysis. New York: Kluwer Academic Publishers.

Cook, W.D., & Zhu, J. (2005). Modelling performance measurement: applications and implementation issues in DEA. Boston: Springer Science & Business Media, Inc.

Dalalah, D., Hayajneh, M., & Batieha, F. (2011). A fuzzy multi-criteria decision making model for supplier selection. Expert Systems with Applications, 38(7), 8384-8391.

Eilat, H., Golany, B., & Shtub, A. (2006). Constructing and evaluating balanced portfolios of R & D projects with interactions: A DEA based methodology. European Journal of Operational Research, 172 (3), 1018–1039.

Eilat, H., Golany, B., Shtub, A. (2008). R & D project evaluation: An integrated DEA and balanced scorecard approach. Omega, 36(5), 895 – 912.

Hiete, M., Merz, M., Comes,T., & Schultmann, F. (2011).Trapezoidal fuzzy DEMATEL method to analyze and correct for relations between variables in a composite indicator for disaster resilience. OR Spectrum.

JalaliNaini, S. GH., Aliahmadi, A.R., & Jafari-Eskandari, M. (2011). Designing a mixed performance measurement system for environmental supply chain management using evolutionary game theory and balanced scorecard: A case study of an auto industry supply chain. Resources, Conservation and Recycling, 55(6), 593-603.

Jassbi, J., Mohamadnejad, F., & Nasrollahzadeh, H. (2011).A Fuzzy DEMATEL framework for modeling cause and effect relationships of strategy map. Expert Systems with Applications, 38(5), 5967–5973.

Kaplan, R.S., & Norton, D.P.(1992). The Balanced Scorecard measures that drive performance. Harvard Business Review, 71–9. January–February.

Kaplan, R. S., & Norton, D.P. (2000). The strategy-focused organization: How balanced scorecard companies thrive in the new business environment. Harvard Business School Press.

Li,Y., Chen, Y., Liang, L., & Xie, J. (2012). DEA models for extended two-stage network structures. Omega, 40(5), 611-618.

Luo,Y., Bi, G., Liang, L. (2012). Input/output indicator selection for DEA efficiency evaluation: An empirical study of Chinese commercial banks. Expert Systems with Applications, 39(1), 1118-1123.

Makhijani, N., & Creelman, J. (2008). How leading organizations successfully implement corporate strategy with the balanced scorecard. The OTI Thought Leadership Series, 1, 1-16.

Manthos, D. M. D., & Papanikolaou, N. L. (2009). Determinants of bank efficiency: evidence from a semi-parametric methodology. Managerial Finance, 35(3), 260-275.

Neely, A.D., Gregory, M., & Platts, K. (1995). Performance measurement system design: A literature review and research agenda. International Journal of Operation, Production and Management. 15(4), 80-116.

Nissen, V. (2006). Modeling corporate strategy with the fuzzy balanced scorecard. In: Hüllermeier E, Kruse R, Nürnberger A, Strackeljan J (eds.): Proceedings Symposium on Fuzzy Systems in Computer Science FSCS, Magdeburg. 121-138.

Otley D. (1999). Performance management: a framework for management control systems research. Management Accounting Research, 10, 363–382.

Rickards R.C. ( 2003). Setting benchmarks and evaluating Balanced Scorecards with data envelopment analysis. Benchmarking: An International Journal, 10(3), 226–245.

Rouse, P., Putterill, M., & Ryan, D. (2002). Integrated performance measurement design: insights from an application in aircraft maintenance. Management Accounting Research,13(2), 229–248.

Seyed-hosseini, S. M., EbrahimiTaleghani, A., Bakhsha, A., & Partovi, S. (2011). Extracting leanness criteria by employing the concept of Balanced Scorecard. Expert Systems with Applications, 38(8), 10454–10461.

Timothy, J. (2005). Fuzzy Logic with Engineering Application, 2nd Ed., Hoboken, NJ, USA:Wiley:114.

Valderrama, T.G., Mulero-Mendigorri, E., Revuelta-Bordoy, D. (2009). Relating the perspectives of the balanced scorecard for R & D by means of DEA. European Journal of Operational Research, 196(3), 1177–1189.

Wang, J. (2006). Corporate performance efficiency investigated by Data Envelopment Analysis and Balanced Scorecard. The Journal of American Academy of Business, 9(2), 312–319.

Xu, Y., Yeh, C-H. (2012). An integrated approach to evaluation and planning of best practices. Omega, 40(1), 65-78.

Zadeh, L.A. (1975). The concept of a linguistic variable and its application to approximate reasoning. Information Science, 8, 199-249.