Multicriteria Analysis of the Success of Research Projects

• Authors: Gładysz Barbara , Kuchta Dorota
• Languages of publication: EN

Abstracts

EN

The present paper considers the problem of the success of a research project evaluated by its outputs (which can be seen as the project’s success measures) related to its inputs (constituting the project’s success factors). Data Envelopment Analysis (DEA) models are used. The inputs and outputs are selected on the basis of a review of the literature. Two models are applied to a set of research projects implemented in Poland. Advantages and disadvantages of the approach are shown. In particular, the selection of inputs, outputs and their weights needs to be researched further. But the models used in the paper, in spite of their imperfection and lack of generality, considerably help to assess and compare the projects. Therefore, DEA is an important tool for the evaluation of R&D activities.

Keywords

EN

Data Envelopment Analysis; Project success; Research project

PL

Data Envelopment Analysis; Projekt badawczy; Sukces projektu

• Publisher: Uniwersytet Ekonomiczny w Katowicach
• Journal: Multiple Criteria Decision Making
• Year: 2017
• Volume: 12
• Pages: 22-35

Contributors

author

Gładysz Barbara

• Wrocław University of Science and Technology. Faculty of Computer Science and Management. Wrocław, Poland

author

Kuchta Dorota

• Wrocław University of Science and Technology. Faculty of Computer Science and Management. Wrocław, Poland

References

• Betta J., Jastrzębska J., Frączkowski K., Gładysz B., Kuchta D., Prałat E., Ptaszyńska E., Rola P., Walecka-Jankowska K., Ropuszyńska-Surma E., Skomra A., Ryńca R., Klaus-Rosińska A., Mrzygłocka-Chojnacka J. (2017), Success and Failure Factors of R&D Projects at Universities in Poland and France, submitted to International Conference Business Risk in Changing Dynamics of Global Village April 26-27, 2017 at PWSZ in Nysa, Poland.
• Castelli L., Pesenti R., Ukovich W. (2010), A Classification of DEA Models when the Internal Structure of the Decision Making Units Is Considered, Annals of Operations Research, 173, 207-235.
• Chan A.P.C., Chan A.P.L. (2004), Key Performance Indicators for Measuring Construction Success, Benchmarking: An International Journal, 11(2), 203-221.
• Chan A.P.C., Scott D. & Lam E.W.M. (2002), Framework of Success Criteria for Design/ Build Projects, Journal of Management in Engineering, 18(3), 120-128.
• Davis K. (2014), Different Stakeholder Groups and Their Perceptions of Project Success, International Journal of Project Management, 32(2), 189-201.
• Debreu G. (1951), The Coefficient of Recourse Utilisation, Econometrica, 19(3), 273-292.
• Eilat H., Golany B. & Shtub A. (2008), Research Project Evaluation: An Integrated DEA and Balanced Scorecard Approach, Omega, 36(5), 895-912.
• Farrell M.J. (1957), The Measurement of Productive Efficiency, Journal of the Royal Statistical Society. Series A, 120(III), 253-281.
• Frascati Manual (2002), Proposed Standard Practice for Surveys on Research and Experimental Development, 6th edition, www.oecd.org/sti/frascatimanual.
• Gładysz B., Kuchta D. (2016), Research Projects Success Factors, Proceedings of EDULEARN16 Conference 4th-6th July 2016, Barcelona, Spain, 7591-7597.
• Kuchta D., Gładysz B., Skowron D., Betta J. (2017), Research Projects in Science Sector, R&D Management, 47(1), 88-110.
• Podinovski V.V. (2016), Optimal Weights in DEA Models with Weight Restrictions, European Journal of Operational Research, 254(3), 916-924.
• Revilla E., Sarkis J. & Modrego A. (2003), Evaluating Performance of Public-private Research Collaborations: A DEA Analysis, Journal of the Operational Research Society, 54(2), 165-174.
• Yuan B., Huang J.-N. (2002), Applying Data Envelopment Analysis to Evaluate the Efficiency of R&D Projects − A Case Study of R&D in Energy Technology, Technology Commercialization, 111-134.

Identifiers

ISSN

2084-1531