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DEA-based benchmarking for performance evaluation in pay-for-performance incentive plans

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  • Cook, Wade D.
  • Ramón, Nuria
  • Ruiz, José L.
  • Sirvent, Inmaculada
  • Zhu, Joe

Abstract

Incentive plans involve payments for performance relative to some set of goals. In this paper, we extend Data Envelopment Analysis (DEA) to the evaluation of performance in the specific context of pay-for-performance incentive plans. The approach proposed ensures that the evaluation of performance of decision making units (DMUs) that follow the implementation of incentive plans, is made in terms of targets that are attainable, as well as representing best practices. A model is developed that adjusts the benchmarking to the goals through the corresponding payment of incentives, thus DEA targets are established taking into consideration the improvement strategies that were set out in the incentive plans. To illustrate, we examine an application concerned with the financing of public Spanish universities.

Suggested Citation

  • Cook, Wade D. & Ramón, Nuria & Ruiz, José L. & Sirvent, Inmaculada & Zhu, Joe, 2019. "DEA-based benchmarking for performance evaluation in pay-for-performance incentive plans," Omega, Elsevier, vol. 84(C), pages 45-54.
    • Handle: RePEc:eee:jomega:v:84:y:2019:i:c:p:45-54
    DOI: 10.1016/j.omega.2018.04.004
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    1. A. Bessent & W. Bessent & J. Elam & T. Clark, 1988. "Efficiency Frontier Determination by Constrained Facet Analysis," Operations Research, INFORMS, vol. 36(5), pages 785-796, October.
    2. Stefano Nigsch & Andrea Schenker-Wicki, 2015. "Frontier Efficiency Analysis in Higher Education," Springer Books, in: Isabell M. Welpe & Jutta Wollersheim & Stefanie Ringelhan & Margit Osterloh (ed.), Incentives and Performance, edition 127, pages 155-170, Springer.
    3. 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.
    4. Aparicio, Juan & Cordero, Jose M. & Pastor, Jesus T., 2017. "The determination of the least distance to the strongly efficient frontier in Data Envelopment Analysis oriented models: Modelling and computational aspects," Omega, Elsevier, vol. 71(C), pages 1-10.
    5. Joe Zhu, 2014. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Quantitative Models for Performance Evaluation and Benchmarking, edition 3, chapter 1, pages 1-9, Springer.
    6. Cook, Wade D. & Tone, Kaoru & Zhu, Joe, 2014. "Data envelopment analysis: Prior to choosing a model," Omega, Elsevier, vol. 44(C), pages 1-4.
    7. Dyson, R. G. & Allen, R. & Camanho, A. S. & Podinovski, V. V. & Sarrico, C. S. & Shale, E. A., 2001. "Pitfalls and protocols in DEA," European Journal of Operational Research, Elsevier, vol. 132(2), pages 245-259, July.
    8. Stewart, Theodor J., 2010. "Goal directed benchmarking for organizational efficiency," Omega, Elsevier, vol. 38(6), pages 534-539, December.
    9. Yang, Jian-Bo & Wong, Brandon Y.H. & Xu, Dong-Ling & Stewart, Theodor J., 2009. "Integrating DEA-oriented performance assessment and target setting using interactive MOLP methods," European Journal of Operational Research, Elsevier, vol. 195(1), pages 205-222, May.
    10. Liu, W.B. & Zhang, D.Q. & Meng, W. & Li, X.X. & Xu, F., 2011. "A study of DEA models without explicit inputs," Omega, Elsevier, vol. 39(5), pages 472-480, October.
    11. Shwartz, Michael & Burgess, James F. & Zhu, Joe, 2016. "A DEA based composite measure of quality and its associated data uncertainty interval for health care provider profiling and pay-for-performance," European Journal of Operational Research, Elsevier, vol. 253(2), pages 489-502.
    12. Shannon W. Anderson & Henri C. Dekker & Karen L. Sedatole, 2010. "An Empirical Examination of Goals and Performance-to-Goal Following the Introduction of an Incentive Bonus Plan with Participative Goal Setting," Management Science, INFORMS, vol. 56(1), pages 90-109, January.
    13. Cook, Wade D. & Ruiz, José L. & Sirvent, Inmaculada & Zhu, Joe, 2017. "Within-group common benchmarking using DEA," European Journal of Operational Research, Elsevier, vol. 256(3), pages 901-910.
    14. Cook, Wade D. & Zhu, Joe, 2007. "Within-group common weights in DEA: An analysis of power plant efficiency," European Journal of Operational Research, Elsevier, vol. 178(1), pages 207-216, April.
    15. O. B. Olesen & N. C. Petersen, 1996. "Indicators of Ill-Conditioned Data Sets and Model Misspecification in Data Envelopment Analysis: An Extended Facet Approach," Management Science, INFORMS, vol. 42(2), pages 205-219, February.
    16. Ruiz, José L. & Sirvent, Inmaculada, 2016. "Common benchmarking and ranking of units with DEA," Omega, Elsevier, vol. 65(C), pages 1-9.
    17. Banker, Rajiv D. & Lee, Seok-Young & Potter, Gordon, 1996. "A field study of the impact of a performance-based incentive plan," Journal of Accounting and Economics, Elsevier, vol. 21(2), pages 195-226, April.
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    6. Ramón, Nuria & Ruiz, José L. & Sirvent, Inmaculada, 2020. "Cross-benchmarking for performance evaluation: Looking across best practices of different peer groups using DEA," Omega, Elsevier, vol. 92(C).
    7. Yin, Pengzhen & Chu, Junfei & Wu, Jie & Ding, Jingjing & Yang, Min & Wang, Yuhong, 2020. "A DEA-based two-stage network approach for hotel performance analysis: An internal cooperation perspective," Omega, Elsevier, vol. 93(C).
    8. An, Qingxian & Zhang, Qiaoyu & Tao, Xiangyang, 2023. "Pay-for-performance incentives in benchmarking with quasi S-shaped technology," Omega, Elsevier, vol. 118(C).
    9. Ming-Fu Hsu & Ying-Shao Hsin & Fu-Jiing Shiue, 2022. "Business analytics for corporate risk management and performance improvement," Annals of Operations Research, Springer, vol. 315(2), pages 629-669, August.
    10. Contreras, I. & Lozano, S., 2020. "Allocating additional resources to public universities. A DEA bargaining approach," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    11. Panagiotis Ravanos & Giannis Karagiannis, 2022. "In search for the Most Preferred Solution in Value Efficiency Analysis," Discussion Paper Series 2022_05, Department of Economics, University of Macedonia, revised Jul 2022.
    12. Monge, Juan F. & Ruiz, José L., 2023. "Setting closer targets based on non-dominated convex combinations of Pareto-efficient units: A bi-level linear programming approach in Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 311(3), pages 1084-1096.
    13. Kalinichenko, Olena & Amado, Carla A.F. & Santos, Sérgio P., 2022. "Exploring the potential of Data Envelopment Analysis for enhancing pay-for-performance programme design in primary health care," European Journal of Operational Research, Elsevier, vol. 298(3), pages 1084-1100.
    14. Xiangrui Chao & Yucheng Dong & Gang Kou & Yi Peng, 2022. "How to determine the consensus threshold in group decision making: a method based on efficiency benchmark using benefit and cost insight," Annals of Operations Research, Springer, vol. 316(1), pages 143-177, September.
    15. An, Qingxian & Tao, Xiangyang & Xiong, Beibei, 2021. "Benchmarking with data envelopment analysis: An agency perspective," Omega, Elsevier, vol. 101(C).
    16. Onesmo Kaiya Mackenzie, 2021. "Efficiency of tax revenue administration in Africa," Working Papers 02/2021, Stellenbosch University, Department of Economics.
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    19. Matthias Klumpp & Dominic Loske, 2021. "Long-Term Economic Sustainability of Humanitarian Logistics—A Multi-Level and Time-Series Data Envelopment Analysis," IJERPH, MDPI, vol. 18(5), pages 1-19, February.

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