Behavioral DEA model in evaluating the regional carrying states in China

S.I.: BOM in Social Networks
  • 44 Downloads

Abstract

In this paper, we have proposed a behavioral DEA model to evaluate Chinese provincial carrying states. To introduce the behavioral DEA model, we take the individual decision maker’s preference into account, including fairness concern, reference dependence and loss aversion. By considering those decision preferences, the proposed models can help to make fair planning with accounting for decision maker’s utilities. Our proposed model provides the detailed technique to demonstrate the fairness concern, reference dependence and loss aversion quantificationally. An empirical study in evaluating Chinese provincial carrying states is used to demonstrate our methods. We also provide comparative analysis and correlation analysis to discuss the results and point out the managerial implications of this study.

Keywords

Data envelopment analysis Regional carrying states Fairness concern Reference dependence Loss aversion 

Notes

Acknowledgements

This research work has been supported by National Natural Science Foundation of China (Grant Nos. 71631006 and 71771071).

References

  1. Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39(10), 1261–1264.CrossRefGoogle Scholar
  2. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092.CrossRefGoogle Scholar
  3. Charnes, A., Cooper, W. W., Golany, B., Seiford, L., & Stutz, J. (1985). Foundations of data envelopment analysis for Pareto–Koopmans efficient empirical production functions. Journal of Econometrics, 30(1–2), 91–107.CrossRefGoogle Scholar
  4. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.CrossRefGoogle Scholar
  5. Doyle, J., & Green, R. (1994). Efficiency and cross-efficiency in DEA: Derivations, meanings and uses. Journal of the Operational Research Society, 45(5), 567–578.CrossRefGoogle Scholar
  6. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica: Journal of the Econometric Society, 47, 263–291.CrossRefGoogle Scholar
  7. Li, Y., Yang, F., Liang, L., & Hua, Z. (2009). Allocating the fixed cost as a complement of other cost inputs: A DEA approach. European Journal of Operational Research, 197(1), 389–401.CrossRefGoogle Scholar
  8. Liang, L., Yang, F., Cook, W. D., & Zhu, J. (2006). DEA models for supply chain efficiency evaluation. Annals of Operations Research, 145(1), 35–49.CrossRefGoogle Scholar
  9. Tversky, A., & Kahneman, D. (1991). Loss aversion in riskless choice: A reference-dependent model. The Quarterly Journal of Economics, 106(4), 1039–1061.CrossRefGoogle Scholar
  10. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323.CrossRefGoogle Scholar
  11. Yang, F., Ang, S., Xia, Q., & Yang, C. (2012). Ranking DMUs by using interval DEA cross efficiency matrix with acceptability analysis. European Journal of Operational Research, 223(2), 483–488.CrossRefGoogle Scholar
  12. Yang, F., Wu, D. D., Liang, L., & O’Neill, L. (2011). Competition strategy and efficiency evaluation for decision making units with fixed-sum outputs. European Journal of Operational Research, 212(3), 560–569.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Management and EconomicsBeijing Institute of TechnologyBeijingPeople’s Republic of China
  2. 2.School of EconomicsHefei University of TechnologyHefeiPeople’s Republic of China

Personalised recommendations