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Behavioral DEA model and its application to the efficiency evaluation of manufacturing transformation and upgrading in the Yangtze River Delta

  • Xiaoqing Chen
  • Xinwang LiuEmail author
  • Weizhong Wang
  • Zaiwu Gong
Methodologies and Application

Abstract

The Yangtze River Delta (YRD) is a Chinese region with the highest level of economic development, which has an important impact on the level of China’s modernization. The development of manufacturing is the driving and critical force for the industrialization of the YRD. Transformation and upgrading are considered as an effective measure to improve the development of manufacturing industry. The purpose of this study is to develop a behavioral DEA (BDEA) model-based efficiency evaluation method by integrating the traditional DEA model with prospect theory. In this BDEA model framework, prospect theory is combined with the traditional DEA model to evaluate the development efficiency of manufacturing transformation and upgrading in the YRD, which takes behavioral preference of the decision-makers (DMs) in decision-making problems into account. In addition, the technique for order of preference by similarity to ideal solution method is applied to identify the reference points, in which maximum input minimum output is regarded as a negative reference point and the minimum input maximum output is regarded as a positive reference point. Then, the gain value and the loss value are obtained on the basis of the value function with reference points of prospect theory, and the overall utility function is further constructed to depict the behavioral preference of DMs. Finally, a case study is employed to illustrate the applicability of the proposed BDEA method by evaluating the relative efficiency of manufacturing transformation and upgrading in the YRD, and the comparative analysis with the result of efficiency values also shows the feasibility of the proposed BDEA model.

Keywords

Data envelopment analysis Behavioral DEA model Manufacturing transformation and upgrading Yangtze river delta 

Notes

Acknowledgements

The authors wish to thank the anonymous reviewers for the valuable suggestions and comments which improved the quality of this paper. The work was supported by the National Science Foundation of China (NSFC) (71771051 and 71971121) and the Key Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province (2017ZDIXM014), and the 2019 Jiangsu Province Policy Guidance Program (Soft Science Research) (BR2019064).

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Abdellaoui M, Bleichrodt H, Paraschiv C (2007) Loss aversion under prospect theory: a parameter-free measurement. Manag Sci 53(10):1659–1674CrossRefGoogle Scholar
  2. Alka A, Prasad YS (2019) Development of intuitionistic fuzzy data envelopment analysis models and intuitionistic fuzzy input-output targets. Soft Comput 23:8975–8993zbMATHCrossRefGoogle Scholar
  3. Alvarado R, Ponce P, Criollo A et al (2018) Environmental degradation and real per capita output: new evidence at the global level grouping countries by income levels. J Clean Prod 189:13–20CrossRefGoogle Scholar
  4. An QX, Meng FY, Xiong BB (2018) Interval cross efficiency for fully ranking decision making units using DEA/AHP approach. Ann Oper Res 271:297–317MathSciNetzbMATHCrossRefGoogle Scholar
  5. Anwar S, Sun SZ (2018) Foreign direct investment and export quality upgrading in China’s manufacturing sector. Int Rev Econ Financ 54:289–298CrossRefGoogle Scholar
  6. Bahaji H (2016) Are employee stock option exercise decisions better explained through the prospect theory? Ann Oper Res 262(2):335–359MathSciNetzbMATHCrossRefGoogle Scholar
  7. Banker RD, Charnes A, Cooper WW (1984) Some methods for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci 30:1078–1092zbMATHCrossRefGoogle Scholar
  8. Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision-making units. Eur J Oper Res 6(2):429–444MathSciNetzbMATHCrossRefGoogle Scholar
  9. Chen P (2019) Effects of normalization on the entropy-based TOPSIS method. Expert Syst Appl 136:33–41CrossRefGoogle Scholar
  10. Chen W, Zhou KL, Yang SL (2017) Evaluation of China’s electric energy efficiency under environmental constraints: a DEA cross efficiency model based on game relationship. J Clean Prod 164:38–44CrossRefGoogle Scholar
  11. Chen ZY, Huang ZH, Nie PY (2018) Industrial characteristics and consumption efficiency from a nexus perspective-based on Anhui’s empirical statistics. Energy Policy 115:281–290CrossRefGoogle Scholar
  12. Cheong TS, Wu YR (2014) The impacts of structural transformation and industrial upgrading on regional inequality in China. China Econ Rev 31:339–350CrossRefGoogle Scholar
  13. Chiang TC, Cheng PY, Leu FY (2017) Prediction of technical efficiency and financial crisis of Taiwan’s information and communication technology industry with decision tree and DEA. Soft Comput 21(18):5341–5353CrossRefGoogle Scholar
  14. Chu JF, Chin KS, Liu XW, Wang YM (2017) A prospect theory based approach to multiple attribute decision making considering the decision maker’s attitudinal character. J Intell Fuzzy Syst 32(3):2563–2578zbMATHCrossRefGoogle Scholar
  15. Costantini V, Crespi F, Palma A (2017) Characterizing the policy mix and its impact on eco-innovation: a patent analysis of energy-efficient technologies. Res Pol 46:799–819CrossRefGoogle Scholar
  16. Fu ZG, Liao HC (2019) Unbalanced double hierarchy linguistic term set: the TOPSIS method for multi-expert qualitative decision making involving green mine selection. Inform Fusion 51:271–286CrossRefGoogle Scholar
  17. Geng ZQ, Yang X, Han YM, Zhu QX (2017) Energy optimization and analysis modeling based on extreme learning machine integrated index decomposition analysis: application to complex chemical processes. Energy 120:67–78CrossRefGoogle Scholar
  18. Golany B, Roll Y (1989) An application procedure for DEA. Omega 17(3):237–250CrossRefGoogle Scholar
  19. Gong ZW, Chen XQ (2017) Analysis of interval data envelopment efficiency model considering different distribution characteristics-based on environmental performance evaluation of the manufacturing industry. Sustainability 9(12):2080CrossRefGoogle Scholar
  20. Goyal KK, Jain PK, Jain M (2012) Optimal configuration selection for reconfigurable manufacturing system using NSGA II and TOPSIS. Int J Prod Res 50(15):4175–4191CrossRefGoogle Scholar
  21. Grunwald A (2018) Diverging pathways to overcoming the environmental crisis: A critique of eco modernism from a technology assessment perspective. J Clean Prod 197:1854–1862CrossRefGoogle Scholar
  22. Han CJ, Thomas SR, Yang M, Ieromonachou P, Zhang HR (2017) Evaluating R&D investment efficiency in China’s high-tech industry. J High Technol Manag Res 28(1):93–109CrossRefGoogle Scholar
  23. Hatami-Marbini A, Pourmahmoud J, Babazadeh E (2018) A modified super-efficiency in the range directional model. Comput Ind Eng 120:442–449CrossRefGoogle Scholar
  24. Hwang CL, Yoon K (1981) Methods for multiple attribute decision making// Multiple attribute decision making. Springer, Berlin, pp 58–191zbMATHCrossRefGoogle Scholar
  25. Ji X, Sun JS, Wang YY, Yuan QQ (2017) Allocation of emission permits in large data sets: a robust multi-criteria approach. J Clean Prod 142:894–906CrossRefGoogle Scholar
  26. Ji A-B, Chen H, Qiao YH, Pang JH (2019) Data envelopment analysis with interactive fuzzy variables. J Oper Res Soc 70(9):1502–1510CrossRefGoogle Scholar
  27. Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 47(2):263–292MathSciNetzbMATHCrossRefGoogle Scholar
  28. Karasakal E, Aker P (2017) A multicriteria sorting approach based on data envelopment analysis for R&D project selection problem. Omega 73:79–92CrossRefGoogle Scholar
  29. Khoshnevis P, Teirlinck P (2018) Performance evaluation of R&D active firms. Socio-Econ Plan Sci 61:16–28CrossRefGoogle Scholar
  30. Koronakos G, Sotiros D, Despotis DK (2019) Reformulation of Network Data Envelopment Analysis models using a common modelling framework. Eur J Oper Res 278:472–480MathSciNetzbMATHCrossRefGoogle Scholar
  31. Li K, Lin BQ (2017) Economic growth model, structural transformation, and green productivity in China. Appl Energy 187:489–500CrossRefGoogle Scholar
  32. Liang HM, Xiong W, Dong YC (2018) A prospect theory-based method for fusing the individual preference-approval structures in group decision making. Comput Ind Eng 117:237–248CrossRefGoogle Scholar
  33. Lin RY, Yang W, Huang HL (2019) A modified slacks-based super-efficiency measure in the presence of negative data. Comput Ind Eng 135:39–52CrossRefGoogle Scholar
  34. Liu SL, Liu XW, Qin JD (2017) Three-way group decisions based on prospect theory. J Oper Res Soc 69(1):25–35CrossRefGoogle Scholar
  35. Liu HH, Song YY, Yang GL (2019a) Cross-efficiency evaluation in data envelopment analysis based on prospect theory. Eur J Oper Res 273(1):364–375MathSciNetzbMATHCrossRefGoogle Scholar
  36. Liu JP, Song JM, Xu Q, Tao ZF, Chen HY (2019b) Group decision making based on DEA cross-efficiency with intuitionistic fuzzy preference relations. Fuzzy Optim Decis Ma 18(3):345–370MathSciNetzbMATHCrossRefGoogle Scholar
  37. Liu Y, Wang Y, Xu MZ, Xu GC (2019c) Emergency alternative evaluation using extended trapezoidal intuitionistic fuzzy thermodynamic approach with prospect theory. Int J Fuzzy Syst 21(6):1801–1817CrossRefGoogle Scholar
  38. Lozano S, Adenso-Diaz B (2018) Network DEA-based biobjective optimization of product flows in a supply chain. Ann Oper Res 264:307–323MathSciNetzbMATHCrossRefGoogle Scholar
  39. Makiela K, Ouattara B (2018) Foreign direct investment and economic growth: exploring the transmission channels. Econ Modell 72:296–305CrossRefGoogle Scholar
  40. Ning T, Wang XP (2018) Study on disruption management strategy of job-shop scheduling problem based on prospect theory. J Clean Prod 194:174–178CrossRefGoogle Scholar
  41. Orlic E, Hashi I, Hisarciklilar M (2018) Cross sectoral FDI spillovers and their impact on manufacturing productivity. Int Bus Rev 27(4):777–796CrossRefGoogle Scholar
  42. Peng XD, Yang Y (2017) Algorithms for interval-valued fuzzy soft sets in stochastic multi-criteria decision making based on regret theory and prospect theory with combined weight. Appl Soft Comput 54:415–430CrossRefGoogle Scholar
  43. Rentizelas A, Costa MI et al (2019) Multi-criteria efficiency assessment of international biomass supply chain pathways using data envelopment analysis. J Clean Prod 237:1–16CrossRefGoogle Scholar
  44. Ruiz JL, Sirvent I (2017) Fuzzy cross-efficiency evaluation: a possibility approach. Fuzzy Optim Decis Ma 16:111–126MathSciNetzbMATHCrossRefGoogle Scholar
  45. Song W, Zhu JJ (2019) Three-reference-point decision-making method with incomplete weight information considering independent and interactive characteristics. Inf Sci 503:148–168MathSciNetCrossRefGoogle Scholar
  46. Su Y, Sun W (2018) Sustainability evaluation of the supply chain with undesired outputs and dual-role factors based on double frontier network DEA. Soft Comput 22:5525–5533CrossRefGoogle Scholar
  47. Sueyoshi T, Yuan Y, Li AJ, Wang DP (2017) Methodological comparison among radial, non-radial and intermediate approaches for DEA environmental assessment. Energy Econ 67:439–453CrossRefGoogle Scholar
  48. Tan CQ, Liu ZD, Wu DD, Chen XH (2016) Cournot game with incomplete information based on rank-dependent utility theory under a fuzzy environment. Int J Prod Res 56(5):1789–1805CrossRefGoogle Scholar
  49. Tavakoli IM, Mostafaee A (2019) Free disposal hull efficiency scores of units with network structures. Eur J Oper Res 277:1027–1036MathSciNetzbMATHCrossRefGoogle Scholar
  50. Tavana M, Khalili-Damghani K, Santos Arteaga FJ, Hosseini A (2019) A fuzzy multi-objective multi-period network DEA model for efficiency measurement in oil refineries. Comput Ind Eng 135:143–155CrossRefGoogle Scholar
  51. Vancauteren M (2018) The effects of human capital, R&D and firm’s innovation on patents: a panel study on Dutch food firms. J Technol Transf 43(4):901–922CrossRefGoogle Scholar
  52. Wang EC, Huang W (2007) Relative efficiency of R&D activities: a cross-country study accounting for environmental factors in the DEA approach. Res Pol 36:260–273CrossRefGoogle Scholar
  53. Wang K, Lu B, Wei YM (2013a) China’s regional energy and environmental efficiency: a Range-Adjusted Measurec based analysis. Appl Energy 112:1403–1415CrossRefGoogle Scholar
  54. Wang K, Wei YM, Zhang X (2013b) Energy and emissions efficiency patterns of Chinese regions: a multi-directional efficiency analysis. Appl Energy 104:105–116CrossRefGoogle Scholar
  55. Wang L, Wang YM, Luis M (2017a) A group decision method based on prospect theory for emergency situations. Inf Sci 3:418–419Google Scholar
  56. Wang RQ, Wang FJ, Xu LY, Yuan CH (2017b) R&D expenditures, ultimate ownership and future performance: evidence from China. J Bus Res 71:47–54CrossRefGoogle Scholar
  57. Wang Q, Han R, Huang QL et al (2018a) Research on energy conservation and emissions reduction based on AHP-fuzzy synthetic evaluation model: a case study of tobacco enterprises. J Clean Prod 201:88–97CrossRefGoogle Scholar
  58. Wang WZ, Liu XW, Qin Y, Fu Y (2018b) A risk evaluation and prioritization method for FMEA with prospect theory and Choquet integral. Safety Sci 110:152–163CrossRefGoogle Scholar
  59. Wang XM, Ding H, Liu L (2019) Eco-efficiency measurement of industrial sectors in China: a hybrid super-efficiency DEA analysis. J Clean Prod 229:53–64CrossRefGoogle Scholar
  60. Wu J, Xiong BB, An QX, Sun JS, Wu HQ (2015) Total-factor energy efficiency evaluation of Chinese industry by using two-stage DEA model with shared inputs. Ann Oper Res 255(1–2):257–276MathSciNetGoogle Scholar
  61. Wu J, Yu YF, Zhu QY, An QX, Liang L (2018) Closest target for the orientation-free context-dependent DEA under variable returns to scale. J Oper Res Soc 69:1819–1833CrossRefGoogle Scholar
  62. Wu J, Li MJ, Zhu QY et al (2019) Energy and environmental efficiency measurement of China’s industrial sectors: a DEA model with non-homogeneous inputs and outputs. Energy Econ 78:468–480CrossRefGoogle Scholar
  63. Yang DW, Xia Q (2018) Behavioral DEA model in evaluating the regional carrying states in China. Ann Oper Res 10:1–17Google Scholar
  64. Yang T, Chen W, Zhou KL, Ren ML (2018) Regional energy efficiency evaluation in China: a super efficiency slack-based measure model with undesirable outputs. J Clean Prod 198:859–866CrossRefGoogle Scholar
  65. Zareie A, Sheikhahmadi A, Khamforoosh K (2018) Influence maximization in social networks based on TOPSIS. Expert Syst Appl 108:96–107CrossRefGoogle Scholar
  66. Zhang ZX, Wang L, Wang YM (2018) An emergency decision making method based on prospect theory for different emergency situations. Int J Disaster Risk Sci 9(3):407–420CrossRefGoogle Scholar
  67. Zhang K, Zhan JM, Yao YY (2019a) TOPSIS method based on a fuzzy covering approximation space: An application to biological nano-materials selection. Inf Sci 502:297–329CrossRefGoogle Scholar
  68. Zhang JJ, Wu Q, Zhou ZX (2019b) A two-stage DEA model for resource allocation in industrial pollution treatment and its application in China. J Clean Prod 228:29–39CrossRefGoogle Scholar
  69. Zhao X (2014) TOPSIS method for interval-valued intuitionistic fuzzy multiple attribute decision making and its application to teaching quality evaluation. J Intell Fuzzy Syst 26(6):3049–3055MathSciNetzbMATHCrossRefGoogle Scholar
  70. Zhong W, Yuan W, Li SX, Huang ZM (2011) The performance evaluation of regional R&D investments in China: an application of DEA based on the first official China economic census data. Omega 39:447–455CrossRefGoogle Scholar
  71. Zhou H, Wang JQ, Zhang HY (2017) Grey stochastic multi-criteria decision-making approach based on prospect theory and distance measures. J Grey Syst 29(1):15–34Google Scholar
  72. Zhu QY, Wu J, Ji X, Li F (2018) A simple MILP to determine closest targets in non-oriented DEA model satisfying strong monotonicity. Omega 79:1–8CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Economics and ManagementSoutheast UniversityNanjingChina
  2. 2.School of Economics and ManagementNanjing University of Information Science and TechnologyNanjingChina

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