Efficiency assessment of public sector management and culture-led urban regeneration using the enhanced Russell-based directional distance function with stochastic data

References

• Al Tamimi, H. A. H., Duqi, A., Kanas, A., & Zervopoulos, P. D. (2022). Directional distance function DEA estimators for evaluating efficiency gains from possible mergers and acquisitions. Journal of the Operational Research Society, 73(6), 1240–1257. https://doi.org/10.1080/01605682.2021.1907243
   Web of Science ®Google Scholar
• Alcaraz, J., Anton-Sanchez, L., Aparicio, J., Monge, J. F., & Ramón, N. (2021). Russell graph efficiency measures in data envelopment analysis: The multiplicative approach. European Journal of Operational Research, 292(2), 663–674. https://doi.org/10.1016/j.ejor.2020.11.001
   Web of Science ®Google Scholar
• Amirteimoori, A., Charles, V., & Mehdizadeh, S. (2023). Stochastic data envelopment analysis in the presence of undesirable outputs. Journal of the Operational Research Society, 74(2), 1–14. https://doi.org/10.1080/01605682.2023.2172366
   Google Scholar
• Amirteimoori, A., Mehdizadeh, S., & Kordrostami, S. (2022). Stochastic performance measurement in two-stage network processes: A data envelopment analysis approach. Kybernetika, 58(2), 200–217. https://doi.org/10.14736/kyb-2022-2-0200
   Web of Science ®Google Scholar
• Azadeh, A., Nazari, T., & Charkhand, H. (2015). Optimisation of facility layout design problem with safety and environmental factors by stochastic DEA and simulation approach. International Journal of Production Research, 53(11), 3370–3389. https://doi.org/10.1080/00207543.2014.986294
   Web of Science ®Google Scholar
• Azadi, M., & Farzipoor Saen, R. (2013). A combination of QFD and imprecise DEA with enhanced Russell graph measure: A case study in healthcare. Socio-Economic Planning Sciences, 47(4), 281–291. https://doi.org/10.1016/j.seps.2013.05.001
   Web of Science ®Google Scholar
• Balak, S., Behzadi, M. H., & Nazari, A. (2021). Stochastic copula-DEA model based on the dependence structure of stochastic variables: An application to twenty bank branches. Economic Analysis and Policy, 72, 326–341. https://doi.org/10.1016/j.eap.2021.09.002
   Web of Science ®Google Scholar
• Banker, R. D. (1993). Maximum likelihood, consistency and data envelopment analysis: A statistical foundation. Management Science, 39(10), 1265–1273. https://doi.org/10.1287/mnsc.39.10.1265
   Web of Science ®Google Scholar
• Banker, R. D., Kotarac, K., & Neralić, L. (2015). Sensitivity and stability in stochastic data envelopment analysis. Journal of the Operational Research Society, 66(1), 134–147. https://doi.org/10.1057/jors.2012.182
   Web of Science ®Google Scholar
• Buallay, A. (2019). Is sustainability reporting (ESG) associated with performance? Evidence from the European banking sector. Management of Environmental Quality: An International Journal, 30(1), 98–115. https://doi.org/10.1108/MEQ-12-2017-0149
   Google Scholar
• Chambers, R. G., Chung, Y., & Färe, R. (1996). Benefit and distance functions. Journal of Economic Theory, 70(2), 407–419. https://doi.org/10.1006/jeth.1996.0096
   Web of Science ®Google Scholar
• Chambers, R. G., Fāure, R., & Grosskopf, S. (1996). Productivity growth in APEC countries. Pacific Economic Review, 1(3), 181–190. https://doi.org/10.1111/j.1468-0106.1996.tb00184.x
   Google Scholar
• Charles, V., & Cornillier, F. (2017). Value of the stochastic efficiency in data envelopment analysis. Expert Systems with Applications, 81, 349–357. https://doi.org/10.1016/j.eswa.2017.03.061
   Web of Science ®Google Scholar
• Charles, V., & Díaz, G. (2017). A non-radial DEA index for Peruvian regional competitiveness. Social Indicators Research, 134(2), 747–770. https://doi.org/10.1007/s11205-016-1444-9
   Web of Science ®Google Scholar
• Charnes, A., & Cooper, W. W. (1959). Chance-constrained programming. Management Science, 6(1), 73–79. https://doi.org/10.1287/mnsc.6.1.73
   Web of Science ®Google Scholar
• Chen, K., & Zhu, J. (2019). Computational tractability of chance constrained data envelopment analysis. European Journal of Operational Research, 274(3), 1037–1046. https://doi.org/10.1016/j.ejor.2018.10.039
   Web of Science ®Google Scholar
• Chen, T-y. (2002). A comparison of chance-constrained DEA and stochastic frontier analysis: Bank efficiency in Taiwan. Journal of the Operational Research Society, 53(5), 492–500. https://doi.org/10.1057/palgrave.jors.2601318
   Web of Science ®Google Scholar
• Chen, Z., Matousek, R., & Wanke, P. (2018). Chinese bank efficiency during the global financial crisis: A combined approach using satisficing DEA and Support Vector Machines⋆. The North American Journal of Economics and Finance, 43, 71–86. https://doi.org/10.1016/j.najef.2017.10.003
   Web of Science ®Google Scholar
• Chen, Z., Wanke, P., Antunes, J. J. M., & Zhang, N. (2017). Chinese airline efficiency under CO2 emissions and flight delays: A stochastic network DEA model. Energy Economics, 68, 89–108. https://doi.org/10.1016/j.eneco.2017.09.015
   Web of Science ®Google Scholar
• Chiu, C. R., Lu, K. H., Tsang, S. S., & Chen, Y. F. (2013). Decomposition of meta‐frontier inefficiency in the two‐stage network directional distance function with quasi‐fixed inputs. International Transactions in Operational Research, 20(4), 595–611. https://doi.org/10.1111/itor.12008
   Web of Science ®Google Scholar
• Chung, Y. H., Färe, R., & Grosskopf, S. (1997). Productivity and undesirable outputs: A directional distance function approach. Journal of Environmental Management, 51(3), 229–240. https://doi.org/10.1006/jema.1997.0146
   Web of Science ®Google Scholar
• Cooper, W. W., Park, K. S., & Pastor, J. T. (1999). RAM: A range adjusted measure of inefficiency for use with additive models, and relations to other models and measures in DEA. Journal of Productivity Analysis, 11(1), 5–42. https://doi.org/10.1023/A:1007701304281
   Web of Science ®Google Scholar
• De Jorge-Moreno, J., & De Jorge-Huertas, V. (2020). Measuring European cultural and creative cities efficiency: A metafrontier DEA approach. Journal of Economic Studies, 47(4), 891–909. https://doi.org/10.1108/JES-06-2019-0265
   Web of Science ®Google Scholar
• Dehghani Sadrabadi, M. H., Sabouhi, F., Bozorgi-Amiri, A., & Sheikhalishahi, M. (2023). A robust-stochastic data envelopment analysis model for supplier performance evaluation of the telecommunication industry under uncertainty. RAIRO - Operations Research, 57(1), 263–290. https://doi.org/10.1051/ro/2023008
   Web of Science ®Google Scholar
• Eslami, R., Khodabakhshi, M., Jahanshahloo, G. R., Hosseinzadeh Lotfi, F., & Khoveyni, M. (2012). Estimating most productive scale size with imprecise-chance constrained input–output orientation model in data envelopment analysis. Computers & Industrial Engineering, 63(1), 254–261. https://doi.org/10.1016/j.cie.2012.02.009
   Web of Science ®Google Scholar
• Färe, R., & Charles, V. (2018). A directional distance function approach to void the non-Archimedean in DEA. Journal of the Operational Research Society, 69(5), 772–775. https://doi.org/10.1057/s41274-017-0264-x
   Web of Science ®Google Scholar
• Färe, R., Grosskopf, S., & Weber, W. L. (2006). Shadow prices and pollution costs in US agriculture. Ecological Economics, 56(1), 89–103. https://doi.org/10.1016/j.ecolecon.2004.12.022
   Web of Science ®Google Scholar
• Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A (General), 120(3), 253–281. https://doi.org/10.2307/2343100
   Web of Science ®Google Scholar
• Ghobadi, S. (2020). A dynamic DEA model for resource allocation. International Journal of Mathematics in Operational Research, 17(1), 50–77. https://doi.org/10.1504/IJMOR.2020.109053
   Google Scholar
• Huang, T. H., Lin, C. I., & Chen, K. C. (2017). Evaluating efficiencies of Chinese commercial banks in the context of stochastic multistage technologies. Pacific-Basin Finance Journal, 41, 93–110. https://doi.org/10.1016/j.pacfin.2016.12.008
   Web of Science ®Google Scholar
• Izadikhah, M., & Farzipoor Saen, R. (2023). Developing a linear stochastic two-stage data envelopment analysis model for evaluating sustainability of supply chains: A case study in welding industry. Annals of Operations Research, 322(1), 195–215. https://doi.org/10.1007/s10479-021-04160-7
   Web of Science ®Google Scholar
• Izadikhah, M., & Saen, R. F. (2018). Assessing sustainability of supply chains by chance-constrained two-stage DEA model in the presence of undesirable factors. Computers & Operations Research, 100, 343–367. https://doi.org/10.1016/j.cor.2017.10.002
   Web of Science ®Google Scholar
• Izadikhah, M., Azadi, M., Shokri Kahi, V., & Farzipoor Saen, R. (2019). Developing a new chance constrained NDEA model to measure the performance of humanitarian supply chains. International Journal of Production Research, 57(3), 662–682. https://doi.org/10.1080/00207543.2018.1480840
   Web of Science ®Google Scholar
• Jradi, S., & Ruggiero, J. (2019). Stochastic data envelopment analysis: A quantile regression approach to estimate the production frontier. European Journal of Operational Research, 278(2), 385–393. https://doi.org/10.1016/j.ejor.2018.11.017
   Web of Science ®Google Scholar
• Kao, C., & Liu, S.-T. (2019). Stochastic efficiency measures for production units with correlated data. European Journal of Operational Research, 273(1), 278–287. https://doi.org/10.1016/j.ejor.2018.07.051
   Web of Science ®Google Scholar
• Land, K. C., Lovell, C. K., & Thore, S. (1993). Chance‐constrained data envelopment analysis. Managerial and Decision Economics, 14(6), 541–554. https://doi.org/10.1002/mde.4090140607
   Google Scholar
• Li, W., Wang, W., Gao, H., Zhu, B., Gong, W., Liu, Y., & Qin, Y. (2020). Evaluation of regional metafrontier total factor carbon emission performance in China’s construction industry: Analysis based on modified non-radial directional distance function. Journal of Cleaner Production, 256, 120425. https://doi.org/10.1016/j.jclepro.2020.120425
   Web of Science ®Google Scholar
• Lin, R. (2020). Cross-efficiency evaluation capable of dealing with negative data: A directional distance function based approach. Journal of the Operational Research Society, 71(3), 505–516. https://doi.org/10.1080/01605682.2019.1567652
   Web of Science ®Google Scholar
• Lin, R., & Liu, Q. (2022). Directional distance based efficiency decomposition for series system in network data envelopment analysis. Journal of the Operational Research Society, 73(8), 1873–1888. https://doi.org/10.1080/01605682.2021.1931498
   Web of Science ®Google Scholar
• Lin, R., & Tu, C. (2021). Cross-efficiency evaluation and decomposition with directional distance function in series and parallel systems. Expert Systems with Applications, 177, 114933. https://doi.org/10.1016/j.eswa.2021.114933
   Web of Science ®Google Scholar
• Lin, S.-W., Lu, W.-M., & Lin, F. (2021). Entrusting decisions to the public service pension fund: An integrated predictive model with additive network DEA approach. Journal of the Operational Research Society, 72(5), 1015–1032. https://doi.org/10.1080/01605682.2020.1718011
   Web of Science ®Google Scholar
• Liu, W., Wang, Y.-M., & Lyu, S. (2017). The upper and lower bound evaluation based on the quantile efficiency in stochastic data envelopment analysis. Expert Systems with Applications, 85, 14–24. https://doi.org/10.1016/j.eswa.2017.05.023
   Web of Science ®Google Scholar
• Liu, X., & Liu, J. (2016). Measurement of low carbon economy efficiency with a three-stage data envelopment analysis: A comparison of the largest twenty CO2 emitting countries. International Journal of Environmental Research and Public Health, 13(11), 1116. https://doi.org/10.3390/ijerph13111116
   PubMed Web of Science ®Google Scholar
• Liu, Y., Lee, J. M., & Lee, C. (2020). The challenges and opportunities of a global health crisis: The management and business implications of COVID-19 from an Asian perspective. Asian Business & Management, 19(3), 277–297. https://doi.org/10.1057/s41291-020-00119-x
   Web of Science ®Google Scholar
• Mehdizadeh, S., Amirteimoori, A., Charles, V., Behzadi, M. H., & Kordrostami, S. (2021). Measuring the efficiency of two-stage network processes: A satisficing DEA approach. Journal of the Operational Research Society, 72(2), 354–366. https://doi.org/10.1080/01605682.2019.1671151
   Web of Science ®Google Scholar
• Mensah, E. K. (2020). Robust data envelopment analysis via ellipsoidal uncertainty sets with application to the Italian banking industry. Decisions in Economics and Finance, 43(2), 491–518. https://doi.org/10.1007/s10203-020-00299-3
   Web of Science ®Google Scholar
• Mitropoulos, P., Talias, Μ. A., & Mitropoulos, I. (2015). Combining stochastic DEA with Bayesian analysis to obtain statistical properties of the efficiency scores: An application to Greek public hospitals. European Journal of Operational Research, 243(1), 302–311. https://doi.org/10.1016/j.ejor.2014.11.012
   Web of Science ®Google Scholar
• Moheb-Alizadeh, H., & Handfield, R. (2018). An integrated chance-constrained stochastic model for efficient and sustainable supplier selection and order allocation. International Journal of Production Research, 56(21), 6890–6916. https://doi.org/10.1080/00207543.2017.1413258
   Web of Science ®Google Scholar
• Nasseri, S. H., Ebrahimnejad, A., & Gholami, O. (2018). Fuzzy stochastic data envelopment analysis with undesirable outputs and its application to banking industry. International Journal of Fuzzy Systems, 20(2), 534–548. https://doi.org/10.1007/s40815-017-0367-1
   Web of Science ®Google Scholar
• Olesen, O. B., & Petersen, N. (1995). Chance constrained efficiency evaluation. Management Science, 41(3), 442–457. https://doi.org/10.1287/mnsc.41.3.442
   Web of Science ®Google Scholar
• Olesen, O. B., & Petersen, N. C. (2016). Stochastic data envelopment analysis-A review. European Journal of Operational Research, 251(1), 2–21. https://doi.org/10.1016/j.ejor.2015.07.058
   Web of Science ®Google Scholar
• Pastor, J. T., Ruiz, J. L., & Sirvent, I. (1999). An enhanced DEA Russell graph efficiency measure. European Journal of Operational Research, 115(3), 596–607. https://doi.org/10.1016/S0377-2217(98)00098-8
   Web of Science ®Google Scholar
• Peykani, P., Mohammadi, E., & Emrouznejad, A. (2021). An adjustable fuzzy chance-constrained network DEA approach with application to ranking investment firms. Expert Systems with Applications, 166, 113938. https://doi.org/10.1016/j.eswa.2020.113938
   Web of Science ®Google Scholar
• Phung, M.-T., Cheng, C.-P., Guo, C., & Kao, C.-Y. (2020). Mixed network DEA with shared resources: A case of measuring performance for banking industry. Operations Research Perspectives, 7, 100173. https://doi.org/10.1016/j.orp.2020.100173
   Web of Science ®Google Scholar
• Sengupta, J. K. (1982). Efficiency measurement in stochastic input-output systems. International Journal of Systems Science, 13(3), 273–287. https://doi.org/10.1080/00207728208926348
   Web of Science ®Google Scholar
• Shephard, R. W. (1953). Cost and Production Functions. Princeton: Princeton University Press.
   Google Scholar
• Shi, Y., Yu, A., Higgins, H. N., & Zhu, J. (2021). Shared and unsplittable performance links in network DEA. Annals of Operations Research, 303(1–2), 507–528. https://doi.org/10.1007/s10479-020-03882-4
   Web of Science ®Google Scholar
• Shih, N. J., & Lin, C. Y. (2019). The evolving urban fabric and contour of old mountain streets in Taiwan. Tourism Geographies, 21(1), 24–53. https://doi.org/10.1080/14616688.2017.1388437
   Web of Science ®Google Scholar
• Shiraz, R. K., Hatami-Marbini, A., Emrouznejad, A., & Fukuyama, H. (2020). Chance-constrained cost efficiency in data envelopment analysis model with random inputs and outputs. Operational Research, 20(3), 1863–1898. https://doi.org/10.1007/s12351-018-0378-1
   Web of Science ®Google Scholar
• Shwartz, M., Burgess, J. F., Jr., & Zhu, J. (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, 253(2), 489–502. https://doi.org/10.1016/j.ejor.2016.02.049
   Web of Science ®Google Scholar
• Simar, L., & Wilson, P. W. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136(1), 31–64. https://doi.org/10.1016/j.jeconom.2005.07.009
   Web of Science ®Google Scholar
• Sitas, R. (2020). Creative cities, graffiti and culture-led development in South Africa: Dlala Indima (‘Play Your Part’). International Journal of Urban and Regional Research, 44(5), 821–840. https://doi.org/10.1111/1468-2427.12894
   Web of Science ®Google Scholar
• Su, Z., Aaron, J. R., McDowell, W. C., & Lu, D. D. (2019). Sustainable synergies between the cultural and tourism industries: An efficiency evaluation perspective. Sustainability, 11(23), 6607. https://doi.org/10.3390/su11236607
   Web of Science ®Google Scholar
• Tang, J., Zhu, H. L., Liu, Z., Jia, F., & Zheng, X. X. (2019). Urban sustainability evaluation under the modified TOPSIS based on grey relational analysis. International Journal of Environmental Research and Public Health, 16(2), 256. https://doi.org/10.3390/ijerph16020256
   PubMed Web of Science ®Google Scholar
• Tavana, M., Izadikhah, M., Di Caprio, D., & Farzipoor Saen, R. (2018). A new dynamic range directional measure for two-stage data envelopment analysis models with negative data. Computers & Industrial Engineering, 115, 427–448. https://doi.org/10.1016/j.cie.2017.11.024
   Web of Science ®Google Scholar
• Tavana, M., Khanjani Shiraz, R., & Hatami-Marbini, A. (2014). A new chance-constrained DEA model with birandom input and output data. Journal of the Operational Research Society, 65(12), 1824–1839. https://doi.org/10.1057/jors.2013.157
   Web of Science ®Google Scholar
• Tavassoli, M., & Farzipoor Saen, R. (2022). A stochastic data envelopment analysis approach for multi-criteria ABC inventory classification. Journal of Industrial and Production Engineering, 39(6), 415–429. https://doi.org/10.1080/21681015.2022.2037761
   Web of Science ®Google Scholar
• Tavassoli, M., Fathi, A., & Farzipoor Saen, R. (2021). Developing a new super-efficiency DEA model in the presence of both zero data and stochastic data: A case study in the Iranian airline industry. Benchmarking: An International Journal, 28(1), 42–65. https://doi.org/10.1108/BIJ-01-2020-0044
   Web of Science ®Google Scholar
• Tavassoli, M., Saen, R. F., & Zanjirani, D. M. (2020). Assessing sustainability of suppliers: A novel stochastic-fuzzy DEA model. Sustainable Production and Consumption, 21, 78–91. https://doi.org/10.1016/j.spc.2019.11.001
   Web of Science ®Google Scholar
• Thompson, B. S., & Friess, D. A. (2019). Stakeholder preferences for payments for ecosystem services (PES) versus other environmental management approaches for mangrove forests. Journal of Environmental Management, 233, 636–648. https://doi.org/10.1016/j.jenvman.2018.12.032
   PubMed Web of Science ®Google Scholar
• Tsionas, E. G., & Papadakis, E. N. (2010). A Bayesian approach to statistical inference in stochastic DEA. Omega, 38(5), 309–314. https://doi.org/10.1016/j.omega.2009.02.003
   Web of Science ®Google Scholar
• Van Puyenbroeck, T., Montalto, V., & Saisana, M. (2021). Benchmarking culture in Europe: A data envelopment analysis approach to identify city-specific strengths. European Journal of Operational Research, 288(2), 584–597. https://doi.org/10.1016/j.ejor.2020.05.058
   Web of Science ®Google Scholar
• Wang, K., Xian, Y., Lee, C.-Y., Wei, Y.-M., & Huang, Z. (2019). On selecting directions for directional distance functions in a non-parametric framework: A review. Annals of Operations Research, 278(1-2), 43–76. https://doi.org/10.1007/s10479-017-2423-5
   Web of Science ®Google Scholar
• Wanke, P., Barros, C. P., & Emrouznejad, A. (2016). Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping: A case of Mozambican banks. European Journal of Operational Research, 249(1), 378–389. https://doi.org/10.1016/j.ejor.2015.10.018
   Web of Science ®Google Scholar
• Wu, Y.-C., & Lin, S.-W. (2021). Integrated approach for exploring critical elements that affect sustainable development of cultural and creative industries. Journal of Business Economics and Management, 22(3), 596–615. https://doi.org/10.3846/jbem.2021.14261
   Web of Science ®Google Scholar
• Wu, Y.-C., & Lin, S.-W. (2022). Efficiency evaluation of Asia’s cultural tourism using a dynamic DEA approach. Socio-Economic Planning Sciences, 84, 101426. https://doi.org/10.1016/j.seps.2022.101426
   Web of Science ®Google Scholar
• Xu, H., Zheng, Y., Li, Y., Xu, X., & Xie, Y. (2023). Operational management efficiency and club convergence of Chinese state-owned toll road companies: A three-stage SBM-DEA model. Research in Transportation Business & Management, 46, 100915. https://doi.org/10.1016/j.rtbm.2022.100915
   Web of Science ®Google Scholar
• Yu, A., Zhang, P., & Rudkin, S. (2022). Simultaneous action or protection after production? Decision making based on a chance-constrained approach by measuring environmental performance considering PM2.5. Socio-Economic Planning Sciences, 80, 101147. https://doi.org/10.1016/j.seps.2021.101147
   Web of Science ®Google Scholar
• Yu, M.-M., See, K. F., & Hsiao, B. (2022). Integrating group frontier and metafrontier directional distance functions to evaluate the efficiency of production units. European Journal of Operational Research, 301(1), 254–276. https://doi.org/10.1016/j.ejor.2021.10.054
   Web of Science ®Google Scholar
• Zeng, S., Hu, M., & Su, B. (2016). Research on investment efficiency and policy recommendations for the culture industry of China based on a three-stage DEA. Sustainability, 8(4), 324. https://doi.org/10.3390/su8040324
   Web of Science ®Google Scholar
• Zha, Y., Zhao, L., & Bian, Y. (2016). Measuring regional efficiency of energy and carbon dioxide emissions in China: A chance constrained DEA approach. Computers & Operations Research, 66, 351–361. https://doi.org/10.1016/j.cor.2015.07.021
   Web of Science ®Google Scholar
• Zhang, Z., & Liao, H. (2022). A stochastic cross-efficiency DEA approach based on the prospect theory and its application in winner determination in public procurement tenders. Annals of Operations Research, 1–29. https://doi.org/10.1007/s10479-022-04539-0
   Web of Science ®Google Scholar
• Zhao, L., Zha, Y., & Liang, L. (2018). Uncertainty, risk, and the efficiencies of the principal and the agent: A chance constrained data envelopment analysis approach. Mathematical Problems in Engineering, 2018, 1–13. https://doi.org/10.1155/2018/8373614
   Web of Science ®Google Scholar
• Zhou, Z., Lin, L., Xiao, H., Ma, C., & Wu, S. (2017). Stochastic network DEA models for two-stage systems under the centralized control organization mechanism. Computers & Industrial Engineering, 110, 404–412. https://doi.org/10.1016/j.cie.2017.06.005
   Web of Science ®Google Scholar
• Zhu, Q. Y., Wu, J., Li, X. C., & Xiong, B. B. (2017). China’s regional natural resource allocation and utilization: A DEA-based approach in a big data environment. Journal of Cleaner Production, 142, 809–818. https://doi.org/10.1016/j.jclepro.2016.02.100
   Web of Science ®Google Scholar