Abstract
How to evaluate and optimize the allocative efficiency of multiple research resources is of great importance for decision-makers and has great practical application value. This is especially true in the case of unbalanced allocation and inefficient utilization of research resources in Chinese higher education institutions (HEIs). Data Envelopment Analysis (DEA) brings a new perspective on the resource allocation problem. However, the conventional paradigm of the resource allocation model in DEA assumes that inputs or outputs can be expanded freely when projecting decision-making units (DMUs) onto the efficient frontier. Thus, it is very difficult to deal with a situation where there is a trade-off between the evaluated units with the fixed-sum output. Considering a common resource allocation problem that occurs in multi-period activities where a carry-over indicator has the fixed-sum characteristic, we extend the study of Yang et al. (Eur J Oper Res 246(1):209–217, 2015) into a dynamic framework by taking the time dimension into account. The proposed approach is applied to reallocate the social scientific resources of Chinese HEIs from 2017 to 2019. The results show that all DMUs are in effective condition after reallocation and the optimal adjustments of newly approved projects can be obtained.
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Notes
Data collected from Educational Statistics Yearbook of China.
Data sources: China Statistical Yearbook on Science and Technology (2020).
References
Agasisti, T., Yang, G.-L., Song, Y.-Y., & Tran, C.-T.T.D. (2021). Evaluating the higher education productivity of Chinese and European “elite” universities using a meta-frontier approach. Scientometrics, 126(7), 5819–5853.
Amirteimoori, A., Masrouri, S., Yang, F., & Kordrostami, S. (2017). Context-based competition strategy and performance analysis with fixed-sum outputs: An application to banking sector. Journal of the Operational Research Society, 68(11), 1461–1469.
Bi, G. B., Feng, C. P., Ding, J. J., Liang, L., & Chu, F. (2014). The linear formulation of the ZSG-DEA models with different production technologies. Journal of the Operational Research Society, 65(8), 1202–1211.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978) Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8.
Chen, L., Guo, M., Li, Y., Liang, L., & Salo, A. (2021). Efficiency intervals, rank intervals and dominance relations of decision-making units with fixed-sum outputs. European Journal of Operational Research, 292(1), 238–249.
Chen, Z., Yang, Z. B., & Yang, L. L. (2020). How to optimize the allocation of research resources? An empirical study based on output and substitution elasticities of universities in Chinese provincial level. Socio-Economic Planning Sciences, 69, 100707.
Chu Ng, Y., & Li, S. K. (2000). Measuring the research performance of Chinese higher education institutions: An application of data envelopment analysis. Education Economics, 8(2), 139–156.
Esmaeilzadeh, A., & Kazemi Matin, R. (2019). Multi-period efficiency measurement of network production systems. Measurement, 134, 835–844.
Ghiyasi, M. (2018). Performance assessment and capital budgeting based on performance. Benchmarking: An International Journal, 25(6), 1729–1745.
Gomes, E. G., & Lins, M. P. E. (2008). Modelling undesirable outputs with zero sum gains data envelopment analysis models. Journal of the Operational Research Society, 59(5), 616–623.
Han, U., Asmild, M., & Kunc, M. (2016). Regional R&D efficiency in Korea from static and dynamic perspectives. Regional Studies, 50(7), 1170–1184.
Jiang, J., Lee, S. K., & Rah, M.-J. (2020). Assessing the research efficiency of Chinese higher education institutions by data envelopment analysis. Asia Pacific Education Review, 21(3), 423–440.
Kao, C. (2013). Dynamic data envelopment analysis: A relational analysis. European Journal of Operational Research, 227(2), 325–330.
Kumar, A., & Thakur, R. R. (2019). Objectivity in performance ranking of higher education institutions using dynamic data envelopment analysis. International Journal of Productivity and Performance Management, 68(4), 774–796.
Lee, B. L., & Worthington, A. C. (2016). A network DEA quantity and quality-orientated production model: An application to Australian university research services. Omega, 60, 26–33.
Lee, S., & Lee, H. (2015). Measuring and comparing the R&D performance of government research institutes: A bottom-up data envelopment analysis approach. Journal of Informetrics, 9(4), 942–953.
Lewis, H. F., & Sexton, T. R. (2004). Network DEA: Efficiency analysis of organizations with complex internal structure. Computers & Operations Research, 31(9), 1365–1410.
Li, Y. J., Hou, W. H., Zhu, W. W., Li, F., & Liang, L. (2021). Provincial carbon emission performance analysis in China based on a Malmquist data envelopment analysis approach with fixed-sum undesirable outputs. Annals of Operations Research, 304(1–2), 233–261.
Lins, M. P. E., Gomes, E. G., de Mello, J., & de Mello, A. (2003). Olympic ranking based on a zero sum gains DEA model. European Journal of Operational Research, 148(2), 312–322.
Lu, W. M. (2012). Intellectual capital and university performance in Taiwan. Economic Modelling, 29(4), 1081–1089.
Ma, Z., See, K. F., Yu, M.-M., & Zhao, C. (2021). Research efficiency analysis of China’s university faculty members: A modified meta-frontier DEA approach. Socio-Economic Planning Sciences, 76, 100944.
Meng, W., Zhang, D., Qi, L., & Liu, W. (2008). Two-level DEA approaches in research evaluation. Omega-International Journal of Management Science, 36(6), 950–957.
Mohan, S. R. (2005). Benchmarking evaluation of performance of public research institutes using data envelopment analysis. Journal of Scientific & Industrial Research, 64(6), 403–410.
Sagarra, M., Mar-Molinero, C., & Agasisti, T. (2017). Exploring the efficiency of Mexican universities: Integrating data envelopment analysis and multidimensional scaling. Omega, 67, 123–133.
Shamohammadi, M., & Oh, D.-H. (2019). Measuring the efficiency changes of private universities of Korea: A two-stage network data envelopment analysis. Technological Forecasting and Social Change, 148, 119730.
Tavares, R. S., Angulo-Meza, L., & Sant’Anna, A. P. (2021). A proposed multistage evaluation approach for Higher Education Institutions based on network Data envelopment analysis: A Brazilian experience. Evaluation and Program Planning, 89, 101984.
Thanassoulis, E., Dey, P. K., Petridis, K., Goniadis, I., & Georgiou, A. C. (2017). Evaluating higher education teaching performance using combined analytic hierarchy process and data envelopment analysis. Journal of the Operational Research Society, 68(4), 431–445.
Villano, R. A., & Tran, C. (2018). Performance of private higher education institutions in Vietnam: Evidence using DEA-based bootstrap directional distance approach with quasi-fixed inputs. Applied Economics, 50(55), 5966–5978.
Wang, D. D. (2019). Performance-based resource allocation for higher education institutions in China. Socio-Economic Planning Sciences, 65, 66–75.
Wang, X., & Hu, H. (2017) Sustainable evaluation of social science research in higher education institutions based on data envelopment analysis. Sustainability, 9(4), 644. https://doi.org/10.3390/su9040644.
Wang, E. C., & Huang, W. (2007). Relative efficiency of R & D activities: A cross-country study accounting for environmental factors in the DEA approach. Research Policy, 36(2), 260–273.
Wang, K., Zhang, X., Wei, Y. M., & Yu, S. W. (2013). Regional allocation of CO2 emissions allowance over provinces in China by 2020. Energy Policy, 54, 214–229.
Wu, J., Zhang, G., Zhu, Q., & Zhou, Z. (2020). An efficiency analysis of higher education institutions in China from a regional perspective considering the external environmental impact. Scientometrics, 122(1), 57–70.
Wu, J., Zhu, Q., Ji, X., Chu, J., & Liang, L. (2016a). Two-stage network processes with shared resources and resources recovered from undesirable outputs. European Journal of Operational Research, 251(1), 182–197.
Wu, J., Zhu, Q. Y., An, Q. X., Chu, J. F., & Ji, X. (2016b). Resource allocation based on context-dependent data envelopment analysis and a multi-objective linear programming approach. Computers & Industrial Engineering, 101, 81–90.
Wubetie, H. T. (2017). Missing data management and statistical measurement of socio-economic status: Application of big data. Journal of Big Data, 4, 47. https://doi.org/10.1186/s40537-017-0099-y
Yaisawarng, S., & Ng, Y. C. (2014). The impact of higher education reform on research performance of Chinese universities. China Economic Review, 31, 94–105.
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.
Yang, G.-L., Fukuyama, H., & Song, Y.-Y. (2018). Measuring the inefficiency of Chinese research universities based on a two-stage network DEA model. Journal of Informetrics, 12(1), 10–30.
Yang, M., Li, Y., Chen, Y., & Liang, L. (2014). An equilibrium efficiency frontier data envelopment analysis approach for evaluating decision-making units with fixed-sum outputs. European Journal of Operational Research, 239(2), 479–489.
Yang, M., Li, Y. J., & Liang, L. (2015). A generalized equilibrium efficient frontier data envelopment analysis approach for evaluating DMUs with fixed-sum outputs. European Journal of Operational Research, 246(1), 209–217.
Zhang, D., Banker, R. D., Li, X., & Liu, W. (2011). Performance impact of research policy at the Chinese Academy of Sciences. Research Policy, 40(6), 875–885.
Zhang, G., Wu, J., & Zhu, Q. (2020). Performance evaluation and enrollment quota allocation for higher education institutions in China. Evaluation and Program Planning, 81, 101821.
Zhu, J., & Cook, W. D. (2007). Modeling data irregularities and structural complexities in data envelopment analysis. Springer.
Acknowledgements
This work is financially supported by the Fundamental Research Funds for the Central Universities (No. NS2022076, No. NK2022003); Jiangsu Planned Projects for Postdoctoral Research Funds (No. 2021K293B); Shandong Provincial Education Science Planning Project (No. 2021QZD003); the Frontier Exploration Project "Optimizing the Allocation of Scientific and Technological Resources and Application of the Double Helix Method" of the Institutes of Science and Development, Chinese Academy of Sciences (No. E2X1201Z).
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Xiong, X., Yang, Gl., Liu, Kd. et al. A proposed fixed-sum carryovers reallocation DEA approach for social scientific resources of Chinese public universities. Scientometrics 127, 4097–4121 (2022). https://doi.org/10.1007/s11192-022-04411-9
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DOI: https://doi.org/10.1007/s11192-022-04411-9