考虑决策自主程度的多中心化系统投入分配研究

doi:10.16381/j.cnki.issn1003-207x.2023.1332

Abstract

Abstract:

Whether or not each operating unit in a centralized organization performs in the best interests of the whole system often depends on the incentives in the allocation mechanism. However， the traditional DEA approach is a tool designed to assess past performance， with insufficient recognition of incentives in the allocation mechanism. Considering that operating units belong to different centralized organizations and face different constraints in the technical environment， it is difficult to motivate all units with a single target system. Furthermore， given their varying degrees of decision-making autonomy in resource allocation and production planning， methods for measuring performance using a uniform benchmark are no longer applicable. Therefore， focusing on the characteristics of “decision-making autonomy” and “inter-group heterogeneity”， a DEA-based allocation methodology is proposed， and the “boundary benchmark” is redifined by constructing the appropriate quantity and location of incentive maps to measure efficiency. And then inputs are reallocated based on efficiency values by expanding the scope of compensation and maximizing the incentive effect of the distribution. Finally， an empirical study is conducted using 26 undergraduate institutions in Jiangsu Province as a case example. The proposed allocation scheme exerts positive incentive effects on both efficient and inefficient units， providing a new methodological tool for optimizing the allocation of science and technology inputs in regional higher education institutions.

Key words: multi-centralized organizations, input allocation, decision-making autonomy, DEA

CLC Number:

F224.31

Xi Xiong,Guoliang Yang,Dequn Zhou, et al. Research on Incentive Allocation of Inputs in Multi-centralized Organizations Considering the Degree of Decision-making Autonomy:Reallocation of Scientific and Technological Inputs in Higher Education Institutions[J]. Chinese Journal of Management Science, 2026, 34(11): 99-107.

Figures/Tables 8

所属系统	变量	自主权下放程度
所属系统	变量	$K = 1$	$K = 2$	$K = 3$	$K = 4$
部属高校系统	Eff>1	1（10.00%）	5（50.00%）	5（50.00%）	5（50.00%）
	效率均值	0.6361	0.8053	0.8038	0.8036
省属高校系统	Eff>1	3（18.75%）	3（18.75%）	——	——
	效率均值	0.6724	0.6484	——	——

References 31

[1]	李艺萌. 目标困难度、灵活度和企业绩效的关系研究［D］. 西安：西安科技大学， 2021.
	Li Y M. Target difficulty， target flexibility， and firm performance ［D］. Xi’an： Xi’an University of Science and Technology， 2021.
[2]	Cook W D， Ramón N， Ruiz J L， et al. DEA-based benchmarking for performance evaluation in pay-for-performance incentive plans［J］. Omega， 2019， 84： 45-54.
[3]	Golany B. An interactive MOLP procedure for the extension of DEA to effectiveness analysis［J］. Journal of the Operational Research Society， 1988， 39（8）： 725-734.
[4]	Lozano S， Villa G. Centralized resource allocation using data envelopment analysis［J］. Journal of Productivity Analysis， 2004， 22（1）： 143-161.
[5]	Yadollahi A H， Ebrahimnejad A， Kazemi Matin R. Centralized resource reallocation in two-stage network structures with undesirable products［J］. Computational and Applied Mathematics， 2022， 41（6）： 228.
[6]	Yang J， Li Y， Fang L. Financing capacity planning with environmental considerations： A non-parametric analysis［J］. Omega， 2023， 118： 102866.
[7]	张倩伟，刘一博，王新宇.基于新型交叉效率的资源分配机制［J］.系统工程理论与实践，2024，44 （5）： 1522-1533.
	Zhang Q W， Liu Y B， Wang X Y. A resource allocation mechanism based on novel cross-efficiency［J］. Systems Engineering-Theory & Practice， 2024，44 （5）： 1522-1533
[8]	Lozano S， Contreras I. Centralised resource allocation using lexicographic goal programming. Application to the spanish public university system［J］. Socio-Economic Planning Sciences， 2022， 84： 101419.
[9]	Afsharian M， Ahn H， Thanassoulis E. A DEA-based incentives system for centrally managed multi-unit organisations［J］. European Journal of Operational Research， 2017， 259（2）： 587-598.
[10]	Jahanshahloo G R， Memariani A， Lotfi F H， et al. A note on some of DEA models and finding efficiency and complete ranking using common set of weights［J］. Applied Mathematics and Computation， 2005， 166（2）： 265-281.
[11]	Saati S， Hatami-Marbini A， Agrell P J， et al. A common set of weight approach using an ideal decision making unit in data envelopment analysis［J］. Journal of Industrial and Management Optimization， 2012， 8（3）： 623-637.
[12]	孙雪莲，郭良栋. 改进的公共权重的区间DEA效率评价［J］. 数学的实践与认识， 2023， 53（10）： 106-115.
	Sun X L， Guo L D. Improved public weights for interval DEA efficiency evaluation［J］. Mathematics in Practice and Theory， 2023， 53（10）： 106-115.
[13]	Ji Z， Wu X， Guo J， et al. Goal-oriented common benchmarking based on global and stepwise reallocation： An application to 18 ports in Korea［J］. Expert Systems with Applications， 2024， 240： 122462.
[14]	Beasley J E. Allocating fixed costs and resources via data envelopment analysis［J］. European Journal of Operational Research， 2003， 147（1）： 198-216.
[15]	Kao C， Hung H T. Data envelopment analysis with common weights： The compromise solution approach［J］. Journal of the Operational Research Society， 2005， 56（10）： 1196-1203.
[16]	Bogetoft P. DEA-based yardstick competition： The optimality of best practice regulation［J］. Annals of Operations Research， 1997， 73： 277-298.
[17]	Varmaz A， Varwig A， Poddig T. Centralized resource planning and Yardstick competition［J］. Omega- The International Journal of Management Science， 2013， 41（1）： 112-118.
[18]	Afsharian M， Ahn H， Thanassoulis E. A DEA-based incentives system for centrally managed multi-unit organisations［J］. European Journal of Operational Research， 2017， 259（2）： 587-598.
[19]	Fang L. An incentive approach based on data envelopment analysis for intra-organization yardstick competition［J］. Journal of the Operational Research Society， 2020， 71（1）： 153-160.
[20]	Chao S L， Yu M M. Applying data envelopment analysis to allocate incentive bonuses for container terminal operators［J］. Transport Policy， 2022， 125： 231-240.
[21]	Thanassoulis E. A data envelopment analysis approach to clustering operating units for resource allocation purposes［J］. Omega-The International Journal of Management Science， 1996， 24（4）： 463-476.
[22]	Afsharian M， Ahn H， Thanassoulis E. A frontier-based system of incentives for units in organisations with varying degrees of decentralisation［J］. European Journal of Operational Research， 2019， 275（1）： 224-237.
[23]	Dai Q Z， Li Y J， Lei X Y， et al. A DEA-based incentive approach for allocating common revenues or fixed costs［J］. European Journal of Operational Research， 2021， 292（2）： 675-686.
[24]	O’Donnell C J， Prasada Rao D S， Battese G E. Metafrontier frameworks for the study of firm-level efficiencies and technology ratios［J］. Empirical Economics， 2008， 34（2）： 231-255.
[25]	Zhang N， Kong F， Yu Y. Measuring ecological total-factor energy efficiency incorporating regional heterogeneities in China［J］. Ecological Indicators， 2015， 51： 165-172.
[26]	Wang X， Hu H. Sustainability in Chinese higher educational institutions’ social science research： A performance interface toward efficiency［J］. Sustainability， 2017， 9（11）： 1952.
[27]	苗玉宁，杨冬英. 基于综合评价方法的中部地区科技资源配置效率分析［J］. 中国软科学， 2020（3）： 134-149.
	Miao Y N， Yang D Y. Analysis on the efficiency of allocation of scientific and technological resources in central China based on the comprehensive evaluation method［J］. China Soft Science， 2020（3）： 134-149.
[28]	李康，范跃进. “双一流”目标下一流大学科研效率评价［J］. 科研管理， 2022， 43（9）： 41-47.
	Li K， Fan Y J. An evaluation of the scientific research efficiency of first-class universities under the goal of construction of “double first-class university”［J］. Science Research Management， 2022， 43（9）： 41-47.
[29]	杨敏，费锡玥，魏宇琪，等. 基于资源共享与子系统交互的两阶段DEA评价方法——兼对我国“一流大学”科研绩效的评价［J］. 中国管理科学， 2022， 30（2）： 256-263.
	Yang M， Fei X Y， Wei Y Q， et al. Two-stage DEA evaluation method based on resource sharing and subsystem interaction—Evaluation of scientific research performance for “first-class” universities in China［J］. Chinese Journal of Management Science， 2022， 30（2）： 256-263.
[30]	周文泳，金为开，柏方云. 研究型大学科研效率分类评价［J］. 科研管理， 2018， 39（S1）： 44-51.
	Zhou W Y， Jin W K， Bai F Y. A classified evaluation of scientific research efficiency of research oriented universities［J］. Science Research Management， 2018， 39（S1）： 44-51.
[31]	Xiong X， Yang G L， Guan Z C. Estimating the multi-period efficiency of high-tech research institutes of the Chinese academy of sciences： A dynamic slacks-based measure［J］. Socio-Economic Planning Sciences， 2020， 71： 100855.

中心化系统	权力下放程度（K=2）	DMU聚类	效率得分				效率≥1
中心化系统	权力下放程度（K=2）	DMU聚类	25%分位数	50%分位数	75%分位数	均值	效率≥1
部属系统	集群a1	MU1、MU2、MU3、MU8、MU9	0.6437	0.7935	1.0000	0.8058	2/5
部属系统	集群a2	MU4、MU5、MU6、MU7、MU10	0.6050	1.0000	1.0000	0.8048	3/5
省属系统	集群b1	PU9、PU10、PU12、PU14、PU16	0.2088	0.6920	0.7460	0.5687	1/5
省属系统	集群b2	PU1、PU2、PU3、PU4、PU5、PU6、PU7、PU8、PU11、PU13、PU15	0.5396	0.6338	0.9044	0.6846	2/11

DMU	传统DEA-LP 模型（1）	单一中心化系统模型（2）	多中心化系统模型（4）	多中心化系统模型（6）
MU1	0.4680	0.3445	0.5767	0.3445
MU2	0.5510	0.4377	0.6437	0.4220
MU3	0.8595	0.6521	1.0149	0.5996
MU4	0.6145	0.6761	1.0003	0.6761
MU5	0.9663	0.8751	1.0000	0.8136
MU6	1.0000	1.0000	1.0000	1.0000
MU7	0.2808	0.2761	0.4185	0.2761
MU8	0.6115	0.4939	0.7935	0.4939
MU9	0.6444	0.6776	1.0000	0.6776
MU10	0.3650	0.4121	0.6050	0.4121
PU1	0.5462	0.4446	0.4446	0.4446
PU2	1.0000	1.0000	1.0000	1.0000
PU3	1.0479	1.0000	1.0000	1.0000
PU4	0.6258	0.7229	0.7229	0.7229
PU5	1.0893	0.9250	0.6039	0.7967
PU6	0.9558	0.9044	0.9044	0.9044
PU7	0.6299	0.6576	0.6576	0.6576
PU8	0.6715	0.5396	0.5396	0.5396
PU9	1.3389	1.0000	1.0000	1.0000
PU10	0.5806	0.6048	0.7460	0.6048
PU11	0.8746	0.7302	0.5618	0.7115
PU12	0.2134	0.1881	0.2088	0.1881
PU13	0.4142	0.4622	0.4622	0.4622
PU14	0.1501	0.1868	0.1966	0.1868
PU15	0.8034	0.6338	0.6338	0.6338
PU16	0.4963	0.6250	0.6920	0.6250
均值	0.6846	0.6335	0.7087	0.6228
部属高校系统均值	0.6361	0.5845	0.8053	0.5716
省属高校系统均值	0.7149	0.6641	0.6484	0.6549

DMU	原始科技投入	分配后科技投入	调整方向
MU1	92451.47	53315.72	-39135.75
MU2	44583.62	28697.87	-15885.75
MU3	18333.39	18606.59	273.20
MU4	9657.79	9660.90	3.11
MU5	30706.47	30706.47	0.00
MU6	50467.01	50467.01	0.00
MU7	80792.31	33813.68	-46978.63
MU8	50341.63	39947.55	-10394.08
MU9	20857.73	20857.73	0.00
MU10	6463.67	3910.75	-2552.93
PU1	66858.19	29724.31	-37133.88
PU2	10224.70	10224.70	0.00
PU3	10338.55	10338.55	0.00
PU4	20183.02	14591.14	-5591.88
PU5	27808.00	16794.07	-11013.93
PU6	8752.96	7916.32	-836.64
PU7	24633.68	16198.87	-8434.81
PU8	27742.57	14970.70	-12771.87
PU9	13638.35	13638.35	0.00
PU10	8744.65	6523.72	-2220.93
PU11	43630.04	24510.39	-19119.65
PU12	133428.44	27861.34	-105567.10
PU13	49449.60	22854.52	-26595.08
PU14	34293.18	6743.11	-27550.07
PU15	33204.17	21045.24	-12158.93
PU16	2425.00	1678.05	-746.95
总计	920010.20	535597.67	-384412.54
均值	35385.01	20599.91	-14785.10

分配结果	整体系统				部属高校系统	省属高校系统
分配结果	均值	25%分位数	50%分位数	75%分位数	部属高校系统	省属高校系统
成本补偿(Eff>1)/%	0.17	0.00	0.00	0.02	0.21	0.00
成本节约(Eff<1) /%	-50.09	-53.78	-39.55	-30.80	-41.85	-56.06

Research on Incentive Allocation of Inputs in Multi-centralized Organizations Considering the Degree of Decision-making Autonomy:Reallocation of Scientific and Technological Inputs in Higher Education Institutions

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