农业产业集群创新效率及影响因素——基于山东省寿光蔬菜产业集群的实证分析

doi:10.11820/dlkxjz.2014.07.015

摘要/Abstract

摘要：

创新是当前农业产业集群研究的重要内容,掌握集群创新状况对指导农业产业集群发展具有重要意义。结合实地调研的数据,利用DEA-Tobit两阶段模型,对山东省寿光蔬菜产业集群企业创新效率及其影响因素进行研究。结果表明:寿光蔬菜产业集群规模已接近最优状态,综合创新效率处于中等偏上水平,但仍未达到最优状态,主要原因是集群在科研技术投入、创新资源利用配置等方面不足造成的。进一步Tobit回归的影响因素分析发现:企业知识技术吸收扩散能力、政府财政支持力度、企业家创新精神和企业发展的外向性与农业产业集群企业创新效率存在显著的正相关关系,而企业规模对农业产业集群企业创新效率影响并不显著。最后,根据集群创新影响因素分析,提出发展建议。

关键词: 农业产业集群, 创新效率, DEA, Tobit回归分析, 寿光蔬菜产业集群

Abstract:

Innovation is an important research topic of agricultural cluster at present. However, existing research on agricultural industry cluster has focused on the evolution mechanism, innovation models and competitiveness of such clusters and studies on innovation efficiency of agricultural clusters are relatively few. Exploring the key influencing factors of innovation will play an important role in guiding the development of agricultural clusters. Innovation of agricultural industry cluster is often reflected in product innovation and benefit, mainly through investment in scientific research, developing innovation networks, among others. The two-phase DEA-Tobit model has been used to analyze efficiency and influencing factors of multiple inputs and outputs for national, regional, and agricultural innovation systems and is considered an effective quantitative analysis method. Based on survey data, this paper analyzes the innovation efficiency and influencing factors of the vegetable industrial cluster in Shouguang County, Shandong Province, China, using a two-phase DEA-Tobit model. The DEA model analysis shows that innovation in this vegetable industrial cluster is in a good condition, with the large enterprises being the best. The scale of the vegetable industrial cluster in Shouguang County is at approximately the optimal state, while the comprehensive innovation efficiency is only at the average level, not reaching the optimal state. The main reason for this is the pure technical inefficiency derived from the lack of scientific research and technology strength. Tobit regression analysis indicates that knowledge dissemination and absorption capacity of enterprises, financial support of the government, innovative spirit of entrepreneurs and enterprises' development extroversion strength have significant positive correlation with agriculture innovation, while the scale of enterprises do not have significant influence on the cluster's innovation efficiency. The knowledge dissemination and absorption capacity of enterprises not only enables enterprises to have innovation resources in the cluster innovation network, but also has effects on innovation efficiency. Financial support of the government has the greatest influence on industrial cluster innovation, and the government has an important role in innovation of agricultural industry cluster. The innovative spirit of entrepreneurs represents an attitude towards innovation and development, thereby affecting enterprise investment in research and innovation. The paper also presents suggestions for encouraging agricultural innovation. Through facilitating the development of the cluster's innovation network, strengthening support from government agencies and foreign investment and cultivating the innovative spirit of entrepreneurs, efficiency oriented enterprise resource utilization will prevail.

Key words: agricultural industrial cluster, innovation efficiency, DEA, Tobit regression analysis, Shouguang vegetable industrial cluster

中图分类号:

K902

史焱文, 李二玲, 李小建. 农业产业集群创新效率及影响因素——基于山东省寿光蔬菜产业集群的实证分析[J]. 地理科学进展, 2014, 33(7): 1000-1008.

Yanwen SHI, Erling LI, Xiaojian LI. Innovation efficiency and influencing factors:analysis of 32 enterprises in the Shouguang vegetable industrial cluster of Shandong Province[J]. PROGRESS IN GEOGRAPHY, 2014, 33(7): 1000-1008.

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类型	大	中	小	合计
样本企业数	6	9	17	32
企业总数	15	48	347	410
样本企业所占比例/%	40	18.75	4.89	7.8
规模(固定资产)	>3亿	5000万~3亿	<5000万

指标维度	指标	均值	标准差	最大值	最小值
投入	科研经费投入/万元	309.063	452.444	2000.000	10.000
	科研人员投入/人	22.719	24.936	100.000	2.000
	合作企业数量/家	4.313	2.494	11.000	1.000
	土地投入/公顷	3774.063	8946.092	50000.000	20.000
	合作科研院校/家	1.500	1.566	5.000	0.000
产出	新品种/种	12.063	13.026	50.000	1.000
产出	新产品利润收入/万元	5633.497	11629.431	53333.300	10.000

影响因素指标	均值	标准差	最大值	最小值
企业知识技术吸收扩散能力	7.281	2.317	15.000	3.000
企业规模	0.340	0.611	2.500	0.001
政府财政支持	0.390	0.156	0.600	0.100
企业家创新精神	5.220	1.497	7.000	1.000
企业外向性强度	9.875	4.070	17.000	3.000

DMU	TE	PTE	SE	RTS	DMU	TE	PTE	SE	RTS
DMU₁	1	1	1	---	DMU₁₈	1	1	1	---
DMU₂	0.986	1	0.986	drs	DMU₁₉	0.997	1	0.997	irs
DMU₃	1	1	1	---	DMU₂₀	1	1	1	---
DMU₄	1	1	1	---	DMU₂₁	0.960	1	0.960	irs
DMU₅	1	1	1	---	DMU₂₂	0.800	1	0.800	irs
DMU₆	0.521	0.628	0.829	irs	DMU₂₃	1	1	1	---
DMU₇	0.378	0.378	1	---	DMU₂₄	0.694	1	0.694	irs-
DMU₈	1	1	1	---	DMU₂₅	1	1	1	---
DMU₉	1	1	1	---	DMU₂₆	1	1	1	---
DMU₁₀	0.777	0.788	0.986	irs	DMU₂₇	1	1	1	---
DMU₁₁	0.914	1	0.914	irs	DMU₂₈	1	1	1	---
DMU₁₂	0.320	0.353	0.907	irs	DMU₂₉	0.532	1	0.532	irs
DMU₁₃	1	1	1	---	DMU₃₀	0.714	0.719	0.993	irs
DMU₁₄	0.461	0.462	0.998	drs	DMU₃₁	1	1	1	---
DMU₁₅	0.800	1	0.800	irs	DMU₃₂	1	1	1	---
DMU₁₆	1	1	1	---	Mean	0.713	0.718	0.992	irs
DMU₁₇	1	1	1	---

企业类型	综合创新效率值	样本企业数量	达到最优企业数量	所占比例/%	创新最优企业的总比例/%
大型企业	=1	6	5	83.33	56.25
中型企业		9	4	44.44
小型企业		17	9	52.94