京津冀高技术产业R&D活动效率测度
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摘要
协同创新环境因素是影响区域高技术产业技术创新效率的重要因素。采用京津冀高技术产业1995-2015年的追踪数据,运用三阶段DEA-Windows模型,对京津冀高技术产业R&D活动效率进行比较分析。突出协同创新的环境因素,既剥离了外部环境和随机因素对创新效率的干扰,又考虑了京津冀创新效率的动态演进特征。结果表明:外部环境因素与随机噪声对京津冀高技术产业R&D活动投入产出效率均有显著影响,利用SFA回归法对环境因素和随机噪声进行剔除性分析是合理的。其中,技术获取结构、区域经济关联度、区域贸易依存度、科技型基础设施投资强度、区域比较劳动生产率对R&D经费和新产品开发经费投入冗余均有显著负向影响,而对R&D人员全时当量的影响则是多元的。剔除环境和随机干扰后,北京和天津的R&D活动投入产出效率有所降低,而河北的效率值有所提高。在上述研究的基础上,提出相应的对策建议。
The environmental factors of collaborative innovation are the important factors influencing the efficiency of regional high-tech industry's technological innovation.Based on the tracking data of the Beijing-Tianjin-Hebei high-tech industry from 1995 to 2015,the efficiency of R&D activities in the Beijing-Tianjin-Hebei high-tech industry is compared and analyzed with the three-stage DEA-Windows model.Highlight the environmental factors of collaborative innovation,which not only interfere with the external environment and random factors,but also consider the dynamic evolution of innovation efficiency.The results shows that external environmental factors and random noise have significant effects on the input and output efficiency of R&D activities in Beijing-Tianjin-Hebei high-tech industry.It is reasonable to use SFA regression to analyze environmental factors and random noise.Among them,the technology acquisition structure,regional economic relevance,regional trade dependency,technological infrastructure investment intensity,regional comparative labor productivity have significant negative effects on R&D funding and new product development funding redundancy,while the influence on the R&D personnel of full-time equivalent is diverse.Excluding the environment and random interference,Beijing and Tianjin R&D activities efficiency has been reduced,while the efficiency of Hebei has increased.On the basis of the above research,this paper puts forward the corresponding countermeasures and suggestions.
The environmental factors of collaborative innovation are the important factors influencing the efficiency of regional high-tech industry's technological innovation.Based on the tracking data of the Beijing-Tianjin-Hebei high-tech industry from 1995 to 2015,the efficiency of R&D activities in the Beijing-Tianjin-Hebei high-tech industry is compared and analyzed with the three-stage DEA-Windows model.Highlight the environmental factors of collaborative innovation,which not only interfere with the external environment and random factors,but also consider the dynamic evolution of innovation efficiency.The results shows that external environmental factors and random noise have significant effects on the input and output efficiency of R&D activities in Beijing-Tianjin-Hebei high-tech industry.It is reasonable to use SFA regression to analyze environmental factors and random noise.Among them,the technology acquisition structure,regional economic relevance,regional trade dependency,technological infrastructure investment intensity,regional comparative labor productivity have significant negative effects on R&D funding and new product development funding redundancy,while the influence on the R&D personnel of full-time equivalent is diverse.Excluding the environment and random interference,Beijing and Tianjin R&D activities efficiency has been reduced,while the efficiency of Hebei has increased.On the basis of the above research,this paper puts forward the corresponding countermeasures and suggestions.
引文
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[21]成力为,孙玮,王九云.要素市场不完全视角下的高技术产业创新效率——基于三阶段DEA-Windows的内外资源配置效率和规模效率比较[J].科学学研究,2011,29(6):930-938.
[22]童健,武康平.经济发展进程中的基础设施投资结构变迁[J].数量经济技术经济研究,2016(12):61-77.
[2]白俊红,蒋伏心.协同创新、空间关联与区域创新绩效[J].经济研究,2015(7):174-187.
[3]汪涛,王慧.地理邻近对企业创新绩效的影响:基于企业能力视角[J].商业经济与管理,2017(2):25-32.
[4]孙慧,王慧.政府补贴、研发投入与企业创新绩效——基于创业板高新技术企业的实证研究[J].科技管理研究,2017(12).
[5]TCOOPER W W,SEIFORD L M,TONE.Data envelopment analysis:a comprehensive text with methods applications references and DEA-Solver Software[M].Berlin:Springer,2007.
[6]HORTA I M,CAMANHO A S,COSTA J M D.Performance assessment of construction companies:a study of factors promoting financial soundness and innovation in the industry[J].International Journal of Production Economics,2012,137(1):84-93.
[7]AKGUN A E,KESKIN H,BYRNE J C,et al.Emotional and learning capability and their impact on product innovativeness and firm performance[J].Technovation,2007(9):501-513.
[8]王立军,张伯伟,朱春礼.产业特征、宏观调控与技术创新策略选择——来自中国工业层面的证据[J].世界经济研究,2011(3):3-8.
[9]李向东,李南,刘东皇.高技术产业创新效率影响因素分析[J].统计与决策,2015(6):109-113.
[10]SIPOS G L,BZOI G,IONESCU A.The impact of hampering innovation factors on innovation performance——European countries case[J].Procedia—Social and Behavioral Sciences,2014,124:415-424.
[11]朱晋伟,王杨阳,梅静娴.高技术产业间创新绩效及影响因素差异比较分析——基于面板数据的半参数模型[J].系统工程理论与实践,2015,35(12):3135-3143.
[12]YUNG-CHING HO,CHING-TZU TSAI.Front end of innovation of high technology industries:the moderating effect of front-end fuzziness[J].Journal of High Technology Management Research,2011(22):47-58.
[13]郑霞.我国高技术企业技术创新能力影响因素研究[J].财经问题研究,2014(11):127-132.
[14]LIN B,TAN R.Ecological total-factor energy efficiency of China′s energy intensive industries[J].Ecological Indicators,2016,70:480-497.
[15]KAO C,HWANG S N.Efficiency decomposition in twostage data envelopment analysis:an application to non-life insurance companies in Taiwan[J].European Journal of Operational Research,2008,185(1):418-429.
[16]HALKOS G E,TZEREMES N G,KOURTZIDIS S A.A unified classification of two-stage DEA models[J].Surveys in Operations Research&Management Science,2014,19(1):1-16.
[17]刘满凤,李圣宏.基于三阶段DEA模型的我国高新技术开发区创新效率研究[J].管理评论,2016,28(1):42-52.
[18]乔元波,王砚羽.基于三阶段DEA-Windows分析的中国省域创新效率评价[J].科学学与科学技术管理,2017,38(1):88-97.
[19]FRIED H O,LOVELL C A K,SCHMIDT S S,et al.Accounting for environmental effects and statistical noise in data envelopment analysis[J].Journal of Productivity Analysis,2002,17(1-2):157-174.
[20]NGUYEN T P T,ROCA E,SHARMA P.How efficient is the banking system of Asia′s next economic dragon evidence from rolling DEA windows[J].Applied Economics,2014,46(22):2665-2684.
[21]成力为,孙玮,王九云.要素市场不完全视角下的高技术产业创新效率——基于三阶段DEA-Windows的内外资源配置效率和规模效率比较[J].科学学研究,2011,29(6):930-938.
[22]童健,武康平.经济发展进程中的基础设施投资结构变迁[J].数量经济技术经济研究,2016(12):61-77.