中国高新技术产业研发效率及其影响因素——基于面板数据SFPF模型的实证研究

以2000—2007年中国高新技术产业研发活动为研究对象,分别建立不考虑/考虑影响因素的面板数据随机前沿生产函数(SFPF)模型对研发中间产出效率与最终产出效率进行测算,进而加以比较分析,并进一步探讨各影响因素对研发效率的具体效应。研究发现:(1)中国高新技术产业的研发效率与效率前沿相比存在较大差距;2003年以前研发的最终产出效率高于中间产出效率,而2003年以后结果正好相反;(2)在不考虑影响因素的前提下,中国高新技术产业各行业的研发效率普遍被低估;(3)中国高新技术产业研发效率受诸多因素影响,其中,市场结构、企业规模对研发中间产出效率和最终产出效率的作用存在显著差异。     

上市公司创新能力评价研究——来自陕西省41家上市公司的数据

本研究基于陕西省2011-2013年41家上市公司的数据,利用结构方程模型和主成分分析方法,考察了上市公司所属行业性质、创新投入以及创新驱动因素对创新能力的影响。结果显示:行业性质对创新能力测评存在约束性,要积极发挥行业优势,调整和优化产业结构,重视行业性质对创新能力测评的约束性;创新投资对提高创新能力水平、提升上市公司研发效率具有显著的正向影响;上市公司公开信息的披露程度有利于对创新能力进行全面评价;创新驱动发展阶段在创新驱动要素对上市公司创新能力的影响中起到积极的调控作用,政府部门应为提高上市公司的创新能力水平营造良好的宏观环境。     

Impacts of grants and contracts on academic researchers’ interactions with industry

Based on a representative national sample of 1564 academic researchers, we investigate the impacts of research grants and contracts on the nature and extent of faculty research and technology activities with industry. A particular focus is on understanding the independent contributions of industry and government grant sources on levels of industrial involvement. In addition to examining the source of grants, the study controls for a number of independent factors including: scientific field, research center affiliation, tenure status, and gender. Results suggest independent effects of grants and contracts on industrial activities. Grants and contracts from industry have a significant effect on academic researchers’ propensity to work with industry, as measured by an “industrial involvement scale.” Federally-sponsored grants also have an impact in increasing work with industry, but a more moderate one. Further, those with more grants and contracts (of each type) have a greater propensity for industrial involvement than those who have such contracts but fewer. This holds even when proxies for productivity and career stage are introduced in regression equations. The analysis also considers whether provision of grants and contracts is best viewed as a predictor of industrial involvement or just another type of industrial involvement using factor analysis and nested multivariate modeling to compare effects.     

科技创业双重卷入对创业成长的影响机理研究

   创业卷入作为一种认知活动,在科技创业过程中发挥着重要作用。本文基于卷入理论,根据科技创业者角色认同特征将创业卷入划分为创业情境卷入和创业行业卷入,并从创业学习和吸收能力视角,构建了科技创业者创业双重卷入对创业成长的影响机理模型,通过对收集到的193份问卷进行有调节的中介实证检验。结果表明：科技创业者创业双重卷入有助于提升创业成长水平,创业学习在创业双重卷入与创业成长之间起到了中介作用,吸收能力正向调节了创业学习与创业成长之间关系。进一步的研究发现,吸收能力调节了创业学习在创业双重卷入与创业成长之间的中介效应。     

要素集聚对高技术产业创新能力发展的影响研究

基于1996年至2016年的我国省际面板数据,本文聚焦产业要素集聚视角研究高技术产业创新能力的来源,利用莫兰指数(Moran′s I)进行空间相关检验,通过LM等系列检验确定本研究采用空间杜宾模型。结论指出：（1）总体来看我国高技术产业创新能力具有显著的空间集聚特点,创新能力存在区域间的空间正相关;（2）劳动、技术、人才、规模等要素集聚及税收优惠政策都对地区内高技术产业各阶段创新能力发展具有积极作用,资本集聚对产业创新转化存在逆向作用;（3）FDI和政策支持都对我国高技术产业总体创新能力存在约束,原因是FDI与政府资金目的导向差异性所致。     

Government centrality to university–industry interactions: University research centers and the industry involvement of academic researchers

This paper uses data from a national survey of academic researchers in the US to detect how different types of university research centers affect individual-level university–industry interactions. The results suggest that while affiliation with an industry-related center correlates positively with the likelihood of an academic researcher having had any research-related interactions with private companies, affiliation with centers sponsored by government centers programs correlates positively with the level of industry involvement, no matter whether these centers additionally have ties to private companies. The analysis takes the “scientific and technical human capital” approach, which draws from theories of social capital and human capital and proves useful for framing the institutional and resource-based perspectives that characterize much of the literature on university–industry interactions. The scientific and technical human capital approach is taken because its emphasis on the research capacities of individual academic researchers provides a more direct explanation of government centrality to academic researchers’ industry involvement than provide either the resource-based or institutional views. Implications for policy and management as well as for future applications of the scientific and technical human capital approach are discussed.     

Knowledge transfer activities in social sciences and humanities: Explaining the interactions of research groups with non-academic agents

The aim of this research is to achieve a better understanding of the processes underlying knowledge transfer (KT) in social sciences and humanities (SSH). The paper addresses: first, the extent of SSH research groups’ engagement in KT and the formal KT activities used to interact with non-academic communities; and second, how the characteristics of research groups may influence engagement in various types of KT. The empirical analysis is at research group level using data derived from a questionnaire of SSH research groups belonging to the Spanish Council for Scientific Research (CSIC). We find that KT activities are based on relational rather than commercial activities. The most frequent relational activities in which SSH research groups engage are consultancy and contract research. We find also that the characteristics of research groups (e.g. size and multidisciplinarity) and individuals (e.g. academic status and star scientist) are associated with involvement in KT activities and that a deliberate focus on the societal impacts and relevance of the research conducted is strongly related to active engagement of research groups in all the modes of KT considered in this study. From a managerial perspective, our findings suggest that measures promoting a focus on the societal impact of research could enhance research groups’ engagement in KT activities.     

Contemporaneous peer effects,career age and the industry involvement of academics in biotechnology

This study explores the role of contemporaneous peer effects in driving an academic's involvement with industry. Specifically, we examine the influence of workplace peers and personal collaborators and how these effects are moderated by the career age of the scientist. Moreover, we look at situations in which both types of social influence are incongruent and the academic is faced with “dissonance”. Based on survey data of 355 German academics in the field of biotechnology and publication data from the Science Citation Index Expanded (SCIE), we find that the scientist's involvement with industry increases with the orientation of the scientist's department toward industry (“localized peer effect”). This effect turns out to be moderated by the scientist's age, such that the localized peer effect decreases with age and finally turns negative for very senior scientists. Moreover, we find that a scientist's involvement increases with the industry orientation of the scientist's co-authors (“personal peer effect”), irrespective of the scientist's age. In case both types of social influence are incongruent, younger scientists will revert to localized norms while more experienced scientists will orient themselves more toward their personal collaborators.     

服务外包的空间格局：理论框架与实证分析

全球化的推进对产业空间格局产生了深远影响.90年代中期以来,外包发展超越了制造业的范畴,服务外包成为新的商业经营模式.不同于大量文献中专注于外包的形成原因.文章以产业空间格局的决策者为视角,鲒合宏观因素和行业特征,构建了一个分析新时代背景下产业空间格局的框架.企业在今天必须以全球化为背景来制定竞争策略.行业特征影响到了企业竞争策略的制定,也规定了区位优势的内涵.企业竞争策略与区位优势的匹配塑造了产业的空间格局.最后,以中国服务外包为例对理论框架进行了检验.     

Teaching benefits of multi-helix university-industry research collaborations: Towards a holistic framework

Despite universities' effort towards contributing to society and economy through multi-helix or multi-party university-industry research collaborations (UIRCs) involving industry, government and community organisations, the impact of such activities on teaching remains unclear. This paper aims to fill this research lacuna. Using a multiple case study approach, the paper provides evidence from seven multi-helix UIRCs taking place in five Indian universities. We first unveil the different roles played by individual actors from the collaborating partner organisations involved in the multi-helix UIRCs, specifically industry scientists (from the collaborating partner company), funding administrators (from the governmental funding body that sponsors the collaboration), community representatives (from the collaborating community organisation) and UIRC-affiliated academics involved in the teaching activities of the collaborating university. Then, we explain various individual-level and organisational-level conditions that enable and constrain these actors' involvement in teaching, and ultimately their efficacy. These findings help us develop a holistic framework for involving UIRC actors in teaching at partner universities.     

Engaging excellence? Effects of faculty quality on university engagement with industry

We investigate how universities’ research quality shapes their engagement with industry. Previous research has predominantly found a positive relationship between academics’ research quality and their commercialization activities. Here we use industry involvement measures that are broader than commercialization and indicate actual collaboration, i.e. collaborative research, contract research and consulting. We hypothesise that the relationship between faculty quality and industry engagement differs across disciplines, depending on complementarities between industrial and academic work, and resource requirements. Using a dataset covering all UK universities, we find that in technology-oriented disciplines, departmental faculty quality is positively related to industry involvement. In the medical and biological sciences we find a positive effect of departmental faculty quality but establish that this does not apply to star scientists. In the social sciences, we find some support for a negative relationship between faculty quality and particularly the more applied forms of industry involvement. The implication for science policy makers and university managers is that differentiated approaches to promoting university-industry relationships are required.     

Innovation and government intervention: A comparison of Singapore and Hong Kong

Government is one of the determinants for innovation capacity although its role and degree of involvement in innovation is debatable. Government intervention can be vital in supporting R&D and innovation as market alone cannot provide adequate incentives for knowledge production. Degrees of government intervention, however, vary in different economies and range from directive intervention by actively advising industrial policy and investing in selected areas, to facilitative intervention by creating positive environment and providing public goods for industry. This study uses Singapore and Hong Kong as two cases to explore the influence of government intervention on innovation performance. Singapore is known for strong government intervention while Hong Kong is famous for its positive non-intervention policy that minimizes the power of government in influencing the market. The comparison shows that innovation activities in Singapore are largely policy driven and dominated by big players, while in Hong Kong industry innovation is less active but the local industry has a dynamic innovation base contributed by small firms. Using a difference-in-differences analysis of USPTO patents filed by Singapore and Hong Kong, we find evidence for the effectiveness of government intervention on enhancing the technological significance and scope of innovation. The findings could shed light on the implication of government involvement in innovation.     

服务企业顾客参与对员工创新行为的影响研究

激发员工的创新行为,让员工参与到服务创新过程中是研究人员和服务企业共同面临的重要课题。而顾客参与有利于员工创造力的发挥,本文在对相关文献进行回顾与分析的基础上提出了顾客参与、顾客满意度和员工创新行为之间关系的理论假设模型。采用问卷调查法进行结构方程分析,得出实证结果包括:顾客参与对员工创新行为的影响;顾客满意度对员工创新行为的影响;创造性地将顾客满意度引入到顾客参与和员工创新行为之间的关系,发现了顾客参与影响员工创新行为的内在机理。为顾客行为和员工行为关系的研究提供了理论基础,并得到了一些具有实践价值的结果和启示。     

A股上市公司股权激励的创新激励效应研究

本文基于A股2337家（次）上市公司2009-2013年的微观数据,运用OLS回归和倾向得分匹配法考察了我国上市公司实施的股权激励是否激励了企业创新。多种形式的OLS回归结果显示影响企业创新的主要因素是企业的创新传统、盈利能力和负债能力。OLS回归和倾向得分匹配法的处理结果都表明股权激励并没有显著提升我国企业的创新活动。改变匹配方法和对样本进行分年份、行业、规模、最终控制人性质等的匹配处理均证明股权激励没有明显的创新激励效应。这表明我国A股上市公司实施的股权激励还主要是一种福利型的制度安排,尚未发挥出创新激励效应,因此仍需加以改革完善。     

高技术产业研发投入与产业绩效的关系研究

本文利用我国2008年~2013年31个省市、自治区的面板数据,通过单位根检验和Hausman检验确定回归模型的影响形式,同时,利用F检验判定需要拟合的回归模型形式,建立变截距固定效应模型,以分析高科技产业的研发行为对产业绩效的影响。研究结果表明,研发经费的投入能够有效提升产业绩效;然而,研发人员数量的投入却削弱了产业绩效;目前,我国高技术产业的研发投入显现出非均衡状态。最后,基于估计结果我们有针对性地提出了相关的政策建议。     

技术体制与医药制造业创新效率研究

现有的创新效率研究,多是站在“技术给定”的逻辑基点上完成,忽略各产业技术发展特征,导致医药制造业相关研究没有“药味”。首先,借鉴技术体制理论,将医药制造业的“创造性积累”创新模式,从一般的“创造性破坏”模式中区分出来。然后,将技术积累水平,纳入创新效率评价模型中,对中国2009—2016年区域医药制造业创新效率进行再评价。结果表明：（1）含技术积累因素模型的评价结果,显著优于传统模型,测度结果更贴近产业实际;（2）规模效率低是制约产业创新效率提升的主要因素,而产业集聚度和技术引进投入对创新效率的提升有促进作用;（3）从创新效率角度看,我国区域医药制造业呈金字塔型分布,且缺乏效率次优组,存在大量基础层的重复建设。最后,提出继续淘汰医药制造业的落后产能,促进优质企业技术有效积累,弥补全国制药业产业结构短板等政策建议。     

多维邻近性下新能源合作创新网络演化研究

通过手动检索中国国家知识产权局的专利数据库,以1999—2018年内排名前100的新能源企业间合作申请专利数据为样本,借助UCINET6.0软件对各阶段合作创新网络进行可视化操作,对新能源产业合作创新网络图谱的相关参数进行统计和分析。选取合作创新绩效作为因变量,选取地理、技术、组织和社会邻近性作为自变量,选取规模相似性和研发经验相似性作为控制变量,并对这些变量数据进行标准化处理。在此基础上,将1999—2018年分为四个阶段,每5年为一个阶段,建立研究模型,采用QAP多元回归分析方法,探究地理、技术、社会和组织邻近性在不同阶段对新能源产业合作创新网络演化的影响。研究结果表明：地理邻近性对新能源产业合作创新网络演化的影响呈下降趋势,组织邻近性始终对该合作创新网络演化发挥着积极作用,技术邻近性对该合作创新网络演化的影响程度逐渐下降,社会邻近性是推动该合作创新网络演化的重要因素,且其显著性一直较高。为加快我国清洁低碳、安全高效的现代化新能源网络建设,并推动新能源产业良好发展,基于实证研究结果,提出相应的对策建议：第一,搭建地区间新能源产业合作平台,推动新能源产业战略联盟建设;第二,实施技术领先战略,打造鲜明的技术特点,建立新能源技术区域互鉴机制和复杂技术联合攻关机制。     

中国科技服务业的异质性研究

中国各地区的科技服务业发展差异性较为明显,本文运用基尼系数、因子分析、面板模型对中国科技服务业发展的异质性进行了分析。首先,计算各个省份、各个区域的基尼系数,对各区域间的基尼系数进行对比,并对区域内基尼系数的贡献量进行分析；其次运用因子分析对各省份科技服务业发展的投入产出效率进行了测度；最后利用面板模型对影响各地区科技服务业发展的因素进行回归。研究结果表明：华北地区与华东地区的基尼系数较其他地区的大,这是由于位于华北地区的北京市与位于华东地区的上海市的科技服务业发展水平较高引起的；北京、上海、天津这三个地区科技服务业的投入产出效率较高,而江苏、山东的效率较低,从因子得分情况看,广东、北京、江苏的整体发展情况位于全国前三；地区生产总值与技术成交额与基尼系数之间呈现反比例的关系,而科技服务业的从业人员与基尼系数之间则呈现正相关的关系,各个省份之间的情况略有差异,需要进行具体情况具体分析。     

技术创新对吉林省信息技术制造业和竞争力影响研究

针对技术创新对吉林省的信息技术制造业和竞争力的影响进行了深入的研究。首先确定技术创新和产业增长的指标体系,选择了合适的分析方法,同时建立了技术创新与产业增长的实证框架;然后分析了吉林省十多年来信息技术制造业的创新和产业增长的速度,并检验了各种技术创新活动与信息技术制造业产出绩效的相关程度,重点分析了R&D投入对产品和产出增长的影响;最后测算了R&D对信息技术制造业的竞争力影响程度,揭示了R&D是提高产业竞争力的关键因素并得出结论和政策建议。     

创新驱动发展战略下优势传统企业创新特性研究——基于苏州200家优秀科技创新企业的实证分析

电子信息、装备制造、轻工、纺织、冶金和化工六大产业是对苏州工业经济起着重要支撑作用的传统产业.准确把握企业创新特性,无论是对于优势传统企业寻找转型之路,还是对于政府相关职能部门提供相应的激励手段和扶持政策,都具有非常重要的指导意义.通过企业创新相关主题的文献回顾,归纳企业创新特性变量,并基于苏州200家优秀科技创新企业进行实证分析.首先分析各创新特征变量的显著水平;其次运用结构方程模型验证优势传统产业的创新效应和企业效益的相关程度;最后选取部分控制变量进行探索性分析.