3D打印技术对物流企业仓储的影响——以UPS公司为例

随着3D打印技术的快速发展,传统物流企业的管理模式必然会受到影响与冲击。聚焦快递类物流企业仓储模块,基于3D打印技术、新兴技术管理理论,运用案例研究法对UPS公司单案例做深入探索分析,剖析3D打印技术对UPS公司仓储过程的运作流程、职能调整、仓储布局、仓储成本等诸多方面的影响,进而启示其他快递类物流企业应了解3D打印技术对其仓储模块的全面影响。     

基于专利和科技论文的技术机会识别研究——以金属3D打印技术为例

主要基于Web of Science科技论文和Derwent专利文献,利用SATI、SPSS等分析工具,提取文本关键词并构建相异共现矩阵,采用多维聚类方法识别研究热点主题。在继承和发展Naoki Shibata技术机会理论的基础上,通过对比科技论文和专利文献研究热点主题的差异来识别金属3D打印的技术机会。研究得出,科技论文和专利文献分别有4个研究热点主题,识别了两大类共4个技术机会。首次基于专利和科技论文研究金属3D打印的技术机会,开拓了金属3D打印的研究视野,对金属3D打印行业的企业战略决策具有指导作用。     

基于动态网络的关键实体识别及演化分析——以3D打印技术专利为例

以3D打印技术领域专利信息为基础构建具有多重属性节点的专利网络,利用动态网络分析方法对3D打印技术领域专利信息进行挖掘,从而可以代替传统的分析方法来识别该技术领域中的关键实体(关键专利权人、关键技术、关键区域分布及关键任务),并对关键实体的演化情况进行分析。     

3D打印技术的应用对企业创新独占的挑战与对策研究

综述了3D打印技术的原理和发展现状,并探讨了该技术的应用对企业创新独占以及创新独占机制的挑战,随后采用案例研究方法,对12家国内外企业展开调研,提出了企业应对由于该技术的应用带来的创新独占挑战的具体策略。     

以创新和理论实践为导向的工科大学生课外科技活动探索——以海南大学3D打印研究活动为例

大学生课外科技创新活动是高校培养工科创新型人才的有效载体,应以创新和理论实践为导向,发挥教师指导作用,结合专业教学和科研,推动活动的深入开展。本文以海南大学开展的3D打印课外科技活动为例,分析和探讨了活动开展的主要内容和成果,为我校在培养创新型科技人才长效机制建立方面提供了有益的案例。     

基于演化博弈的产业联盟组织间学习机制分析研究——以3D打印技术产业为例

以3D打印技术产业现状为基点,对3D打印技术产业联盟组织间学习机制进行研究。首先通过演化博弈分析3D打印技术产业联盟组织间学习行为的内在机制及其动态演化过程;其次立足盟员企业间信任关系与策略选择趋向的角度探讨产业联盟组织间学习机制演化路径,为盟员企业制定正确的组织间学习策略提供参考,同时有助于产业联盟组织间学习机制的建立。     

试论公共图书馆创客空间服务的新模式“3D打印”——以苏州第二图书馆为例

“图书馆+3D打印”是一种图书馆界服务的新模式,将影响和改变大众的传统思维模式和未来的生活。文章以苏州第二图书馆创客空间3D打印和相关业务为例,分析了国内外图书馆3D打印服务现状,探讨了苏州第二图书馆创新空间3D打印服务新模式的方法,并提出了3D打印服务的注意事项。     

研发项目对中国大学技术转让合同影响的实证研究——以985工程高校为例

研发项目和科研经费是高校开展科研活动和技术转让的资源基础。采用教育部内部资料提供的22所“985工程”高校在1998-2010年的面板数据,构建研发项目的综合投入指标、研发项目的类型、科研经费的不同来源、专利活动对技术转移合同数量和合同金额影响的经验分析模型。研究发现,相同变量对合同数量影响的显著性要强于对合同金额的影响;政府资金、企事业单位委托资金和其他资金对技术转让合同数量和金额均没有产生显著的正向作用;专利申请和专利授权量对技术转让合同数量有显著正向影响,但是对合同金额的影响在统计意义上并不显著。研究结果为如何从资源配置的角度提高大学技术转让水平提供了有价值的政策启示。     

R&3D技术创新风险与失败案例研究——以洁净煤技术为例

基于R3D的框架,拓展了Tassey的技术风险曲线,分别从理论和实证两方面进行验证。在理论方面,综述了技术创新风险的典型性研究,从融合的角度探讨了技术创新风险的类型和水平,对示范和推广阶段的风险进行了综合评定。在实践方面,以洁净煤技术领域为例,选取了燃煤磁流体技术、增压流化床燃烧联合循环发电技术、多喷嘴对置式水煤浆气化技术作为典型案例进行成功与失败的对比研究。理论研究结果表明,示范阶段交织存在着多种类型、不同程度的风险,极易导致技术创新的失败。实证研究结果表明,(科学)技术就绪水平、市场和产业就绪水平、制度就绪水平是决定R3D中前三个阶段成败的重要因素,应予以特别的关注和重视。     

新兴技术在位企业成长过程与内在机理——以3D打印企业为例研究

本文以3D打印新兴技术为研究背景,对新兴技术在位企业成长过程进行探索性研究,提出新兴技术企业成长机制模型,明晰新兴技术在位企业成长的内在机理。新兴技术在位企业成长过程体现了"内部资源驱动—合法性推动—外生性撬动"的机制。在种子期,特色技术的内部培育是颠覆性驱动要素;在稳步发展期,企业合法化战略是突破新进入缺陷约束关键;在快速发展期,"并购+战略联盟"的外生性发展策略可不断拓展企业实际生态位,从而实现企业成长壮大。新兴技术在位企业持续成长的内在机理体现了组织生态学"变异—选择—保留"的演化本质。     

低碳城市试点对企业绿色技术创新的影响

The low-carbon pilot policy has played an important role when China make efforts to achieve the carbon emission reduction targets. In order to study whether the construction of low-carbon pilot policy can induce enterprises to carry out green technology innovation, this paper uses China′s low-carbon pilot policy as a quasi-natural experiment, constructs a difference-in-difference-in-differences (DDD) model that "before-after the implementation of the low-carbon pilot policy; if or not in the pilot city; if or not belongs to the high carbon emission industry". Since the third batch of low-carbon pilot cities was officially announced in 2017, the duration of the policy is not enough for us to evaluate its effect, so we take the pilot cities announced in 2010 and 2012 as the main research object. The number of enterprises authorized green patents and micro-economic characteristics of listed companies in China from 2006 to 2017 were used to evaluate the effect of the low-carbon pilot policy on enterprises′ green technology innovation, and explore whether the Porter hypothesis based on China′s low-carbon pilot policy is valid. The results show that: (1) The implementation of the low-carbon pilot policy has significantly improved the green technology innovation level of high-carbon emission enterprises in the pilot cities. The policy has increased the proportion of green patents of high-carbon emission enterprises in the pilot area by 0.929%, and the coefficient is significant at the 5% significance level. Furthermore, the low-carbon pilot policy has a positive effect on both green invention patent and green utility model patent of high-carbon emission enterprises in the pilot area, but has a more significant effect on the green utility model patent. The conclusion of the DDD estimation confirms the validity of the Porter hypothesis based on the low-carbon pilot policy in China. (2)In order to solve the problem of sample selection bias caused by the difference in initial conditions between the treat group and the control group, this paper introduces the propensity score matching method (PSM), and adopts the propensity score matching-DDD (PSM-DDD) method for the first time to explore the effect of the low-carbon pilot policy on green technological innovation of high carbon emission enterprises within pilot cities. The effectiveness of the matching process is proved through the matching balance test and drawing the propensity score value density function graph. The results of the PSM-DDD are basically similar to those obtained by the DDD method. At the same time, this paper proves the validity of the conclusions through parallel trend hypothesis test and a series of robustness tests. (3)The analysis of enterprises geographic heterogeneity shows that the low-carbon pilot policy has significantly improved the green technology innovation of high carbon emission enterprises within the pilot cities in the eastern and western regions, but for those in the central region, the low-carbon pilot policy even has a negative inhibitory effect on their green technology innovation. After grouping the data by patent types, we can see that, for green invention patents, the low-carbon pilot policy can significantly promote the green invention patents of high-carbon emission enterprises in the pilot cities in the eastern and western regions, but it has no significant impact on enterprises in the central region; for green utility model patents, the low-carbon pilot policy only has a positive effect on the utility model patents of high-carbon emission enterprises in the pilot cities in the eastern region, but has no significant impact on the green utility model patents of enterprises in the central and western regions.  (4)The analysis of enterprise ownership heterogeneity shows that the low-carbon pilot policy has significantly improved the green technological innovation of non-state-owned high-carbon emission enterprises in the pilot cities, but has no significant impact on the green technology innovation of state-owned enterprises in the pilot cities. Among them, the low-carbon pilot policy has a significant positive effect on promoting both green invention patents and utility model patents of non-state-owned high-carbon emission enterprises in the pilot cities. As for the state-owned high-carbon emission enterprises in the pilot cities, the low-carbon pilot policy only has a positive effect on their green utility model patents, but has no significant impact on green invention patents. The results of this paper further expand the study on the Porter hypothesis and the evaluation of the low-carbon pilot policy.     

超级网络(Supernet)的兴起与衰退--英国对中小企业技术创新政策的一项实证研究

本描述了英国对中小企业创新政策的一项研究实例－Supernet从兴起到衰退的历程,并从其运行机制入手,通过在分析其供需方行为的基础上,对其最终衰退的结果进行了一定的剖析。     

基于SNM理论的技术范式变迁实证研究——以数码化图像显示技术为例

SNM理论是基于演化经济学和技术系统论基础上建立的有关技术生态位和技术演化的分析方法,通过从微观到宏观进路的经验描述,初步建立了技术演化模型。本研究通过分析典型的数码化图像显示LCD和CCD技术的不同演化进路,论证SNM理论分析方法的实用性和可行性。该理论在技术论或STS领域,尤其是在讨论技术范式变迁和技术进化问题中,需要明确其讨论的取向和内容。同时,作为经验描述的分析方法,SNM理论有待进一步实证研究并拓展其理论运用的空间。     

Using big data analytics to study brand authenticity sentiments: The case of Starbucks on Twitter

There is a strong interest among academics and practitioners in studying branding issues in the big data era. In this article, we examine the sentiments toward a brand, via brand authenticity, to identify the reasons for positive or negative sentiments on social media. Moreover, in order to increase precision, we investigate sentiment polarity on a five-point scale. From a database containing 2,282,912 English tweets with the keyword ‘Starbucks’, we use a set of 2204 coded tweets both for analyzing brand authenticity and sentiment polarity. First, we examine the tweets qualitatively to gain insights about brand authenticity sentiments. Then we analyze the data quantitatively to establish a framework in which we predict both the brand authenticity dimensions and their sentiment polarity. Through three qualitative studies, we discuss several tweets from the dataset that can be classified under the quality commitment, heritage, uniqueness, and symbolism categories. Using latent semantic analysis (LSA), we extract the common words in each category. We verify the robustness of previous findings with an in-lab experiment. Results from the support vector machine (SVM), as the quantitative research method, illustrate the effectiveness of the proposed procedure of brand authenticity sentiment analysis. It shows high accuracy for both the brand authenticity dimensions’ predictions and their sentiment polarity. We then discuss the theoretical and managerial implications of the studies.