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开放式创新为企业和其他创新主体进行技术开发提供了新的视角和方法,然而开放式创新对企业创新实效影响的研究仍有待深入。本文以专利维持作为专利质量的代理变量,运用虚拟回归的方法对高校和科研机构等专业机构的参与、开放式创新的采用对专利质量的影响进行了分析。回归分析结果表明,高校和科研机构的参与并未能够提高专利质量,基于合作开发的开放式创新能够明显提升专利质量。据此提出提升开放式创新效果和专利质量的建议。 相似文献
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科学研究与技术创新在推动发展的同时产生潜在的社会负外部性,引发理论与实践关于科技创新治理的关注,以实现科学研究与技术创新活动的社会责任。而“开放”逐渐成为组织中一种强有力的规范和治理机制,从根本上改变了组织的资源获取方式、运作方式、与环境的互动方式,责任式创新的治理目标均能够在开放机制中得以实现。围绕责任式创新研究,从科学研究、技术开发与社会发展三个阶段,构建了以开放科学、开放战略、以及开放社会为基础的科技创新责任治理的开放机制,并结合争议性科学研究（生殖性克隆与预防性基因编译等）和争议性技术创新（区块链与人工智能等）的案例检验予以论证。研究构建的从开放科学到开放社会的开放机制为科技创新的责任治理提供借鉴启示。 相似文献
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《Information processing & management》2022,59(5):103001
Brain–computer interface (BCI) is a promising intelligent healthcare technology to improve human living quality across the lifespan, which enables assistance of movement and communication, rehabilitation of exercise and nerves, monitoring sleep quality, fatigue and emotion. Most BCI systems are based on motor imagery electroencephalogram (MI-EEG) due to its advantages of sensory organs affection, operation at free will and etc. However, MI-EEG classification, a core problem in BCI systems, suffers from two critical challenges: the EEG signal’s temporal non-stationarity and the nonuniform information distribution over different electrode channels. To address these two challenges, this paper proposes TCACNet, a temporal and channel attention convolutional network for MI-EEG classification. TCACNet leverages a novel attention mechanism module and a well-designed network architecture to process the EEG signals. The former enables the TCACNet to pay more attention to signals of task-related time slices and electrode channels, supporting the latter to make accurate classification decisions. We compare the proposed TCACNet with other state-of-the-art deep learning baselines on two open source EEG datasets. Experimental results show that TCACNet achieves 11.4% and 7.9% classification accuracy improvement on two datasets respectively. Additionally, TCACNet achieves the same accuracy as other baselines with about 50% less training data. In terms of classification accuracy and data efficiency, the superiority of the TCACNet over advanced baselines demonstrates its practical value for BCI systems. 相似文献
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开放式创新已成为环境不确定条件下企业创新的新模式。本文构建了基于系统环境观的企业环境不确定性测量量表和开放式技术创新战略的测量量表;用结构方程模型研究了环境不确定性对开放式技术创新战略的影响。结果表明:尽管我国企业的总体开放度不高,致使环境不确定性的某些维度对开放式技术创新战略的影响并不显著,但整体上还是可以断定环境不确定性能促进企业向开放式技术创新战略转变。本研究的启示是:一方面,在不确定环境下企业应注重开放式技术创新战略的构建;另一方面,政府部门应为企业提高开放度营造良好的宏观环境。 相似文献
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Loha Hashimy Horst Treiblmaier Geetika Jain 《The Journal of High Technology Management Research》2021,32(1):100405
Open innovation and distributed ledger technology (DLT) are both based on the underlying principles of distribution and sharing. While open innovation is about sharing knowledge to improve innovation processes and performance, DLT is a distributed data ledger that is utilized to enhance efficiency, reduce costs, and ensure immutability, traceability, security, and transparency. In this paper, we investigate the barriers to open innovation currently faced by small and medium-sized companies (SMEs) that DLT can solve. To achieve this goal, we conducted semi-structured interviews with 11 experts in open innovation and DLTs from Spain, Germany, Australia, and India. The results of our exploratory study show that DLTs can help to solve several problems, including external barriers, such as problems with contracts, financing, lack of trust, raw materials, lack of information, domestic and international market limitations, IP rights, and governmental regulations as well as bureaucracy. Internal challenges include insufficient funding, organizational systems that are out of date, and lack of trust. When it comes to difficulties associated with the management of open innovation, external barriers are frequently caused by customers' demands, while internal barriers are frequently caused by organizational culture or human nature, which cannot be improved by DLTs. Finally, SMEs might face new obstacles when integrating DLTs, such as integration problems, complex transition phases, and high setup costs as well as problems with attracting and retaining qualified employees. 相似文献
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《Information processing & management》2020,57(2):102147
The combination of large open data sources with machine learning approaches presents a potentially powerful way to predict events such as protest or social unrest. However, accounting for uncertainty in such models, particularly when using diverse, unstructured datasets such as social media, is essential to guarantee the appropriate use of such methods. Here we develop a Bayesian method for predicting social unrest events in Australia using social media data. This method uses machine learning methods to classify individual postings to social media as being relevant, and an empirical Bayesian approach to calculate posterior event probabilities. We use the method to predict events in Australian cities over a period in 2017/18. 相似文献
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AbstractThere is a paucity of granular, high-resolution broadband data in the United States, which limits the prospects of informed telecommunications policy debates. In the absence of regulation requiring telecommunication providers to accurately report service footprints, pricing, and service quality data, providers refuse to disclose this information, routinely citing competition as the primary reason for keeping network and pricing data obfuscated. In addition, many secondary datasets needed for broadband planning and policy research, such as parcel or address point data, are equally challenging to gather. These datasets are often controlled by county governments, with no overarching access policy, resulting in neighboring counties with widely divergent policies for the release of these important data. In this article we explore the challenges of open data and its impacts on telecommunication policy development. A recent project which explored the spatial provision of Google Fiber in the Kansas City metropolitan area helps to highlight these difficulties. Basic strategies for mitigating information asymmetries and the lack of data transparency for telecommunication policy analysis are discussed. 相似文献
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异构系统开放封装的技术分析与实现框架 总被引：3，自引：0，他引：3
1 开放封装的意义现实系统环境是一个由众多分布、异构和自主的资源系统组成的开放环境 ,现实应用则要求对这些系统进行整合来向用户提供逻辑化甚至个性化的集成服务。为了形成有效的集成服务 ,我们可以要求新建设的资源系统采用标准的元数据格式、检索方式、检索记录调用机制、检索记录传输格式和传输协议 ,从而支持整合检索、开放链接和基于Web的集成使用[1] 。但对于已经存在的资源系统和第三方资源系统 (在中国科学院这些系统可能占多数 ) ,它们往往各自具有自己的数据结构、检索方式、检索与调用协议、数据记录传输格式等。我们不可… 相似文献
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基于价值共创视角探索核心企业开放式服务创新平台构建过程,采用扎根理论对四组案例展开质性研究,建构开放式服务创新平台形成的前因-过程-结果理论分析框架,分析开放式服务创新平台关键要素,揭示资源整合情境引发差异性影响。研究表明：内向型资源整合情境下,核心企业整合上位资源更注重功能契合和资源多样性,易促进渐进式创新绩效提升;外向型资源整合情境下,核心企业整合下位资源更注重感知契合和资产专用性,易促进突破式创新绩效提升。 相似文献
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Zara Nasar Syed Waqar Jaffry Muhammad Kamran Malik 《Information processing & management》2019,56(6):102088
With the advent of Web 2.0, there exist many online platforms that results in massive textual data production such as social networks, online blogs, magazines etc. This textual data carries information that can be used for betterment of humanity. Hence, there is a dire need to extract potential information out of it. This study aims to present an overview of approaches that can be applied to extract and later present these valuable information nuggets residing within text in brief, clear and concise way. In this regard, two major tasks of automatic keyword extraction and text summarization are being reviewed. To compile the literature, scientific articles were collected using major digital computing research repositories. In the light of acquired literature, survey study covers early approaches up to all the way till recent advancements using machine learning solutions. Survey findings conclude that annotated benchmark datasets for various textual data-generators such as twitter and social forms are not available. This scarcity of dataset has resulted into relatively less progress in many domains. Also, applications of deep learning techniques for the task of automatic keyword extraction are relatively unaddressed. Hence, impact of various deep architectures stands as an open research direction. For text summarization task, deep learning techniques are applied after advent of word vectors, and are currently governing state-of-the-art for abstractive summarization. Currently, one of the major challenges in these tasks is semantic aware evaluation of generated results. 相似文献
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《International Journal of Information Management》2016,36(4):668-672
Public agencies around the globe are liberating their data. Drawing on a case of Data.gov, we outline the challenges and opportunities that lie ahead for the liberation of public data. Data.gov is an online portal that provides open access to datasets generated by US public agencies and countries around the world in a machine-readable format. By discussing the challenges and opportunities faced by Data.gov, we provide several lessons that can inform research and practice. We suggest that providing access to open data in itself does not spur innovation. Specifically, we claim that public agencies need to spend resources to improve the capacities of their organizations to move toward ‘open data by default’; develop capacities of community to use data to solve problems; and think critically about the unintended consequences of providing access to public data. We also suggest that public agencies need better metrics to evaluate the success of open-data efforts in achieving its goals. 相似文献
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‘Open data’ has recently emerged as a label for renewed attempts to promote scientific exchange. As part of such efforts, the posting of data online is often portrayed as commonly beneficial: individual scientists accrue greater prominence while at the same time fostering communal knowledge. Yet, how scientists in non-Western research settings assess such calls for openness has been the subject of little empirical study. Based on extended fieldwork with biochemistry laboratories in sub-Sahara Africa, this paper examines a variety of reasons why scientists opt for closure over openness with regard to their own data. We argue that the heterogeneity of research environments calls into question many of the presumptions made as part of open data. Inequalities in research environments can mean that moves towards sharing create binds and dilemmas. These observations suggest that those promoting openness must critically examine current research governance and funding systems that continue to perpetuate disparities. The paper proposes an innovative approach to facilitating openness: coupling the sharing of data with enabling scientists to redress their day-to-day research environment demands. Such a starting basis provides an alternative but vital link between the aspirations for science aired today as part of international discussions and the daily challenges of undertaking research in low-resourced environments. 相似文献
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J. Nils Foege Ghita Dragsdahl Lauritzen Frank Tietze Torsten Oliver Salge 《Research Policy》2019,48(6):1323-1339
The paradox of openness describes the fundamental tension between knowledge sharing and knowledge protection in open innovation. While sharing is vital for value creation, protecting is critical for value appropriation. Prior research has examined this paradox of openness from the perspective of the seeking firm, focusing on the firm-level challenges of inbound open innovation. In this article, we complement that research by illuminating the tensions between sharing and protecting in individual-level outbound open innovation, where we argue that the paradox of openness is most prevalent, yet much less well understood. Drawing on the experience of individual participants, or solvers, in intermediated crowdsourcing contests, we analyze textual data from 2,149 answers to five open-ended narrative questions embedded in a large-scale solver survey, as well as 43 in-depth interviews of solvers. Our findings indicate that individual solvers face fundamental sharing-protecting tensions that carry considerable economic and psychological costs. We also document how solvers attempt to navigate the paradox of openness by employing three formal and four informal value appropriation practices. They build elaborate configurations of these practices, which they tailor to the idiosyncrasies of each contest. They also dynamically adjust these configurations over time, as the contest and the interaction with the seeker unfold. We end by outlining how these findings contribute to a more multifaceted conceptualization and a richer understanding of the paradox of openness. 相似文献
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In this work, we elaborate on the meaning of metadata quality by surveying efforts and experiences matured in the digital library domain. In particular, an overview of the frameworks developed to characterize such a multi-faceted concept is presented. Moreover, the most common quality-related problems affecting metadata both during the creation and the aggregation phase are discussed together with the approaches, technologies and tools developed to mitigate them. This survey on digital library developments is expected to contribute to the ongoing discussion on data and metadata quality occurring in the emerging yet more general framework of data infrastructures. 相似文献
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开放创新条件下的资源投入测度及政策含义 总被引：1，自引：0，他引：1
基于Lundvall教授提出的技术创新的两种模式和学习型经济理论,探讨开放式创新环境下提高我国自主创新能力的关键因素。认为在经济全球化环境下,开放式创新是当前技术创新的重要特点,开放式创新与自主创新是相辅相成的关系,开放环境下的自主创新强调创新多种元素的集成和整合。提出以全面创新投入替代研发投入来反映技术投入将更为合理和完整,并对国内时间序列数据进行Granger因果检验,对国际面板数据进行计量经济检验加以实证验证。最后根据分析结果提出现阶段增强我国自主创新能力的政策建议。 相似文献
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开放式创新正成为经济发展的关键动力,为创新主体的成果研发、分享和转移提供优良生态环境,这也引起学界对开放式创新与创新绩效关系的探讨。既有研究大多聚焦创新主体个案内部的合作关系,忽略开放式创新多层次表征所带来的外部组织管理因素（如激励系统）对创新绩效的影响。本文以85家国家文化和科技融合示范基地作为研究对象,采用回归模型探究开放式创新对基地创新绩效的直接影响以及政府资金扶持和政策支持的调节效应。研究结果表明:开放式创新对基地创新绩效具有显著的正向影响,资金扶持可以促进开放式创新提高创新成果的作用,政策扶持的调剂效应不存在。落足于国家文化和科技融合示范基地开放式创新模式应用的现实,建议各基地发挥宏观调控优势,提高需求端与供给端技术交易的匹配度,政府部门应该设定合理的政府资金补贴强度区间,提高政策支持的针对性,为市场主体构建开放式创新生态系统提供策略选择和相关实践指导。 相似文献
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Open data is considered an important resource to stimulate innovation and create economic and social value. Open data researchers have examined the importance of data publication by public bodies, but in contrast, much less attention has focused on how open data is exploited. In this paper, we aim to provide insight into the actions of platforms to assist open data exploitation by transformational entrepreneurs, which systemically facilitate transformation in socio-economic development. Our findings investigate two types of mechanism, reflecting the openness of the data movement and the competitive commercial need for closure. First, inclusion mechanisms are social mechanisms of the open data ecosystem which seek to attract a broad range of potential collaborators through supporting entrepreneurs to innovate with open data. Second, exclusion mechanisms are deployed to filter collaborators, to identify those who are seen by platforms to be more likely to successfully exploit their data, or develop revenue sharing relationships. We reveal how these mechanisms are used at different stages of the innovation process, to initially attract a wide pool of collaborators, before seeking to reduce them to better target limited resources. Despite ‘openness’ being a key requirement of open data publication, for successful transformational entrepreneur exploitation, it requires dimensions of closure. 相似文献