共查询到19条相似文献，搜索用时 466 毫秒
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为了探析企业大数据分析能力对制造企业间协同创新的影响,构建了大数据分析能力、协同创新能力与协同创新绩效三者间关系的概念模型,并通过217家制造业企业的调查数据做实证分析。结果表明:大数据分析能力包括大数据有形资源、人力资源、无形资源三个维度,其中,大数据有形资源对人力资源、无形资源有显著的正向影响,对协同创新能力、协同创新绩效没有显著的直接影响,而通过大数据人力资源、无形资源对协同创新能力产生间接影响。大数据人力资源、无形资源均通过协同创新能力对协同创新绩效产生间接影响。协同创新能力对协同创新绩效有显著的正向影响。 相似文献
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基于数据驱动的视角,本文构建了数字经济环境下大数据能力、知识整合对公司创业影响的理论模型。通过310份公司创业数据进行实证检验发现：大数据资源获取能力与分析整合能力对公司创业存在显著正向影响,但大数据洞察预测能力对公司创业存在显著负向影响;大数据资源获取能力与分析整合能力对知识获取、知识融合、知识重构均存在显著正向影响,大数据洞察预测能力对知识获取和知识重构没有显著影响,对知识融合存在显著负向影响;知识获取、知识融合与知识重构对公司创业均存在显著正向影响;知识整合视角下知识融合在大数据能力各维度与公司创业之间存在中介效应,知识获取与知识重构的中介效应不存在。 相似文献
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研究以143家高新技术企业的调查数据为样本,统计分析结果显示:知识导向实践和协作导向实践对探索式学习和开发式学习都有显著的正向影响;探索式学习对管理创新绩效、技术创新绩效都有显著的正向影响,开发式学习对技术创新绩效有显著的正向影响;探索式学习在人力资源实践和管理创新绩效间具有完全中介效应,探索式学习、开发式学习在人力资源实践和技术创新绩效间具有部分中介效应。 相似文献
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本研究构建了资源拼凑、团队即兴与初创企业新产品开发绩效关系的理论模型,通过运用结构方程模型对广州地区133家初创企业研发团队问卷调查数据进行实证研究发现：（1）资源新目的重组不但对初创企业新产品开发绩效产生显著的直接正向影响,而且还分别通过研发团队的认知即兴和行为即兴两个中间变量对初创企业新产品开发绩效产生显著的间接正向影响;（2）资源将就使用和资源就地取材对初创企业新产品开发绩效均没有显著的影响,但资源将就使用仍然会通过研发团队行为即兴,对初创企业新产品开发绩效产生显著的间接正向影响,而研发团队资源就地取材也会通过团队认知即兴和行为即兴,正向影响初创企业新产品开发绩效;（3）研发团队的团队即兴包括认知即兴和行为即兴,均对初创企业新产品开发绩效产生显著的直接正向影响作用。 相似文献
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基于264份问卷调查数据,将创业者先前经验、利用式学习、企业创业拼凑、正式制度环境4个因素整合在一起,构建了一个具有调节的中介效应模型,并进行实证研究。结果表明：创业者先前经验对利用式学习具有显著正向影响;利用式学习对企业创业拼凑具有显著正向影响;利用式学习在创业者先前经验与企业创业拼凑之间具有中介作用;正式制度环境负向调节创业者先前经验对利用式学习的正向影响,也负向调节创业者先前经验通过利用式学习影响企业创业拼凑的中介效应。 相似文献
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技术创新导向下高管激励契约最优整合策略研究——企业生命周期视角 总被引：1，自引：0，他引：1
王旭 《科学学与科学技术管理》2016,(9):143-154
高管激励对技术创新的促进效应依赖于对不同激励契约进行科学动态整合。立足于企业生命周期视角,在对高管创新激励机制的运行原理以及高管创新收益预期分布规律进行分析的基础上,实证检验了不同生命周期内,高管薪酬激励、声誉激励和控制权激励契约对技术创新影响的差异性,以及契约间的互补和互替关系,设计了技术创新导向下高管激励的最优动态整合方案。研究结果显示,成长期,声誉激励对企业技术创新具有显著促进作用,薪酬激励与声誉激励间具有互替效应,控制权激励与声誉激励间具有互补效应;成熟期,薪酬激励对技术创新具有显著正向影响,而控制权激励却表现出显著的抑制作用,并且薪酬激励与声誉激励之间具有互补效应;蜕变期,薪酬激励和控制权激励均能够显著促进企业技术创新,且二者之间存在显著的互补效应。 相似文献
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以江浙沪144家制造和有形服务企业为样本,从制度理论和高阶理论的整合视角考察了利益相关者环保导向对生态创新的影响以及高管环保意识的权变效应。主要结论包括:第一,政府环保导向对生态产品创新具有显著正向影响,与生态工艺创新呈倒U型的关系;客户环保导向对生态管理创新具有显著正向影响,与生态产品创新呈倒U型的关系;竞争者环保导向对生态管理创新、生态工艺创新和生态产品创新均具有显著正向影响。第二,不同高管环保意识对不同利益相关者环保导向与不同生态创新的之间调节效应不同。 相似文献
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基于基本心理需求理论,探讨人—工作匹配、辱虐管理对员工创新行为的影响机制。通过471份对上下级匹配调查问题为数据的实证分析,发现人—工作匹配正向影响员工创新行为,主管辱虐管理负向影响创新行为;人—工作匹配通过能力需求间接对创新行为产生显著正向影响,辱虐管理通过自主需求、关系需求和能力需求间接对创新行为产生显著负向影响;辱虐管理分别弱化了人—工作匹配与关系需求、能力需求之间的正向关系。 相似文献
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Surabhi Verma Som Sekhar Bhattacharyya Saurav Kumar 《Information processing & management》2018,54(5):791-806
Research on the adoption of systems for big data analytics has drawn enormous attention in Information Systems research. This study extends big data analytics adoption research by examining the effects of system characteristics on the attitude of managers towards the usage of big data analytics systems. A research model has been proposed in this study based on an extensive review of literature pertaining to the Technology Acceptance Model, with further validation by a survey of 150 big data analytics users. Results of this survey confirm that characteristics of the big data analytics system have significant direct and indirect effects on belief in the benefits of big data analytics systems and perceived usefulness, attitude and adoption. Moreover, there are mediation effects that exist among the system characteristics, benefits of big data analytics systems, perceived usefulness and the attitude towards using big data analytics system. This study expands the existing body of knowledge on the adoption of big data analytics systems, and benefits big data analytics providers and vendors while helping in the formulation of their business models. 相似文献
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Big data analytics associated with database searching, mining, and analysis can be seen as an innovative IT capability that can improve firm performance. Even though some leading companies are actively adopting big data analytics to strengthen market competition and to open up new business opportunities, many firms are still in the early stage of the adoption curve due to lack of understanding of and experience with big data. Hence, it is interesting and timely to understand issues relevant to big data adoption. In this study, a research model is proposed to explain the acquisition intention of big data analytics mainly from the theoretical perspectives of data quality management and data usage experience. Our empirical investigation reveals that a firm's intention for big data analytics can be positively affected by its competence in maintaining the quality of corporate data. Moreover, a firm's favorable experience (i.e., benefit perceptions) in utilizing external source data could encourage future acquisition of big data analytics. Surprisingly, a firm's favorable experience (i.e., benefit perceptions) in utilizing internal source data could hamper its adoption intention for big data analytics. 相似文献
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The number of firms that intend to invest in big data analytics has declined and many firms that invested in the use of these tools could not successfully deploy their project to production. In this study, we leverage the valence theory perspective to investigate the role of positive and negative valence factors on the impact of bigness of data on big data analytics usage within firms. The research model is validated empirically from 140 IT managers and data analysts using survey data. The results confirm the impact of bigness of data on both negative valence (i.e., data security concern and task complexity), and positive valence (i.e., data accessibility and data diagnosticity) factors. In addition, findings show that data security concern is not a critical factor in using big data analytics. The results also show that, interestingly, at different levels of data security concern, task complexity, data accessibility, and data diagnosticity, the impact of bigness of data on big data analytics use will be varied. For practitioners, the findings provide important guidelines to increase the extent of using big data analytics by considering both positive and negative valence factors. 相似文献
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Firms are increasingly relying on business insights obtained by deploying data analytics. Analytics-driven business decisions have thus taken a strategic imperative role for the competitive advantage of a firm to endure. The extent and effectiveness through which business firms can actually derive benefits by deploying big data-based practices requires deep analysis and calls for extensive research. This study extends the big data analytics capability (BDAC) model by examining the mediatory effects of organizational culture (CL) between internal analytical knowledge (KN) and BDAC, as well as the mediating effects of BDAC between CL and firm performance. The findings bring into focus that CL plays the role of complementary mediation between BDAC and KN to positively impact firm performance (FP); BDAC also plays a similar mediatory role between CL and the performance of a firm. 相似文献
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Over recent years, organizations have started to capitalize on the significant use of Big Data and emerging technologies to analyze, and gain valuable insights linked to, decision-making processes. The process of Competitive Intelligence (CI) includes monitoring competitors with a view to delivering both actionable and meaningful intelligence to organizations. In this regard, the capacity to leverage and unleash the potential of big data tools and techniques is one of various significant components of successfully steering CI and ultimately infusing such valuable knowledge into CI strategies. In this paper, the authors aim to examine Big Data applications in CI processes within organizations by exploring how organizations deal with Big Data analytics, and this study provides a context for developing Big Data frameworks and process models for CI in organizations. Overall, research findings have indicated a preference for a rather centralized informal process as opposed to a clear formal structure for CI; the use of basic tools for queries, as opposed to reliance on dedicated methods such as advanced machine learning; and the existence of multiple challenges that companies currently face regarding the use of big data analytics in building organizational CI. 相似文献
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Information and operations management in libraries presents a unique opportunity to provide insights for the sharing economy. Libraries correspond to a special type of sharing goods, named common-pool resources. Such resources have two characteristics: they are non-exclusive, but rival to each other. Service operations in libraries involve thousands of operations every year, making them a perfect context for the use of big data analytics capabilities (BDAC) to provide real-world evidence on the potential existing challenges in the sharing economy. Employing a novel dataset related to 723,798 library transactions, made by 16,232 individual users during a 10-year period (2006–2015), we estimate peer effects among users via regression analysis, considering the number of books each user borrows. Our main results suggest that a rise in the number of loans among a user’s peer group correlates with her own loans, an evidence of positive peer effects. However, a closer look at the data suggests a high degree of heterogeneity, in terms of behavioral patterns. First, we suggest that peer effects do not occur in the case of users who are not subject to monetary fines. Second, peer effects vary according to users’ category (student or non-student), and area of study (management, accounting, economics, and other courses). Third, there is evidence of different magnitudes of peer effects according to time in school, which suggests the existence of learning effects in a library setting. The results reported in this paper highlight the important role of big data analytics capabilities to uncover new challenges of the sharing economy, having important implications, both in theoretical and practical terms. 相似文献
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Clinicians, healthcare providers-suppliers, policy makers and patients are experiencing exciting opportunities in light of new information deriving from the analysis of big data sets, a capability that has emerged in the last decades. Due to the rapid increase of publications in the healthcare industry, we have conducted a structured review regarding healthcare big data analytics. With reference to the resource-based view theory we focus on how big data resources are utilised to create organization values/capabilities, and through content analysis of the selected publications we discuss: the classification of big data types related to healthcare, the associate analysis techniques, the created value for stakeholders, the platforms and tools for handling big health data and future aspects in the field. We present a number of pragmatic examples to show how the advances in healthcare were made possible. We believe that the findings of this review are stimulating and provide valuable information to practitioners, policy makers and researchers while presenting them with certain paths for future research. 相似文献
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基于172份企业调研数据,从数据赋能视角探讨信息技术(IT)与业务融合对企业商业模式创新的作用机制,并在此基础上基于资源整合、数据挖掘与分析、组织决策的逻辑框架以及企业的环境适配性问题,考察企业大数据能力的中介作用以及环境动态性的调节效应.研究发现:IT与业务融合对商业模式创新有正向影响;大数据能力在IT与业务融合和商业模式创新的关系间具有显著中介作用;IT与业务融合通过大数据能力对商业模式创新的间接影响受到环境动态性的负向调节作用.研究结论对于企业开展商业模式创新实践的主要启示包括:企业应注重信息技术与当前自身基础架构、业务流程与战略方向之间的匹配问题,重视并发挥大数据能力积极的影响,积极开展对外部环境的监测工作等. 相似文献
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Big Data Analytics (BDA) is increasingly becoming a trending practice that generates an enormous amount of data and provides a new opportunity that is helpful in relevant decision-making. The developments in Big Data Analytics provide a new paradigm and solutions for big data sources, storage, and advanced analytics. The BDA provide a nuanced view of big data development, and insights on how it can truly create value for firm and customer. This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies. It provides an overview of the architecture of BDA including six components, namely: (i) data generation, (ii) data acquisition, (iii) data storage, (iv) advanced data analytics, (v) data visualization, and (vi) decision-making for value-creation. In this paper, seven V's characteristics of BDA namely Volume, Velocity, Variety, Valence, Veracity, Variability, and Value are explored. The various big data analytics tools, techniques and technologies have been described. Furthermore, it presents a methodical analysis for the usage of Big Data Analytics in various applications such as agriculture, healthcare, cyber security, and smart city. This paper also highlights the previous research, challenges, current status, and future directions of big data analytics for various application platforms. This overview highlights three issues, namely (i) concepts, characteristics and processing paradigms of Big Data Analytics; (ii) the state-of-the-art framework for decision-making in BDA for companies to insight value-creation; and (iii) the current challenges of Big Data Analytics as well as possible future directions. 相似文献