Impact of Cooperative Innovation on the Technological Innovation Performance of High-Tech Firms: A Dual Moderating Effect Model of Big Data Capabilities and Policy Support.

IF 2.6 4区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Big Data Pub Date : 2024-02-01 Epub Date: 2023-09-14 DOI:10.1089/big.2022.0301

Xianglong Li, Qingjin Wang, Renbo Shi, Xueling Wang, Kaiyun Zhang, Xiao Liu

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Abstract

The mechanism of cooperative innovation (CI) for high-tech firms aims to improve their technological innovation performance. It is the effective integration of the internal and external innovation resources of these firms, along with the simultaneous reduction in the uncertainty of technological innovation and the maintenance of the comparative advantage of the firms in the competition. This study used 322 high-tech firms as our sample, which were located in 33 national innovation demonstration bases identified by the Chinese government. We implemented a multiple linear regression to test the impact of CI conducted by these high-tech firms at the level of their technological innovation performance. In addition, the study further examined the moderating effect of two boundary conditions-big data capabilities and policy support (PS)-on the main hypotheses. Our study found that high-tech firms carrying out CI can effectively improve their technological innovation performance, with big data capabilities and PS significantly enhancing the degree of this influence. The study reveals the intrinsic mechanism of the impact of CI on the technological innovation performance of high-tech firms, which, to a certain extent, expands the application context of CI and enriches the research perspective on the impact of CI on the innovation performance of firms. At the same time, the findings provide insight for how high-tech firms in the digital era can make reasonable use of data empowerment in the process of CI to achieve improved technological innovation performance.

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合作创新对高科技企业技术创新绩效的影响：大数据能力与政策支持的双重调节效应模型。

高科技企业的合作创新（CI）机制旨在提高其技术创新绩效。它有效整合了企业内外部的创新资源，同时降低了技术创新的不确定性，保持了企业在竞争中的比较优势。本研究以中国政府认定的 33 个国家自主创新示范基地中的 322 家高科技企业为样本。我们采用多元线性回归的方法，检验了这些高科技企业开展的 CI 对其技术创新绩效水平的影响。此外，研究还进一步检验了两个边界条件--大数据能力和政策支持（PS）--对主要假设的调节作用。我们的研究发现，高科技企业开展 CI 能有效提高其技术创新绩效，而大数据能力和政策支持能显著提高这种影响程度。研究揭示了CI对高科技企业技术创新绩效影响的内在机理，在一定程度上拓展了CI的应用范围，丰富了CI对企业创新绩效影响的研究视角。同时，研究结果也为数字时代的高科技企业如何在CI过程中合理利用数据赋能实现技术创新绩效的提升提供了启示。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

9.10

自引率

2.20%

发文量

期刊介绍： Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.