研发伙伴多样性、双向学习与企业创新质量

IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Complexity Pub Date : 2024-07-31 DOI:10.1155/2024/7187690
Zhongtao Zhao, Zhaofeng Yu, Yunwei Li, Jing Tian
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引用次数: 0

摘要

研发伙伴多样性(RPD)对于提高企业的创新水平至关重要,而创新质量则是衡量企业整体创新能力的关键指标。然而,RPD 与创新质量之间的关系却鲜有文献关注。本文旨在揭示 RPD 对创新质量的影响及其内在机制。为此,我们利用 463 家中国制造业上市公司的数据进行了实证研究。利用负二项模型进行数据分析,我们发现 RPD 对创新质量有积极影响。微观机制分析表明,探索性学习和利用性学习在 RPD 与创新质量的关系中起着中介作用。此外,我们还发现,研发伙伴类型和企业所有权不同,研发与创新质量之间的关系强度也不同。具体来说,与大学、竞争对手、用户或研究机构合作的企业会增强正效应,而与同一集团内其他实体结成联盟则会减轻正效应。与国有企业相比,RPD 对非国有企业创新质量的积极影响更为显著。这些发现在一系列敏感性测试中都是稳健的,为企业通过促进多样化的研发合作关系来提高创新质量提供了宝贵的启示。
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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R&D Partner Diversity, Ambidextrous Learning, and Innovation Quality of Firms

R&D partner diversity (RPD) is crucial for enhancing a firm’s innovation level, with innovation quality being a pivotal indicator of the firm’s overall capacity for innovation. However, the relationship between RPD and innovation quality has received little attention in the literature. This paper aims to unravel the influence of RPD on innovation quality and the underlying mechanisms. We conduct empirical research utilizing data from 463 publicly listed Chinese manufacturing companies to achieve this goal. Using a negative binomial model for data analysis, we find that RPD has a positive impact on the quality of innovation. The micromechanism analysis reveals that both exploratory learning and exploitative learning play a mediating role in the relationship between RPD and innovation quality. Furthermore, we discover that the strength of the relationship between RPD and innovation quality varies depending on the types of R&D partners and corporate ownership. Specifically, firms that collaborate with universities, competitors, users, or research institutes strengthen the positive effect, whereas forming alliances with other entities within the same group mitigates it. RPD has a more significant positive influence on the quality of innovation in non-state-owned enterprises compared to state-owned enterprises. These findings are robust to a battery of sensitivity tests, which provide valuable insights for firms seeking to enhance their innovation quality by fostering diverse R&D partnerships.

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来源期刊
Complexity
Complexity 综合性期刊-数学跨学科应用
CiteScore
5.80
自引率
4.30%
发文量
595
审稿时长
>12 weeks
期刊介绍: Complexity is a cross-disciplinary journal focusing on the rapidly expanding science of complex adaptive systems. The purpose of the journal is to advance the science of complexity. Articles may deal with such methodological themes as chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory. Papers treating applications in any area of natural science or human endeavor are welcome, and especially encouraged are papers integrating conceptual themes and applications that cross traditional disciplinary boundaries. Complexity is not meant to serve as a forum for speculation and vague analogies between words like “chaos,” “self-organization,” and “emergence” that are often used in completely different ways in science and in daily life.
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