{"title":"R&D Partner Diversity, Ambidextrous Learning, and Innovation Quality of Firms","authors":"Zhongtao Zhao, Zhaofeng Yu, Yunwei Li, Jing Tian","doi":"10.1155/2024/7187690","DOIUrl":null,"url":null,"abstract":"<div>\n <p>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.</p>\n </div>","PeriodicalId":50653,"journal":{"name":"Complexity","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/7187690","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Complexity","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1155/2024/7187690","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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.