Individual performance in a coopetitive R&amp;D alliance: motivation, opportunity and ability

Wang, Xiao; Dolfsma, Wilfred; Bij, Hans van der

doi:10.1111/radm.12370

Cited by 14 publications

(20 citation statements)

References 54 publications

(87 reference statements)

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“…Previous studies mostly defined R&D alliances as innovation-based relationships established by several actors who collaborate to develop a new product or technology [21,22]. Following previous researchers [8,23], our study has used similar alliance network construction criteria to develop the R&D alliance network.…”

Section: Research and Development Alliance Networkmentioning

confidence: 99%

“…Moreover, organizations with a central position (a higher degree centrality) within R&D alliance networks might affect the relationship between knowledge combinatorial potential and organizational innovation capability. Degree centrality is one of the critical indicators to present the central position of an actor in alliance networks, which is regarded as the number of cooperators that an actor is directly linked in alliance networks [22]. Degree centrality reflects a real-world situation in which organizations with a high degree centrality can control more resources and information in R&D alliance networks, enhance their commercial standing, and influence how their capabilities are perceived [43].…”

Section: Knowledge Combinatorial Potential and Degreementioning

confidence: 99%

“…Networks. Degree centrality (DC) is one of the vital structural indicators used to describe the network position [22]. e high degree centrality reflects the central position of an organization in alliance networks.…”

Section: A Moderating Variable From Research and Development Alliancementioning

confidence: 99%

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Interorganizational Knowledge Networks, R&D Alliance Networks, and Innovation Capability: A Multilevel Network Perspective

et al. 2021

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R&D alliances and knowledge networks are vital to the innovation process. Based on the multilevel network approach, our study comprehensively investigates several knowledge attributes of interorganizational knowledge networks and explores how R&D alliance networks are relevant for the relationship between knowledge attributes and organizational innovation capability. Samples in our research include 86 cliques from 2010 to 2015 in five Chinese high-tech industries’ R&D alliance networks. Results from the negative binomial regression model show that different knowledge attributes show a distinct effect on organizational innovation capability, including linear relationship, inverted U-shaped curve relationship, and inverted S-shaped curve relationship. Besides, our results identify that the central position within R&D alliance networks plays a limited role in the relationship between knowledge attributes and organizational innovation capability. Our findings could be used to help organizations sort out their knowledge attributes of knowledge bases, come to understand the impact of the interaction between the interorganizational knowledge network and R&D alliance network on the organizational innovation capability, and then make a targeted strategy to carry out innovation activities.

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Section: Research and Development Alliance Networkmentioning

confidence: 99%

Section: Knowledge Combinatorial Potential and Degreementioning

confidence: 99%

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Interorganizational Knowledge Networks, R&D Alliance Networks, and Innovation Capability: A Multilevel Network Perspective

et al. 2021

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“…partner technological relatedness, partner competitive overlap, partner experience and partner relative size) tie, with regard to the firms’ access to external knowledge (Caner and Tyler, 2015) and consequently to their willingness to achieve high innovation performance (Grant and Baden-Fuller, 2004; Lavie, 2007; Mindruta et al , 2016). Second, this paper contributes to coopetition research because it reveals the beneficial effect of coopetition for the innovation performance of the firms involved in R&D alliances when some of the other knowledge-based partner attributes are considered (Filiou and Massini, 2018; Hani and Dagnino, 2020; Park et al , 2014; Ritala and Hurmelinna-Laukkanen, 2013; Wang et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

“…some of the other knowledge-based partner attributes are considered (Filiou and Massini, 2018;Hani and Dagnino, 2020;Park et al, 2014;Ritala and Hurmelinna-Laukkanen, 2013;Wang et al, 2019).…”

mentioning

confidence: 99%

R&D alliance partner attributes and innovation performance: a fuzzy set qualitative comparative analysis

Ferrigno

Dagnino

Paola

2021

JBIM

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Purpose Drawing upon the importance of research and development (R&D) alliances in driving firm innovation performance, extant research has analyzed individually the impact of R&D alliance partner attributes on firm innovation performance. Despite such analyzes, research has generally underestimated the configurations of partner attributes leading to firm innovation performance. This research gap is interesting to explore, as firms involved in R&D alliances usually face a combination of partner attributes. Moreover, gaining a better understanding of how R&D partner attributes tie into configurations is an issue that is attracting particular interest in coopetition research and alliance literature. This paper aims to obtain a better knowledge of this underrated, but important, aspect of alliances by exploring what configurations of R&D alliance partner attributes lead firms involved in R&D alliances to achieve high innovation performance. To tackle this question, first, this study reviews the extant literature on R&D alliances by relying on the knowledge-based view of alliances to identify the most impactful partner attributes on firms’ innovation performance. This paper then applies a fuzzy set qualitative comparative analysis (fsQCA) to explore the configurations of R&D alliance partner attributes that lead firms involved in R&D alliances to achieve high innovation performance. Design/methodology/approach This study selects 27 R&D alliances formed worldwide in the telecom industry. This paper explores the multiple configurations of partner attributes of these alliances by conducting a fsQCA. Findings The findings of the fsQCA show that the two alternate configurations of partner attributes guided the firms involved in these alliances to achieve high innovation performance: a configuration with extensive partner technological relatedness and coopetition, but no experience; and a configuration with extensive partner experience and competition, but no technological relatedness. Research limitations/implications The research highlights the importance of how partner attributes (i.e. partner technological relatedness, partner competitive overlap, partner experience and partner relative size) tie, with regard to the firms’ access to external knowledge and consequently to their willingness to achieve high innovation performance. Moreover, this paper reveals the beneficial effect of competition on the innovation performance of the firms involved in R&D alliances when some of the other knowledge-based partner attributes are considered. Despite these insights for alliance and coopetition literature, some limitations are to be noted. First, some of the partners’ attributes considered could be further disentangled into sub-partner attributes. Second, other indicators might be used to measure firms’ innovation performance. Third, as anticipated this study applies fsQCA to explore the combinatory effects of partner attributes in the specific context of R&D alliances in the telecom industry worldwide and in a specific time window. This condition may question the extensibility of the results to other industries and times. Practical implications This study also bears two interesting implications for alliance managers. First, the paper suggests that R&D alliance managers need to be aware that potential alliance partners have multiple attributes leading to firm innovation performance. In this regard, partner competitive overlap is particularly important for gaining a better understanding of firm innovation performance. When looking for strategic partners, managers should try to ally with highly competitive enterprises so as to access their more innovative knowledge. Second, the results also highlight that this beneficial effect of coopetition in R&D alliances can be amplified in two ways. On the one hand, when the partners involved in the alliance have not yet developed experience in forming alliances. Partners without previous experience supply ideal stimuli to unlock more knowledge in the alliance because new approaches to access and develop knowledge in the alliance could be explored. On the other hand, this paper detects the situation when the allied partners are developing technologies and products in different areas. When partnering with firms coming from different technological areas, the knowledge diversity that can be leveraged in the alliances could drive alliance managers to generate synergies and economies of scope within the coopetitive alliance. Originality/value Extant research has analyzed individually the impact of R&D alliance partner attributes on firm innovation performance but has concurrently underestimated the configurations of partner attributes leading to firm innovation performance. Therefore, this paper differs from previous studies, as it provides an understanding of the specific configurations of R&D alliance partner attributes leading firms involved in R&D alliances to achieve high innovation performance.

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Motives to Publish, to Patent and to Standardize: An Explorative Study Based on Individual Engineers’ Assessments

Blind

Filipović

Lazina

2022

Technological Forecasting and Social Change

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Individual performance in a coopetitive R&D alliance: motivation, opportunity and ability

Cited by 14 publications

References 54 publications

Interorganizational Knowledge Networks, R&D Alliance Networks, and Innovation Capability: A Multilevel Network Perspective

Interorganizational Knowledge Networks, R&D Alliance Networks, and Innovation Capability: A Multilevel Network Perspective

R&D alliance partner attributes and innovation performance: a fuzzy set qualitative comparative analysis

Motives to Publish, to Patent and to Standardize: An Explorative Study Based on Individual Engineers’ Assessments

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