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Previous research has analyzed the consequence of network stability; however, little is known about how partner type diversity influence network stability in R&D network. Based on knowledge-based view and social network theory, the purpose of this paper is to unravel the internal mechanisms between partner type diversity and network stability through the mediating role of knowledge recombination in R&D network.

The authors collected an unbalanced panel patent data set from information communication technology industry for the period 1994–2016. Then, the authors tested the different dimensions of partner type variety and its relevance in the R&D network and the mediating role of knowledge recombination through adopting the multiple linear regression.

Results indicate an inverted U-shaped relationship between partner type diversity (variety and relevance) and network stability, whereas knowledge recombination partially mediate these relationships.

From the perspective of R&D networks, this paper explores that there are the under-researched phenomena the antecedent of network stability through nodal attributes (i.e. partner type variety and partner type relevance). Moreover, this paper empirically examined the mediating role of knowledge recombination in the partner type diversity–network stability relationships. The novel perspective allows focal firm to recognize importance of nodal attributes, which are critical to fully excavate the potential capabilities of cooperating partners in R&D network.

Starting from the model of the initiation and evolution of inter-firm knowledge transfer in R&D relationships developed by Faems et al. (2007), the purpose of this paper is to refine and improve this model, assessing its reliability in a different and wider context and extending it according to the outcomes.

A multiple case-study approach was implemented, examining 34 dyadic inter-firm R&D relationships. This methodology suited the research goal of exploring the validity of a model in an area where little data or theory exists.

The theoretical model proposed by Faems et al. (2007) was improved, confirming the adequacy of the overall structure of their intuition and highlighting several differences in terms of factors that lead to the dissolution of R&D relationships. These differences mainly refer to partners’ similarities before starting R&D relationships, co-opetition situations, knowledge leakage/opportunistic behavior and reputation issues.

This work is the first to investigate two open research gaps related to the model of the initiation and evolution of inter-firm knowledge transfer in R&D relationships: the need for additional case studies in other contexts to develop a more general theory and the lack of research incorporating issues such as relational capital between partners, governance form and alliance scope in an integrated analysis.

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.

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.

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.

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.

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.

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.

The purpose of this study is to enhance the comprehensive understanding of the roles of resource investments, explicit contracts and three components of guanxi (i.e. renqing, ganqing and mianzi) in asymmetric research and development (R&D) partnerships. Treating dependence asymmetry as a multidimensional construct, this study examines the moderating effects of these elements on the relationships between resources and information asymmetry and opportunism.

The study was executed by issuing questionnaires to R&D managers participating in R&D projects and collaborations in the Shanghai and Jiangsu provinces via e-mail and face to face surveys. A multiple regression analysis was used to test the hypotheses.

The empirical test generally supported the conceptual model and produced the following findings: first, resources and information asymmetry significantly and positively affect opportunism. Second, the partner’s resource investments can weaken the effect of resources and information asymmetry on the partner’s opportunism. Third, explicit contracts can reduce the impact of information asymmetry on the partner’s opportunism. Fourth, renqing and ganqing but not mianzi can weaken the influence of information asymmetry on the partner’s opportunism.

This study provides a comprehensive and clear understanding of how opportunism can be curbed by jointly considering resource investments, explicit contracts and guanxi in asymmetric R&D cooperative relationships. Moreover, dependence asymmetry and guanxi are measured as a multidimensional construct and reveal their underlying structure, which expands previous understandings of risk management in R&D collaborations.

The purpose of this paper is to define co-exploitation, co-exploration, and alliance ambidexterity from the perspective of organizational learning; to analyze how knowledge bases, structural arrangements, and control mechanisms of R&D alliances influence co-exploitation and co-exploration; and to discuss how to achieve alliance ambidexterity by managing paradoxes around knowledge bases, structural arrangements, and control mechanisms.

This is a conceptual paper focussing on how to balance exploitation and exploration at the alliance level through managing three paradoxes of cooperation: similarity vs complementarity, integration vs modularity, and contracts vs trust.

While technological similarity, structural integration, and contracts are more likely to promote co-exploitation, technological complementarity, structural modularity, and trust are more likely to facilitate co-exploration. Alliance ambidexterity, which is beneficial for alliance performance, derives from either the combination of technological complementarity, structural integration, and contracts, or the combination of technological similarity, structural modularity, and trust temporally.

Researchers should analyze the possibility of building alliance ambidexterity in other types of interorganizational relationships, and find other possible antecedents of interorganizational learning.

Managers should not simply treat R&D alliances as one of exploratory interorganizational relationships, but pay equal attention to co-exploitation and co-exploration. To achieve this balance, practitioners should combine technological complementarity with structural integration and contracts, or integrate technological similarity with structural modularity and trust.

This paper is one of the first contributions that analyze how an R&D alliance could gain its ambidexterity through the management of nested cooperation paradoxes.

The purpose of this paper is to explore the relationship between prior experience of organisations and their “co‐opetitive” behaviours in forming research networks when a R&D programme is launched in order to fund future research projects.

Drawing on both resource‐based writings and social embeddedness studies, the paper posits that two types of co‐opetition – namely the intra‐network and inter‐network co‐opetition – should be clearly distinguished in order to understand co‐opetitive behaviours of organisations. These two types of co‐opetition arise as a consequence of the different levels of firms' prior experience in forming successful co‐opetitive networks with their competitors.

A reverse U‐shape trade‐off between the two types of co‐opetition is hypothesised: the maximum level of intra‐network co‐opetition and the lowest of inter‐network co‐opetition are expected for low and highlevels of prior experience; the minimum level of intra‐network co‐opetition and the highest of inter‐network co‐opetition are expected for moderate levels of prior experience.

This paper sheds light on how co‐opetitive relationships emerge in R&D networks and under which circumstances competition prevails on cooperation.

The purpose of this paper is to examine the collaboration strategies employed by collaborating small‐ and medium‐sized enterprises (SMEs) and university researchers for initiating and optimizing the process and outcome of R&D collaboration.

The paper is based upon a qualitative study of the total population of university departments and SMEs involved in collaborative research projects sponsored by a new governmental programme in Denmark, the aim of which was to build new R&D alliances between industry and universities.

The findings show how partners choose to pursue difference short‐ or long‐term strategies to optimize the process and outcome of university‐industry (UI) collaboration. Some collaborations were thus informed by a short‐term strategy aimed at achieving immediate R&D results. However, to a high extent, many SME partners relied upon a long‐term strategy aiming at developing UI relations beyond the immediate project and practical learning. A variety of shifting strategies shape researchers' decisions during UI collaborations, which thus convey different notions of success.

The findings of the present research point to the importance of taking the diverse reasons and micro strategies informing collaborative efforts into account when studying UI collaborations.

Different strategies may prove successful in optimizing the outcome of UI collaborations depending upon, e.g. partners' previous collaborative experiences. Policies should incorporate some openness towards the differential premises and reasons for UI collaboration.

Relatively little research has addressed the development of UI relationships from the micro‐level perspective of the discretionary decisions and strategies of collaborating researchers.

How beneficial is interorganizational learning for research and development (R&D) consortium performance, and what factors drive the learning effectiveness? To answer these underexplored topics, this study aims to investigate the relationship between interorganizational learning and consortium performance and the moderating impact of social capital embedded in the consortia on the relationship.

Based on the literature review conducted, interorganizational learning is conceptualized as a multidimensional construct represented by exploitative and exploratory learning at the consortium level. R&D consortium performance is operationalized as a combination of technological and business performance, corresponding, respectively, to its collective outputs and individual outcomes. This study hypothesizes focusing on technological performance while analyzing business performance for the robustness check.

The hypotheses are tested in an original sample of 218 R&D consortium projects in which Korean ventures participated as focal partners. The results show that both exploitative and exploratory learnings are positively related to R&D consortium performance, and social capital accrued in the consortia leverages the advantages of exploratory learning for technological performance and exploitative learning for business performance.

This study adds new evidence to the literature, suggesting the performing-by-partnering mechanism of R&D consortia is contingent on the social capital that institutionalizes the common learning platform in which the partners interoperate. In practical respects, the combination of interorganizational learning and social capital deserves to be regarded as strategic elements for the value-cocreating consortia, requiring the true exchange of knowledge across partners.

The purpose of this paper is twofold: to analyze to what extent innovation output of R & D collaborations, proxied by co-patenting activities in terms of quantity, characteristics and value, differs depending on whether the engaged R & D partners have a certain type of relationship (allies, suppliers and subsidiaries); to identify possible automakers co-patenting patterns taking into account the differences in the innovation output with their R & D partners.

To reach the aims, the authors matched two types of data: co-assigned patent portfolio of four automakers and relationship type between automakers and their co-assignees. Matching the company names of the two data sources allowed the authors to obtain the final data set used to carry out extensive descriptive and regression analysis, both on a firm- and patent-level.

Results show differences in the characteristics and the technological value of patented inventions in relation with the type of collaboration partner; they also support the authors in the identification of four co-patenting patterns (contingent, purposive, watchful and advanced) according to the co-patenting propensity and the presence of a preferred relationship type.

The paper contributes to the literature by investigating the presence of differences across the patenting activities of a selection of automakers and their supplier, allied and subsidiary firms. The issue related to patent value represents an emerging area of interest in the field of collaborations for innovation. The methodology constitutes a novelty by matching two different sources and standardizing the company names (“name game”) through an automated algorithm and a double manual check, by searching company web sites and corporate trees.

This paper examines the impact of technological alliance announcements in a nonfavorable environment, using event study methodology that includes robust tests to allow heteroskedasticity across firms and over time. The study is based on Spanish data, and focuses on the fact that Spanish market conditions do not favor firms that are deciding whether to enter a technological alliance. The paper is extended to analyze different features of alliances. Results suggest no stock market reaction on the day of the announcement, and a negative reaction on the days that follow. Our results also show that the stock market exacts no penalty on joint venture alliances, alliances involving public entities, alliances between Spanish firms, or alliances between competitors. The common feature revealed in these subgroups is the pursuit of security, a phenomenon that is consistent with the study environment.