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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 paper is to synthesize existing knowledge‐based dynamic capabilities research into a single typology for managerial and academic use.

Based on the resource‐based and knowledge‐based views, this study conducts a theoretically grounded typology development exercise based on an extensive review of the existing dynamic capabilities literature.

The paper identifies seven frameworks presented in the literature that showed some consistency in underlying concepts but conflict in nomenclature and application. Identifying over 80 uses of knowledge‐based dynamic capabilities in the literature review, three complementary dimensions that are common amongst the frameworks are identified and integrated into a consistent typology of eight knowledge‐based dynamic capabilities to encompass the extant literature.

Addressing fragmentation in the knowledge‐based dynamic capabilities discourse, the paper advances the concept of knowledge‐based dynamic capabilities by organizing the existing literature and frameworks into a comprehensive and consistent typology. Moreover, this integrative typology allows managers and researchers to identify those capabilities in use and the commonalities between them. Finally, the paper identifies a new knowledge‐based dynamic capability that has not yet been identified in any existing framework.

The purpose of this paper is to clarify the concept of dynamic capabilities from the knowledge‐based perspective and investigate the mechanisms of dynamic capabilities on innovation performance in networked environments.

This paper designed a seven‐point Likert questionnaire measuring the dynamic capabilities, innovation performance and network embeddedness and a sample of 218 Chinese manufacturing firms were surveyed. Structural equation modeling method was used to statistically test the theoretical hypothesis.

Significant relationships were found between dynamic capabilities and innovation performance and knowledge combination capability played a mediating role in this relationship. For the links between network embeddedness and dynamic capabilities, knowledge acquisition capability was affected mainly by relational embeddedness and the diversity of network and joint problem solving contributed much to knowledge combination capability.

This paper deepened the understanding on dynamic capabilities and the mechanism between network embeddedness, knowledge‐based dynamic capabilities and innovation performance. In the future, the construct of knowledge‐based dynamic capabilities calls for more examination and verification.

Drawing on the literature of dynamic capabilities framework, knowledge‐based view and the network model, this study extends the literature of dynamic capabilities and its link with innovation performance in networked environments. Using survey data and structural equation modeling, this study offers rich evidence on the contribution of dynamic capabilities on innovation performance and the antecedents of dynamic capabilities.

While previous research has demonstrated the positive effects of digital business strategies on operational efficiency, financial performance and value creation, little is known about how such strategies influence innovation performance. To address the gap, this paper aims to investigate the impact of a firm’s digital business strategy on its innovation performance.

Drawing on the dynamic capability view, this study examines the mechanism through which a digital business strategy affects innovation performance. Data were collected from 215 firms in China and analyzed using multiple regression and structural equation modeling.

The empirical analysis reveals that a firm’s digital business strategy has positive impacts on both product and process innovation performance. These impacts are partially mediated by knowledge-based dynamic capability. Additionally, a firm’s digital business strategy interacts positively with its entrepreneurial orientation in facilitating knowledge-based dynamic capability. Moreover, market turbulence enhances the strength of this interaction effect. Therefore, entrepreneurial-oriented firms operating in turbulent markets can benefit more from digital business strategies to enhance their knowledge-based dynamic capabilities and consequently improve their innovation performance.

This study contributes to the understanding of how a firm’s digital business strategy interacts with entrepreneurial orientation in turbulent markets to shape knowledge-based dynamic capability, which in turn enhances the firm’s innovation performance.

Facing the global public health emergency (GPHE), the conflict of cultural differences and the imbalance of vital resources such as knowledge among different organizations are becoming more severe, which affects the enthusiasm and sustainability of firms' innovation heavily. It is an urgent problem to be solved for firms how to make use of internal knowledge and external power to help firms' sustainable innovation (FSI). Thus, the purpose of this study is to deeply analyze how firms' internal knowledge diversity (KD) and external ego-network structures [ego-network density (ED) and honest brokers (HB)] affect FSI, as well as how the ego-network structures (ED and HB) moderate the relationship between KD and FSI based on the perspective of the ego network.

Based on the data of the alliance innovation networks of China's new energy industries in 2009–2019, this study uses the social network analysis method and negative binomial regression model to explore the effect of KD and ego-network structures (ED and HB) on FSI, as well as the moderating effects of ego-network structures (ED and HB) on the relationship between KD and FSI based on the perspective of ego network.

This study finds that KD, ED and HB can boost FSI. Moreover, ED plays a negative moderating role in the relationship between KD and FSI. However, the negative moderating effect of HB on the relationship between KD and FSI is not significant.

This study presents fresh empirical evidence and new insights for firms on how to make full use of firms' internal KD and external ego-network structures to facilitate FSI.

First, this study not only enriches the research on the consequences of KD but also expands our understanding of the knowledge-based view to some extent. Second, this study not only enriches the motivation research of the FSI based on the perspective of ego-network in the context of the GPHE but also expands the application scope of social network theory and sustainable innovation' theory in part. Third, this paper is a new attempt to apply social network theory and knowledge-based view at the same time.

The purpose of this paper is to propose a theoretical framework to distinguish different forms of network resource that govern knowledge‐based alliances and facilitate innovation.

The paper seeks to build theory through a critical analysis of the relevant literature.

The paper draws on the notion of network resources to better understand those assets firms have at their disposal to facilitate knowledge‐based interactions and relationships that catalyze innovation. It seeks to integrate the concept of social capital, which the paper argues largely concerns resources related to the social relations and networks held by those individuals within a particular firm. As a means of describing and identifying network resources that are more strategically held by the firm as a whole, the paper introduces the concept of network capital. Network capital is defined as consisting of investments in calculative relations by firms through which they gain access to knowledge to enhance expected economic returns. Therefore, the paper argues that it is possible to make a distinction between the two types of network resource: network capital and social capital.

Making a distinction between network capital and social capital is relevant to both scholars and decision‐makers as it provides a framework for analyzing the underlying complexity of inter‐firm networks and variability across a range of dimensions, conditions and contingencies. It also provides a framework for evaluating which networks a firm can or cannot manage and invest in to meet its requirements.

The paper develops a new and more refined framework for analyzing and evaluating knowledge‐based alliances and innovation‐driven networks between firms and other actors.

We examine collaborative complexity arising from strategic alliances among competitors. In high technology industries, rapidly evolving and modular technologies increase the likelihood that collaborative alliances will develop between partners who also compete with one another. Partnering under these conditions involves choosing collaborative structures that foster the transfer and integration of some resources, while simultaneously protecting other resources from unintended transfer. Using resource-based, transaction cost, and industrial organization economic theories we develop a model to depict the risks and rewards of collaboration under different modes of competitive interdependence. Two dimensions underlie our conceptual model: resource interdependence and competitive interdependence. Resource interdependence is the degree of integration needed for the resources contributed by alliance partners as reflected in the nature of the resources and their co-specialization. Competitive interdependence gauges the similarity between partners in their overall strategic capabilities and customer markets. We conclude with a discussion of the contingent use of inter-organizational structures to enable partners to balance resource contributions and resource protection in collaborative-competitive relationships.

Because the discontinuous and uncertain characteristics of knowledge-based innovation cannot be reasonably interpreted by conventional management approaches, quantum mechanics which begins with uncertainty and concerns with a dynamic process of the complex system, has been exploratorily used in the management field. Although the theoretical new insights are provided by pioneering studies, quantitative research is in short supply. This paper aims to propose a quantum mechanics-based framework for quantitative research, thus extending the application of quantum mechanics in the knowledge management area from a dynamic system evolutionary standpoint.

Based on the similarity comparison between knowledge-based system evolution and atomic motion, the authors construct the atom-like structure of the knowledge-based system and elaborate the evolutionary mechanism of the knowledge-based system, thereby establishing the quantitative model. Apple and Zhongxing Telecom Equipment were selected for an empirical study to demonstrate the usefulness of the models for research on knowledge-based innovation and explore the unique knowledge-based innovation characteristics of the two firms.

First, the transition force of dynamic knowledge shows an inverted U shape; accumulating dynamic knowledge to a moderate degree not only facilitates transforming dynamic knowledge into static knowledge but also balances the relationship between the influence of knowledge force range and dynamic knowledge transformation. Second, existing knowledge is gradually substituted by new knowledge and knowledge density at a high knowledge energy level distinctly increases with a narrower bandwidth. Third, the investment loss is associated with resource configuration, resource utilization and the amount of accumulative dynamic knowledge before investment. Knowledge loss is negatively correlated with the knowledge compatibility coefficient.

The authors use the advanced method in quantum mechanics to legitimately unveil the emergence mechanism of knowledge-based innovation. Meanwhile, the authors capture the non-linear transformation relationship of heterogeneous knowledge and expose the change in ways of both investment loss and knowledge loss that cannot be quantified by conventional models. In doing so, the authors not only reveal the principle of qualitative knowledge change but also offer practical implications for developing flexible and targeted innovation strategies.

First, by proposing a complete quantum mechanics-based framework, the authors not only supplement the quantitative research contents to knowledge-based innovation literature which proposed calls to conduct research in way of quantum mechanics but also overcome the difficulties of knowledge-based system conceptualization and measurement. Second, the authors reveal the uncertain change of knowledge transformation and measure the loss of investment and knowledge, which contribute to identifying defects of firms in knowledge-based innovation. Third, the authors explore the internal mechanism that led to knowledge-based innovation exhibits non-linear characteristics and capture unique dynamic relationships between different variables which affect the emergence of knowledge-based innovation.

This paper attempts to propose an integrated model for measuring the knowledge transfer effectiveness in university‐industry alliances. The so‐called “RDCE” model is thereby proposed as an integrated model for measuring the knowledge transfer effectiveness. By combining inter‐organizational relations (IORs), knowledge‐based view (KBV) and resource‐based view (RBV) of firms, this paper aims to focus on the influence of determinant factors such as partner complementarities, partner attributes, the characteristics of the coordination and relationship quality between industrial companies and universities that may lead to the effectiveness of knowledge transfer.

This framework thereby clarifies how mediating variables influenced the paths that constitute the direct, indirect and total effects of mediated models by integrating moderated regression analysis together with bootstrap resampling methods to ensure the precision in estimating confidence intervals of indirect effects and path analysis using structural equation models to test all the hypotheses simultaneously for the robustness of the results and conclusions.

The statistical results reveal that the proposed model has a significant mediating effect that contributes to knowledge transfer effectiveness. Only partner attributes and relationship factors have a direct impact on the effectiveness of knowledge transfer. This appears plausible since mere complementarities and coordination between partners may not lead to learning or knowledge transfer, which requires a certain depth of the partner interaction in terms of the specific attributes of partners, coordination and relationship quality.

The authors assumed that the alliance constitutes partnerships between firms of roughly equal size and market power. Therefore, this study provided only broad perspectives of collaboration among alliance partners, and did not capitalize on different degree of alliance integration and different types of collaboration.

Managerial suggestions on how to improve their knowledge transfer effectiveness are also provided at the end of the text.

There are numerous studies examining alliance network performance. Very few studies, however, have examined detailed collaborative activities in dyadic university‐industry partnerships and potential constructs for measuring knowledge transfer and commercialization in the research and development alliance between industrial firms and university context.

This article traces the evolution of strategic management and knowledge management research during the past four decades with particular emphasis on the role of knowledge in interorganizational collaborative arrangements. By outlining the main strategic management perspectives in contemporary business literature and combining them with current knowledge management perspectives, an indication of the evolution of research pertaining to strategic knowledge management emerges. It is shown that most of the current strategic knowledge management research is conducted within the existing paradigms of strategic management. A criticism of this is offered and the article concludes by offering a new, more dynamic perspective of knowledge management, focusing on the synergies of knowledge‐related capabilities in explaining the formation and economic justification of strategic collaborative arrangements.