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This study aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough technological innovation. In contrast to conventional linear net effects, the article explores three possible types of team configuration within enterprises and their breakthrough innovation-driving mechanisms based on machine learning methods.

Based on the patent application data of 2,337 Chinese companies in the biopharmaceutical manufacturing industry to construct the R&D team network, the study uses the K-Means method to explore the configuration types of R&D teams with the principle of greatest intergroup differences. Further, a decision tree model (DT) is utilized to excavate the conditional combined relationships between diverse team network configuration factors, knowledge diversity and breakthrough innovation. The network driving mechanism of corporate breakthrough innovation is analyzed from the perspective of team configurations.

It has been discerned that in the biopharmaceutical manufacturing industry, there exist three main types of enterprise R&D team configurations: tight collaboration, knowledge expansion and scale orientation, which reflect the three resource investment preferences of enterprises in technological innovation, network relationships, knowledge resources and human capital. The results highlight both the crowding-out effects and complementary effects between knowledge diversity and team network characteristics in tight collaborative teams. Low knowledge diversity and high team structure holes (SHs) are found to be the optimal team configuration conditions for breakthrough innovation in knowledge-expanding and scale-oriented teams.

Previous studies have mainly focused on the relationship between the external collaboration network and corporate innovation. Moreover, traditional regression methods mainly describe the linear net effects between variables, neglecting that technological breakthroughs are a comprehensive concept that requires the combined action of multiple factors. To address the gap, this article proposes a combination effect framework between R&D teams and enterprise breakthrough innovation, further improving social network theory and expanding the applicability of data mining methods in the field of innovation management.

The field of Human Resource Management (HRM) has long recognized the importance of interpersonal influence for employee and organizational effectiveness. HRM research and practice have focused primarily on individuals’ characteristics and behaviors as a means to understand “who” is influential in organizations, with substantially less attention paid to social networks. To reinvigorate a focus on network structures to explain interpersonal influence, the authors present a comprehensive account of how network structures enable and constrain influence within organizations. The authors begin by describing how power and status, two key determinants of individual influence in organizations, operate through different mechanisms, and delineate a range of network positions that yield power, reflect status, and/or capture realized influence. Then, the authors extend initial structural views of influence beyond the positions of individuals to consider how network structures within and between groups – capturing group social capital and/or shared leadership – enable and constrain groups’ ability to influence group members, other groups, and the broader organizational system. The authors also discuss how HRM may leverage these insights to facilitate interpersonal influence in ways that support individual, group, and organizational effectiveness.

For many of the current leadership theories, models, and approaches, the answer to the question posed in the title, “Is leadership more than ‘I like my boss’?,” is “no,” as there appears to be a hierarchy of leadership concepts with Liking of the leader as the primary dimension or general factor foundation. There are then secondary dimensions or specific sub-factors of liking of Relationship Leadership and Task Leadership; and subsequently, tertiary dimensions or actual sub-sub-factors that comprise the numerous leadership views as well as their operationalizations (e.g., via surveys). There are, however, some leadership views that go beyond simply liking of the leader and liking of relationship leadership and task leadership. For these, which involve explicit levels of analysis formulations, often beyond the leader, or are multi-level in nature, the answer to the title question is “yes.” We clarify and discuss these various “no” and “yes” leadership views and implications of our work for future research and personnel and human resources management practice.

This study aims to explore the influence of social interaction processes on transactive memory system (TMS) practice, the mediation of knowledge integration to the relationship between TMS and team performance and the moderation of team psychological safety to the relationship among TMS, knowledge intentions and team performance.

The authors collected data from a sample of 366 team members from 55 research and development (R&D) teams in Taiwan and conduct the analysis using the partial least squares method.

The results of this study indicate that social interaction processes have a positive effect on a TMS; a TMS can foster team performance, but knowledge integration mediates the relationship between the TMS and team performance; and team psychological safety can moderate the relationship between the TMS, knowledge integration and team performance.

Existing studies not only fail to explore the influence of social interaction processes on a TMS practice but also lack empirical analyses to explore knowledge integration as a mediator and team psychological safety as a moderator. This study fills that gap by developing a model that includes these types of relationships and suggests the importance of the TMS in the context of R&D.

This study aims to examine direct effects of qualified team gatekeepers on absorptive capacity (AC), and the mediating roles of combinative capabilities – knowledge integration capability (KIC) and interteam coordination.

A social networking analysis was used to analyze a unique data set collected from all members of 32 Japanese research and development (R&D) teams to identify key individuals who perform daily gatekeeping functions. This study analyzed the data through partial least squares structural equation modeling with higher-order latent variables. Finally, cross-validation tests were used with holdout samples to test the model’s predictive validity.

Qualified gatekeepers directly contribute to teams’ realized AC but not to their potential AC. Furthermore, qualified gatekeepers can improve their teams’ capability to absorb and exploit external knowledge by facilitating their capability to consolidate knowledge, that is, its KIC and interteam coordination.

Unlike prior research that asks top managers to identify team gatekeepers, this study used social network analysis to identify these vital individuals. This study provides a new framework indicating how qualified gatekeepers impact the AC of R&D teams through the examination of both the direct and indirect paths of gatekeeping abilities, two combinative capabilities as mediators and team AC.

There are often relational interactions in teams that lead to and drive the spread of work engagement. Despite the potential social impacts on work engagement, such as coworker support and organizational citizenship behaviors within teams, they have rarely been studied from a social perspective using social network analysis (SNA). This review draws on the crossover model and conservation of resources theory to suggest that the effects of social diffusion and the exchange of resources can impact Well-Being, specifically work engagement, in teams and that SNA can help measure those social interactions. Linking several network concepts – closeness centrality, density, degree centrality, and tie strength – to work engagement propositions related to the spread of work engagement as well as the number and quality of network ties, this review elucidates the potential for integrating SNA methodology to the field of Well-Being for teams.

The purpose of this paper is to study the relationship between network ties (NT) and transactive memory systems (TMS), observed through three dimensions – specialization (TMSS), credibility (TMSCR), and coordination (TMSCO) – in the presence of leadership (LDR) as a moderating variable, in university research-and-development (R&D) groups.

The data are composed of 257 university R&D groups. To confirm the hypotheses, the authors use multiple linear regression analysis with a moderating effect.

The conclusions show that the relationships between NT and two of the three dimensions of TMS (TMSCR and TMSCO) are significant when LDR is included as a moderating variable. Although the effect of TMSS is positive, it is not significant. Including the interaction element enables better explanation of two of the dimensions of TMS in the sector analyzed. Thus, LDR is perfectly applicable to the university R&D environment.

This research has several limitations that suggest further possibilities for empirical research. The limitations include the cross-sectional nature of the research and the judgment of a single manager as the basis of the perception analyzed for each group.

The authors provide several implications for R&D practitioners. The results of this study could be validated in other universities in other geographic areas, enabling better generalization and applicability of the results. The results described may serve as a guide for group leaders of university R&D. This research helps us to see the importance of LDR in forming internal research networks that help researchers to perform common projects in order to obtain better results in the group. Thus, the groups provided better results to society.

No studies have tested the moderating effect of LDR in university R&D empirically. The results provide information to fill this gap and demonstrate the applicability of LDR as a key element in the organization, improvement, and cohesion of R&D groups.

Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.