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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.

Agile project teams are self-managing and self-organizing teams, and these two characteristics are pivotal attributes of emergent leadership. Emergent leadership is thus common in agile teams – however, how these (informal) emergent leaders can be identified in teams remains far from understood. The purpose of this research is to uncover techniques that enable top management to identify emergent agile leaders.

We approached six agile teams from four organizations. We employ social network analysis (SNA) and aggregation approaches to identify emergent agile leaders.

We approached six agile teams from four organizations. We employ SNA and aggregation approaches to identify emergent agile leaders.

Seven emergent leaders are identified using the SNA and aggregation approaches. The same leaders are also identified using the KeyPlayer algorithms. One emergent leader is identified from each of the five teams, for a total of five emergent leaders from the five teams. However, two emergent leaders are identified for the remaining sixth team.

Emergent leadership is a relatively new phenomenon where leaders emerge from within teams without having a formal leadership assigned role. A challenge remains as to how such leaders can be identified without any formal leadership status. We contribute by showing how network analysis and aggregation approaches are suitable for the identification of emergent leadership talent within teams. In addition, we help advance leadership research by describing the network behaviors of emergent leaders and offering a way forward to identify more than one emergent leader in a team. We also show some limitations of the approaches used and offer some useful insights.

Purpose – The purpose of this chapter is to explore the effects of team composition on justice climate strength. Specifically, we adopt a social network approach to justice in teams to explore the social-psychological mechanisms underlying diversity effects.

Design/methodology/approach – Using data from 80 self-managed project teams, we consider the impact of surface-level and deep-level diversity in teams on member social network ties and subsequently dispersion in their perceptions of procedural and interpersonal justice.

Findings – The results showed that diversity in team members’ psychological attributes – specifically, preferences for individualism – were associated with variability in members’ attachment to the team. In contrast, team gender and racial diversity were not significantly related to member social network ties. The results also demonstrated a relationship between network tie diversity and both procedural and interpersonal justice climate strength, such that variability in members’ attachment to the team was related to variability in their justice perceptions.

Overall, these findings demonstrate that teams characterized by higher levels of deep-level diversity may experience greater variability in their social interactions, which amplify variability in members’ justice perceptions.

Implications – Practically, these findings suggest that potential performance advantages of informational diversity in teams may come at a cost, as such diversity may reduce the quality of members’ justice experiences. Theoretically, they provide insight into the nature of the relationship between diversity and justice, which is largely dependent on the social psychological processes evoked by diversity. They also highlight team social networks as a useful means for examining such processes and understanding the operation of justice in teams.

Organizational networks are generally considered major antecedents of mutual influence in adopting similar practices, typically via a structure of dense ties, or closure. We propose that under conditions of competitive interdependence, closure may be associated with links established to access resources and knowledge and become a possible source of differentiation rather than imitation. We test these and other antecedents of imitative behavior and performance in the Italian TV industry with 12 years of data on 501 productions. We find that network closure is associated with lower imitation, centrality, but not status, leads to imitation, and that imitation lowers performance.

Research on organizational teamwork is increasingly highlighting the patterned nature of the relational processes (e.g., communication, backup behavior) and psychological states (e.g., trust, shared cognition) that underlie team effectiveness. However, studies of teams often rely on methodologies that do not explicitly assess the underlying patterns of relational processes and states. Social network approaches offer an appealing alternative to the typical methodologies used in team research given that network approaches provide both the theory and methodology necessary to conceptualize and investigate patterns of interactions among group members. Despite the advantages of social network approaches, many team researchers are unfamiliar with the network paradigm and its associated methodologies. The purpose of this chapter is to clarify how networks can be leveraged to answer key research questions related to the study of team functioning and effectiveness. We begin by discussing the evolution and eventual convergence of team research and network approaches. Then, we examine the current state of the literature at the intersection of teams and networks in order to identify key takeaways and remaining questions. We conclude by highlighting opportunities for the future of team network science.

The purpose of this paper is to trace and understand informal knowledge sharing networks for various competencies in project teams. This will help establish a baseline and thereby enable further knowledge management interventions to be outlined.

Two project teams were identified for this study. While one of the teams had a semi‐structured knowledge management system already in place, the other had not adopted any knowledge management practices. The knowledge network analysis was rolled out for both the teams for the competencies that they were working on. This was more of an exploratory study. The results are compared across both the teams and inferences are made on the knowledge networks for the teams.

The various measures involved in social network analysis can help from a knowledge management perspective to: identify experts; provide indicators to the extent of knowledge sharing for various competencies; and baseline current knowledge management practices in a team.

This methodology would not be very feasible for large teams with more than 500 people.

This is a very useful diagnostic tool for managers to know more about the knowledge sharing dynamics in their teams. This may help them design interventions to build the capabilities of key team members along specific knowledge areas.

The paper provides indicators on the capability of the teams and their knowledge repositories based on the interactions between them.

To obtain optimal deliverables, more and more crowdsourcing platforms allow contest teams to submit tentative solutions and update scores/rankings on public leaderboards. Such feedback-seeking behavior for progress benchmarking pertains to the team representation activity of boundary spanning. The literature on virtual team performance primarily focuses on team characteristics, among which network closure is generally considered a positive factor. This study further examines how boundary spanning helps mitigate the negative impact of network closure.

This study collected data of 9,793 teams in 246 contests from Kaggle.com. Negative binomial regression modeling and linear regression modeling are employed to investigate the relationships among network closure, boundary spanning and team performance in crowdsourcing contests.

Whereas network closure turns out to be a negative asset for virtual teams to seek platform feedback, boundary spanning mitigates its impact on team performance. On top of such a partial mediation, boundary spanning experience and previous contest performance serve as potential moderators.

The findings offer helpful implications for researchers and practitioners on how to break network closure and encourage boundary spanning with the establishment of facilitating structures in crowdsourcing contests.

The study advances the understanding of theoretical relationships among network closure, boundary spanning and team performance in crowdsourcing contests.

This study investigates the impact of network positions on inter-team conflicts and project success in megaprojects.

Network position is measured with centrality and structural holes. Substantive conflict and affective conflict reflect inter-team conflicts. A questionnaire survey was implemented in Chinese megaprojects, and 309 valid questionnaires were collected. The data were analyzed using structural equation modeling and bootstrapping methods.

The results show that centrality negatively impacts project success, while the presence of a team in a structural hole has no significant impact on project success. Centrality is negatively related to substantive conflict and is positively related to affective conflict. The team in a structural hole has a positive effect on substantive conflict and a negative effect on affective conflict. Substantive conflict and affective conflict have positive and negative effects on project success, respectively. The effect of network position on project success is mediated by inter-team conflict.

This research provides a reference for megaproject managers to better conduct network governance, manage inter-team conflict, and successfully manage projects. The study did not investigate the effects of changes in teams' network positions on project success. Future research should explore this facet of megaprojects.

This research adds to existing research on network position, and reveals that project network governance is important for megaproject success. This provides a new direction for megaproject management. Furthermore, the results validate constructive and non-constructive roles and the mediating role of inter-team conflict. This complements the literature on conflict management, providing a reference for megaproject managers when managing inter-team conflict.

The aim of this study was to investigate the impact of the transactive memory system (TMS) on green innovation and examine the mediation role of the social network at all hierarchical levels.

Three hypotheses were examined by performing regression analyses on survey data from manufacturing firms in China. Especially, the nested sets of data from 389 individual observations nested in 53 work teams, including individual level and collective level have been investigated.

The study results show that the TMS has a positive effect on green innovation. Furthermore, the results indicate that at the team level, structure holes' mediation in this relationship is stronger than degree centrality; at the individual level, weak ties mediation in the relationship of specialization and green innovation is stronger than strong ties, conversely, strong ties mediation in the relationship of credibility and green innovation is stronger than weak ties.

This study expands previous research by highlighting the significance of multilevel social network elements in the context of the TMS and sustainable development and enriches the present research on green innovation.

Team‐based innovation requires a balance of creative and pragmatic processes both within teams and between teams and their organizational stakeholders. However, prior research has focused primarily on the internal team dynamics that facilitate innovation, paying comparatively little attention to team‐stakeholder dynamics. The purpose of this study is to address this limitation by studying the impact of team‐stakeholder networks and shared cognition on the effectiveness of innovation teams.

This study investigates the knowledge and trust linkages between 51 new product development (NPD) teams and their organizational stakeholders using a mixed methods design that combines network analysis, surveys, and qualitative interviews. Multiple indicators of team effectiveness were collected at various stages of the innovation process.

The results show that effective NPD teams establish knowledge ties with many non‐redundant organizational stakeholders and foster a high level of agreement among stakeholders about team innovation factors. Conversely, effective NPD teams also establish highly centralized trust networks that are focused on only a few key stakeholders in the organization.

This study focuses on NPD teams in chemical and pharmaceutical manufacturing. Future studies should seek to replicate the findings using larger samples of teams involving diverse innovation tasks.

These results have implications for the most effective way to build and manage innovation teams, considering both pre‐existing stakeholder linkages and networking strategies for the future.

The results suggest that the optimal characteristics of team‐stakeholder knowledge and trust networks differ and highlight the unique importance of shared understanding about risk‐taking and creativity beyond higher overall levels.