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The environment in high-tech industries is highly dynamic, and after COVID-19, it has become even more unpredictable. Hence, it has become critical for firms to develop strategies to cope with a highly dynamic environment. This paper aims to analyze how the impact of the scientific collaboration networks with URIs (universities and research institutes) on firm innovation performance is contingent on technological and market dynamics.

Using a sample of 174 Chinese firms in the new-energy vehicle industry during 2004–2015, the authors applied a random-effects negative binomial modeling approach to model these relationships.

A broad and strong scientific collaboration network promotes firm innovation network effects are contingent on technological and market dynamics. While technological dynamics strengthen the effect market dynamics weaken it due to the different purposes of collaboration for firms and URIs.

Firms should adjust the structure of scientific collaboration networks with URIs when facing different environments. The government should encourage firms to jointly research with diverse URIs and play an active role in stabilizing market environments.

This study contributes to the academic debate on university-industry scientific collaborations. Applying the temporary competitive advantage (TCA) framework, we provide nuances to the literature that studies the factors that condition the effects of networks. This study also adds to the research on firm scientific collaboration networks by measuring networks based on the coauthorship between firms and URIs.

Based on social influence theory (SIT) and social capital theory, this paper aims to explore the mediating role of entrepreneurial networks between technological entrepreneurs' political skills and entrepreneurial performance and whether market dynamics positively moderates this relationship.

This study collected data from 454 technological entrepreneurs in Beijing, Shanghai, Hangzhou, Guangzhou, Zhengzhou in China and examined four hypotheses by hierarchical regression analysis and bootstrapping analysis in an empirical design.

Results reveal that technological entrepreneurs' political skills not only have a direct positive impact on entrepreneurial performance (β = 0.544, t = 12.632, p < 0.001), but also have an indirect positive impact on entrepreneurial performance through entrepreneurial networks (β = 0.473, t = 10.636, p < 0.001). Entrepreneurial networks play a mediating role between entrepreneurs' political skills and entrepreneurial performance with 95% bias-corrected confidence intervals [0.034, 0.015]. Market dynamics plays a moderating role in the relationship among technological entrepreneurs' political skills, entrepreneurial networks and entrepreneurial performance (entrepreneurial performance: β = 0.190, t = 4.275, p < 0.001; entrepreneurial networks: β = 0.135, t = 4.455, p < 0.001). When market dynamics is high, technological entrepreneurs' political skills have a significant positive effect on entrepreneurial networks (simple slope = 0.309, t = 7.656, p < 0.001); but when market dynamics is low, there is no significant correlation between political skills and entrepreneurial networks (simple slope = 0.039, t = 0.966, p > 0.05).

The study relies on self-reported data from single informants. Although the severity of common method bias is tested through two methods, future research designs should avoid the influence of common method bias. Future research should adopt a vertical tracking design, collect data from multiple sources and use subjective assessment and objective indicators to measure variables. In addition, the applicability of the results outside China is worth further empirical exploration. Therefore, the authors hope that future studies can replicate the research to different countries, different cultural backgrounds and different organizational sections to explore the generalizability of the results.

The findings provide useful suggestions for entrepreneurs, who can use political skills to build a strong entrepreneurial network to improve their entrepreneurial performance. The results also suggest that entrepreneurs should pay more attention to cultivating and developing their political skills through methods such as training and practice. In addition, the conclusion is of great implications to enrich the content of entrepreneurship education and guide entrepreneurship practice.

These findings enrich SIT and social capital theory by providing the empirical evidence of the effect of entrepreneurs' political skills on entrepreneurial performance through entrepreneurial network. They also provide deeper insights into market dynamics research by uncovering the moderating role of market dynamics in the relationship between entrepreneurs' political skills, entrepreneurial networks and entrepreneurial performance.

My purpose is to describe for strategic management scholars in Africa, particularly graduate students and new faculty members, dynamic analysis and its significance in the African context so as to assist in the study of dynamic phenomena.

I discuss various types and methods of dynamic analysis. Dynamic analysis has been used extensively in such fields as cognitive psychology, social psychology, and management in Western countries.

I illustrate the various dynamics by reviewing four illustrative studies. I also provide procedures for studying dynamics in the African context.

I discuss the strengths and limitations of dynamic analysis and suggest ways of maximizing its potential.

The technique is a source particularly for graduate students of strategy in Africa. They can use it to supplement other approaches in studying strategic management phenomena.

This chapter discusses a typology of dynamic analysis consistent with empirical or variable modeling approaches. The lack of such a typology in the context of Africa makes it a valuable contribution. Thus, it fills a contextual gap in the research methodology literature.

The purpose of this paper is to have a review on the analysis of information diffusion based on evolutionary game theory. People now get used to interact over social networks, and one of the most important functions of social networks is information sharing. Understanding the mechanisms of the information diffusion over social networks is critical to various applications including online advertisement and rumor control.

It has been shown that the graphical evolutionary game theory (EGT) is a very efficient method to study this problem.

By applying EGT to information diffusion, the authors could predict every small change in the process, get the detailed dynamics and finally foretell the stable states.

In this paper, the authors provide a general review on the evolutionary game-theoretic framework for information diffusion over social network by summarizing the results and conclusions of works using graphical EGT.

This chapter aims to identify the main determinants that define the architectural properties of network emergence and significantly influence the dynamics underlying network evolution in time. The identification and analysis of these determinants, as well as the dynamic processes tied to them, allows to appreciate the competitive bases and consequences of network morphology. To this purpose, using a complex systems perspective as an integrative conceptual approach, we represent networks as complex dynamic systems of knowledge and capabilities. We perform a comparative in-depth analysis of the processes underlying the emergence and evolution of STMicroelectronic's global network and of Toyota's supplier network in the US so as to allow an elucidatory empirical assessment of the theoretical representation elaborated in the article.

Purpose – Criminal groups have long been central to explanations of crime and deviance. Yet, challenges in measuring their dynamic and transient nature meant that group-level explanations were often displaced in favor of individual-level ones. This chapter outlines how network methods provide a powerful tool for modeling the dynamic nature of criminal groups.

Approach – The chapter starts by providing a brief introduction to social network analysis, including key concepts and terminology. The chapter then focuses on the types of relational data available to study criminal groups, and how network methods can be used to delineate group boundaries. The chapter concludes by presenting a framework for understanding group dynamics from a network perspective, describing the contributions of network analysis to theories of group processes.

Findings – Network methods have provided meaningful advances to the study of group dynamics, leading scholars to revisit assumptions about the impact of group’ structure on delinquent behavior. Network studies of group dynamics have primarily focused on the cohesion–delinquency link (within-group structure) and the social contagion of conflict (between-group structure), highlighting important opportunities for the intersection of these two inquiries.

Value – Network methods provide a means to revisit and extend theories of crime and delinquency with a focus on social structure. The unique affinity between group dynamics and network methods highlights immense opportunities for expanding the knowledge of collective trajectories.

Traditionally, organizational theory has been a receptacle of methods and mechanisms from network theory. In this paper, we argue that organizational theory can also be an active contributor to network theory’s conceptual development. To that end, we make explicit a theoretical strategy that has only been used informally by network theorists so far, which – following Vaughan (2002) – we refer to as analogical theorizing. Using the basic correspondence between dyadic relationships as the most minimal form of “organization,” we show that processes and mechanisms extracted from various theoretical strands of organizational theory can be mapped onto the dynamics of social relationships. This allows us to build novel theoretical insight as it pertains to issue of relationship emergence, maintenance, and decay in social networks.

The research community currently employs four very different versions of the social network concept: A social network is seen as a set of socially constructed role relations (e.g., friends, business partners), a set of interpersonal sentiments (e.g., liking, trust), a pattern of behavioral social interaction (e.g., conversations, citations), or an opportunity structure for exchange. Researchers conventionally assume these conceptualizations are interchangeable as social ties, and some employ composite measures that aim to capture more than one dimension. Even so, important discrepancies often appear for non-ties (as dyads where a specific role relation or sentiment is not reported, a specific form of interaction is not observed, or exchange is not possible).

Investigating the interplay across the four definitions is a step toward developing scope conditions for generalization and application of theory across these domains.

This step is timely because emerging tools of computational social science – wearable sensors, logs of telecommunication, online exchange, or other interaction – now allow us to observe the fine-grained dynamics of interaction over time. Combined with cutting-edge methods for analysis, these lenses allow us to move beyond reified notions of social ties (and non-ties) and instead directly observe and analyze the dynamic and structural interdependencies of social interaction behavior.

This unprecedented opportunity invites us to refashion dynamic structural theories of exchange that advance “beyond networks” to unify previously disjoint research streams on relationships, interaction, and opportunity structures.

The purpose of this paper is to address the topic of business network dynamics and identify different relational paths, as forms of change in business relationships and related types of network change. The paper contributes to the literature on business network dynamics by providing an understanding of relational paths in the context of firms’ internationalization and shading light on different forms of change in business relationships and types of network change. The paper also contributes to the understanding of liabilities in internationalization that has to do with business networks, namely the liability of outsidership.

This paper is based on qualitative research following the multiple case study research approach. The authors propose three cases of internationalizing firms in the fashion industry that follows relational paths corresponding to different forms of change in business relationships that imply different types of network change.

Changes to a firm’s business network may be addressed through relational paths, namely creation of new business relationships (activation), maintenance of existing relationships (integration) and ending of existing relationships (substitution). These relational paths can be considered forms of incremental (integration) and radical (activation and substitution) change in business relationships and lead to different types of network change for business network dynamics that coexist and influence each other within the same firm.

This paper contributes to a better understanding of business network dynamics by showing how firms change their business relationships following different relational paths as the best way to respond to the challenges and new opportunities offered especially by international markets. The present paper has relevant managerial implications since coping with change in business relationships is perhaps one of the most critical and difficult tasks for management, even more critical if compared to the increasing complexity of doing business internationally and the liabilities that firms face in their internationalization process, especially the liability of outsidership.

The purpose of this paper is to introduce a novel approach to studying disaster management operations: the emergence of coordination-clusters in long-term rehabilitation projects and innovation dynamics in coordination-clusters.

The problem addressed is examining the coordination dynamics in long-term rehabilitation operations. A mixed methods research approach was adopted where a combination of qualitative and quantitative techniques was used for data collection and analysis to study the phenomenon of the coordination evolution in long-term rehabilitation projects.

The results indicate resilience in the behavior of involved actors from different organizations as they re-organize into coordination-clusters and collectively work to overcome the unfolding challenges of long-term rehabilitation projects in areas affected by major disaster.

The results provide some answers to the question of how to map and analyze the phenomenon of coordination-clusters and their consequent coordination dynamics, and thereby steps to redesign the approach to execute long-term rehabilitation projects in places affected by major disasters.

The combination of Actor-network theory and critical incident technique with social network analysis and community detection provides an integrated network-based view of coordination dynamics in long-term recovery operations. Such perspective would broaden the empirical basis for the planning and management of complex disaster management operations.

The results of the research offer a new approach to study coordination dynamics in disaster management operations. The proposed method provides a tool to examine the evolution of processes involved with the recovery phase of a disaster management cycle.