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Communities representing groups of agents with similar interests or functions are one of the essential features of complex networks. Finding communities in real-world networks is critical for analyzing complex systems in various areas ranging from collaborative information to political systems. Given the different characteristics of networks and the capability of community detection in handling a plethora of societal problems, community detection methods represent an emerging area of research. Contributing to this field, the authors propose a new community detection algorithm based on the hybridization of node and link granulation.

The proposed algorithm utilizes a rough set-theoretic concept called closure on networks. Initial sets are constructed by using neighborhood topology around the nodes as well as links and represented as two different categories of granules. Subsequently, the authors iteratively obtain the constrained closure of these sets. The authors use node mutuality and link mutuality as merging criteria for node and link granules, respectively, during the iterations. Finally, the constrained closure subsets of nodes and links are combined and refined using the Jaccard similarity coefficient and a local density function to obtain communities in a binary network.

Extensive experiments conducted on twelve real-world networks followed by a comparison with state-of-the-art methods demonstrate the viability and effectiveness of the proposed algorithm.

The study also contributes to the ongoing effort related to the application of soft computing techniques to model complex systems. The extant literature has integrated a rough set-theoretic approach with a fuzzy granular model (Kundu and Pal, 2015) and spectral clustering (Huang and Xiao, 2012) for node-centric community detection in complex networks. In contributing to this stream of work, the proposed algorithm leverages the unexplored synergy between rough set theory, node granulation and link granulation in the context of complex networks. Combined with experiments of network datasets from various domains, the results indicate that the proposed algorithm can effectively reveal co-occurring disjoint, overlapping and nested communities without necessarily assigning each node to a community.

This study carries important practical implications for complex adaptive systems in business and management sciences, in which entities are increasingly getting organized into communities (Jacucci et al., 2006). The proposed community detection method can be used for network-based fraud detection by enabling experts to understand the formation and development of fraudulent setups with an active exchange of information and resources between the firms (Van Vlasselaer et al., 2017). Products and services are getting connected and mapped in every walk of life due to the emergence of a variety of interconnected devices, social networks and software applications.

The proposed algorithm could be extended for community detection on customer trajectory patterns and design recommendation systems for online products and services (Ghose et al., 2019; Liu and Wang, 2017). In line with prior research, the proposed algorithm can aid companies in investigating the characteristics of implicit communities of bloggers or social media users for their services and products so as to identify peer influencers and conduct targeted marketing (Chau and Xu, 2012; De Matos et al., 2014; Zhang et al., 2016). The proposed algorithm can be used to understand the behavior of each group and the appropriate communication strategy for that group. For instance, a group using a specific language or following a specific account might benefit more from a particular piece of content than another group. The proposed algorithm can thus help in exploring the factors defining communities and confronting many real-life challenges.

This work is based on a theoretical argument that communities in networks are not only based on compatibility among nodes but also on the compatibility among links. Building up on the aforementioned argument, the authors propose a community detection method that considers the relationship among both the entities in a network (nodes and links) as opposed to traditional methods, which are predominantly based on relationships among nodes only.

The purpose of this study is to develop a model of a starting situation for relationship initiation in turbulent business networks.

The study is designed as an extreme single case study that takes its point of departure in a company’s bankruptcy in the Swedish automotive industry.

This study illustrates how a new business relationship can start from a resource combination previously controlled by one actor (i.e. a single company) in a turbulent business network, thereby bringing nuances to the common understanding that new relationships start in stable business networks where resource combinations are developed between actors in established business relationships.

Previous studies have stated that the development of a mutual orientation between actors leads to the formation of a business relationship. The business relationship then leads to resource adaptations between the two companies. The developed model, however, illustrates that this pattern can be reversed in situations of turbulence. Hence, previously adapted resources might lead to the formations of a business relationship. Based on this observation, the authors argue that there are reasons to question if previous models of business relationship initiation and development in business networks are adequately equipped for analysis in turbulent business networks.

The goal of this paper is to examine the mediating role of organizational social capital between family firms' organizational culture, characterized by their group vs individual orientation and external vs internal orientation, and their performance.

A structural equation model is developed and tested in a sample of 176 US family firms recruited through Prolific Academic.

The authors show that group vs individual cultural orientation fosters bonding social capital, while external vs internal cultural orientation fosters bridging social capital. In turn, family firm performance is only enhanced by bridging social capital, not bonding social capital, which appears to have neutral to negative direct performance effects. Nevertheless, it is noteworthy that bonding social capital facilitates the establishment of bridging ties, leading to overall positive performance outcomes.

The understanding of how organizational culture influences family business heterogeneity and performance, along with the clarification of how bonding social capital fosters or hinders performance, provides novel insights for researchers and practitioners seeking to understand the complexities within the unique context of family businesses.

This study aims to explore the different effects of inter-community group networks and intra-community group networks on group innovation.

The authors used a pooled panel dataset of 12,111 self-organizing innovation groups in 463 game product creative workshop communities from Steam support to test the hypothesis. The pooled ordinary least squares (OLS) model is used for analyzing the data.

The results show that network constraint is negatively associated with the innovation performance of online groups. The average path length of the inter-community group network negatively moderates the relationship between network constraint and group innovation, while the average path length of the intra-community group network positively moderates the relationship between network constraint and group innovation. In addition, both the network density of inter-community group networks and intra-community group networks can negatively moderate the negative relationship between network constraint and group innovation.

The findings of this study suggest that network structural characteristics of inter-community networks and intra-community networks have different effects on online groups’ product innovation, and therefore, group members should consider their inter- and intra-community connections when choosing other groups to form a collaborative innovation relationship.

Drawing on social network and information diffusion theories, the authors study the impact of the structural characteristics of a seller’s local social network on her promotion effectiveness in social commerce.

The authors define a local social network as one formed by a focal seller, her directly connected users and all links among these users. Using data from a large social commerce website in China, the authors build econometric models to investigate how the density, grouping and centralization of local social networks affect the number of likes received by products posted by sellers.

Local social networks with low density, grouping and centralization are associated with more likes on sellers’ posted products. The negative effects of grouping and centralization are reduced when density is high.

The paper deepens the understanding of the determinants of social commerce success from a network structure perspective. In particular, it draws attention to the role of sellers’ local social networks, forming a foundation for future research on social commerce.

Prior literature has long argued that knowledge networks contain great opportunities for innovation, and researchers can identify these opportunities using the properties of knowledge networks (PKNs). However, previous studies have examined only the relationship between structural PKNs (s-PKNs) and innovation, ignoring the effect of qualitative PKNs (q-PKNs), which refer to the quality of the relationship between two elements. This study aims to further investigate the effects of q-PKNs on innovation.

Using a panel data set of 2,255 patents from the Chinese wind energy industry, the authors construct knowledge networks to identify more PKNs and examine these hypotheses.

The results show that q-PKNs significantly influence recombinant innovation (RI), reflecting the importance of q-PKNs analysed in this study. Moreover, the results suggest that the combinational potential of an element with others may be huge at different levels of q-PKNs.

This study advances the understanding of PKNs and RI by exploring how q-PKNs impact RI. At different levels of PKNs, the potential of the elements to combine with others and form innovation are different. Researchers can more accurately identify the opportunities for RI using two kinds of PKNs. The findings also provide important implications on how government should provide support for R&D firms.

The purpose of this study is to examine the impact of political relations on trade in strategic emerging industries (SEIs) in the Belt and Road initiative (BRI) associated countries. This investigation encompasses not only from the perspective of bilateral political relations but also the political intervention of third parties.

The study employs the temporal exponential random graphmodel to analyze the dynamic structure and influencing factor of SEIs trade network among 150 BRI-associated countries from 2015 to 2020.

The results indicate that the trade of SEIs in the BRI-associated countries exhibits a pattern of concentrated exporters and decentralized importers. Amicable bilateral political relations foster trade cooperations in SEIs, while political pressure from the United States has the opposite effect. Furthermore, compared with the influence of third parties, the BRI has created a more robust trade environment characterized by political mutual trust.

BRI-associated countries should strengthen their political communication, and endeavor to transform political consensus and shared vision into concrete collaborative projects, while mitigating geopolitical uncertainties through a sound risk evaluation system. Moreover, they should establish a more transparent and consistent consultation mechanism and leverage the BRI trade network to foster balanced and mutually beneficial partnerships that minimize rivalry and dependence on a single market.

This study goes beyond observed trade cost and incorporates the political factor into the determinants of the BRI trade, thereby expanding the theoretical boundaries of existing BRI research. Also, this study employs bilateral trade data to construct SEIs trade networks (SEITNs) along the BRI route. It provides a comprehensive understanding of the dynamic determinates of the SEITNs will provide valuable practical guidance for enhancing and expanding trade and cooperation among BRI-associated countries.

This study aims to determine the pedagogical and socio-emotional impacts of school closures caused by the COVID-19 pandemic in Guinea.

A descriptive, survey-based methodology was used to collect quantitative and qualitative data directly from parents and caregivers. Between February 24 and March 13, 2022, data was gathered from a study population comprising 2,955 adults residing in five communes and five prefectures of Guinea.

Half of all respondents stated that school closures had no particular impact on children in their household, and 42% stated that no intentional pedagogical activities took place during school closures. Approximately 15% of respondents stated that children experienced boredom, loneliness, sadness, depression, stress and anxiety during the school closures.

The study underscores the significance of school closure readiness and interactive learning while revealing limited emotional impact on children. The findings, while specific to Guinea, provide a foundational understanding, highlighting the complexity of pandemic effects on education and the need for adaptive strategies in vulnerable regions.

The purpose of this study is to examine the propagation of consumer and business confidence in the euro area with a particular focus on the global financial crisis (GFC), the European sovereign debt crisis (ESDC) and the COVID-19-induced Great Lockdown.

The authors apply Diebold and Yilmaz’s connectedness framework and the improved method based on the time-varying parameter vector autoregressive model.

The authors find that although the evolution of business confidence marked the GFC and the ESDC the role of consumer confidence (mainly in those countries with stricter containment and closure measures) increased in the COVID-19-induced crisis.

The findings are related to the different origins of the examined crisis periods, and the analysis of their interrelationship is a very relevant topic for future research.

Through the reconstruction of the events of some foreign entrepreneurs who worked in the territory of the Italian city of Bari in the first half of the 19th century, this paper aims to analyze the role of entrepreneurial migration in the economic development of Apulia land in this period.

This study adopts a theoretical framework that combines the concept of mixed embeddedness in a multifocal perspective, with the model of the diffusion of innovation focusing on the role of the so-called agency of actors, and of the network, in the dissemination of innovation. The theoretical framework is applied to multiple case studies to compare the evidence that emerged from the simultaneous analysis of several situations.

By analyzing how innovations have spread within the network of entrepreneurs of that time, it is possible to identify some relevant aspects related to the mechanisms of dissemination of innovations in the context of entrepreneurial migration. Specifically, the opportunity structure is intended in an even broader sense than indicated in the classic approach to mixed embeddedness: it is considered as the result of the joint interaction of the political, institutional and economic context of several places, and the behavioral dynamics of several groups.

Due to the specific method chosen, the outcomes of the research might apply to a narrow context. Therefore, the results need to be tested and confirmed in further empirical studies, and by applying multiple research methods.

Findings are useful and significant in the analysis of the link that exists between the diffusion of innovations and migrant entrepreneurship, and then the conclusions can be applied and extended to the current phenomenon of migration-related innovations, with specific reference to developing countries.

Findings can be applied and extended to the current phenomenon of migration-related innovations and highly skilled migration, with specific reference to developing countries.

This paper contributes to shed new light on the contextual and multifocal factors that influence the development of innovations in the networks of migrant entrepreneurship, in a specific historical period and a specific context. Combining social, human and financial capital with the wider opportunity structure, this study also provides a comprehensive understanding of the modalities through which migrant and high-skilled entrepreneurs could innovate.