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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.

Community detection of dynamic networks provides more effective information than static network community detection in the real world. The mainstream method for community detection in dynamic networks is evolutionary clustering, which uses temporal smoothness of community structures to connect snapshots of networks in adjacent time intervals. However, the error accumulation issues limit the effectiveness of evolutionary clustering. While the multi-objective evolutionary approach can solve the issue of fixed settings of the two objective function weight parameters in the evolutionary clustering framework, the traditional multi-objective evolutionary approach lacks self-adaptability.

This paper proposes a community detection algorithm that integrates evolutionary clustering and decomposition-based multi-objective optimization methods. In this approach, a benchmark correction procedure is added to the evolutionary clustering framework to prevent the division results from drifting.

Experimental results demonstrate the superior accuracy of this method compared to similar algorithms in both real and synthetic dynamic datasets.

To enhance the clustering results, adaptive variances and crossover probabilities are designed based on the relative change amounts of the subproblems decomposed by MOEA/D (A Multiobjective Optimization Evolutionary Algorithm based on Decomposition) to dynamically adjust the focus of different evolutionary stages.

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.

This paper aims to integrate the consumption perspective within the Industrial Marketing and Purchasing (IMP) debate. The study delves into how consumer communities can be conceived like other network business actors. The perspective of sustainable new ventures (SNVs) in the fashion industry is adopted, considering their specific connection with consumer communities.

Adopting a multiple case study methodology, this paper uses a qualitative approach. Data collection mainly relies on interviews conducted with 10 SNVs in the fashion industry; this sector is a fertile ground for studying sustainability and consumer communities. For data analysis, the abductive approach of systematic combining is applied.

The paper identifies four distinct types of consumer communities and four roles that they can assume as business actors in the business network. Owing to their engagement in these specific roles, consumer communities become part of the SNVs’ network, akin to other business-to-business players.

This study represents one of the initial endeavors to introduce consumption into the IMP theoretical framework. In this paper’s conceptualization, consumer communities are groups of consumers and collective actors in the business network. Additionally, this study advances the research on sustainability as a network concept by including consumer communities’ roles in business networks.

Corporate philanthropy is increasingly a vital decision-making basis for consumers to purchase and establish relationships with enterprises. However, few studies have examined corporate philanthropy from the perspective of community evolution. To address this gap, this study aims to provide a more in-depth and holistic investigation of corporate philanthropy by examining the evolution of social media brand communities caused by corporate philanthropy and the characteristics of consumer interactive behavior.

Web crawlers developed by Python were employed to collect data of ERKE from Sina Weibo (the Chinese equivalent of Twitter). A total of 2,736 posts and 7,774 comments were collected and investigated using social network and sentiment tendency analyses.

The results showed that the evolution of the social media brand community presented a prominent three-stage characteristic influenced by corporate philanthropy. The findings not only support the benefits of corporate philanthropy but also show the possible disadvantages. Besides, this study further concluded the characteristics of consumer interactive behavior in the social media brand community.

This paper addresses an attractive and practical issue related to the impact of corporate philanthropy. Moreover, this study is one of the first studies to examine the impact of corporate philanthropy in the context of the social media brand community. The findings of this study will provide a valuable reference for community operations and practitioners of brands.

This study aims to examine digital consumer culture and behavior in the community, namely, 180° Movement Digital Training Center (DTC), in Jakarta, Indonesia. It aims to describe the dynamics of digital consumer culture in contemporary society, particularly as experienced by the youth community in Jakarta in the context of socio-technology relations and incorporates it into the diagram of digital consumer culture network.

This research uses a constructivist qualitative approach and socio-technical relation analysis through actor-network theory and digital consumer culture.

The study finds that the individual model of digital consumption is constructed through the process of problematization, interessement, enrollment and mobilization of individuals. It generates a culture in which consumers are constantly up to date with high-intensity information, but within increasingly shorter timeframes, while also considering principles of affordability, needs, desires and satisfaction. The network of digital consumer culture construction among informants is peculiar and unstable.

The study of digital consumer culture within the 180° Movement DTC community highlights how consumer behaviors of its members are facilitated and interconnected within a digital cultural network. However, this research is constrained by the dialectical interplay between Christian principles and the emerging values of consumer culture, a result of the scarcity of theoretical resources and information. This study also provides a specific contribution as a foundation for mapping the volatile digital consumer culture for researchers.

Understanding the socio-technological relationships and consumption behavior of the youth community could help digital platforms tailor their services more effectively. It could also guide the 180° Movement DTC in developing programs that resonate with the youth, bridging the gap between the physical and virtual realms. Ultimately, this could lead to a more engaged and digitally literate society.

This study contributes to a broader societal understanding of how digital technology is shaping consumer behavior and identity within youth communities, which can influence social dynamics and interactions. It provides insights into the potential social impacts of digital technology, such as changes in relationships, communication patterns and self-perception, informing societal discourse on digital culture.

In addition to presenting socio-technological analysis on Indonesian consumer culture using actor-network theory, some also show that studies on digital connectivity ambivalence that concern the relationship between humans as actors and non-humans as actors have become one of the popular sociology studies at present.

This study investigates how a competitor orientation (knowledge of and acting on competitors' strengths and weaknesses) facilitates coopetition activities (collaboration with competitors), within networks of competing micro-sized, independent, family restaurants, owned by entrepreneurs from ethnic minority backgrounds.

An instrumental case study features data collected from interviews with 30 owners (as key informants) of micro-sized, independent, family-owned restaurants, in two urban clusters within the Midlands (UK). Specifically, the context involves restaurants offering South Asian cuisine and where the owner originated from the Indian sub-continent (Bangladesh, India or Pakistan). Secondary data were collected wherever possible. These two clusters (not named for ethics reasons) are highly populated by members of these respective ethnic communities; also, they contain a relatively large number of restaurants offering South Asian cuisine.

A competitor orientation facilitated strong coopetition-oriented partnerships comprised of extended family and intra-community members that helped enhance individual firms' performance, maintained family employment and sustained their cluster. It also helped owners develop subtle counter strategies where weak ties existed, such as via inter-community networks. For example, strategies attracted customers that were not loyal to a particular restaurant, or indeed, sub-ethnic cuisine (within Bangladesh, India or Pakistan, like the Punjab region). Subtle as opposed to outright counter strategies minimised retaliation, since restaurant owners wanted to avoid price wars, or spreading misinformation where the reputation of a cluster may suffer alongside the likely survival of individual businesses within that regional cluster.

Mixed evidence exists in earlier studies regarding the competitive rivalry in certain sectors where ethnic minority ownership is prominent; not least, restaurants located in regional clusters. However, this investigation considers the notion – what if some of these earlier studies are wrong? More specifically, does certain prior research under-represent the extent that rival entrepreneurs of an ethnic minority origin collaborate rather than compete for mutually beneficial purposes? New evidence emerges regarding ways in which a competitor orientation can influence the performance-enhancing nature of coopetition activities among business owners originating from both intra and inter-ethnic communities.

This study focuses on a regional education network in the Mid-Atlantic that aims to facilitate equitable learning practices by providing ongoing teacher/leader support, cross-sector collaboration and professional learning for educators. The authors probe networks as providing core support for systems level change and serving as precursors to the community engagement that is essential for deeper learning. Specifically, this study is driven by the hypotheses that (1) deeper learning may be supported by pathways for students and educators to meaningfully engage with the local community; (2) deeper learning is more likely to happen when educators connect to communities beyond their own school or organization and (3) education networks can help facilitate those functions.

The authors aimed to understand how participation in an education network influenced members (which include school leaders, teachers and leaders of youth programs) and how experiences might differ by level of participation. The authors conducted interviews with individuals across three groups of adults (n = 111 total): core members (n = 16), members with mid-level engagement (n = 30) and peripheral members (n = 65).

Educators who participated most intensely and deeply described the network as a vehicle for learning about and advancing equity through specific practices including individualized learning, increasing access and resource redistribution. Mid-level participants emphasized the professional network building function of the network. For peripheral or new participants, the most salient function of the network was celebration of education and educators. These findings suggest that education networks have a role in strengthening the structures that support leaders to make deeper learning happen.

More research is needed on how participants move from the periphery to more core involvement in education networks, where they may gain the full benefits of participation. Further research is also needed to explore the link between education network engagement among school leaders and the deeper learning environments in schools.

Research on education networks is limited. To the authors' knowledge, the present study is one of the largest collections and analyses of interviews with education network members to date. The authors present education network engagement as a precursor for community embedded deeper learning in schools.

This study aims to explore the social networks and network interactions of bed-and-breakfast (B&B) entrepreneurs in rural China. In addition, it evaluates how such network interactions relate to rural resilience.

In-depth interviews were performed in two locations: Ningbo and Dujiangyan, China. Purposive sampling was combined with snowball sampling to select interviewees. The 154 interviews involved 29 B&B owners and relevant social actors. All codes and data were analyzed using the discourse analysis framework.

The B&B owners’ social networks were identified based on strategic goals, revealing a business operation network, business development network and business citizenship network. Challenges in seeking financial support for rural B&Bs during the pandemic were specified along with network interactions. The institutional adaptation approach was used to evaluate network interaction in rural B&B business. It was argued that other networks would react based on primary network members’ goal compatibility and the effectiveness of the primary network in addressing obstacles.

This study indicates that the rural B&B entrepreneurs’ interactions with various networks could influence on business resilience, community resilience as well as rural resilience.

By combining the institutional adaptation typology with social network theory, this study generates a new typology of network interactions for rural B&Bs. The typology helps to explain how and why B&B entrepreneurs make decisions and provides a broader scope of social networks involved in these business operations.