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This study explores the influence of organizational structure on relationship formation and tacit knowledge sharing within a family business context.

Utilizing a single case study approach, data were collected through interviews and questionnaires from 12 participants at a family-owned advertising and communication firm in Beirut, Lebanon.

The research highlights the critical role of organizational structure in enhancing organizational effectiveness through knowledge transfer. It underscores how both intraorganizational and interorganizational ties influence knowledge sharing processes and demonstrates the varying impacts of tie strength on tacit knowledge distribution.

This paper contributes to the literature by examining the interdependence between organizational structure, tacit knowledge transfer and tie strength in family businesses. By analyzing these elements across internal and external boundaries, the study offers a fresh perspective on network dynamics. The research highlights that traditional definitions of network ties may not fully capture the unique environment of family firms, where structural nuances impact knowledge sharing and performance. Practically, the findings provide actionable insights for managers to design organizational structures that optimize tacit knowledge flow, fostering innovation and competitiveness. This work challenges existing frameworks and offers guidance for improving knowledge management in family businesses, supporting sustainable growth and success.

Networks linking tourist attractions or organizations are a major focus of tourism research. Despite extensive research on tourism networks, academic research on the spatial structure and formation of wine tourism networks is limited. This study aims to investigate the spatial structure and factors influencing the development of a network among Ningxia wineries, an emerging wine tourism destination in China.

This study uses social network analysis to uncover “what” the spatial structure of wine tourism networks looks like. Sixteen in-depth interviews were conducted among key stakeholders to explain the “why” of such structural characteristics.

The results show that in an emerging wine tourism destination, popular tourist attractions enjoy high centrality and hold key positions in the wine tourism network. Small wineries exhibit high closeness centrality, and only one winery serves as a network broker. According to the stakeholders, the importance of network actors will increase as their economic and political importance increase, while small wineries that lack differentiation in the network may perish.

Local governments can implement the suggested measures for improving network connections, and wineries are advised to find suitable positions to improve the experiences of tourists.

This study pioneers the identification of the distinct structure and factors influencing the network of an emerging wine tourism destination, thus enriching the understanding of the interplay and roles of different actors.

In this paper, we investigate the coevolution of the supply network and procurement strategies in the context of semiconductors and electronics for the automotive industry over 3 decades. We aim to explain how procurement strategy interrelates with changes in supply network structure and what the implications of a hub-centric structure network structure are for procurement in supply.

We collected in-depth primary and secondary data that stretched back to 1996 from a leading automotive European original equipment manufacturer (OEM) and its network. Using social network analysis (SNA), we identified OEMs’ procurement focus and mapped the evolution of the supply network, the links in the network, and the environmental forces impacting the strategies and the network.

Our findings describe the supply network for semiconductor and electronic components to the automotive industry. The findings suggest that a focus on cost can lead to a Tier 1-centric network structure with many tiers that can fail to assure supply or capture innovation when the external environment is marked by high uncertainty. In such situations, increasing complexity by creating more links in the network can improve transparency and contribute to supply assurance and innovation.

The findings indicate that managers should consider the role of the supply network in selecting their strategy to attain objectives of cost, innovation, and supply assurance.

This paper presents empirical-based insights into the automotive semiconductor and electronic component supply chain (SC), the unexpected implications of hub-centric supply networks, and the use of SNA in the SC in context.

Community detection is a key factor in analyzing the structural features of complex networks. However, traditional dynamic community detection methods often fail to effectively solve the problems of deep network information loss and computational complexity in hyperbolic space. To address this challenge, a hyperbolic space-based dynamic graph neural network community detection model (HSDCDM) is proposed.

HSDCDM first projects the node features into the hyperbolic space and then utilizes the hyperbolic graph convolution module on the Poincaré and Lorentz models to realize feature fusion and information transfer. In addition, the parallel optimized temporal memory module ensures fast and accurate capture of time domain information over extended periods. Finally, the community clustering module divides the community structure by combining the node characteristics of the space domain and the time domain. To evaluate the performance of HSDCDM, experiments are conducted on both artificial and real datasets.

Experimental results on complex networks demonstrate that HSDCDM significantly enhances the quality of community detection in hierarchical networks. It shows an average improvement of 7.29% in NMI and a 9.07% increase in ARI across datasets compared to traditional methods. For complex networks with non-Euclidean geometric structures, the HSDCDM model incorporating hyperbolic geometry can better handle the discontinuity of the metric space, provides a more compact embedding that preserves the data structure, and offers advantages over methods based on Euclidean geometry methods.

This model aggregates the potential information of nodes in space through manifold-preserving distribution mapping and hyperbolic graph topology modules. Moreover, it optimizes the Simple Recurrent Unit (SRU) on the hyperbolic space Lorentz model to effectively extract time series data in hyperbolic space, thereby enhancing computing efficiency by eliminating the reliance on tangent space.

With the introduction of graph structure learning into service classification, more accurate graph structures can significantly improve the precision of service classification. However, existing graph structure learning methods tend to rely on a single information source when attempting to eliminate noise in the original graph structure and lack consideration for the graph generation mechanism. To address this problem, this paper aims to propose a graph structure estimation neural network-based service classification (GSESC) model.

First, this method uses the local smoothing properties of graph convolutional networks (GCN) and combines them with the stochastic block model to serve as the graph generation mechanism. Next, it constructs a series of observation sets reflecting the intrinsic structure of the service from different perspectives to minimize biases introduced by a single information source. Subsequently, it integrates the observation model with the structural model to calculate the posterior distribution of the graph structure. Finally, it jointly optimizes GCN and the graph estimation process to obtain the optimal graph.

The authors conducted a series of experiments on the API data set and compared it with six baseline methods. The experimental results demonstrate the effectiveness of the GSESC model in service classification.

This paper argues that the data set used for service classification exhibits a strong community structure. In response to this, the paper innovatively applies a graph-based learning model that considers the underlying generation mechanism of the graph to the field of service classification and achieves good results.

This paper explains why some firms manage to capture disproportionate value from their network of relationships, leading to superior performance. The paper examines how a firm's dependencies affect its value appropriation potential (VAP) in economic networks.

The paper follows the axiomatic method and the embeddedness perspective of firms to develop an index called nodal power, which captures the power that accrues to a firm in exchange-based economic networks. Thereafter, using the formal method and simulation, it shows nodal power reflects a firm's VAP in economic networks.

The study analysis and findings prove that a firm's dyadic level exchange relations and the embedded network structure determine its VAP by affecting the nodal power. A firm with lesser nodal power is likely to appropriate less value from its relations even if it equally contributes to the value creation. This finding explains how the structural and relational characteristics of a firm's network enable disproportionate value appropriation.

Nodal power furthers the scope of analyzing firms' economic relationships and changing power equations in dynamic networks. It can help firms build optimal strategic networks and manage the portfolio of relationships by predicting the impact of changing relations on firms' VAP.

The paper's original contribution is to explain, through formal analysis, why and how the structure and nature of relations of firms affect their VAP. The paper also formalizes the power-dependence principle through a dependency-based index called nodal power and uses it to show how interfirm dependencies are key to value appropriation.

The role of inventors' creativity is crucial for technological innovation within enterprises. The mobility of inventors among different enterprises is a primary source for companies to acquire external knowledge. The mechanism of “learning-by-hiring” is widely recognized by companies. Therefore, it is important to determine how to allocate network resources to enhance the creativity of inventors when companies hire mobile inventors.

The study suggests an analytical framework that analyzes alterations in tie strength and structural holes resulting from the network embeddedness of mobile inventors as well as the effect of the interaction between these two variables on changes in inventor’s creativity after the mobility. In addition, this paper examines the moderating impact of cognitive richness of mobile inventors and cognitive distance between mobile inventors and new employers on the correlation between network embeddedness and creativity.

This study found that: (1) The increase of tie strength has a significant boost in creativity. (2) Increasing structural holes can significantly improve the creativity of mobile inventors. (3) When both the tie strength and the structural holes increase, the creativity of the mobile inventors significantly increases. (4) It is important to note that when there is a greater cognitive distance, stronger tie strength promotes the creativity of mobile inventors. Additionally, cognitive richness plays a significant role in moderating the relationship between changes in structural holes and the creativity of mobile inventors.

These findings provide theoretical guidance for firms to effectively manage mobile inventors and optimize collaborative networks within organizations.

Organizations are increasingly joining inter-organizational networks to foster sustainable employability for their employees. The purpose of this study is to identify the factors and mechanisms central to their success as experienced by key stakeholders.

An explorative, qualitative approach was adopted, using four focus groups with network coordinators (N = 18) and HR professionals (N = 14). Fourteen Dutch inter-organizational networks were represented. Respondents were recruited through purposive and snowballing sampling techniques. Thematic analysis was applied using open coding to generate themes.

The results of this study outline environmental, structural, and inter-personal factors and mechanisms that contribute to the success of inter-organizational networks that aim to foster sustainable employability for their employees. The environmental factors and mechanisms consist of challenges stemming from the labor market. The structural factors and mechanisms include: a network’s flat structure, flat fee, lack of informal rules, the allocation of roles and expectations for stakeholders and shared network activities. Lastly, the inter-personal factors and mechanisms are: communication among stakeholders, establishing reciprocity, interaction and collaboration between stakeholders, the valuation of trust, a convivial culture and shared vision among stakeholders. The dynamics between these factors and mechanisms are compared to other forms of inter-organizational networks. Furthermore, several recommendations for network coordinators and practitioners regarding the development of networks are presented.

This study provides insights into the factors and mechanisms that are regarded by stakeholders as influencing the success of inter-organizational networks in their ability to foster sustainable employability for workers. We have identified a unique model that captures this new way of inter-organizational collaboration and builds on insights from literature on collaborative governance regimes, institutional fields and entrepreneurial ecosystems. Specifically, the model provides a framework that consists of environmental, structural and interpersonal factors and mechanisms for network success. This study increases our understanding of how collaborative efforts can be fostered beyond organizational boundaries and existing Human Resource Management practices.

This study aims to examine the role of key network actors in relation to the discourse structure of a microblogging hashtag stream within a global agricultural educators’ conference over two years. Prior work in online networks suggests that participation is dominated by highly active members, and in this study, the authors focus on examining what types of discourse are shared and reshared by key actors.

The authors used a combination of social network analyses and qualitative discourse coding to examine approximately 1,390 posts associated with the conference hashtag over two consecutive years.

The study analyses uncovered a set of common key participants over both years and common types of discourse used by those key participants. Key participants took on roles of resharing messages and contributed to discourse by retweeting posts that highlighted participants’ thoughts and feelings related to the conference and the discipline.

This research has implications for encouraging diverse participants and diverse discourses related to key community goals. Design suggestions include identifying and inviting key actors as collaborators to reshare discourse that clearly aligns with community goals and using smaller hashtag spaces to encourage broader participation.

Prior work on microblogging has highlighted either the types of discourse and information sharing or the structures of the network interactions within conference hashtag streams. This study builds on this prior work and combines discourse and structure to understand the ways in which key network figures reshare discourse within the community, a facet that has been underreported in the literature.

The aim of this research is to elucidate the correlation between open innovation, digital strategies and networking in enhancing agricultural enterprises within the new perspective of Agrifood 5.0. As such, it contributes to making businesses more competitive, especially in the Italian agricultural sector, where small and medium-sized enterprises are highly fragmented. Numerous studies have asserted that the competitiveness of actors operating within a specific territory is closely linked to local identity and image enhancement. Agricultural organizations are undergoing a profound transformation, with technological assets emerging as catalysts for new synergies. Advanced technologies such as robotics, the Internet of Things (IoT) and automation (AI) are emerging as differentiating elements capable of further advancing the agricultural sector, transitioning it from Agrifood 4.0 to Agrifood 5.0. The empirical analysis of the research shows a positive correlation between a collaborative attitude and a propensity for innovation. Indeed, the data demonstrated that digital strategies and open innovation positively influence competitiveness in agricultural SMEs.

The methodology employed in this study is mixed, incorporating both qualitative and quantitative approaches. The quantitative aspect involves analysis of the dataset from the Italian Statistical Institute (ISTAT) through logistic regression, while the qualitative component entails analysis of semi-structured interviews conducted with a sample of 174 agricultural cooperatives in southern Italian regions (Campania). This approach allows for a comprehensive understanding of the research topic, capturing both numerical trends and nuanced insights from interviews.

After analyzing the data from the 7th General Census of Agriculture conducted by ISTAT, a clear understanding of the sector has emerged, revealing several potential research avenues. It is evident that innovation in the agricultural sector is often driven by the largest and best-capitalized production entities, primarily located in Italy. Conversely, smaller agricultural entities can benefit from networking as new technological assets act as catalysts for new synergies, innovation and competitiveness.

Enhancing the relational contribution within the network and humanizing a fragmented sector are crucial elements for promoting open innovation. Network structuring facilitates the transmission of managerial knowledge, contributing to an overall increase in the intellectual and relational capital of the agricultural sector. These factors, combined with open innovation, enhance the competitiveness of individual firms and elevate the brand of the entire sector, creating a conducive environment for transitioning toward Agrifood 5.0. This transition is characterized by increased interconnection, continuous innovation and overall prosperity. Specific studies on this topic are lacking in Italy, particularly in the southern regions. Therefore, this contribution focuses on investigating the Campania region.

The novelty of this study lies in its investigation of the relationship between agricultural enterprises and innovation in the context of enterprises networking strategies (i.e. associationism and/or cooperation), promoting competitiveness. The limitations of this study are related to the dimension of the sample selected and its relationship with other productive sectors.