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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.

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.

Thin cracks on the surface, such as those found in nuclear power plant concrete structures, are difficult to identify because they tend to be thin. This paper aims to design a novel segmentation network, called U-shaped contextual aggregation network (UCAN), for better recognition of weak cracks.

UCAN uses dilated convolutional layers with exponentially changing dilation rates to extract additional contextual features of thin cracks while preserving resolution. Furthermore, this paper has developed a topology-based loss function, called ℓ_cl Dice, which enhances the crack segmentation’s connectivity.

This paper generated five data sets with varying crack widths to evaluate the performance of multiple algorithms. The results show that the UCAN network proposed in this study achieves the highest F1-Score on thinner cracks. Additionally, training the UCAN network with the ℓ_cl Dice improves the F1-Scores compared to using the cross-entropy function alone. These findings demonstrate the effectiveness of the UCAN network and the value of incorporating the ℓ_cl Dice in crack segmentation tasks.

In this paper, an exponentially dilated convolutional layer is constructed to replace the commonly used pooling layer to improve the model receptive field. To address the challenge of preserving fracture connectivity segmentation, this paper introduces ℓ_cl Dice. This design enables UCAN to extract more contextual features while maintaining resolution, thus improving the crack segmentation performance. The proposed method is evaluated using extensive experiments where the results demonstrate the effectiveness of the algorithm.

Technological innovations provide huge opportunities to expand and revolutionize the scope of products and services offered. This is particularly true for tourism, which is undergoing significant changes due to the development of new technologies. The level of technology diffusion depends on several factors like the exchange of information among peers, and the attitude and shared perception among the contributors. The aim of the study is to explore the diffusion of technology in tourism with a specific focus on the social media discourse around new technologies. Thus, the paper investigates the level of interest in these new technologies analysing the information exchange occurring between individuals on Twitter in order to explore the influence of reciprocal networking.

To capture the attitudes expressed in the industry, the study analyses the ongoing discourse on Twitter as a proxy for the participants “interest in new technologies. Through a social network analysis of the tweets and retweets conducted over a period of nine months, the research maps the level of information exchange about the diffusion of new technologies. Moreover, the sentiment analysis provides an interesting overview of the individuals” attitudes towards the awareness or the adoption of new technologies.

Our analysis has provided several insights: (1) the information network on blockchain in tourism consists of participants who change very quickly over time (high turnover of accounts); (2) some contributors have an extremely important role in influencing the flow of information in the system (information centralization), they can have a generalist (discussing several topics) or a specialist (focusing on a specific topic) behaviour and this strategic choice influences their network's structure; (3) these central nodes also have an impact on the definition of positive and negative sentiment towards a topic (sentiment influencer).

The paper contributes to the literature on technology diffusion, by focusing on one of the preconditions of diffusion that is the shared positive attitude towards technological innovation. More specifically, we adopt a network-based approach, which is useful to explain the level of information exchange and the public discourse that can impact the shared perception and attitude towards technological innovation. The study also highlights the role of knowledge brokers in influencing this public discourse. Future studies can deepen the association between positive perception, higher levels of information exchange and increasing usage of specific technologies. Our results also suggest further exploring the opportunity to combine social media data and other sources of information to shed more light on the technological innovation diffusion processes.

This paper shows how practitioners can benefit from the analysis of information exchange about new technologies in tourism adopting a network perspective with the aim of understanding the level of influence among contributors. Moreover, the increasing interest in blockchain technology and the potential combination between social media data and other sources of information can offer promising insights.

The present study explores the level of technology diffusion through the analysis of information exchange on social media (Twitter). Furthermore, the dynamics of individual user behaviour offers a better understanding about media effects.

While previous research is focused on the users' perception towards the development of new technologies in tourism, the aim of this study is to investigate the dynamics behind the level of diffusion of information and awareness about these new technologies, which still represents an unexplored area of research.

The purpose of this study is to forecast the mechanical properties of ternary blended concrete (TBC) modified with oyster shell powder (OSP) and shea nutshell ash (SNA) using deep neural network (DNN) models.

DNN models with three hidden layers, each layer containing 5–30 nodes, were used to predict the target variables (compressive strength [CS], flexural strength [FS] and split tensile strength [STS]) for the eight input variables of concrete classes 25 and 30 MPa. The concrete samples were cured for 3–120 days. Levenberg−Marquardt's backpropagation learning technique trained the networks, and the model's precision was confirmed using the experimental data set.

The DNN model with a 25-node structure yielded a strong relation for training, validating and testing the input and output variables with the lowest mean squared error (MSE) and the highest correlation coefficient (R) values of 0.0099 and 99.91% for CS and 0.010 and 98.42% for FS compared to other architectures. However, the DNN model with a 20-node architecture yielded a strong correlation for STS, with the lowest MSE and the highest R values of 0.013 and 97.26%. Strong relationships were found between the developed models and raw experimental data sets, with R² values of 99.58%, 97.85% and 97.58% for CS, FS and STS, respectively.

To the best of the authors’ knowledge, this novel research establishes the prospects of replacing SNA and OSP with Portland limestone cement (PLC) to produce TBC. In addition, predicting the CS, FS and STS of TBC modified with OSP and SNA using DNN models is original, optimizing the time, cost and quality of concrete.

The purpose of this paper is to investigate the fault estimation issue of nonlinear dynamical systems via distributed sensor networks. Furthermore, based on the communication topology of sensor networks, the nonfragile design strategy considering the gain fluctuation is also adopted for distributed fault estimators.

By means of intensive dynamical model transformation, sufficient conditions with disturbance attenuation performance are established to design desired fault estimator gains with the help of convex optimization.

A novel distributed fault estimation framework for a class of nonlinear dynamical systems is established over a set of distributed sensor networks, where sampled data of sensor nodes via local information exchanges can be used for more efficiency.

The proposed distributed fault estimator gain fluctuations are taken into account for the nonfragile strategy, such that the distributed fault estimators are more applicable for practical sensor networks implementations. In addition, an illustrative example with simulation results are provided to validate the effectiveness and applicableness of the developed distributed fault estimation technique.

The mega railway infrastructure projects are faced with complex environments and multi-level management challenges. Thus, the mega railway infrastructure project management system not only needs to focus on its composition, but also needs to consider changes and impacts of internal and external environment.

This study attempts to introduce the concept of dissipative structure from the perspective of complexity theory and constructs a positive entropy and negentropy flow index system for mega railway infrastructure project management system in order to analyze the factors of management system more deeply. The Brusselator model is used to construct the structure of the mega railway infrastructure project management system, and the entropy method is used to calculate the positive entropy and negentropy values to verify whether the management system is a dissipative structure.

A plateau railway project in China was used as an example for an empirical study, not only its own characteristics are analyzed, but also the role of constraints and facilitation of the internal and external environment. Based on the research results, several effective suggestions are put forward to improve the stability and work efficiency of mega railway infrastructure project management system.

This study demonstrates that mega railway infrastructure project management system has the characteristics of dissipative structure. It can provide theoretical support for the development of mega railway infrastructure project management system from disorderly state to orderly state.

This study aims to examine the underlying process through which learning organization culture positively influences knowledge sharing. It specifically explored the mediating role of social capital, underscoring its critical impact on enhancing both knowledge sharing and fostering learning organization culture.

To test the proposed hypotheses, structural equation modeling (SEM) analysis was conducted with a sample of 231 employees from a manufacturing firm in South Korea.

The results of this study indicate significant direct effects of learning organization culture on social capital. Also, social capital indicates a positive effect on knowledge sharing. Although learning organization culture had no direct effect on knowledge sharing, it indirectly affected learning organization culture and knowledge sharing by mediating social capital.

This study proposes that a learning organization culture will be interconnected with social capital and knowledge sharing. Organizations that can effectively harness the wealth of knowledge unlocked by social capital, and subsequently integrate this knowledge into their activities, are poised for competitive advantage.

First, this study places a special emphasis on the mediating role of social capital between learning organization culture and knowledge sharing. Despite extensive research exploring diverse knowledge-sharing factors (Wang and Noe, 2010), it is plausible that examining social capital as a mediator could offer insights for facilitating knowledge sharing through its structural, relational and cognitive dimensions. Second, while a plethora of literature examines knowledge sharing, this study also seeks to unravel the multifaceted pathways through which the learning organization culture influences knowledge sharing and how these processes could be optimized in organizations.

This study aims to work toward understanding the entrepreneurship ecosystem of agricultural social enterprises in the Philippines by exploring the interactions between policy, culture, supports and human capital domains.

The authors considered using an exploratory single-embedded case study approach, involving methodological triangulation of document analysis, semistructured interviews and participant observation. The authors analyzed the data using a narrative approach to map the ecosystem.

Through the research, the authors discovered that while each domain functions effectively individually, disconnects exist when interacting collectively as an ecosystem. The authors come to know that there is no policy consensus on social enterprise definitions, which limits specialized policy support. Although support services like incubators are available, the authors observed that awareness and accessibility vary based on location and business maturity. The authors also noted that human capital helps translate concepts into frameworks, but research tailored to agriculture and social entrepreneurship is limited. The authors come to the conclusion that collaboration and openness across domains are needed to strengthen connections and synergies.

The study was geographically limited to Luzon Island, and the authors did not include the finance and markets domains of the ecosystem model in the analysis.

Based on the findings, the authors identify strategies to reinforce connections, such as increasing awareness of support services, developing tailored policies for social enterprises, conducting specialized research and promoting collaboration across domains. The authors are convinced that implementing these strategies can further develop the agricultural social entrepreneurship ecosystem.

The study provides unique empirical insights into the agricultural social entrepreneurship ecosystem in the Philippines. The authors captured the narratives and experiences of key ecosystem stakeholders along the process. The authors have confidence that what the authors found can strategically guide policymakers and support organizations, educational institutions and social entrepreneurs to accelerate ecosystem development for greater social impact.

This paper aims to investigate sustainability-led innovation, focusing on the interplay between product and process innovation for sustainability goals and the underlying supplier–customer relationships. Thus, the paper delves into sustainability-led innovation and how it affects supplier–customer relationships, and vice versa, thus providing a twofold perspective.

The textile industry is the empirical context of this study, which is exploratory research based on in-depth, semi-structured interviews with entrepreneurs, managers and experts in the textile industry.

In the textile industry, sustainability-led product innovation concerns mainly product durability and performance, product recyclability and the use of waste for new product development. Process innovation deals with circular economy, traceability and water and chemical use minimization. The paper also shows how sustainability-led innovation is implemented in more technical terms and regarding supplier–customer relationships.

The paper adopts an original perspective on how processes take place in the relationships between suppliers and customers, where there is no dominance of one actor, but innovation emerges from interdependence and interaction. Such perspective allows to provide an in-depth analysis of the supplier–customer relationships and underlying dynamics that affect sustainability-led innovation; moreover, the authors study how such innovation impacts supplier–customer relationships and the underlying relational dynamics. The value of the paper also stands in delivering a real representation of the innovation processes grounded in the textile industry.