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The purpose of this study is to begine to address this question. The concept of the ideas-informed society (IIS) represents a desired situation in which citizens actively and critically engage with new ideas, developments and claims to truth. Its successful actualisation is dependent on high-quality educational opportunity at all stages of the life course. Social networks represent our connections to one another. Features of our social networks impact on how we engage with ideas. For instance, homophily dictates that individuals form networks with others seen as being like themselves. A key question, however, is whether there are forms of homophily that, by the nature of those they bring together, promote ideas engagement by individuals and the implications of consequent networks for the IIS?

This study re-analysed survey data from 1,000 voting-age citizens in England. Focusing on friendship networks, the authors used a structural equation model approach to explore the existence and potency of homophilic friendship networks; whether such networks drive respondents’ ideas-engagement with friends; and whether ideas discussions with friends impacts on the importance respondents place on staying up to date.

Political homophily has the strongest influence on whether people discuss new ideas with their friends (ES = 0.326, p < 0.01). In turn, ideas discussion has a significant impact on the extent to which people value engaging with ideas (ES = −0.345, p < 0.01).

The authors consider whether ideas-related discussion within politically homophilous networks is problematic for the IIS and what is required from education systems if we are to build individuals’ capacity to engage with ideas while escaping echo chambers.

Professional learning networks (PLNs) involve teachers’ collaboration with others outside of their school to improve teaching and learning. PLNs can facilitate teachers’ professional growth and school improvement. This study aims to explore the drivers for participation within PLNs, the enactment process and the impact of PLN participation on teachers, students and schools in Greece.

A descriptive phenomenological study was conducted to explore the lived experience of primary school teachers participating in PLNs.

The findings showed that individuals who were open to change were driving innovation to address a need or a lack in their daily practice that was not satisfied within their usual community of practice. The key element of the participation was peer collaboration with openness of communication without attendant accountability pressures. The change was mainly identified in teacher skills and the school climate. An individual could bring change only if the school is already open to change. In some cases, resistance to change in schools was identified before enactment or during enactment. The transformation of teachers’ and leaders’ stances is discussed, enabling the opportunity to maximise school improvement.

The study examines PLNs as European Union-funded initiatives that are developed by teachers in centralised education systems under the phenomenological research paradigm. It explores the PLNs in a different setting compared to the existing conceptual theory of change for PLNs.

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.

Unlike many existing methods that are primarily focused on two-dimensional localization, this research paper extended the scope to three-dimensional localization. This enhancement is particularly significant for unmanned aerial vehicle (UAV) applications that demand precise altitude information, such as infrastructure inspection and aerial surveillance, thereby broadening the applicability of UAV-assisted wireless networks.

The paper introduced a novel method that employs recurrent neural networks (RNNs) for node localization in three-dimensional space within UAV-assisted wireless networks. It presented an optimization perspective to the node localization problem, aiming to balance localization accuracy with computational efficiency. By formulating the localization task as an optimization challenge, the study proposed strategies to minimize errors while ensuring manageable computational overhead, which are crucial for real-time deployment in dynamic UAV environments.

Simulation results demonstrated significant improvements, including a channel capacity of 99.95%, energy savings of 89.42%, reduced latency by 99.88% and notable data rates for UAV-based communication with an average localization error of 0.8462. Hence, the proposed model can be used to enhance the capacity of UAVs to work effectively in diverse environmental conditions, offering a reliable solution for maintaining connectivity during critical scenarios such as terrestrial environmental crises when traditional infrastructure is unavailable.

Conventional localization methods in wireless sensor networks (WSNs), such as received signal strength (RSS), often entail manual configuration and are beset by limitations in terms of capacity, scalability and efficiency. It is not considered for 3-D localization. In this paper, machine learning such as multi-layer perceptrons (MLP) and RNN are employed to facilitate the capture of intricate spatial relationships and patterns (3-D), resulting in enhanced localization precision and also improved in channel capacity, energy savings and reduced latency of UAVs for wireless communication.

The numerous spoil grounds brought about by mega transportation infrastructure projects which can be influenced by the ecological environment. To achieve better management of spoil grounds, this paper aims to assess their comprehensive risk levels and categorize them into different categories based on ecological environmental risks.

Based on analysis of the environmental characteristics of spoil grounds, this paper first comprehensively identified the ecological environmental risk factors and developed a risk assessment index system to quantitatively describe the comprehensive risk levels. Second, this paper proposed a comprehensive model to determine the risk assessment and categorization of spoil ground group in mega projects integrating improved projection pursuit clustering (PPC) method and K-means clustering algorithm. Finally, a case study of a spoil ground group (includes 50 spoil grounds) in a mega infrastructure project in western China is presented to demonstrate and validate the proposed method.

The results show that our proposed comprehensive model can efficiently assess and categorize the spoil grounds in the group based on their comprehensive ecological environmental risk. In addition, during the process of risk assessment and categorization of spoil grounds, it is necessary to distinguish between sensitive factors and nonsensitive factors. The differences between different categories of spoil grounds can be recognized based on nonsensitive factors, and high-risk spoil grounds which need to be focused more on can be identified according to sensitive factors.

This paper develops a comprehensive model of risk assessment and categorization of a group of spoil grounds based on their ecological environmental risks, which can provide a reference for the management of spoil grounds in mega projects.

Organizational sociology and organization studies have a long history together, while also sharing a proclivity to self-diagnose crises. Instead of taking these assessments at face value, this paper treats them as an object of study, asking what conditions have fueled them. In the case of organizational sociology, there are indications of a connection between rising levels of discontent and community building: self-identified organizational sociologists have progressively withdrawn from general debates in the discipline and turned their attention to organization studies, which, they suspect, has seen dramatic levels of growth at their expense. Organization studies, on the other hand, are still haunted by “a Faustian bargain”: leaning heavily on the authority of the social sciences, business school faculty were able to facilitate the emergence of a scholarly field of practice dedicated to the study of organizations, which they control. However, in doing so, they also set organization studies on a path of continued dependence on knowledge produced elsewhere: notably, by university disciplines such as sociology.

This study aims to present the concept of aircraft turbofan engine health status prediction with artificial neural network (ANN) pattern recognition but augmented with automated features engineering (AFE).

The main concept of engine health status prediction was based on three case studies and a validation process. The first two were performed on the engine health status parameters, namely, performance margin and specific fuel consumption margin. The third one was generated and created for the engine performance and safety data, specifically created for the final test. The final validation of the neural network pattern recognition was the validation of the proposed neural network architecture in comparison to the machine learning classification algorithms. All studies were conducted for ANN, which was a two-layer feedforward network architecture with pattern recognition. All case studies and tests were performed for both simple pattern recognition network and network augmented with automated feature engineering (AFE).

The greatest achievement of this elaboration is the presentation of how on the basis of the real-life engine operational data, the entire process of engine status prediction might be conducted with the application of the neural network pattern recognition process augmented with AFE.

This research could be implemented into the engine maintenance strategy and planning. Engine health status prediction based on ANN augmented with AFE is an extremely strong tool in aircraft accident and incident prevention.

Although turbofan engine health status prediction with ANN is not a novel approach, what is absolutely worth emphasizing is the fact that contrary to other publications this research was based on genuine, real engine performance operational data as well as AFE methodology, which makes the entire research very reliable. This is also the reason the prediction results reflect the effect of the real engine wear and deterioration process.

Through the subject of business network dynamics, this study aims to examine how business network relationships impact company entry market and development within fast growing economies as China. The paper looks at business network relationships in a fast-growing economy and provides an understanding of the relational perspectives in internationalisation of three luxury fashion companies and their entry models affected by the China context-related variables.

The methodology is following a qualitative approach, based on multiple case-studies research, proposing three cases of companies entering the Chinese market in the luxury and fashion industry.

The research analyses the local business network paths and how they affect the entry strategy through the socio-cultural and political players. The paper adds knowledge to studies in market entry and business networks, in the fast-growing economies area, with its new norms and values.

This study tries to analyse how business networks and related relationships “with Chinese characteristics” affect the market entry strategy in the internationalisation path from the perspective of the luxury fashion industry sector. In so doing it tries to provide an original further development of business network models within the new Chinese context.

As sustainable performance has a central role in the small and medium enterprises (SMEs) performance literature, this study aims to examine the influence of networking capabilities in enhancing sustainable performance through knowledge workers’ productivity and digital innovation. It also examines the sequential mediating role of knowledge workers’ productivity and digital innovation on networking capabilities and SMEs’ sustainable performance relationship.

Data were collected from 308 knowledge workers in the information technology sector and analyzed using the Hayes Process Macro bootstrapping method to test the proposed hypotheses.

Results indicate that knowledge workers’ productivity and digital innovation individually and sequentially mediate the relationship between networking capabilities and SME’s sustainable (economic and environmental) performance, surprisingly, they do not act as a mediator between networking capability and SME’s social performance. SMEs should prioritize investments in the professional development of their knowledge workers through training and skill enhancement programs. This investment equips knowledge workers with the tools to effectively use the knowledge and resources acquired through networking. Thus, knowledge workers may improve performance by using these resources to tackle challenges.

Although this research focused on this specific context, it is prudent to acknowledge that additional factors may also exert influence on sustainable performance within SMEs, factors that managers may consider when making decisions. Methodologically, the cross-sectional design of this research poses a potential limitation, as it does not allow for the complete elimination of endogeneity concerns. However, it is worth noting that scholars have endorsed the use of cross-sectional data in cases where management researchers aim to expand beyond well-documented and longitudinal data sets.

This research offers practical recommendations for SMEs to improve their sustainable performance through networking. SMEs should seek partnerships with complementary knowledge to improve operations and for other performance-oriented benefits.

This study adds significantly to the literature on sustainable SME performance by studying the interdependent effects of networking capabilities. It also represents the individual and sequential mediation mechanism that links networking capabilities to SME success through knowledge worker productivity and digital innovation.

This study aims to develop an in-depth understanding of how supplier selection helps social enterprises achieve their social missions while maintaining commercial viability.

The paper applies a multiple-case design to study the supplier selection processes of 15 Dutch social enterprises.

Social enterprises tend to build supply relationships through existing networks and evaluate suppliers based on value alignment, relationship commitment, resource complementarity, and cost. Depending on the possibility of social value creation in supplier selection, the importance of these criteria varies across different social enterprise models and between key and non-key suppliers. Moreover, suppliers’ long-term relationship commitment can help reconcile tensions between the social and commercial logic of a social enterprise and facilitate impact creation.

Data collection is limited to the perspectives of buyers – the social enterprises. Future research could collect supplier-side data to explore how they engage with social enterprises during the selection process.

Managers of social enterprises can use our research findings as guidance for selecting the most suitable suppliers, while organizations that want to collaborate with social enterprises should actively build network ties to be identified.

We contribute to the cross-sector collaboration literature by showing the underlying reasons for the preference for network reinforcing and indirect networking in supplier identification. We contribute to the social impact supply chain literature by revealing the critical role of supplier selection in shaping collaboration outcomes.