Abstract
Purpose - The objective of this study is to integrate various theories of identity within entrepreneurship and derive insights and propositions that enhance the understanding of how an incubation program influences the formation and development of entrepreneurial team identity. Design/methodology/approach - This study adopts a qualitative multiple case study design to explore how entrepreneurial team identity develops within ventures incubated at CEU iLab. The analysis is based primarily on interviews with individual entrepreneurs from five selected ventures, complemented by secondary data to enrich and contextualize the findings. Findings - The findings revealed the interconnections between entrepreneurial team formation processes, social interactions, networking, entrepreneurial team stability, feedback mechanisms, team dynamics and intrateam trust and legitimacy. Moreover, the cultivation of a culture defined by trust, open communication and the active integration of feedback mechanisms played a pivotal role in the creation of collaborative team environments. Furthermore, the process of building an entrepreneurial team is heavily reliant on shared vision, common values, complementary skill sets, intrateam trust and pre-existing relationships. Originality/value - This study addresses a notable gap in the existing literature by studying how entrepreneurial teams and individual entrepreneurial team members manage to form and develop their entrepreneurial identity. By focusing on the dynamic processes behind identity formation within teams, this research provides novel insights into the motivations that drive individuals and teams to engage in entrepreneurial activities. This focus on the interplay between identity and team processes represents a distinctive and timely addition to the field.

Abstract
This paper investigates the relationship between school principals' managerial practices and two key mensions of school performance: students' cognitive outcomes and school climate. School performance assessed using a classical Data Envelopment Analysis (DEA) framework, complemented by both unconditional robust and conditional robust models to evaluate the influence of managerial practices on school efficiency. We introduce a methodological innovation that allows for a nuanced analysis of how contextual variables-specifically, principals' managerial practices-affect performance, both individually and through their interactions. The analysis is based on 2019 INVALSI data from a nationally representative sample of 8th grade students in Italian schools. The findings show that principals' practices, as well as the ways in which these practices interact, play a significant role in shaping school efficiency, particularly by promoting a positive supportive school climate.

Abstract
Integrated Access and Backhaul (IAB) technology in cellular networks operating in the 3.x GHz band combines access and backhaul functionalities within a wireless framework, reducing dependence on fiber-based solutions and enabling cost-efficient, flexible network expansion. Deploying a mobile IAB (MIAB) in obstruction-prone environments, such as seaports, offers on-demand capacity and resilience but poses unique challenges due to severe shadowing from dense physical obstacles. This paper presents a three-dimensional, obstacle-aware model for optimal MIAB placement and scheduler selection in networks comprising user equipments (UEs) and fixed IABs (FIABs). We evaluate user and backhaul association patterns under different scheduling strategies, including Round-Robin (RR) and Weighted Round-Robin (WRR), ensuring that both MIABs and FIABs meet UE application-layer capacity demands without exceeding backhaul limits. A genetic algorithm (GA)-based optimizer is employed to explore deployment configurations under varying FIAB densities, number of UEs, and obstacles. Results show that MIAB assistance yields the greatest benefits in sparse FIAB networks and low-UE scenarios, with capacity gains reaching up to 350%. MIAB delivers the greatest added value in the presence of obstacles. In contrast, dense FIAB deployments exhibit diminishing returns from MIAB integration. Across most of the evaluated conditions, WRR outperforms RR by enabling fairer and more adaptive resource blocks (RBs) allocation. These findings provide practical guidance for targeted MIAB deployment strategies that balance infrastructure investment, environmental constraints, and scheduling policies.

Abstract

Healthcare organizations increasingly rely on complex digital systems, but software onboarding often depends on manuals and classroom-based training that do not fit well with fast-paced clinical workflows. Interactive in-app guidance may better support learning during real work, although healthcare-specific evidence is still limited.

Abstract
The integration of artificial intelligence (AI) into safety-critical systems, where human operators remain central to decision-making, introduces various challenges that existing AI frameworks struggle to address comprehensively. Key concerns involve designing a socio-technical system that balances AI transparency, trust, and explainability with the imperative for robust and reliable decision-making. Presently, while numerous sector-specific solutions exist, a holistic framework that effectively integrates human expertise with AI capabilities remains absent, leaving critical gaps in system design, deployment, and oversight. This chapter proposes a multidisciplinary conceptual framework to enhance human-AI collaboration in critical infrastructures such as power grids, railways, and air traffic management. The different design steps were guided by the requirements of these industrial domains. The framework combines key design principles that support human cognition, leveraging insights from decision theory, mathematics, and specialized engineering domains to optimize AI-assisted decision-making. Furthermore, it embeds trustworthiness and risk assessment methodologies, using tools such as the Assessment List for Trustworthy Artificial Intelligence (ALTAI) tool to ensure compliance with ethical and regulatory requirements. © The Editor(s) (if applicable) and The Author(s) 2026.

Abstract
Federated learning (FL) has emerged as a promising paradigm for enabling distributed intelligence in Internet of Things (IoT) environments while preserving data privacy and reducing the need for centralized data collection. However, the practical deployment of FL in IoT scenarios remains challenging due to heterogeneous data distributions, unreliable communication conditions, and the presence of faulty or malicious edge devices that can disrupt collaborative training. These limitations can significantly degrade convergence stability and predictive performance, particularly in resource-constrained and intermittently connected networks. This paper proposes a simulation-driven trust-aware federated learning framework for robust intelligent IoT networks. The proposed approach incorporates a dynamic trust-based aggregation mechanism that adaptively weights client contributions based on the consistency of their local model updates with the global model state. In addition, a controlled IoT-oriented federated simulation environment is developed to emulate heterogeneous edge conditions, including non-independent and identically distributed (non-IID) data partitioning, adversarial model manipulation, and intermittent client connectivity caused by communication dropouts. Extensive multi-seed experiments were conducted on the UCI Human Activity Recognition (UCI HAR) dataset and complemented with an auxiliary CIFAR-10 convolutional neural network (CNN) validation scenario. The evaluation considered multiple adversarial settings, including sign-flip, Gaussian-noise, scaling, and label-flip attacks, as well as communication-dropout probabilities up to 50%. In contrast with the initial FedAvg-only evaluation, the revised experimental analysis includes comparisons with representative robust aggregation baselines, namely Median, Trimmed Mean, Krum, Multi-Krum, and an auxiliary Bulyan configuration. The experimental results demonstrate that the proposed Trust-FedAvg framework substantially improves robustness over conventional FedAvg and remains competitive with established robust aggregation strategies, particularly under directional model-manipulation attacks and intermittent-connectivity conditions. Under a 20% sign-flip attack on UCI HAR, the proposed method achieved a final test accuracy of 86.2%, whereas conventional FedAvg degraded to approximately 44.7%. Furthermore, under combined adversarial and intermittent-connectivity conditions with 50% communication dropout, Trust-FedAvg maintained a final accuracy of 57.0%, compared with 21.3% for FedAvg, 24.8% for Median, and 14.6% for Trimmed Mean. The additional experiments also show that Trust-FedAvg is not universally superior across all perturbation types: under severe Gaussian-noise attacks, coordinate-wise Median and Multi-Krum provided stronger robustness in some settings. Overall, the results suggest that trust-aware aggregation can improve robustness against unreliable or malicious simulated clients while preserving a relatively simple aggregation procedure. Runtime measurements further indicate that the proposed method introduces only limited round-level overhead compared with FedAvg, while remaining simpler than more complex Byzantine-resilient alternatives. Further validation with real IoT deployments, additional sensor datasets, asynchronous communication models, energy profiling, and communication-overhead measurements is required to fully assess deployment feasibility in real IoT environments. The proposed framework provides a practical, extensible basis for the design and evaluation of resilient AI-enabled IoT networks operating under controlled but practically relevant edge-learning constraints.