Abstract
PurposeThis study aims to investigate the relationship between fun at work (FW) and organizational cohesion (OC), using an integrative model where FW acts as a precursor to cohesion. Psychological empowerment (PE) and intrinsic motivation (IM) are examined as potential mediating mechanisms.Design/methodology/approachData was collected through a questionnaire distributed via Google Forms between April and July 2025 to small and medium-sized enterprises (SMEs) identified through Informa D&B. Of the 315 responses, 288 valid cases were retained for analysis. Structural equation modeling was applied to test the proposed integrative model linking FW, PE, IM and OC.FindingsThe results show that FW positively influences PE and OC, both directly and indirectly. PE also significantly enhances cohesion, reinforcing its mediating role. In contrast, IM does not significantly mediate the relationship between FW and cohesion, suggesting that enjoyable workplace practices may strengthen empowerment and collective bonds without necessarily fostering deeper intrinsic drivers.Originality/valueThis study advances knowledge by integrating two previously disconnected domains (i.e. FW and OC) into a single empirical model. The research offers practical value for managers seeking to enhance team dynamics through targeted workplace interventions, highlighting the strategic role of fun in fostering empowerment and collective bonds. In addition, it identifies the nuanced, limited role of IM, opening avenues for further exploration of contextual and cultural factors shaping these relationships.

Abstract
PurposeThis study aims to examine the use of the serious game FLIGBY to recognize and enhance conflict management skills in international business education. It explores how the game's realistic scenarios and embedded conflict resolution frameworks (i.e. Thomas-Kilmann Instrument, Interest-Based Relational approach and Harvard Negotiation Principles) support the development of key competencies through experiential, gamified learning.Design/methodology/approachThis study analyzes data from 16,597 FLIGBY players using multiple regression models to examine how the game facilitates the recognition of conflict situations and the application of resolution strategies. The risk-free, simulated environment of FLIGBY provides a controlled setting to assess players' conflict management competencies.FindingsThe findings demonstrate that serious games like FLIGBY not only enhance students' understanding of conflict management theories but also actively support the development of practical skills and self-awareness. Players are challenged to analyze interpersonal dynamics, make strategic decisions and reflect on the outcomes of their actions, fostering the development of conflict management skills.Originality/valueThe inclusion of FLIGBY in a business education program has the potential to facilitate experiential learning in conflict management, which is a key topic in international business. Furthermore, it offers data-driven insights into how individuals and teams approach conflict resolutions which may be relevant to refine theoretical models based on gameplay analytics.

Abstract
High-throughput plant phenotyping (HTPP) enhances the throughput, resolution, and dimensionality of conventional manual phenotyping techniques. However, existing platforms face significant challenges, including high acquisition and maintenance costs, limited adaptability to field conditions, and inadequate data management capabilities. This paper introduces GREENTRIBE, an open-source, multi-sensor HTPP architecture that integrates Internet of Things sensing devices and robotics to collect, process, and manage comprehensive phenotypic and environmental data. GREENTRIBE features a multiscale sensing network, built on a sensor-independent communication protocol. An ontology-driven data management layer was designed in accordance with common standards and metadata guidelines, ensuring FAIR (Findable, Accessible, Interoperable, and Reusable) (meta)data. The architecture combines Computer Vision and Artificial Intelligence data analysis pipelines with a process-based crop model for data assimilation, allowing the quantitative traits derived from the sensing layer to be linked to contextual data (genotype, environment, and management conditions). The architecture and performance indicators are presented, demonstrating efficient data collection, processing, and management. Phenotyping is the cornerstone of GREENTRIBE, offering a valuable platform for generating data-rich, reproducible workflows, multimodal datasets, and analysis systems with high impact on Precision Agriculture, improving real-time monitoring, input application, and environmental impacts assessment towards maximized crop productivity, quality, and sustainability.

Abstract
In the competitive landscape of higher education, institutions increasingly rely on international rankings to secure funding, attract talent, and enhance their global reputation. Concurrently, these institutions have expanded their presence on social media, utilizing sophisticated posting strategies not only to disseminate information but also to boost recognition and engagement. This study examines the relationship between the rankings of Higher Education Institutions (HEIs) and their social media posting strategies. We collected and analyzed tweets from 22 HEIs featured in a consolidated ranking system, focusing on various features of their social media posts. The analysis identified six distinct clusters of posting strategies. This paper categorizes the HEIs into these clusters and discusses the implications of differing social media strategies on their rankings The findings suggest a nuanced interaction between social media engagement and the perceived prestige of HEIs.

Abstract
ABSTRACT The study aims to understand how, to what extent, and through what mechanisms startups contribute to the achievement of the sustainable development goals (SDGs), distinguishing between impact-driven initiatives and approaches in which sustainability is used instrumentally. To this end, a mixed-methods methodology was applied, combining quantitative and qualitative analysis on a sample of 1864 startups accelerated by Y Combinator. The results indicate that the most addressed SDGs are SDG 13 (Climate Action) and SDG 3 (Good Health and Well-being), with an emphasis on clean technologies, low-carbon models, data-based climate monitoring, personalized and preventive medicine, mental health, and clinical data management. In contrast, goals related to biodiversity and reducing inequalities are less represented, revealing gaps and areas with potential for expansion. The study's contributions include identifying patterns and gaps in startups' engagement with the SDGs, reinforcing the importance of public policies and incentives to balance efforts in less addressed areas, and demonstrating the central role of digital technologies and data analysis in promoting sustainability.

Abstract
Data leakage is a critical issue in deep learning inflating performance and compromise validity, especially in sensitive areas like medical imaging. This study systematically evaluates two common leakage types in oral squamous cell carcinoma classification from histopathology images: (1) preprocessing leakage (global normalization before dataset splitting) and (2) a severe sample-related (patient-related) contamination scenario created by mixing closely related original and augmented images across splits. We trained 11 CNN and Transformer-based models on a public oral cancer histopathology dataset, benchmarking results against published leakage-free baselines. The results obtained show that the configuration with random splitting of original and augmented images (Scenario 2) artificially increased accuracy by up to 18% (mean +14.3%) compared to leakage-free conditions, while the preprocessing-based leakage (Scenario 1) showed smaller deviations (+1.8%). These inflated metrics arise from a combination of cross-split contamination between closely related samples and increased dataset redundancy, rather than genuine gains in generalization ability. Transformers improved leak-free accuracy (+3.9%) but degraded performance in Scenario 2 (-1.4%), revealing sensitivity to sample-specific biases. The observed performance gains under data leakage conditions are methodological artifacts that undermine clinical reliability, with a severe sample-related contamination scenario (Scenario 2) with random splitting of original and augmented images being particularly detrimental due to its promotion of non-generalizable feature learning. The quantitative benchmarks established here-including a mean accuracy gap of 12.5% (Scenario 2 vs. Scenario 1) across 11 models and Transformer architectures’ sensitivity to contamination-reveal fundamental tradeoffs between metric inflation and model trustworthiness. These findings establish quantitative benchmarks for leakage impacts in medical imaging and inform future guidelines for trustworthy AI development in pathology. © The Author(s) 2026.