In the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive data produced by numerous connected devices. Our study introduces a set of TinyML algorithms designed and developed to improve Big Data management in large-scale IoT systems. These algorithms, named TinyCleanEDF, EdgeClusterML, CompressEdgeML, CacheEdgeML, and TinyHybridSenseQ, operate together to enhance data processing, storage, and quality control in IoT networks, utilizing the capabilities of Edge AI. In particular, TinyCleanEDF applies federated learning for Edge-based data cleaning and anomaly detection. EdgeClusterML combines reinforcement learning with self-organizing maps for effective data clustering. CompressEdgeML uses neural networks for adaptive data compression. CacheEdgeML employs predictive analytics for smart data caching, and TinyHybridSenseQ concentrates on data quality evaluation and hybrid storage strategies. Our experimental evaluation of the proposed techniques includes executing all the algorithms in various numbers of Raspberry Pi devices ranging from one to ten. The experimental results are promising as we outperform similar methods across various evaluation metrics. Ultimately, we anticipate that the proposed algorithms offer a comprehensive and efficient approach to managing the complexities of IoT, Big Data, and Edge AI.

While research on unit‐level ambidexterity is burgeoning, the role of a unit's power advantage has been largely overlooked. Yet, organizational units are interdependent, and gaining an advantageous position across a network of multiple power relationships with other units enables them to create a context favorable to pursuing both exploration and exploitation. This study, therefore, examines how a unit's power advantage shapes its ability to achieve ambidexterity through a tripartite mediation of affective, behavioral, and cognitive contextual factors. Employing a multisource, time‐separated sample of 146 marketing units engaging in 901 dyadic (power) relationships with other units, we find that power‐advantaged units are more likely to attain ambidexterity directly or indirectly through fostering unit positive affect and cognitive diversity, but not through behavioral autonomy. We conclude by discussing the theoretical and practical implications of our findings.

Although benefits of absorptive capacity for new product development have been widely acknowledged, scholars have largely ignored potential costs and challenges involved. We develop a model that examines whether the interplay between a team’s potential (PACAP) and realized absorptive capacity (RACAP) relates to task conflict and knowledge breadth, which in turn lead to radical and incremental innovation outcomes. We further suggest that two key organizational-level characteristics (i.e., CEO servant leadership and TMT information exchange frequency) may foster team-level social integration which, in turn, differentially shapes the effects of task conflict and knowledge breadth on the team’s innovation outcomes. Through the multilevel nature of our study, we aim to demonstrate the benefits and risks that NPD teams are confronted with when leveraging absorptive capacity components for innovation purposes and the contingency role of CEO and board characteristics in shaping team outcomes.

Onshore Power Supply (OPS) allows ships at berth to use electricity from the onshore grid instead of relying on auxiliary diesel engines, reducing emissions, noise, and local air pollution in port areas. Despite its growing importance for maritime decarbonisation, OPS deployment remains fragmented and uneven, creating a coordination problem that involves welfare, investment efficiency, and grid stability. We address this problem by developing a multi-objective optimisation model that combines demandelasticity functions with swing-equation dynamics to capture the interaction between market design and electricity network conditions. Our analysis shows that existing market designs face important limitations. Specifically, "Intermediary" market designs pose risks of monopoly pricing and inefficient investment, while "Facilitator" market designs improve welfare but compromise grid stability. To address these problems, we propose a new "Extension-to-Grid" market design, in which the electricity network operator assumes a coordinating role. This achieves a welfare-superior balance between efficiency, stability, and consumer protection. Through illustrative scenarios, real-world pricing data and a case study with two ports in Greece, we demonstrate how the proposed model can identify and help avoid distorted price signals or blackout risks under poorly designed OPS market structures. By integrating infrastructure optimization with institutional planning, our study provides actionable insights for sustainable port electrification and demonstrates how operations research can inform the regulatory decisions for complex socio-technical systems.