Background: Information and communication technologies (ICT) have introduced “smart” concepts across industries, including ports. Smart ports, leveraging IoT, cybersecurity, and cloud computing, are trending in maritime operations. They optimize data for informed decision-making, cutting costs, enhancing efficiency, mitigating risks, and fostering growth. Methods: To consolidate knowledge in this area, we are conducting a systematic literature review and meta-analysis using the PRISMA framework. Our goal is to synthesize existing insights, minimize biases, increase reliability, and effectively communicate our findings. To address the research needs mentioned, the current study focuses on implementing a systematic literature review (SLR). Results: The goals of this review are: (i) to present and describe the main categories and themes within the research topic, and (ii) to identify research gaps that will aid future research. Key findings include the identification and classification of current literature trends in the smart port performance evaluation framework and the examination of fundamental themes discussed within this area of research. Conclusions: In our review, we emphasize the smart port concept, clarifying its common interpretations amid the industry 4.0 revolution. We discuss recent advancements in emerging technologies and identify key challenges driving researchers’ exploration of the evolving smart port landscape.

Purpose: The purpose of this paper is to identify the factors that managers have to take into consideration when they implement the strategic information systems planning (SISP) process to develop information systems (IS) in the logistics sector. Design/methodology/approach: Data were collected using questionnaires to IS executives in Greek small- and medium-sized enterprises (SMEs) of the logistics sector. Factor analysis is performed on the detailed items of the SISP process constructs. Findings: The results of this paper indicate that IS executives do not focus on defining goals, which will be used to improve the operations of the supply chain for the IS development. Both IS executives and managers from the logistics department of each organization should participate in the process of information technology (IT) strategy formulation and implementation. Executives from the logistics department of each organization should be aware of implementing environmental and organizational analysis with greater meticulousness, to identify IT opportunities and apply IT strategy with greater agility rather than now. Practical implications: A strategic planning process for IS development in the logistics sector is required to achieve the consistently IS planning objectives. This is an important challenge for executives in SMEs who do not concentrate on implementing a strategic planning process for IS development. Originality/value: This paper examines the factors that influence the strategic development of IS in the logistics sector and improve the effectiveness of decision-making regarding the supply chain’s operation.

Proper pricing strategy is a key component in hotel revenue maximization but determining the right room rate is not an easy task. Although many advanced revenue management systems have been introdu...

Individual productivity is a key determinant of organizational performance and employee well-being, yet its underlying drivers are inherently multi-factorial and context-dependent. Existing productivity support systems typically monitor environmental, physiological, or behavioural factors in isolation, limiting their ability to provide personalized and explainable interventions. This paper presents ADAPTS, an adaptive Knowledge Graph (KG)-driven smart system for incentive-based supporting workplace productivity. The proposed framework integrates indoor environmental measurements, wearable-derived physiological and behavioural indicators, and questionnaire-based user profiles into an RDF/OWL Knowledge Graph. A rule-based reasoning layer enriches the graph with high-level semantic knowledge and computes a multi-factor Daily Productivity Score (DPS). The enriched graph is subsequently exploited through a Graph Retrieval-Augmented Generation (GraphRAG) mechanism to construct a Unified User Context that grounds a Large Language Model (LLM) for generating personalized behavioural interventions and adaptive incentive strategies. User interactions are continuously incorporated into the Knowledge Graph, enabling iterative personalization through a closed feedback loop. The proposed framework was evaluated using data collected from 25 participants. Experimental results show that KG-grounded recommendations were consistently preferred over non-grounded LLM recommendations, achieving higher scores in perceived relevance, personalization, explainability, and user trust. These findings demonstrate the effectiveness of combining semantic knowledge representation, graph reasoning, and generative AI to deliver personalized and explainable recommendations targeting environmental, physiological, and behavioural conditions associated with workplace productivity.