Ioanna Kalliampakou

Background: Converging technologies of Big Data and Internet of Things (IoT) in sustainable tourism need systematic review of existing applications, efficacy, and implementation issues to enable evidence-based strategies for development. Methods: Systematic review in line with PRISMA guidelines of five principal databases across eight years searched peer-reviewed articles. Systematic screening was used to select studies with high inter-rater reliability, as confirmed during study selection. Results: Three main application fields appeared with different evidence profiles. Customer experience enhancement was well empirically supported by AI-enabled personalization systems utilizing neural networks, collaborative filtering techniques, and IoT-enabled infrastructure, including smart environmental controls, wearable device integration, and automated service delivery. Sustainability applications offered compelling proof through IoT sensor networks facilitating real-time environmental monitoring, predictive crowd control analytics, and intelligent resource optimization with substantial efficiency gains over traditional methods. Ethical concerns emerged in substantial implementation hurdles: though most research recognized privacy and data management issues, few suggested feasible agendas for ethical data stewardship. Geographical analysis offered extreme urban-bias with rural tourism environments represented far less. Explainable AI methods were not forthcoming even as transparency requirements increased. The key implementation obstacles were infrastructure expenses, technical interoperability issues, and lack of skilled personnel, mainly impacting resource-scarce settings. Conclusions: There is support for the effectiveness of IoT and Big Data technologies for enhancing customer experiences and environmental sustainability in tourism contexts. However, there are key deficiencies in ethical frameworks, rural tourism applications, and explainable AI implementations. Research agendas include the creation of scalable solutions in diverse geographical contexts, tourism-specific privacy frameworks, and participatory governance frameworks. These findings inform evidence-based approaches to responsible smart tourism development that reconcile technological innovation with ethical regard and fair application in varied tourism settings.

This paper presents a systematic review of research (2020–2025) on the role of Large Language Models (LLMs) in cybersecurity, with emphasis on their integration into Big Data infrastructures. Based on a curated corpus of 235 peer-reviewed studies, this review synthesizes evidence across multiple domains to evaluate how models such as GPT-4, BERT, and domain-specific variants support threat detection, incident response, vulnerability assessment, and cyber threat intelligence. The findings confirm that LLMs, particularly when coupled with scalable Big Data pipelines, improve detection accuracy and reduce response latency compared with traditional approaches. However, challenges persist, including adversarial susceptibility, risks of data leakage, computational overhead, and limited transparency. The contribution of this study lies in consolidating fragmented research into a unified taxonomy, identifying sector-specific gaps, and outlining future research priorities: enhancing robustness, mitigating bias, advancing explainability, developing domain-specific models, and optimizing distributed integration. In doing so, this review provides a structured foundation for both academic inquiry and practical adoption of LLM-enabled cyberdefense strategies. Last search: 30 April 2025; methods followed: PRISMA-2020; risk of bias was assessed; random-effects syntheses were conducted.

This paper investigates the complex interplay between emotional intelligence (EI) and consumer decision-making processes. It defines and discusses the key elements of emotional intelligence, including emotional awareness, emotional facilitation, emotional knowledge, and reflection. The study explained how emotions, through all stages of the decision process—from problem recognition to information search, evaluation of alternatives, purchase decision, and finally, post-purchase evaluation— metabatically affect consumers' preferences, judgments, and behaviors. It tackles the interface between psychological variables, habitual behaviors, and external influences modulating consumer choices. The paper will review the theoretical frameworks of trait emotional intelligence theory and the ability model and discuss both qualitative and quantitative methodologies used in the study of EI and consumer behavior. These findings underline the impact of EI on consumer outcomes and shed light on its implications in marketing strategies that include personalized experiences and emotional data analysis. The paper finally provides some practical recommendations on how to integrate EI into business strategies and infers further research directions regarding the evolving role that emotional intelligence could assume in consumer behavior, including the impact of digital emotional intelligence and authenticity.

The accounting division plays a crucial role in providing accurate financial data and insights, which are instrumental to informed decision-making and strategic planning. Organizational financial transparency and effective support for resource management depend on accounting. This paper presents a deep neural network (DNN)-based accounting analytics, NeuralACT, to support the decision-making process using real-time predictions made from big data. It uses a Bidirectional Long Short-Term Memory (BiLSTM) network trained with Visegrad Group Companies (VGC) dataset customized and improved for accounting features prediction. The incorporation of bootstrapping while ensuring proper data variance significantly improved the performance of the system. Experimental results demonstrate the outstanding performance of NeuralACT, with an RMSE of 0.0181, MAE of 0.0145, MAPE of 5.14%, and an average R ² of 0.99, significantly outperforming existing methods. The experimental setup involves 10 categories of data from 2000 sources, sending data at a rate of 512 Mbps to 720 Mbps. The NeuralACT framework demonstrates consistent performance for big data, maintaining an average throughput of 0.1338 Gbps and latency of 0.039 s. In a nutshell, NeuralACT is a high-performing, innovative, and real-time accounting analytics framework capable of handling big data with exceptional performance.