Dimitris C. Gkikas

Digital marketing has evolved into a significant tool for competitiveness and growth in the tourism industry, enabling engagement among consumers, destinations, and travel brands. The study presents a narrative review of research spanning 10 years from 2014 to 2024, examining digital marketing tools and platforms, strategies, and consumer behaviors in the tourism sector. Specifically, the review examines how the integration of Artificial Intelligence (AI)-based user personalization tools, social media marketing, influences marketing strategies, web analytics, mobile marketing, location optimization services, targeted content curation, and the adoption of digital tools by tourists in the funnel. A narrative review methodology is employed to examine these areas comprehensively. The searched keywords included smart tourism, digital marketing tools, personalized travel, responsive design, mobile marketing, etc. The findings benefit tourism marketers and stakeholders, demonstrating that social media platforms are crucial marketing channels for optimizing campaigns, facilitating electronic word-of-mouth, providing personalized solutions using AI, responding to real-time marketing demands, enabling instant bookings via mobile devices, and promoting destination reviews.

In recent years, tourists have sought personalized, authentic, and emotionally engaging content, including storytelling approaches and experiences, which creates marketing value by sharing their experiences, photos, and reviews. Social media platforms and content-promotional apps enable tourists to express their travel choices and shape the destinations’ digital identity. Influencer marketing and short-form video content have also emerged as influential factors. Challenges include measuring campaign effectiveness, maintaining authenticity, and addressing concerns related to trust, transparency, and ethics. This study employs a narrative literature review methodology, combining research sources from 2014 to 2024 to examine the newly introduced digital marketing trends in the tourism industry. This review examines the impact of digital storytelling, user-generated content, and personalized experiences on tourists’ and consumers’ decision-making behavior, offering insights for tourism marketers and researchers. The findings provide strategic insights for optimizing consumer engagement throughout the entire digital tourism journey, while addressing research gaps in sustainable travel and ethical marketing tactics.

The aquaculture industry is growing rapidly. It is the fastest growing food industry in the world, with production expanding 16-fold between 1985 and 2018, according to the Food and Agriculture Organization FAO. The industry operates in an environment of high uncertainty, as the management of biological and environmental risks is critical. The aim of this research is to identify machine learning (ML) algorithms applied to quantify risks, categorize applications by sector, and evaluate data linkage to the extent that they feed into formal risk management protocols. A systematic review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. This search was conducted in Scopus and Science Direct for publications up to January 2026. Initially, 134 records were identified, of which 38 studies were ultimately included in the analysis. The results showed that artificial intelligence (AI) and ML offer new predictive capabilities. Integrating Internet of Things (IoT) sensors, AI methods and ML algorithms improve risk mitigation. However, there is a significant disconnection between algorithmic predictions and operational action. Only 3 of 38 studies demonstrated integration with standardized risk management frameworks (e.g., ISO31000). The study concludes that while AI tools provide predictive efficiency, interdisciplinary frameworks are required to filter predictions through economic and ethical criteria. Strengthening this connection will bring the use of AI as a tool for proactive and standardized risk mitigation.

This study examines and evaluates key performance indicators (KPIs) that impact user engagement on social media platforms, with a primary focus on fashion retail within seasonal tourism contexts. The primary objective is to determine which engagement metrics most accurately predict user interaction levels and to enhance strategic decision-making in digital marketing. Using a dataset of 2500 Facebook photos and videos from a women’s retail store, collected between 2016 and 2024, the study employs descriptive analysis and predictive modeling. Three KPIs—such as 3 s video views, reach from organic posts, and other clicks—are examined for their impact on user engagement. The posts are categorized into engagement levels, and classification models, including Random Forests (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Naïve Bayes (NB), are evaluated. Results show that short video views and post reach are key predictors of user engagement. With XGBoost achieving a classification accuracy of 94.73%, the models perform effectively, and Cronbach’s alpha analysis confirms the consistency among the variables selected. The findings underscore the significance of KPI analysis in social media strategy and illustrate the value of data mining techniques in uncovering user behavior patterns that offer practical insights for optimizing digital marketing efforts.

This study aims to explore aquaculture professionals’ perspectives on, attitudes towards and understanding of Mediterranean farm fish health management, regarding Artificial Intelligence (A.I.), and to shed light on the factors that affect its adoption. A survey was distributed during a major fish health management conference, representing more than 70% of Greek domestic production. A total of 73 questionnaires were collected, for which descriptive statistics and statistical analysis followed. Gender and age were shown to affect interest in A.I. and in viewing A.I. as a partner rather than a competitor. Age was additionally shown to affect trust in A.I. estimates and anticipation that A.I. will contribute to professional development. Education level shows no significant effect. Knowledge of A.I. is positively correlated with A.I. usage (r = 0.43, p < 0.05), as is interest in learning about A.I. (r = 0.64). A.I. usage is in turn positively correlated with eagerness to see its contribution (r = 0.72). Despite the fact that 64.4% characterized their knowledge as little or non-existent, 67.1% expressed interest in learning more, while 43.8% believe that A.I. will revolutionize aquaculture and 74% do not fear they will be replaced by A.I. in the future. The findings highlight the importance of targeted educational initiatives to bridge the knowledge gap and encourage trust in A.I. technologies.

User engagement metrics, including engaged sessions, average engagement time, bounce rate, and conversions, provide significant insights into online behavior. This study utilizes Google Analytics data insights and predictive statistics to analyze these metrics and apply classification models to enhance digital marketing strategies. Relationships among key metrics including event count, sessions, purchase revenue, transactions, and bounce rate, were examined using descriptive statistics, revealing factors affecting user engagement. Machine learning classifiers, such as decision trees (DTs), Naive Bayes (NB), and k-nearest neighbors (k-NN), were assessed for their effectiveness in classifying engagement levels. DTs achieved a classification accuracy of 97.98%, outperforming NB (65.00%) and k-NN (97.90%). Furthermore, techniques like pruning are applied for performance optimization. Primarily, this paper goas is to generate a series of recommendations to help the decision-makers and marketers optimizing the marketing strategies. This study highlights the significance of artificial intelligence (AI) integration in digital marketing as a best practice for optimizing decision-making processes.

The current study evaluates the performance of three machine learning models—Decision Trees, Random Forest, and Linear Regression—applied to aquaculture data to mitigate risks in aquaculture management. The performances of these models are analyzed and properly demonstrated using metrics including the Mean Squared Error (MSE), R-squared (R²), Root Mean Squared Error (RMSE), and Concordance Index (C-index). The Random Forest model achieved the highest prediction accuracy among all machine learning models, followed by Linear Regression and the Decision Trees. The scatter plot for Linear Regression demonstrates good predictive accuracy for mid-range values. However, it shows significant deviations at the extremes, indicating that the model struggles to capture the full range of variability in the data. The bar chart of coefficients pinpoints the variables with the greatest impact on the predictions, providing suggestions for potential areas that can be improved and providing model interpretability. Future work could incorporate more predictive statistics models focusing on improving the models for extreme values by assessing non-linear models, feature engineering methods, and expanding research into less influential variables. The results greatly impact several sections, including aquaculture management, policy-making, and operational strategies, providing valuable insights for stakeholders and decision-makers. Apache Spark was used for data processing and machine learning model implementation; Apache Cassandra was also used for data storage, ensuring efficient large dataset management and SQL tools for structured data handling; Oracle VM VirtualBox for cross-platform virtualization; and Spark Connector was also used.