Dimitris C. Gkikas

@upatras.gr

Post Doctoral Researcher, Department of Fisheries and Aquaculture, School of Agricultural Sciences
Professor John A. Theodorou

Dimitris C. Gkikas


                        Download Compact PDF  
https://researchid.co/dcgkikas

EDUCATION

Doctor of Philosophy - PhD in Artificial Intelligence and Marketing, Department of Business Administration of Food and Agricultural Enterprises, School of Economics and Business, Department of Business Administration of Food and Agricultural Enterprises, University of Patras. Thesis: Data mining for enhanced decision making. Applications in consumers’ behavior data in online and offline environment using a machine learning model.
Master of Science - MSc in e-Business and Digital Marketing, Department of Science and Technology, School of Science and Technology, International Hellenic University.
Master of Science - MSc in Computer Science (Artificial Intelligence and Agents), School of Computer Science and Electronic Engineering, University of Essex.
Bachelor of Science - BSc in Informatics Engineering, Department of Informatics and Computer Engineering, School of Engineering, Technology Institute of Athens (University of West Attica).

RESEARCH INTERESTS

Artificial intelligence, Machine learning, Digital Strategy, e-Business, Bioinformatics, e-Governance
21

Scopus Publications

851

Scholar Citations

9

Scholar h-index

9

Scholar i10-index

Scopus Publications

  • Artificial Intelligence in Aquaculture Risk Management: A Systematic Review by PRISMA
    Marios C. Gkikas, Michele Thornton, Dimitris C. Gkikas, Spyros Sioutas, John A. Theodorou
    Applied Sciences Switzerland, 2026
    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.
  • Digital Tools in Tourism Marketing: A Narrative Review of Social Media, Artificial Intelligence, and Mobile Strategies
    Dimitris C. Gkikas, Prokopis K. Theodoridis
    Springer Proceedings in Business and Economics, 2026
    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.
  • AI in Citizen-Centric Smart Cities: Exploring Data Privacy, Algorithmic Transparency and Trustworthiness Through Regulation
    Dimitris C. Gkikas, Marios C. Gkikas
    Learning and Analytics in Intelligent Systems, 2026
  • Digital Storytelling and Tourist Behavior: A Narrative Review of Content, Trust, and Engagement
    Dimitris C. Gkikas, Prokopis K. Theodoridis
    Springer Proceedings in Business and Economics, 2026
    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.
  • Maximizing Social Media User Engagement Through Predictive Analytics in Retail Tourism: Identifying Key Performance Indicators That Trigger User Interactions
    Prokopis K. Theodoridis, Dimitris C. Gkikas
    Applied Sciences Switzerland, 2025
    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.
  • Introduction to the European Artificial Intelligence Act
    Markus Mueck, Christophe Gaie, Dimitris C. Gkikas
    Intelligent Systems Reference Library, 2025
  • Predicting Online Shopping Behavior: Using Machine Learning and Google Analytics to Classify User Engagement
    Dimitris C. Gkikas, Prokopis K. Theodoridis
    Applied Sciences Switzerland, 2024
    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.
  • Exploring Aquaculture Professionals’ Perceptions of Artificial Intelligence: Quantitative Insights into Mediterranean Fish Health Management
    Dimitris C. Gkikas, Vasileios P. Georgopoulos, John A. Theodorou
    Water Switzerland, 2024
    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.
  • Application of Machine Learning for Predictive Analysis and Management of Mediterranean-Farmed Fish Mortalities: A Risk Management Case Study Using Apache Spark
    Marios C. Gkikas, Dimitris C. Gkikas, Gerasimos Vonitsanos, John A. Theodorou, Spyros Sioutas
    Applied Sciences Switzerland, 2024
    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.
  • Fostering Sustainable Aquaculture: Mitigating Fish Mortality Risks Using Decision Trees Classifiers
    Dimitris C. Gkikas, Marios C. Gkikas, John A. Theodorou
    Applied Sciences Switzerland, 2024
    A proposal has been put forward advocating a data-driven strategy that employs classifiers from data mining to foresee and categorize instances of fish mortality. This addresses the increasing concerns regarding the death rates in caged fish environments because of the unsustainable fish farming techniques employed and environmental variables involved. The aim of this research is to enhance the competitiveness of Greek fish farming through the development of an intelligent system that is able to diagnose fish diseases in farms. This system concurrently addresses medication and dosage issues. To achieve this, a comprehensive dataset derived from various aquaculture sources was used, including various factors such as the geographic locations, farming techniques, and indicative parameters such as the water quality, climatic conditions, and fish biological characteristics. The main objective of the research was to categorize fish mortality cases through predictive models. Advanced data mining classification methods, specifically decision trees (DTs), were used for the comparison, aiming to recognize the most appropriate method with high precision and recall rates in predicting fish death rates. To ensure the reliability of the results, a methodical evaluation process was adopted, including cross-validation and a classification performance assessment. In addition, a statistical analysis was performed to gain insights into the factors that identify the correlations between the various factors affecting fish mortality. This analysis contributes to the development of targeted conservation and restoration action strategies. The research results have important implications for sustainable management actions, enabling stakeholders to proactively address issues and monitor aquaculture practices. This proactive approach ensures the protection of farmed fish quantities while meeting global seafood requirements. The data mining using a classification approach coincides with the general context of the UN sustainability goals, reducing the losses in seafood management and production when dealing with the consequences of climate change.
  • How Data Mining is Used in Social Media. Key Performance Indicators’ Impact on Image Post Data Characteristics for Maximum User Engagement
    Dimitris C. Gkikas, Prokopis K. Theodoridis
    Springer Proceedings in Business and Economics, 2024
  • A Comprehensive Marketing and Diffusion Strategy Protocol for Marine Life Protection, Restoration and Conservation; the Case of Endangered Pinna Nobilis
    Dimitris C. Gkikas, Marios C. Gkikas, John A. Theodorou
    Springer Proceedings in Business and Economics, 2024
  • Factors Influencing the Adoption of Artificial Intelligence Technologies in Agriculture, Livestock Farming and Aquaculture: A Systematic Literature Review Using PRISMA 2020
    Vasileios P. Georgopoulos, Dimitris C. Gkikas, John A. Theodorou
    Sustainability Switzerland, 2023
  • Finding Good Attribute Subsets for Improved Decision Trees Using a Genetic Algorithm Wrapper; a Supervised Learning Application in the Food Business Sector for Wine Type Classification
    Dimitris C. Gkikas, Prokopis K. Theodoridis, Theodoros Theodoridis, Marios C. Gkikas
    Informatics, 2023
  • Enhanced Marketing Decision Making for Consumer Behaviour Classification Using Binary Decision Trees and a Genetic Algorithm Wrapper
    Dimitris C. Gkikas, Prokopis K. Theodoridis, Grigorios N. Beligiannis
    Informatics, 2022
  • How do text characteristics impact user engagement in social media posts: Modeling content readability, length, and hashtags number in Facebook
    Dimitris C Gkikas, Katerina Tzafilkou, Prokopis K Theodoridis, Aristogiannis Garmpis, Marios C Gkikas
    International Journal of Information Management Data Insights, 2022
  • AI in Consumer Behavior
    Dimitris C. Gkikas, Prokopis K. Theodoridis
    Learning and Analytics in Intelligent Systems, 2022
  • Assessment of E-Government Portals
    Dimitris C. Gkikas, Georgia Tzavella, Melpomeni Tzioli, Georgia Vlachopoulou, Isidora Kondili, Ioannis Magnisalis
    International Journal of Information Systems in the Service Sector, 2022
  • Optimal Feature Selection for Decision Trees Induction Using a Genetic Algorithm Wrapper - A Model Approach
    Prokopis K. Theodoridis, Dimitris C. Gkikas
    Springer Proceedings in Business and Economics, 2020
  • How Artificial Intelligence Affects Digital Marketing
    Prokopis K. Theodoridis, Dimitris C. Gkikas
    Springer Proceedings in Business and Economics, 2019
  • Artificial Intelligence (AI) Impact on Digital Marketing Research
    Dimitris C. Gkikas, Prokopis K. Theodoridis
    Springer Proceedings in Business and Economics, 2019

RECENT SCHOLAR PUBLICATIONS

  • Digital Storytelling and Tourist Behavior: A Narrative Review of Content, Trust, and Engagement
    DC Gkikas, PK Theodoridis
    The International Conference on Strategic Innovative Marketing and Tourism … , 2026
    2026
    Citations: 1
  • Digital Tools in Tourism Marketing: A Narrative Review of Social Media, Artificial Intelligence, and Mobile Strategies
    DC Gkikas, PK Theodoridis
    In: Kavoura, A., Gretzel, U., Vrana, V. (eds) Strategic Innovative Marketing … , 2026
    2026
  • Artificial Intelligence in Aquaculture Risk Management: A Systematic Review by PRISMA
    MC Gkikas, M Thornton, DC Gkikas, S Sioutas, JA Theodorou
    Applied Sciences 14 (6) , 2026
    2026
  • AI in Citizen-Centric Smart Cities: Exploring Data Privacy, Algorithmic Transparency and Trustworthiness Through Regulation
    DC Gkikas, MC Gkikas
    Learning and Analytics in Intelligent Systems 58, 75–99 , 2026
    2026
  • Maximizing Social Media User Engagement Through Predictive Analytics in Retail Tourism: Identifying Key Performance Indicators That Trigger User Interactions
    PK Theodoridis, DC Gkikas
    Applied Sciences 15 (21), 25 , 2025
    2025
    Citations: 3
  • Introduction to the European artificial intelligence act
    M Mueck, C Gaie, DC Gkikas
    European Digital Regulations, 53-90 , 2025
    2025
    Citations: 6
  • Exploring Aquaculture Professionals’ Perceptions of Artificial Intelligence: Quantitative Insights into Mediterranean Fish Health Management
    DC Gkikas, VP Georgopoulos, JA Theodorou
    Water 16 (24), 3595 , 2024
    2024
  • Predicting Online Shopping Behavior: Using Machine Learning and Google Analytics to Classify User Engagement
    DC Gkikas, PK Theodoridis
    Applied Sciences 14 (23), 31 , 2024
    2024
    Citations: 36
  • Application of Machine Learning for Predictive Analysis and Management of Mediterranean-Farmed Fish Mortalities: A Risk Management Case Study Using Apache Spark
    MC Gkikas, DC Gkikas, G Vonitsanos, JA Theodorou, S Sioutas
    Applied Sciences 14 (22), 10112 , 2024
    2024
    Citations: 9
  • A Comprehensive Marketing and Diffusion Strategy Protocol for Marine Life Protection, Restoration and Conservation; the Case of Endangered Pinna Nobilis
    DC Gkikas, MC Gkikas, JA Theodorou
    ICSIMAT: The International Conference on Strategic Innovative Marketing and … , 2024
    2024
    Citations: 1
  • How Data Mining is Used in Social Media. Key Performance Indicators’ Impact on Image Post Data Characteristics for Maximum User Engagement
    DC Gkikas, PK Theodoridis
    ICSIMAT: The International Conference on Strategic Innovative Marketing and … , 2024
    2024
    Citations: 1
  • Fostering Sustainable Aquaculture: Mitigating Fish Mortality Risks Using Decision Trees Classifiers
    DC Gkikas, MC Gkikas, JA Theodorou
    Applied Sciences 14 (5), 2129 , 2024
    2024
    Citations: 22
  • Understanding Social Media User Behavior Through Post Types: A Data Mining Analysis to Predict User Engagement
    DC Gkikas, PK Theodoridis
    International Conference on Business & Economics of the Hellenic Open … , 2024
    2024
  • Factors influencing the adoption of artificial intelligence technologies in agriculture, livestock farming and aquaculture: A systematic literature review using PRISMA 2020
    VP Georgopoulos, DC Gkikas, JA Theodorou
    Sustainability 15 (23), 16385 , 2023
    2023
    Citations: 36
  • AI-Driven Digital Marketing Approaches for the Aquaculture based Conservation of the endangered Pinna nobilis: Bridging Technology and Environmental Stewardship
    JA Theodorou, DC Gkikas, MC Gkikas
    International Symposium on Fisheries and Aquatic Sciences “SOFAS 2023” , 2023
    2023
  • Predictive Classification Of Aquaculture Fish Mortality Using Data Mining Classifiers
    DC Gkikas, MC Gkikas, JA Theodorou
    XIV International Scientific Agricultural Symposium “Agrosym 2023”, 900-907 , 2023
    2023
  • Finding Good Attribute Subsets for Improved Decision Trees Using a Genetic Algorithm Wrapper; a Supervised Learning Application in the Food Business Sector for Wine Type …
    DC Gkikas, PK Theodoridis, T Theodoridis, MC Gkikas
    Informatics 10 (3), 63 , 2023
    2023
    Citations: 8
  • Artificial Intelligence (AI) Use for e-governance in agriculture: exploring the bioeconomy landscape
    DC Gkikas, PK Theodoridis, MC Gkikas
    Recent advances in data and algorithms for e-government, 141-172 , 2023
    2023
    Citations: 15
  • Enhancing EU Services with Chatbot Design: A Model Proposal and Analysis for Efficient Implementation
    DC Gkikas, PK Theodoridis
    European Marketing Academy (EMAC) 117 (264) , 2023
    2023
  • Data Mining for Enhanced Marketing Decision Making. Applications In Consumers' Behavior Data in Online and Offline Environment Using A Machine Learning Model.
    DC Gkikas
    University of Patras , 2022
    2022

MOST CITED SCHOLAR PUBLICATIONS

  • How do text characteristics impact user engagement in social media posts: Modeling content readability, length, and hashtags number in Facebook
    DC Gkikas, K Tzafilkou, PK Theodoridis, A Garmpis, MC Gkikas
    International Journal of Information Management Data Insights 2 (1), 100067 , 2022
    2022
    Citations: 217
  • AI in Consumer Behavior
    DC Gkikas, PK Theodoridis
    Advances in Artificial Intelligence-based Technologies 22, 147-176 , 2022
    2022
    Citations: 180
  • How Artificial Intelligence Affects Digital Marketing
    PK Theodoridis, DC Gkikas
    Strategic Innovative Marketing and Tourism, 1319-1327 , 2019
    2019
    Citations: 135
  • Artificial Intelligence (AI) Impact on Digital Marketing Research
    DC Gkikas, PK Theodoridis
    Strategic Innovative Marketing and Tourism, 1251-1259 , 2019
    2019
    Citations: 106
  • Enhanced marketing decision making for consumer behaviour classification using binary decision trees and a genetic algorithm wrapper
    DC Gkikas, PK Theodoridis, GN Beligiannis
    Informatics 9 (2), 45 , 2022
    2022
    Citations: 41
  • Predicting Online Shopping Behavior: Using Machine Learning and Google Analytics to Classify User Engagement
    DC Gkikas, PK Theodoridis
    Applied Sciences 14 (23), 31 , 2024
    2024
    Citations: 36
  • Factors influencing the adoption of artificial intelligence technologies in agriculture, livestock farming and aquaculture: A systematic literature review using PRISMA 2020
    VP Georgopoulos, DC Gkikas, JA Theodorou
    Sustainability 15 (23), 16385 , 2023
    2023
    Citations: 36
  • Fostering Sustainable Aquaculture: Mitigating Fish Mortality Risks Using Decision Trees Classifiers
    DC Gkikas, MC Gkikas, JA Theodorou
    Applied Sciences 14 (5), 2129 , 2024
    2024
    Citations: 22
  • Artificial Intelligence (AI) Use for e-governance in agriculture: exploring the bioeconomy landscape
    DC Gkikas, PK Theodoridis, MC Gkikas
    Recent advances in data and algorithms for e-government, 141-172 , 2023
    2023
    Citations: 15
  • Application of Machine Learning for Predictive Analysis and Management of Mediterranean-Farmed Fish Mortalities: A Risk Management Case Study Using Apache Spark
    MC Gkikas, DC Gkikas, G Vonitsanos, JA Theodorou, S Sioutas
    Applied Sciences 14 (22), 10112 , 2024
    2024
    Citations: 9
  • AI in Consumer Behavior Advances in Artificial Intelligencebased Technologies (pp. 147-176)
    DC Gkikas, PK Theodoridis
    Springer , 2022
    2022
    Citations: 9
  • Finding Good Attribute Subsets for Improved Decision Trees Using a Genetic Algorithm Wrapper; a Supervised Learning Application in the Food Business Sector for Wine Type …
    DC Gkikas, PK Theodoridis, T Theodoridis, MC Gkikas
    Informatics 10 (3), 63 , 2023
    2023
    Citations: 8
  • Optimal Feature Selection for Decision Trees Induction Using a Genetic Algorithm Wrapper - A Model Approach
    PK Theodoridis, DC Gkikas
    Strategic Innovative Marketing and Tourism, 583-591 , 2020
    2020
    Citations: 7
  • Introduction to the European artificial intelligence act
    M Mueck, C Gaie, DC Gkikas
    European Digital Regulations, 53-90 , 2025
    2025
    Citations: 6
  • Assessment of E-Government Portals
    DC Gkikas, G Tzavella, M Tzioli, G Vlachopoulou, I Kondili, I Magnisalis
    International Journal of Information Systems in the Service Sector (IJISSS … , 2022
    2022
    Citations: 5
  • Online Consumer Behaviour in Social Media Post Types: A Data Mining Approach
    DC Gkikas, T Theodoridis, PK Theodoridis, A Kavoura
    European Marketing Academy (EMAC) 49 (63455) , 2020
    2020
    Citations: 5
  • How artificial intelligence affects digital marketing (pp. 1319–1327)
    PK Theodoridis, DC Gkikas
    2019
    Citations: 5
  • Maximizing Social Media User Engagement Through Predictive Analytics in Retail Tourism: Identifying Key Performance Indicators That Trigger User Interactions
    PK Theodoridis, DC Gkikas
    Applied Sciences 15 (21), 25 , 2025
    2025
    Citations: 3
  • Chatbot Tools Evaluation
    DC Gkikas, PK Theodoridis, G Tzavella, G Vlachopoulou, I Kondili, ...
    International Conference on Contemporary Marketing Issues 8 (10.6084/m9 … , 2021
    2021
    Citations: 2
  • Digital Storytelling and Tourist Behavior: A Narrative Review of Content, Trust, and Engagement
    DC Gkikas, PK Theodoridis
    The International Conference on Strategic Innovative Marketing and Tourism … , 2026
    2026
    Citations: 1