This study proposes the Tourism Adaptable Digital Marketing Framework (TADMF), a flexible, cyclical model tailored to optimize digital marketing strategies for hotels and destinations. By leveraging data-driven insights and neuromarketing principles, the framework addresses critical gaps in traditional linear models to maximize bookings for hotels and enhance awareness of destinations. The three-stage cyclical process, attraction, engagement, and conversion, ensures continuous feedback and refinement across the customer journey. Hotels benefit from tailored techniques, such as dynamic pricing and personalized recommendations, while destinations focus on storytelling and user-generated content to forge emotional connections. Compared to traditional marketing models, this framework uniquely integrates online and offline interactions to create cohesive customer experiences. Key findings reveal that the TADMF fosters a dynamic interplay between theoretical innovation and practical applicability, demonstrating scalability and adaptability to diverse tourism contexts. The study concludes that the TADMF offers a robust foundation for addressing the evolving challenges of digital marketing in tourism, paving the way for future research into advanced technologies such as AR, VR, and AI.

This study conducts a literature analysis on integrating tourism development into urban planning to understand the current state of research in the field and identify potential areas for future study. The analysis, based on 11,134 articles published between 2010 and 2023, in the Scopus database, aims to identify emerging research themes, emphasize interdisciplinary collaboration, and reveal global research networks. Utilizing citation analysis, co-authorship analysis, co-occurrence analysis, bibliographic coupling analysis, and co-citation analysis with the use of VOSviewer (version 1.6.19) and Python programming language, the study sheds light on key trends in the integration of tourism development into urban planning. The findings provide a comprehensive understanding of the field’s current landscape, highlighting the importance of interdisciplinary cooperation and global research networks. This study offers valuable insights for researchers, practitioners, and policymakers, facilitating the advancement of sustainable urban tourism practices and guiding future research endeavors in integrating tourism development into urban planning.

This paper builds on Shift-Share analysis and aims to provide a methodological framework for studying the inequalities of Greek regions in the tourism industry. The method applied to yearly data of overnight stays, both for foreigners, and domestic visitors, for the periods 2003–2008, 2008–2013, 2013–2018, and results showing that disparities among Greek regions are generally very large for the tourism industry. The overall analysis is a useful tool for tourism management and regional policy. The paper advances Shift-Share analysis to be used as a tool of region classification, and it incorporates one-way ANOVA to examine the relationship between the inequalities concerns the policies from the last development law and tourism specialization in Greek regions.

Within the context that tourism-seasonality is a composite phenomenon described by temporal, geographical, and socio-economic aspects, this article develops a multilevel method for studying time patterns of tourism-seasonality in conjunction with its spatial dimension and socio-economic dimension. The study aims to classify the temporal patterns of seasonality into regional groups and to configure distinguishable seasonal profiles facilitating tourism policy and development. The study applies a multilevel pattern recognition approach incorporating time-series assessment, correlation, and complex network analysis based on community detection with the use of the modularity optimization algorithm, on data of overnight-stays recorded for the time-period 1998–2018. The analysis reveals four groups of seasonality, which are described by distinct seasonal, geographical, and socio-economic profiles. Overall, the analysis supports multidisciplinary and synthetic research in the modeling of tourism research and promotes complex network analysis in the study of socio-economic systems, by providing insights into the physical conceptualization that the community detection based on the modularity optimization algorithm can enjoy to the real-world applications.