This study examines the influence of the various attributes of smart tourism technologies (STTs) on tourists’ intentions to revisit locations and engage in word-of-mouth (WOM) activities regarding emerging and smart rural tourist destinations in Iran. A sample of 590 tourists who visited these destinations following the COVID-19 pandemic participated in the study. The findings reveal that three attributes of STTs, namely, informativeness, accessibility, and interactivity, positively contribute to tourists’ memorable experiences (ME). Furthermore, ME, satisfaction, and the willingness to engage in WOM recommendations are identified as predictors of tourists’ intention to revisit rural destinations. The study also reveals that user competence serves as a mediator between the attributes of STTs (informativeness, accessibility, and interactivity) and the tourists’ ME. Specifically, tourists with greater skills and knowledge of using STTs tend to have more memorable experiences in these emerging and smart rural destinations. The study discusses both the theoretical and practical implications of these findings.

Rural deteriorated textures (RDTs) are vulnerable against natural hazards (particularly earthquakes); thus, they need planning and coordinated intervention so that they can be organized. In this regard, identification and prioritization of rural deteriorated areas is one of the fundamental and basic actions. Hence, the present study seeks to identify and zone the areas having RDTs (with an emphasis on residential buildings) in Tehran province through the application of the kernel density estimation (KDE) and geographical information system (GIS). Based on this, the present study is applied and is investigated by library, documentary, and field studies. In this regard, using statistical data available for 655 villages and 14 counties in the region, as well as using a questionnaire, ideas of 16 elites and scientific experts were analyzed in order to discuss the criteria. The results obtained from a kernel density surface of deteriorating locations showed that the highest concentration of rural deterioration areas are located mainly on the Tehran metropolitan fringe. Also, the results showed that the overall accuracy for classification was 79.5% and Kappa statistics was 0.728. Hence, KDE is an appropriate method in the identification and zoning of areas with RDTs.

Despite growing interest in community-based ecotourism as a pathway to sustainable development, there is a critical lack of research on how localized configurations of marketing and policy factors shape ecolodge performance at the village level. This study addresses this gap by pioneering a comprehensive, contextually grounded analysis of the availability and desirability of key marketing and policy indicators across 25 rural villages in Iran’s culturally and ecologically rich Hawraman region. By employing an innovative mixed-methods approach blending statistical analysis, GIS spatial modeling, surveys, and interviews, the research reveals striking heterogeneity in ecolodge constraints and opportunities, even within a localized setting. Spatial analysis uncovers distance decay effects, with peripheral villages facing exacerbated accessibility deficits compared to centrally located ones. Substantial variations in marketing effectiveness and governance capacities underscore the inadequacy of one-size-fits-all tourism planning. The findings make a novel contribution by demonstrating the imperative for transitioning from monolithic interventions to hyper-localized, place-based strategies tailored to each village’s unique barrier and asset profiles. Embracing this paradigm shift promises to enhance the equity and sustainability of ecolodge tourism in rural contexts globally. The study provides an original methodological blueprint for harnessing multi-modal diagnostics to unpack complex tourism dynamics and catalyze transformative, community-centric solutions.

Adaptation to climate change in vulnerable tourism destinations, particularly desert regions, requires a systematic understanding of tourists’ psychological responses and behavioral adaptations. This study develops and empirically validates an integrated theoretical framework combining the Theory of Planned Behavior and Protection Motivation Theory to examine psychological determinants of tourist adaptation in Iran’s desert regions. The research systematically evaluates the relationships between risk perception, adaptation assessment, climate change beliefs, and behavioral outcomes through structural equation modeling analysis of data collected across three desert destinations in Qom Province. Results demonstrate that tourist motivation (β = 0.416, p < 0.001) and climate change beliefs (β = 0.158, p < 0.001) significantly influence adaptation intentions, while subjective norms show no significant effect despite the collectivist cultural context. The model explains 38.6% of the variance in adaptation intentions and 21.0% in maladaptive behaviors, revealing differential predictability between positive and negative responses. Analysis indicates that immediate experiential factors demonstrate a more decisive influence than abstract climate change beliefs, suggesting context-specific adaptation mechanisms in desert tourism settings. This research advances the theoretical understanding of tourist adaptation behavior while providing evidence-based recommendations for destination management in climatically vulnerable regions. The findings inform the development of targeted communication strategies and management interventions that balance environmental awareness with destination attractiveness, contributing to sustainable desert tourism development.