Purpose: The study aims to investigate the impact of Government Support on revitalizing the Environmental and Financial Performance of Tourism MSMEs with the Resource Advantage theory perspective. Methodology/approach: Data were collected from 384 TMSMEs through a snowball sampling technique using a self-administered structured questionnaire. Three multiple linear regression models were constructed to investigate the impact of Government Support as an independent variable on performance as a dependent variable, and the Statistical Package for Social Sciences (SPSS) was employed to analyze the data. Results/findings: The results indicate that both Government Financial (GFS) and Non-Financial Support (GNFS) significantly influence the financial and environmental performance of TMSMEs at a 5% level. Moreover, GFS shows a slightly high impact on financial performance GNFS, although both supports show a significant impact on revitalizing the performance of businesses in emerging economies. Conclusion: The study concludes that government financial and non-financial support are critical determinants of tourism MSME performance. Strengthened support not only enhances financial viability but also fosters environmental accountability, aligning with the principles of sustainable development. Limitations: The study focused on TMSMEs in selected emerging tourism regions of Sri Lanka, a least-developed economy, using a quantitative approach. Business performance was assessed through subjective measures due to owners' reluctance to disclose financial data. Contribution: The study contributes to R-A theory by highlighting how government support strengthens firms' resource capabilities, leading to superior performance. It also facilitates policymakers, tourism entrepreneurs, and regulators in fostering regional development by revitalizing TMSMEs, reducing socio-economic disparities, and increasing per capita income.

Purpose: This study examines how beach attributes influence preferences among resident recreationists in Mombasa County, Kenya, and explores how recreationists' characteristics moderate these preferences. Methodology/approach: A survey of 77 frequent beachgoers (visiting ?3 times weekly) was conducted at four beaches using enumerator-administered questionnaires. Descriptive statistics profiled demographics, exploratory factor analysis assessed environmental attitudes, conjoint analysis evaluated beach profile preferences (cleanliness, safety, congestion), and binary logistic regression examined moderating effects of demographics and attitudes. Results/findings: Clean, litter-free, patrolled, and uncrowded beaches were strongly preferred. Heavy litter and the absence of patrols significantly detracted from appeal, while congestion moderately reduced preferences. Gender significantly moderated the influence of congestion, highlighting differing valuations between male and female recreationists. These findings underscore the need for targeted management strategies to enhance beach experiences. Conclusion: The study reveals that extreme levels of key beach attributes-cleanliness, patrolling frequency, and congestion-significantly influence recreationists' preferences. Limitations: The cross-sectional design limits insights into the evolution of preferences over time. Longitudinal studies are recommended to assess how environmental education, management changes, and societal norms influence preferences. Additionally, the context-specific findings suggest a need for comparative studies across diverse regions. Contribution: This study contributes empirical evidence on resident beach preferences, an often-overlooked segment in tourism research. By applying conjoint analysis, it provides nuanced insights into how varying levels of cleanliness, safety, and congestion shape preferences, offering actionable guidance for beach management and policy.

Purpose: This study aims to explore the relationship between service quality and tourist satisfaction in Lebakmuncang Tourism Village, located in Bandung Regency. The research emphasizes the importance of service quality in enhancing visitor satisfaction, particularly within the framework of community-based tourism. Research Methodology: A descriptive quantitative approach was used in this study. Data were collected from 95 tourist respondents through a structured questionnaire utilizing the Likert scale. The instrument was adapted from the SERVQUAL model and included additional dimensions such as willingness to serve, openness, reputation, and credibility. Results: The results indicate that tourists provided highly positive evaluations of various service quality dimensions, including physical evidence, reliability, responsiveness, assurance, empathy, and emotional aspects. These assessments correspond with high levels of tourist satisfaction in terms of contentment, enjoyment, favorable impressions, and overall experience. Conclusion: The study confirms a strong correlation between service quality and tourist satisfaction. Quality service not only boosts visitor loyalty and destination appeal but also supports operational efficiency through positive word-of-mouth. Limitations: This study is limited to a single destination and uses self-reported data, which may not capture broader variations or longitudinal changes in perception. Contributions: This research contributes empirical evidence to the tourism service literature and offers strategic insights for stakeholders in community-based tourism to enhance service delivery and destination sustainability.

Purpose: This study aims to examine seasonal patterns in tourist preferences for luxury resort stays in Bali, with a focus on how cultural backgrounds influence accommodation choices. The goal is to help resorts better understand guest behavior and optimize occupancy strategies. Methodology/approach: The research analyzes monthly online review data from Tripadvisor for Bvlgari Resort Bali, a prominent luxury hotel. A time-series analysis using the ARIMA (Autoregressive Integrated Moving Average) model is applied to forecast occupancy trends. Prior to modeling, the data is tested for stationarity. In addition to forecasting, the study explores guest preferences by analyzing cultural characteristics inferred from reviews, categorizing them into collectivist and individualist orientations. Results/findings: Findings reveal that occupancy trends do not strictly align with the hotel’s predefined seasonal categories. Instead, they are shaped by global travel trends and cultural factors. Guests from collectivist cultures tend to prefer facilities that support group interaction and shared experiences, while those from individualist cultures prioritize privacy, exclusivity, and personalized services. The ARIMA model delivers accurate forecasting results, helping to predict future occupancy rates effectively. Conclusion: IoT integration enhances the reliability of hospital-based PV systems. Tourist behavior is not solely dictated by conventional seasons but also by cultural expectations and travel motivations. Leveraging these insights allows hotels to better align operations, marketing, and pricing strategies with actual guest preferences. Limitations: The study is limited to a single resort and uses data from one online review platform, which may not fully capture the diversity of all guests. Contribution: This study contributes to tourism analytics, cross-cultural marketing, and hotel management by offering data-driven strategies to enhance occupancy performance.