This research focuses on the impact of the tourist experience on the relationship among destination image, tourist motivation, and tourist satisfaction on destination loyalty by examining the theoretical and empirical evidence on the causal relationship between constructs. A research model was proposed, in which nine hypotheses were developed, and the empirical data were collected from Hue city, which is a major tourist destination in Vietnam. A total of 204 questionnaires were returned, and the data were analyzed using PLS-SEM. The moderating effect of the tourist experience on the impact of the destination image, tourist motivation, and tourist satisfaction on destination loyalty is analyzed in this article. The proposed conceptual model was tested, and the results reveal a significant relationship between the two constructs to destination loyalty (destination image and tourist satisfaction). Furthermore, the findings support the proposed destination loyalty model: destination image, tourist satisfaction directly influenced destination loyalty, and tourist experience play an important role as a moderator in the relationship between tourist motivation and tourist loyalty.

The main purpose of this study is to investigate the best predictor of firm performance among different proxies. A sample of 287 Czech firms was taken from automobile, construction, and manufacturing sectors. Panel data of the firms was acquired from the Albertina database for the time period from 2016 to 2020. Three different proxies of firm performance, return of assets (RoA), return of equity (RoE), and return of capital employed (RoCE) were used as dependent variables. Including three proxies of firm’s performance, 16 financial ratios were measured based on the previous literature. A machine learning-based decision tree algorithm, Chi-squared Automatic Interaction Detector (CHAID), was deployed to gauge each proxy’s efficacy and examine the best proxy of the firm performance. A partitioning rule of 70:30 was maintained, which implied that 70% of the dataset was used for training and the remaining 30% for testing. The results revealed that return on assets (RoA) was detected to be a robust proxy to predict financial performance among the targeted indicators. The results and the methodology will be useful for policy-makers, stakeholders, academics and managers to take strategic business decisions and forecast financial performance.

As the sharing economy continues to diffuse in the global sphere, this paper seeks to understand the relevant behavioral antecedents for individuals to supply resources in technology-enabled platforms, especially in environments where resource scarcity subsists and the cultural landscape varies. Explicitly, we examine the impact of a set of belief factors and the individual-level collectivistic orientation on the core constructs of the Theory of Planned Behavior. A web-based survey was carried out in the Philippines, and a total of 365 valid responses were analyzed using partial least squares structural equation modeling. Results show that attitude and perceived behavioral control strongly drive the consumers’ intention to share resources in the sharing economy. Attitude is markedly preceded by altruistic and social tenets traditionally attached to sharing practices, while perceived behavioral control is endorsed by perceived trust and ease of use. Also, this work recognizes the attributions of culture to behavior as evidenced by the significant positive effect of collectivism towards the subjective norm. However, the potency of subjective norms on behavioral intention is argued. The study findings offer relevant insights to existing and prospective sharing economy platform providers in fostering peer-provider participation in the less developed world.

Small and medium‐sized hotels (SME hotels), which constitute the majority of the global hospitality sector, face mounting pressure to align with sustainability goals. However, their transition to circular economy (CE) practices remains under‐theorised and poorly implemented, particularly in emerging economies where institutional fragility and resource constraints prevail. Existing literature predominantly emphasises large firms and linear modelling approaches, overlooking the configurational, capability‐driven processes that enable SMEs to adopt CE under complexity. This study addresses this gap by developing a tri‐theoretical framework—grounded in the Resource‐Based View, Dynamic Capabilities Theory and Configurational Theory—to examine how internal resources and capabilities interact to shape CE adoption in SME hotels. Empirical insights were drawn from 300 executive respondents across Indian SME hotels. The study employs a hybrid analytical design integrating Partial Least Squares Structural Equation Modelling (PLS‐SEM) to capture net effects, and Fuzzy‐Set Qualitative Comparative Analysis (fsQCA) to explore causal asymmetries and equifinal pathways to CE implementation. Findings reveal Green Absorptive Capacity (GAC) as a consistent enabler and Green Dynamic Capabilities (GDC) as essential for adaptive CE strategies. While Green Human Capital (GHC) does not appear in dominant configurations, its role is found to be context‐dependent rather than absent. The study contributes theoretically by advancing an integrated model that explains CE adoption beyond linear assumptions and practically by offering strategic guidance for SME hoteliers navigating environmental transformation. It advocates for investment in organisational learning, dynamic capability‐building and targeted policy engagement to support sustainable transitions under constraint.

The COVID-19 pandemic has had a significant and enduring effect on the tourism industry, specifically impacting travel agencies. To respond effectively, it is essential to comprehend the financial repercussions and challenges that these agencies confront. This study seeks to address existing gaps in knowledge by conducting a comprehensive examination of the tourism industry's financial performance in the post-COVID-19 era. Our methodology utilizes a Difference-In-Difference (D-I-D) approach, considering multiple investigations to establish a causal connection between the pandemic and the financial stability of tourism service providers. We analyse changes in the overall financial health of the tourism industry relative to other sectors both before and after the COVID-19 outbreak using Altman Z scores. Furthermore, we assess accounting harmonization across diverse jurisdictions and financial frameworks (IFRS, US GAAP, Czech GAAP, German HGB). Data is sourced from 11 multinational corporations in the tourism sector, supplemented with financial data from sectors such as Automotive, Manufacturing, Food and beverage, and Real Estate. Our empirical findings indicate that non-tourism entities experienced a 19% lower likelihood of bankruptcy post-COVID-19. This finding underscores the uniqueness of the tourism industry's challenges during the pandemic. In addition to these findings, we provide practical recommendations designed to assist companies in the tourism industry as they navigate the recovery phase following the pandemic. These insights are pivotal in enabling the industry to build resilient strategies and ensure its sustainability in a post-pandemic landscape.

This research study focuses on the positive impact of eWOM in determining the selection of eco-tourism destinations. A positive review of tourism destinations has significant images, content and experience. Its natural beauty, environment-friendly practices, sustainability, and other factors determine an eco-tourism destination. The paper's findings suggest that tourists research before selecting a destination. An honest review of a previous consumer has more impact on a potential customer. Travellers opt for online and offline media to judge the authenticity of the destination. This research paper also examines how electronic word of mouth (e-WOM) impacts the tourists' decision to select and visit eco-friendly destinations?