Due to the rising appeal of travel, as well as the appearance of low-cost carriers that fly to both popular and exotic destinations, people nowadays tend to book their accommodation through the more convenient and flexible sharing economy platforms. The success of such platforms among consumers depends on various factors, such as satisfaction and platform credibility, and on sustainable performance among contributors, increasing their relevance and visibility. This research proposes a conceptual model for measuring the success of sharing economy platforms in tourism, based on added value, unique experience, and enhanced social interactions, together with credibility and sustainable performance. In this vein, the authors conduct an empirical investigation in an emerging market—Romania—where the development of such sharing economy platforms is still in its infancy. Data gathered by the authors by means of a self-developed questionnaire distributed to 500 respondents are modelled via structural equations with SmartPLS. The results indicate that the platform credibility and sustainable performance of sharing economy platforms are key elements in generating their success. The research adds value to the stimulus–organism–response approach, highlighting some insights from a relevant emerging market, which in the future could become a turning point for inbound and outbound tourism.

In the last decades, wine tourism (WT) has been a topic of study, especially for researchers from large producer countries. The relationship between wine tourism and rural tourism (RT) has been approached from many angles in the past. Ranking sixth in Europe in having its surface cultivated with vines, Romania is one of the major players in the Old World of wine producers. In Romania, the scientific literature on wine tourism is scarce and has a limited coverage on the subject. The present paper, by its main purpose, raises the curtain of Romanian wine tourism drawing a consumer profile and analyzes the relationship between WT and RT. Through quantitative research with 850 valid wine tourist respondents and a margin of error of 3.36%, we discovered that people who know what wine tourism means are 1.85 times more confident that it will contribute to the development of rural tourism compared to those who know less about the subject. A high number of visits leads to a decrease in the confidence that rural tourism can be developed through wine tourism due to the poor diversification of leisure activities offered by wine cellars and a weak connection between economic activity and local space. In the end, we draw some practical implications for wine cellars. By developing local-based leisure activities, vineyards can contribute to the sustainable development of the local community. Due to the COVID-19 impact on tourism, there are some limitations to our study. Future studies are needed to cover the changes and evolution of this type of tourism.

The sharing economy has seen a worldwide boom in recent years. In tourism, such platforms are being increasingly used; however, analysis of consumers’ behavior toward such platforms in the context of technological innovation since the COVID-19 pandemic is almost non-existent. The aim of the present research is to fill this gap by studying, with the aid of structural equations, factors that influence the attitudes and behavior of consumers of tourist services offered through sharing economy platforms. Under the impact of the adoption of new technologies, the perceived value generated by these platforms is considered in the context of the COVID-19 pandemic. Trust in tourism sharing economy platforms and their perceived value is determined by consumers’ experience of using them, the authenticity of platforms, propensity toward technological innovation, and users’ demand for novelty. Data collected from 548 respondents were modeled using structural equations in SmartPLS. Results show that experience, the authenticity of platform, openness to new things, and technological innovation are the determining factors that influence consumers’ behavior on sharing economy tourism platforms. The originality of the research consists of the implementation, in a sharing economy context, of a new construct for this sector, which can also be used in other fields, namely that of pandemic fear. Furthermore, the way in which other constructs are included in the proposed conceptual model and the links between them presents additional novelty. Given the trend of increasing demand for such platforms and tourists’ appetite for technological innovation, it will become a managerial challenge for businesses in this field to keep up with the constant evolution of cutting-edge technologies.

This study examines the key factors influencing investment decisions through decision tree regression, grounded in behavioral finance theory. By analyzing a comprehensive dataset incorporating behavioral, demographic, and financial variables—including investment attitudes, decision-making behaviors, financial education, age, income, and education—this study identifies significant predictors of investment outcomes. While the model shows moderate predictive performance (R² = 0.185; MAPE = 172.96%), it identifies hierarchical relationships among behavioral, cognitive, and demographic predictors. These results highlight the complexity of investment decisions and the need for integrative, behavioral-driven approaches in predictive modeling. Investment attitudes (25.88%), decision-making behaviors (19.53%), and financial education (16.68%) emerge as the most influential variables, while traditional demographic factors such as income and age have a lower impact. The hierarchical structure of the decision tree highlights critical decision-making patterns, particularly regarding speculative behaviors and investment attitudes. These findings challenge classical models of rationality by emphasizing the dominant role of behavioral factors in investment decision making. This study contributes to bridging computational modeling with financial economics, demonstrating the utility of decision tree regression in uncovering complex investor behavior. Practical implications include enhancing personalized financial advisory services and designing targeted financial literacy programs to improve decision-making efficiency. These insights, while exploratory, can guide future research and decision-support systems in behavioral finance.