Purpose: This paper aims to propose a data mining approach to evaluate a conceptual model in tourism, encompassing a large data set characterized by dimensions grounded on existing literature. Design/methodology/approach: The approach is tested using a guest satisfaction model encompassing nine dimensions. A large data set of 84 k online reviews and 31 features was collected from TripAdvisor. The review score granted was considered a proxy of guest satisfaction and was defined as the target feature to model. A sequence of data understanding and preparation tasks led to a tuned set of 60k reviews and 29 input features which were used for training the data mining model. Finally, the data-based sensitivity analysis was adopted to understand which dimensions most influence guest satisfaction. Findings: Previous user’s experience with the online platform, individual preferences, and hotel prestige were the most relevant dimensions concerning guests’ satisfaction. On the opposite, homogeneous characteristics among the Las Vegas hotels such as the hotel size were found of little relevance to satisfaction. Originality/value: This study intends to set a baseline for an easier adoption of data mining to evaluate conceptual models through a scalable approach, helping to bridge between theory and practice, especially relevant when dealing with Big Data sources such as the social media. Thus, the steps undertaken during the study are detailed to facilitate replication to other models.

Background: Human capital is a critical asset for organizations, impacting financial performance and market value. Maintaining a stable, committed workforce is challenging. Job satisfaction emerges as a pivotal factor influencing employees’ intentions to leave or turnover intention. However, both encompass diverse dimensions that can exert positive or negative effects. Objectives: To explore the common factors affecting job satisfaction and turnover intention, focusing on the significant positive and negative relationships. Method: A systematic review based on the PRISMA framework has been conducted. The data has been precisely identified, evaluated, and extracted from the SCOPUS database based on set benchmarks. Result: The investigation revealed a variety of determinants that exhibit a significant correlation with job satisfaction and turnover intention, manifesting in both positive and negative associations. Furthermore, these elements have been linked to established motivational theories, suggesting a theoretical underpinning for the observed relationships between workplace dynamics and employee behavior. Conclusion: The result intended to provide a comprehensive understanding of the factors by offering a framework for studying job satisfaction and turnover intention guiding future research in the field of knowledge.

Augmented Reality (AR) is an increasingly prevalent subject with various areas of application. This work explores the results of a survey especially developed to document the ease of acceptance of possible characteristics to implement in mobile outdoor applications enhanced with AR. This survey has been devised for a better assessment of user preferences regarding the use of AR in outdoor scenarios. Preference level has been evaluated based on indicators from models concerning various existing and important models. The paper presents the survey results about the preference level in relation the targeted questions and proposes a conceptual framework for feedback on AR technology acceptance.