Dataset on anthropomorphism, perceived risk characteristics, and continued use of AI assistants for travel planning among Generation Z
Description
The data evaluates the factors influencing the continued use of AI assistants for travel planning among Generation Z in emerging markets. It operates under a multidimensional framework, hypothesizing that anthropomorphism features (voice cues, message interactivity cues, emotional cues, and intelligence cues) and perceived risk dimensions (financial, time, privacy, performance, and psychological) directly and indirectly impact continued usage intention, mediated by user satisfaction. The study utilized a quantitative approach, collecting data through a structured online survey targeting Generation Z individuals with prior experience using AI assistants for travel planning. A non-probability convenience sampling method was employed. The survey instrument captured demographic profiles, behavioral characteristics, and user perceptions across the established constructs. The data collection yielded 552 valid responses, which are structured within a dataset encompassing 54 distinct variables. The dataset, AIassistantsfortravelplanning_dataset.xls, provides comprehensive insights into the interplay between human-like characteristics and perceived vulnerabilities. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS version 3 software. The analysis includes an assessment of the measurement model (outer loadings, reliability, and validity metrics such as Cronbach’s alpha, Composite Reliability, Average Variance Extracted, Fornell-Larcker criterion, and HTMT ratio) and the evaluation of the structural model to test the hypothesized direct and indirect relationships, detailing path coefficients, T-statistics, and P-values. This data suggests that both anthropomorphism cues and perceived risk factors significantly shape post-adoption behaviors. It highlights the mediating function of satisfaction, indicating that enhancing human-like interactions and mitigating perceived risks are intrinsically linked to fostering a satisfying user experience that drives long-term engagement. The findings offer empirical evidence for developers and tourism marketers to refine AI interfaces, cultivate trust, and address hesitations among young travelers. This dataset serves as a resource for understanding the psychological and interactive factors driving sustained adoption of AI in the tourism sector. Researchers can leverage this data to validate theoretical models concerning intelligent system adoption, perform comparative analyses across different demographic or cultural contexts, and inform the calibration of novel psychometric scales for evaluating AI integration in consumer services.
Files
Institutions
- FPT UniversityHanoi, Hanoi