Data mapping the impact of multifaceted information quality on customer satisfaction, trust, and actual behavior towards AI customer service chatbots in online hotel reservations
Description
The data evaluates the multifaceted dimensions of information quality generated by AI customer service chatbots and their impact on customer evaluations in the context of online hotel reservations. It maps the pathways from cognitive information assessment to actual user behavior, operating under the framework that distinct information components, accuracy, completeness, interestingness, relevance, timeliness, understandability, and value-added, directly influence customer trust and customer satisfaction. The study utilized a quantitative approach through a structured online survey targeted at individuals in Vietnam who have practical experience using AI chatbots for hotel bookings. A non-probability convenience sampling method was applied to efficiently reach the target demographic. Responses were measured using a Likert scale adapted from established literature. After the screening process to exclude invalid submissions, a robust dataset of valid responses was established. The data were rigorously analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Data screening procedures confirmed acceptable normal distribution parameters through skewness and kurtosis evaluations. The dataset demonstrates robust measurement model validity; internal consistency was established with Cronbach’s Alpha and Composite Reliability metrics exceeding standard benchmarks, while convergent validity was confirmed with Average Variance Extracted (AVE) values meeting accepted criteria. Furthermore, discriminant validity was successfully established utilizing both the Fornell-Larcker criterion and the rigorous Heterotrait-Monotrait (HTMT) ratio, with values remaining below conservative thresholds, ensuring all constructs are empirically distinct. The data suggests that the nuanced dimensions of system outputs critically dictate user reliance during automated service encounters. It indicates that hospitality managers and software developers should prioritize specific attributes, such as information accuracy, completeness, and relevance, to optimize digital customer service interactions and positively shape consumer behavior. This dataset serves as a foundational resource for scholars and practitioners in hospitality management, human-computer interaction, and digital marketing. Industry leaders can leverage these insights to evaluate the efficacy of current AI communication strategies, particularly within emerging tourism markets experiencing rapid digitalization. The study advocates for the use of this validated measurement instrument in future empirical investigations. Researchers can utilize this dataset as a comparative baseline to measure longitudinal shifts in consumer trust and to validate complex theoretical frameworks across diverse service industries and technological platforms.
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Institutions
- FPT UniversityHanoi, Hanoi