Data mapping the impact of multifaceted information quality on customer satisfaction, trust, and actual behavior towards AI customer service chatbots in online hotel reservations

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.本数据集评估了AI客服聊天机器人生成的信息质量的多维度属性，及其在在线酒店预订场景下对顾客评价的影响。本研究构建了从认知信息评估到实际用户行为的作用路径，其理论框架为：差异化的信息维度——准确性、完整性、趣味性、相关性、时效性、易懂性与增值性——直接影响顾客信任度与顾客满意度。 本研究采用定量研究方法，针对曾实际使用AI聊天机器人进行酒店预订的越南民众开展结构化在线问卷调查。研究采用非概率便利抽样法以高效触达目标受众群体。问卷采用改编自成熟学术文献的李克特量表 (Likert scale) 进行数据收集。经筛查剔除无效提交的问卷后，最终形成了包含高质量有效应答的数据集。本研究通过偏最小二乘结构方程模型 (Partial Least Squares Structural Equation Modeling, PLS-SEM) 对数据开展严谨的统计分析。 数据筛查流程通过偏度与峰度检验，确认数据集符合可接受的正态分布参数。本数据集具备稳健的测量模型效度：内部一致性层面，克朗巴哈α系数 (Cronbach’s Alpha) 与组合信度 (Composite Reliability) 指标均超出标准基准值；收敛效度层面，平均方差抽取量 (Average Variance Extracted, AVE) 值满足公认的检验标准；区别效度层面，本研究同时采用福内尔-拉克准则 (Fornell-Larcker criterion) 与严格的异特质-同特质比 (Heterotrait-Monotrait, HTMT) 进行验证，所有相关数值均低于保守阈值，确保所有研究构念均具备经验上的独立性。 本数据集的分析结果表明，AI系统输出信息的精细化维度，在自动化服务交互场景中对用户信任度起到关键性决定作用。研究结果显示，酒店管理方与软件开发人员应优先关注信息准确性、完整性与相关性等核心属性，以优化数字化客户服务交互体验，并正向引导消费者行为。 本数据集可为酒店管理、人机交互与数字营销领域的学者与从业者提供基础性研究资源。行业领导者可借助本研究结论评估当前AI沟通策略的有效性，尤其适用于正经历快速数字化转型的新兴旅游市场。 本研究倡导在未来实证研究中使用本经过验证的测量工具。研究人员可将本数据集作为比较基准，用于衡量消费者信任度的纵向变化，以及在多样化服务行业与技术平台中验证复杂理论框架。