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In spite of wide civic and academic interest in obesity, there are no bibliometric records of this issue in the marketing corpus. Thus, this inquiry is conceived to address this shortcoming with a bibliometric analysis of Scopus indexed articles published on the subject.

The analysis followed a five-step science mapping approach of study design, data collection, data analysis, data visualisation and data interpretation. R programming software was used to review 88 peer reviewed journals published between 1987 and 2021.

A sizable stream of literature exploring obesity has accrued in the marketing area as authors have drawn parallels between the influence of persuasive communication and advertising on human wellbeing and child health. The United States of America is found to be by far the country with the highest number of publications on obesity, followed by Australia and the United Kingdom. The topic dendrogram indicates two strands of obesity discourse: (1) social and policy intervention opportunities and (2) the effects on social groups in the population.

This review will shape future enquiries investigating obesity. Beyond the focus on children, males and females, an emerging focus on cola, ethics, food waste, milk, policy-making and students is highlighted.

This is the first bibliometric review of obesity in the marketing literature. This is especially timely for weighing up the utility of research aimed at understanding and reporting the trends, influences and role of stakeholders in addressing obesity.

This study aims to apply the predictive processing theory to examine the influence of artificial intelligence (AI)-driven robotic performers on audience emotions and the audience’s resulting electronic word-of-mouth (eWOM) behaviors during tourism service encounters.

Using a quantitative research methodology, survey responses from 339 regular customers of performing arts in tourism destinations were analyzed. The respondents were recruited through Prolific, a professional data collection platform. SPSS 23.0 was used for the preliminary analysis, from which a research model to achieve the aim was proposed. SmartPLS 3 was used for partial least squares structural equation modeling to test the model.

Interactive and novel robotic performances significantly encouraged the consumers to share their experiences online, thereby enhancing eWOM. However, melodic resonance had no significant impact on eWOM intentions. The consumers’ emotional responses fully mediated the relationship of the novelty and interactivity of the performances to the consumers’ eWOM intentions but did not mediate the relationship of the musical elements to their eWOM intentions.

This study enriches the understanding of how AI-driven performances impact consumers’ emotional engagement and sharing behaviors. It extends the application of the predictive processing theory to the domain of consumer behavior, offering valuable insights for enhancing audience engagement in performances through technological innovation.

本研究旨在运用预测处理理论, 考察人工智能（AI）驱动的机器人表演对观众情感及其在旅游服务接触中的电子口碑（eWOM）行为的影响。。

采用定量研究方法, 分析了339名经常观看旅游景点表演艺术的常客的调查问卷。受访者通过专业数据收集平台Prolific招募。初步分析使用SPSS 23.0进行, 从中提出了实现研究目标的研究模型。使用SmartPLS 3进行偏最小二乘结构方程模型测试该模型。

互动性和新颖性的机器人表演显著鼓励消费者在线分享他们的体验, 从而增强电子口碑。然而, 旋律共鸣对电子口碑意图没有显著影响。消费者的情感反应完全中介了表演的新颖性和互动性与消费者电子口碑意图之间的关系, 但没有中介音乐元素与电子口碑意图之间的关系。

本研究丰富了对AI驱动表演如何影响消费者情感参与和分享行为的理解。将预测处理理论的应用扩展到消费者行为领域, 为通过技术创新增强观众参与度提供了宝贵的见解。