Search results

The purpose of this study is to gather insights into digital consumer behaviour related to Chinese restaurents by examining visual contents on Tripadvisor platform.

Using the deep learning approach, this research assessed consumer-posted online content of dining experiences by implementing image analysis and clustering. Text mining using word cloud analysis revealed the most frequently repeated keywords.

First, 4,000 photos of nine Chinese restaurants posted on Tripadvisor’s website were analyzed using image recognition via Inception V3 and Google’s deep learning network; this revealed 12 hierarchical image clusters. Then, an open-questionnaire survey of 125 Chinese respondents investigated consumers’ information needs before visiting a restaurant and after purchasing behavior (motives to share).

This study contributes to culinary marketing development by introducing a new analysis methodology and demonstrating its application by exploring a wide range of keywords and visual images published on the internet.

This research extends and contributes to the literature regarding visual user-generated content in culinary tourism.

This study aimed to (a) investigate the effects of robot service efficiency, enthusiastic curiosity, escapist experience and sense of security on perceived experience value (PEV) and identify which dimension is the strongest determinant of PEV; (b) examine PEV’s effect on behavioral intention to dine in a robot-enhanced restaurant (RER); (c) examine cultural differences (Kazakh vs. United States consumers) on these dynamics; and (d) examine gender’s moderating effect between PEV and behavioral intention to dine in an RER.

The data were collected through a self-administered online survey based on a convenience nonprobability sampling technique with participants in Kazakhstan and the US Partial least squares structural equation modeling was used to investigate relationships between the constructs.

The findings indicated that PEV was influenced positively and significantly by robot service efficiency, enthusiastic curiosity, escapist experience and sense of security. Gender moderated the relationship between PEV and behavioral intention. Also, multigroup analyses found notable behavioral differences between Kazakh and US participants.

Grounded in Expectancy-value Theory, the present study demonstrated the effects of experiential (escapist experience), psycho-emotional (enthusiastic curiosity) and quality conditions (service efficiency and sense of security) on PEV and consumers’ behavioral intentions, with variations observed across genders and cultures.

本研究旨在：(a) 探讨机器人服务效率、好奇心、逃避体验和安全感对感知体验价值（PEV）的影响, 并确定哪个维度是PEV的最强决定因素; (b) 检验PEV对在机器人增强餐厅（RER）用餐的行为意图的影响; (c) 探讨这些动态中的文化差异（哈萨克消费者与美国消费者）; (d) 检验性别在PEV与在RER用餐行为意图之间的调节作用。

数据通过自填式在线调查收集, 采用便利非概率抽样方法, 参与者来自哈萨克斯坦和美国。研究采用偏最小二乘结构方程模型来分析构念之间的关系。

结果表明, 机器人服务效率、好奇心、逃避体验和安全感对PEV均具有正向显著影响。性别调节了PEV与行为意图之间的关系。此外, 多组分析发现了哈萨克和美国参与者之间显著的行为差异。

本研究基于期望-价值理论, 展示了体验性因素（逃避体验）、心理情感因素（好奇心）和质量条件（服务效率和安全感）对PEV和消费者行为意图的影响, 并观察到性别和文化上的差异。