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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。

Artificial intelligence (AI) services are vital in enhancing customer experience and purchase intentions in the international online fashion retail sector. This study explores customers’ intentions to use AI-enabled services, focusing on transaction utility, trust and product uniqueness across the customer journey in the context of international online fashion stores. This study also assesses how privacy moderates customer intentions.

This study adopted a longitudinal research design and purposive sampling technique to collect a total of 566 participants. The final data were analyzed using IBM SPSS Amos version 21 software.

The study highlights the significance of transaction utility, trust and product uniqueness in AI integration across the customer journey (pre-purchase, during purchase and post-purchase stages). Most of the direct relationships are significant, except the relationship between the during purchase and post-purchase stages. With a few exceptions, AI integration commonly does not mediate the relationship between antecedents and intention to use AI-enabled services. Privacy moderates AI integration in post-purchase, during purchase and intention to use AI-enabled services, except in the pre-purchase stage.

This study bridges important gaps in the literature by integrating AI-enabled services and customer behavior, contributing to a broader knowledge of customer interactions in global e-commerce fashion stores. The study examines multiple attributes that impact intention, such as transaction utility, trust, product uniqueness, AI integration in three stages of purchases (pre-purchase, during purchase and post-purchase) and privacy, using three major theories: mental accounting theory, trust commitment theory and commodity theory.