Harmony in robotic synergy: exploring how service robot collaborative capability affects hospitality employee value co-creation intentions

款待 共同创造 和声(颜色) 业务 酒店业 营销 知识管理 服务机器人 服务(商务) 机器人 过程管理 旅游 计算机科学 人工智能 地理 艺术 视觉艺术 考古
作者
Xin Liu,Xiaoyun Zheng,Lu Zhang,Shicheng Yang
出处
期刊:International Journal of Contemporary Hospitality Management [Emerald Publishing Limited]
卷期号:37 (10): 3405-3426 被引量:1
标识
DOI:10.1108/ijchm-11-2024-1742
摘要

Purpose This paper aims to explore the impact of service robot collaborative capability on employee value co-creation intentions, the mediating roles of employee cognitive crafting and robotic competency evaluation, and the moderating effect of technological anxiety. Design/methodology/approach A total of 430 Chinese hospitality workers participated in this research. A multi-method quantitative design, comprising a scenario-based experiment (n = 80) and a questionnaire survey (n = 350), was implemented. Findings The results show that service robot collaborative capability positively affects employee value co-creation intentions, with employee cognitive crafting and robotic competency evaluations playing a partial mediating role. Technological anxiety positively moderates the relationship between employee cognitive crafting, robotic competency evaluation and value co-creation intention. When individuals experience a higher level of technological anxiety, intention to implement co-creation was greater. Practical implications Our study offers insights that promote the integration of human employees and service robots, guiding managers to leverage robotic attributes to encourage employee engagement. By identifying employee personal traits, hospitality organizations can enhance the effectiveness of human–robot co-creation. Originality/value Research has largely focused on customer–robot interactions, with limited attention to employee–robot value co-creation. This study applies cognitive evaluation theory to clarify the psychological and behavioral mechanisms of employee–robot co-creation, offering new directions for future research on human–robot interaction.
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