Paying for robotic errors: exploring the relationship between robot service failure stressors, emotional labor and recovery work engagement

压力源 情感劳动 工作(物理) 心理学 服务(商务) 应用心理学 工作投入 服务补救 业务 运营管理 社会心理学 营销 服务质量 工程类 临床心理学 机械工程
作者
Xin Liu,Lu Zhang,Michael S. Lin,Guangmei Jia
出处
期刊:International Journal of Contemporary Hospitality Management [Emerald Publishing Limited]
标识
DOI:10.1108/ijchm-08-2024-1188
摘要

Purpose This paper aims to identify types of robot service failure stressors and explores its impact on emotional labor and recovery work engagement from the employees’ standpoint. Design/methodology/approach The research adopted a mixed-method approach in the hospitality industry in China, which included 25 hospitality workers participating in semi-structured interviews and 435 hospitality employees participating in a two-stage questionnaire survey. Findings Three types of robotic service failure stressors – illegitimate tasks, customer mistreatment and robotic instability – were identified. These stressors significantly influence emotional labor strategies in employee subsequently shaping their recovery work engagement through dual pathways. The sequential mediation effect of deep acting and service empathy serves to enhance recovery work engagement, whereas the sequential mediation effect of surface acting and workplace depersonalization diminishes this engagement. Human–robot collaborative climate moderates these effects in this context. Practical implications The findings from this study yield several implications for hospitality managers in managing employees and service robots to perform human–robot collaboration tasks. Originality/value Current research has primarily delved into how robot service failures impact customer experiences, leaving the effects on employees less explored. To the best of the authors’ knowledge, this paper is the first empirical study to explore the relationship between robot service failure and employee emotional responses and behaviors, enriching the literature on service robots in the hospitality industry and also proposing new directions and frameworks for future human–robot interaction research.
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