灵活性(工程)
独创性
适度
工作(物理)
人力资源
人力资源管理
资源(消歧)
心理学
款待
社会心理学
知识管理
管理
工程类
计算机科学
旅游
政治学
经济
机械工程
计算机网络
创造力
法学
作者
Li Yan,Shu-mei Jin,Qi Chen,Steven J. Armstrong
出处
期刊:International Journal of Contemporary Hospitality Management
[Emerald (MCB UP)]
日期:2023-07-17
卷期号:36 (6): 1766-1783
被引量:2
标识
DOI:10.1108/ijchm-11-2022-1371
摘要
Purpose This research focuses on the work–family facilitation process to theorize and examine the potential positive impact of perceived overqualification (POQ) on an individual’s work–family interface. Drawing on social cognitive theory of self-regulation, this research identifies work–family balance self-efficacy (WFBSE) as a motivational mechanism through which POQ affects work engagement. Additionally, it proposes flexibility human resource (HR) practices as a key moderator of this process. Design/methodology/approach This research collected multi-wave time-lagged data from 342 managers in the hospitality industry. The research focused on managers with the title of headwaiter and above, while front-line service personnel were not included. Findings The results showed that POQ had an indirect positive relationship with work engagement via WFBSE. The results also showed that employee-experienced flexibility HR practices moderated the effectiveness of WFBSE in translating POQ into work engagement. Practical implications This research provides guidance and insights into how HRM systems can be customized to sustain positive outcomes in situations of overqualification. It is crucial that hotels offer flexibility options or individualization of work arrangements for overqualified employees. Originality/value The potential positive impact of POQ on employees’ work–family interface has been neglected. In addition, prior research has devoted little attention to potential organizational factors that enhance the positive effects of POQ. By examining the mediating and moderating effects, this research aims to explain how and under what conditions POQ facilitates work engagement.
科研通智能强力驱动
Strongly Powered by AbleSci AI