人力资本
人力资源
人力资源管理
知识管理
业务
任务(项目管理)
大裂谷
营销
心理学
经济
管理
计算机科学
天文
经济增长
物理
作者
Guanglei Zhang,Puzhen Xiong,Cheng Huan
出处
期刊:Proceedings - Academy of Management
[Academy of Management]
日期:2023-07-24
卷期号:2023 (1)
标识
DOI:10.5465/amproc.2023.12717abstract
摘要
Artificial intelligence (AI) awareness might raise concerns about the layoff in popular consciousness, but it also encourages frontline workers to seek and build more resources to avoid dismission. To survive in the era of big data, those employees might develop specific human capital that could be nurtured by a high-performance work system (HPWS). Drawing on conservation of resources theory and the Ability-Motivation-Opportunity (AMO) framework, this study investigates the impacts of HPWS on specific human capital and subsequent innovation and task performance of frontline workers in the manufacturing industry. In addition, the moderating effect of AI awareness was tested, given the greater risk of unemployment that AI poses to people doing simple repetitive tasks. Structural equation modelling was employed to test the model, with data collected from 386 employees in Chinese manufacturing enterprises through a two-wave online survey. Results indicate that HPWS has a significant positive effect on innovation and task performance at the individual level, and specific human capital mediates the effect of HPWS on frontline workers’ performance. Moreover, AI awareness can strengthen the indirect effect of HPWS on frontline workers’ performance (via specific human capital). Our research focuses on the specific human capital of low-skilled frontline workers who are extremely vulnerable to being affected. It finds the bright side of AI awareness as a boundary condition and contributes to filling the blank of research on softening the blow of emerging technologies.
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