知识管理
变革型领导
知识共享
适度
独创性
人力资源管理
业务
工作设计
竞争优势
工作表现
心理学
营销
计算机科学
工作满意度
社会心理学
创造力
作者
Mai Nguyen,Sharyn Rundle‐Thiele,Ashish Malik,Pawan Budhwar
标识
DOI:10.1108/jkm-07-2022-0552
摘要
Purpose The purpose of this paper is to focus on how adopting technologies impacts employees’ job performance and well-being. One such new job demand is the use of technology-based knowledge sharing (TBKS), which has the potential to influence employees’ job performance and well-being. Therefore, human resource managers must provide resources that facilitate the adoption of TBKS to improve job performance while minimising mental health effects. Design/methodology/approach Guided by social capital theory, social exchange theory and the job demands-resources model, the authors analyse survey data from 281 Vietnamese employees. Findings The results of this paper show that TBKS influences employee mental health and directly and indirectly affects job performance. The authors examine the moderating effects of training, transformational leadership and organisational resources on the relationship between the new job demands of TBKS on job performance and mental health outcomes. Practical implications TBKS platform developers should offer user-friendly interface functions and extend critical features. HRM should communicate more with employees, care about their well-being and consider their goals and values. HRM needs to provide training to help employees adapt to organisational changes. Leadership also needs to make employees perceive that organisational success is closely related to the success of TBKS. Originality/value This paper draws upon the three fundamental tenets of three theories as a triangular base to examine the relationship between TBKS and its outcomes. This paper contributes to the knowledge management literature by delivering a comprehensive understanding and demonstrating how the inclusion of technology in knowledge sharing and human resource practices can impact employee performance and well-being.
科研通智能强力驱动
Strongly Powered by AbleSci AI