E-HR practices and risk management effectiveness: The mediating role of operations strategy in the Saudi Stock Exchange (Tadawul)
Abstract
The objective of this paper will be to determine how electronic human resource (E-HR) practices affect risk management performance mediated by operations strategy in the Saudi Stock Exchange (Tadawul). Under the quantitative research design, 370 managerial and human resource (HR) employees in the financial and investment institutions listed under Tadawul will be used to collect data. The research paper examines how E-HR systems, digital recruitment, performance analytics, and electronic training programs are integrated to lower institutional risk responsiveness, efficiency in operations, and strategic performance. To test the hypothesized relationships and to verify the mediating variable of operations strategy, Structural Equation Modeling (SEM) is used. The results have indicated that E-HR practices are effective in increasing the level of risk management by fostering operational agility, data-driven decision-making, and proactive financial and operational risks identification. Moreover, the relationship between operations strategy plays the mediating role by converting digital HR initiatives into operational improvements which are well-organized and provide organizational resiliency and control mechanisms. The research finds that E-HR practices are an effective enabler of proper risk management practice in the digital age that support the correspondence of technological change, human potential and operational effectiveness. In order to improve the resilience of the institutions within the Tadawul, it is advised that the listed institutions invest more in E-HR infrastructure, digital workforce development, and operational optimization systems that will maintain the sustainability of financial performance.
Fields of Study & Subjects
Employer Branding and e-HRM
AI and HR Technologies
Organizational and Employee Performance
Publication details
- Journal / source
- International Journal of Data and Network Science
- Publisher
- Growing Science
- Volume
- 10
- Issue
- 2
- Pages
- 837–848
- Publication date
- 1 January 2026