The current study validates AMO-enhancing HRM practices measurement in Malaysian construction firms' setting. The study pattern is cross-sectional. First, an examination of the literature was conducted to find out the fundamental components of AMO-enhancing HRM practices and the relevant items. Following that, these items were subjected to content validity with academic specialists. These items were administered to collect data from construction firms. Partial Least Square-Structural Equation Modeling (PLS-SEM) was employed as AMO-enhancing HRM practices were framed as a reflective-formative second-order construct. After that, AMO-enhancing HRM practices were assessed using reliability and validity examination. The result revealed that AMO-enhancing HRM practices have three dimensions: ability-, motivation-and opportunity-enhancing HRM practices. AMO-enhancing HRM practices are defined by these three dimensions, which collectively encompass various aspects of AMO-enhancing HRM practices. If any elements are excluded, AMO-enhancing HRM practices may be altered. This study is unique when conceptualizing AMO-enhancing HRM practices as a multidimensional construct comprising three dimensions, which had not been investigated in prior studies.

This research paper investigates the impact of employees' perceptions of HRM system and practices on employee affective commitment. Additionally, it explores the mediating role of employee psychological capital in the relationship between employee perceptions of the HRM system, practices, and affective commitment. Data was collected from employees of private banks in Pakistan. The collected data was analyzed using the structural equation modeling technique. The findings indicate that HPWPs groups and HRM system strength positively influence employee affective commitment. However, the mediation results only partially support the hypothesis. These findings encourage scholars to delve deeper into the relationship between these variables.

This research aims to examine the impact of Big Data Quality (BDQ) components, including completeness, accuracy, format, and currency, on Big Data-driven Human Resources (BDHRP) management practices and Decision-Making Quality (DMQ) from the viewpoint of HR managers. It also seeks to identify the most impactful components among completeness, accuracy, format, and currency in the context of BDHRP and DMQ. A survey of HR professionals in 108 French organizations deploying Big Data Analytics systems revealed positive relationships between BDQ, BDHRP, and DMQ. Statistical analyses conducted with the Partial Least Squares Structural Equation Modeling (PLSEM) method showed a positive relationship between BDQ components and BDHRP, with currency and accuracy emerging as the most influential factors. Additionally, a strong positive relationship was found between BDQ components and DMQ, with currency and accuracy leading the way. The research also found a significant connection between BDHRP and DMQ, further underscoring the importance of effective HRM practices in enhancing decision-making quality. These findings contribute significantly to understanding the crucial role played by big data quality in BDHRP and decision-making, highlighting the potential for organizations to improve outcomes by focusing on currency and accuracy-related concerns. In practical terms, this research offers insights that can guide data quality practices, resource allocation, and strategic decision-making within organizations.