When Silent isn’t Golden: Measurement Validation Amo-Enhancing Hrm Practices in Malaysian Construction Firms
Publish place: Iranian Journal of Management Studies، Vol: 17، Issue: 1
Publish Year: 1403
نوع سند: مقاله ژورنالی
زبان: English
View: 52
This Paper With 15 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_JIJMS-17-1_012
تاریخ نمایه سازی: 6 دی 1402
Abstract:
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.
Authors
Lee Chin Tay
Faculty of Accountancy, Finance and Business, Tunku Abdul Rahman University of Management and Technology, Kuala Lumpur, Malaysia
Tan Fee Yean
Department of Business Management, University Utara Malaysia, Sintok, Kedah, Malaysia
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :