The aim of the research is to test the effect of outsourcing human resources activities (independent variable) with its dimensions (outsourcing of staffing, outsourcing of training and development, outsourcing of wages and compensation, outsourcing of human resources information systems) on organizational winning (dependent variable) with its dimensions (the culture of winning, successful organizational change, continuous improvement, and adoption of risk). The research problem was the questions posed by the researcher, the most important of which is the extent to which the research sample realizes the importance of applying outsourcing to human resources activities and its role in organizational victory. The research community is represented by all the administrative leaders with their various administrative responsibilities in the premium class hotels in Iraq, their number is (103). The sample was intentional, whose size was (92) managers. The main data collection tool (the questionnaire) was distributed tothem to survey opinions about the variables investigated. While the research used statistical programs (SPSS V.28-SMART PLS V.3.3) to analyze the primary data. With the adoption of descriptive and inferential statistics methods for the test. The statistical analysis showed a number of results, the most prominent of which was the limited interest of premium class hotels in outsourcing staffing. This has weakened the outsourcing of human resource management activities in general, despite its reliance on external parties to conduct job analysis and descriptions.
In this paper, estimation of system reliability of the multi-components in stress-strength model R(s,k) is considered, when the stress and strength are independent random variables and follows the Exponentiated Weibull Distribution (EWD) with known first shape parameter θ and, the second shape parameter α is unknown using different estimation methods. Comparisons among the proposed estimators through Monte Carlo simulation technique were made depend on mean squared error (MSE) criteria