This study aims to investigate the impact of international immigration on the native labor market of host countries. With the growing number of cross-border movements, the relationship between immigration and unemployment in the domestic labor market has become a concern for economists and policy makers. This study utilizes the comprehensive dataset of the years spanning from 2000 to 2020 and employs panel regression analysis to analyze the intricate dynamics of international migration and unemployment of native-born workers of 16 selected OECD countries. The results have disclosed a significant negative relationship between net migration rate and unemployment rate of domestic laborers. Furthermore, it was concluded that an increase in GDP, wages and government expenditure on education will significantly reduce the unemployment rate as well. However, the positive relationship of gross national expenditure with the unemployment rate of natives is found to be insignificant. In the light of this negative relationship between net migration rates and native unemployment rates, policy makers should take into account promoting immigration policies internationally that are consistent with economic growth goals. Moreover, investments in GDP growth, wage increases and education expenditure must be made to further reduce unemployment among native born workers.

Purpose: The purpose of this study is to investigate how female empowerment mediates the relationship between Saudi Arabia’s Vision 2030 initiatives aimed at cracking the glass ceiling and the enhancement of female job involvement opportunities (FIO) in the tourism and hospitality sectors. By investigating this mediation role, the research aims to get a better understanding of the processes by which national reforms and empowerment efforts lead to increased FIO in tourism and hotels. Design/methodology/approach: The study adopted a descriptive method using a self-administered questionnaire (Appendix) instrument for a convenience sample limited to 218 female respondents. The respondents were selected from female employees working in hotels and tourism companies in Saudi Arabia. The analysis of the collected data utilized structural equation modeling and a maximum likelihood estimate considering AMOS to investigate the direct and indirect relationship between the constructs and the proposed model. Findings: The results indicated that Saudi Arabia Vision 2030 for glass ceiling cracking (V2030) highly and positively influenced FIO via empowerment of females, which mediated the link of Saudi Arabia V2030 to job involvement opportunities in the tourism companies and hotels sector. Research limitations/implications: The biggest challenge for the researchers was reaching working women in hotel and tourism companies to complete the questionnaire and, therefore, only a small number of Saudi women working in the tourism and hotel sector were able to participate. Moreover, obtaining the approval of hotel managers in Saudi Arabia to distribute and endorse the questionnaire posed some difficulties. The study used a questionnaire directed to the small number of working females in certain tourism companies and hotel sectors, which limited the generalization of the results to all over the Kingdom of Saudi Arabia (KSA). Also, it didn’t consider the deep interviews with managers and officials of the tourism authority in KSA. Besides, it didn’t cover women poisoning the leadership and higher jobs in tourism and hospitality. Practical implications: The study has important practical implications for concerned officials and their possibilities in helping Saudi women shatter the glass ceiling and change women’s roles in the tourism and hospitality industry via more empowerment and job involvement in available opportunities. Accordingly, the results may pave the way to benefit programs such as positive discrimination as an affirmative action program when adopting the policy of cracking the glass ceiling. Originality/value: This study uniquely addressed the Saudi Arabia V2030 on FIO through empowering females in the tourism and hotel sector, which opens the doors to researchers, practitioners and concerned officials for the significance of such issues to give due attention in a continuing assessment, amendments and development.

This study has a novel conceptual framework and uses SPSS version 23 and AMOS version 23 for analysis. These findings reveal that the use of AI in HR processes has, in fact, a positive significant effect on Employee Engagement and Retention, Data-Driven Decision Making, Smarter Recruitment, and Enhancing Performance Management but has no effect on Personalized Learning and Development. The novelty of this contribution lies in its focus on specific outcomes of the adoption of AI technology in managing the HRM process. This study fills the conceptual gap in the literature on HRM through the critical analysis of AI functions in the human resources process, focusing on the following critical outcome variables: employee engagement and retention, data-driven decision making, smarter recruitment, enhancing performance management, personalized learning, and development-the interrelations between HR artificial intelligence and these outcome variables.

The aim of this study was to build a scale for assessing the quality of electronic learning service in Saudi universities, which has been named “E-L-SQUAL”. The study was carried out during five stages using the scientific methodology for developing scales. The sample of the study consisted of 729 male and female students of King Khalid University. The data were subjected to statistical analysis using the SPSS program. A number of statistical methods were used for checking the validity, consistency and, reliability, as well as exploratory factor analysis (EFA) to determine the quality of the e-learning service. In addition, the confirmatory factor analysis (CFA) conducted by using AMOS statistical program. Finally, all the study results confirmed the validity of the (E-L-SQUAL) scale, which consisted of (43) items, divided into six dimensions of quality: content, technology, support, user interface, assessments, security and communication. Keywords: scale, evaluation, service quality, e-learning, E-L-SQUAL .