This study examines the influence of HR practices on the rate of retention among Generation Y and Generation Z employees in Lebanon. This is accomplished via intrinsic motivation and job satisfaction, which serve as two critical mediating factors. The data were gathered through an online survey involving 906 participants, divided equally into Generation Y (n=455) and Generation Z (n=451). Quantitative methodology was adopted during the data collection process. Structural Equation Modeling and Confirmatory Factor Analysis were utilized for analysis purposes. The findings indicate that both intrinsic motivation (𝛽 = 0.177, 𝑝< .001) and job satisfaction (𝛽 = 0.305, 𝑝< .001) have a positive effect on retention. Generation Y employees were more receptive to HR policies such as servant leadership and Work-life balance, servant leadership, and organizational career development were found to exert significant indirect effects on employee retention through job satisfaction and intrinsic motivation (β = 0.080, p < .001; β = 0.037, p < .001; and β = 0.091, p < .001, respectively). On the other hand, servant leadership and job satisfaction had a negative association for Gen Z employees (𝛽 = -0.256, 𝑝 < .001). According to the results of the current study, HR policies and procedures should be unique to each particular generation. Generation Y employees are keen on career development, whereas Generation Z employees will feel more comfortable in an environment where careers are independent and autonomous. The present research adds to the body of literature on employee retention because there are not many studies that explore the role of generations in employee retention in Lebanon.

This study aims to examine the impacts of AI investment on economic growth, while controlling for labor force participation and gross fixed capital formation in Germany. The analysis is based on data collected on the state of economic development in Germany from the first quarter of 2012 to the fourth quarter of 2022. The study used a nonlinear ARDL bounds approach for these investigations. The outcomes clearly reveal that positive shocks to labor force participation and investment in AI significantly enhance economic growth (GDP) in Germany, whereas a positive shock to gross fixed capital formation (GFCF) has no considerable effect on economic growth. Likewise, negative shocks to gross fixed capital formation and AI investment increase GDP growth. Negative shock to the labor force reduces GDP growth. Recommendations are made that Germany must maintain its measured approach while offering long-term commitment. More spectacular AI expenditure increases are not required; prioritize steadiness and integration. Establish long-time-horizon AI development partnerships between government, universities, and industry with stable funding streams on long-term horizons. The fragile link between traditional capital formation and growth means that Germany needs to redefine productive investment.