Abstract This study integrates advanced statistical modeling approaches to investigate and forecast the human capital development of organizations that integrate AI into their processes. Structural equation modeling was used to estimate the total direct and indirect effects of AI integration into human capital development, testing employee openness to change as a moderator. The estimation of the parameters in SEM, the test for moderation, and the assessment of model fit were conducted using SPSS and AMOS, using maximum likelihood methods along with standard goodness-of-fit indices. In this respect, machine learning-based predictive models were implemented in Python to evaluate the predictive power of AI integration measures with respect to human capital development, considering cross-validation and relevant accuracy metrics for evaluating model performance. The results show statistically significant moderation effects along with adequate predictive performance, thus emphasizing the complementary use of explanatory statistical modeling and predictive statistical learning in understanding and forecasting human capital development.
Rabie et al. (Wed,) studied this question.