Reactive decision-making is at the heart of traditional HRM, leading to less-than-ideal talent outcomes like higher attrition and misaligned hiring practices. Companies that use standardized HR analytics frameworks show a 31% increase in internal mobility success rates, an 18% decrease in voluntary attrition, and a 23% boost in recruit quality. This study investigates to examine how HR analytics affect DDDM and how that affects employee productivity, retention, and the overall success of the firm. The analytics cover hiring, training and development, and engagement. The study also looks at how adopting new technology might make DDDM have a bigger effect on important outcomes for the workforce.

The study uses structural equation modelling (SEM) to look at data from professionals in a variety of fields. It finds that HR analytics greatly improves DDDM (β = 0.32 to 0.41, p < 0.001). DDDM had a positive effect on employee productivity (β = 0.45), retention (β = 0.42), and organizational performance (β = 0.48), all at p < 0.001. Using technology makes these interactions even stronger, showing how important AI-driven HR solutions are in defining current workforce strategies. The indirect path analysis also shows that DDDM (β between 0.13 and 0.22, p < 0.001) shows that HR analytics has an indirect effect on worker outcomes.

The results show how important it is to combine HR analytics with digital tools to make better decisions and improve company results. Companies who use AI-powered HR analytics find it easier to manage their talent, keep their employees engaged, and keep their workers longer. This study adds to both theoretical and practical knowledge by showing that HR professionals need to engage in advanced analytics, predictive modelling, and AI-driven HR strategies to stay ahead of the competition