Choosing the Right Predictive Lens: Evidence-Based Guidelines for Forecasting Employee Performance in Knowledge-Intensive Banking Environments

Abstract

In an era where predictive analytics drive strategic human capital decisions, selecting the appropriate validation methodology is no longer a technical choice it is a competitive imperative. This study provides actionable guidance for banking leaders seeking to forecast employee performance outcomes from knowledge management investments by rigorously comparing two leading predictive assessment frameworks: PLS-Predict and Cross-Validated Predictive Ability Testing (CVPAT).

Drawing on survey data from 310 banking professionals across Ghana's commercial sector, our analysis reveals a critical divergence: while PLS-Predict delivers superior predictive accuracy for knowledge-process constructs (Q² = 0.834 for Knowledge Creation; RMSE = 0.410), its advantage diminishes for employee performance outcomes (Q² = 0.191; RMSE = 0.905), where CVPAT demonstrates comparable robustness against benchmark models. This construct-level heterogeneity challenges one-size-fits-all validation approaches and underscores the need for context-sensitive tool selection.

We propose a decision framework that aligns predictive methodology with organizational objectives: PLS-Predict for forecasting knowledge-process outcomes where precision drives innovation ROI; CVPAT for validating performance models where conservative estimates mitigate implementation risk. By translating methodological nuance into strategic guidance, this study empowers banking executives to allocate analytical resources more effectively, reduce forecasting error in talent development initiatives, and strengthen the evidence base for knowledge-driven transformation.