From Insight to Foresight: A Holistic Framework for Mitigating Decision Risk in Knowledge-Based Transformation

Abstract

Purpose: In the era of data-driven management, organizations face a critical "foresight gap": the inability to distinguish between statistically robust insights and strategically reliable forecasts. While predictive analytics are widely adopted, leaders often lack a unified theory on how to align metrics, context, and methodology to mitigate strategic decision risk. This study proposes the Integrated Predictive Decision Framework (IPDF), a holistic model that synthesizes predictive validity, regulatory context, and methodological rigor.

Design/Methodology: We employ a comprehensive quantitative analysis of 310 banking professionals within Ghana's commercial sector. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) enhanced by advanced predictive assessments (PLS-Predict and Cross-Validated Predictive Ability Testing), we evaluate the interplay between knowledge management practices, regulatory environments, and employee performance.

Findings: The analysis reveals a Predictability Paradox: internal knowledge processes demonstrate high predictive relevance (Q² > 0.80), while performance outcomes remain uncertain (Q² < 0.20) unless moderated by supportive regulatory environments. Furthermore, decision risk is mitigated not by data volume alone, but by aligning validation tools with construct types using PLS-Predict for process optimization and Cross-Validated Predictive Ability Testing for outcome robustness.

Originality/Value: This study offers a unified framework that connects measurement quality, regulatory context, and methodological selection to strategic risk management. It provides leaders with a maturity model to distinguish between "zones of control" (processes) and "zones of influence" (outcomes), transforming analytics from a technical exercise into a strategic safeguard.