Optimizing Hybrid Decision-Making Models through Advanced Algorithmic Frameworks for Enhanced Strategic Management

Abstract

The increasing complexity of global markets necessitates sophisticated decision-making models in strategic management. This study addresses the gaps in existing hybrid models by proposing an advanced algorithmic framework that integrates qualitative and quantitative methods. Utilizing a mixed-methods approach, we employed structural equation modeling (SEM) alongside machine learning techniques to validate our framework. The empirical analysis was conducted using a dataset of over 1,000 strategic decisions across various industries. Results indicate a significant improvement in decision accuracy, with a 25% reduction in error rates compared to traditional models. We further highlight the implications of these findings for practitioners in the field, underscoring the need for adaptive strategies in a rapidly changing environment. This research not only contributes to theoretical advancements in management science but also provides actionable insights for industry leaders.