The Application of Statistical Learning and Machine Learning in Supporting Organizational Strategic Decision-Making

Abstract

This study aims to explore how statistical learning and machine learning support organizational strategic decision-making, with a focus on how organizational actors interpret, trust, and use algorithmic outputs in strategic contexts. The study used a qualitative case study approach because the research examined meanings, experiences, and decision processes within real organizational settings. Data were collected through semi-structured interviews, non-participant observation, and document analysis involving senior managers, strategic planning officers, data analysts, IT managers, and operational decision-makers from selected organizations that had applied predictive analytics, dashboards, or machine learning-based decision support tools. The data were analyzed using reflexive thematic analysis. The findings reveal four main themes: data as a strategic language, trust and interpretation of model outputs, negotiation between human judgment and algorithmic recommendation, and organizational readiness for data-driven decision-making. The study shows that statistical learning and machine learning improve strategic decision-making when organizations combine analytical capability with human interpretation, transparent communication, and collaborative decision routines. These findings contribute to the literature on data-driven decision-making, dynamic capabilities, and human-AI collaboration by showing that machine learning creates strategic value through socio-technical interaction rather than technical accuracy alone. The study implies that organizations need stronger data governance, data literacy, explainability mechanisms, and ethical guidelines to support responsible and effective use of machine learning in strategic decisions.