How do organisations transform data and AI into logistics intelligence performance? A machine learning perspective from emerging economies
TL;DR
Results indicate that data quality, analytical capabilities and alignment between analytics and decision-making are the strongest determinants of LIP, and machine learning models outperform linear regression in predicting LIP.
Abstract
Although data analytics and artificial intelligence are increasingly reshaping logistics decision-making, limited empirical evidence explains how organisations transform these technologies into tangible intelligence-driven performance, particularly in emerging economies. This study aims to develop and empirically examine the concept of logistics intelligence performance (LIP), defined as a multidimensional organisational capability that captures a firm’s capacity to transform logistics data and AI tools into timely, accurate and forward-looking decisions. The study is based on survey data collected from 2,752 Moroccan firms. A quantitative approach is adopted, combining confirmatory factor analysis (CFA) to validate the measurement model of LIP with descriptive statistical analysis and a comparative machine learning framework. Multiple predictive models are implemented and evaluated, including linear regression, decision trees, ensemble methods (Random Forest, Gradient Boosting, XGBoost), support vector machines, k-nearest neighbors and artificial neural networks, to compare their ability to explain variations in LIP. The CFA confirms the validity and reliability of the LIP construct, showing strong model fit and convergent validity. Machine learning models outperform linear regression in predicting LIP, with artificial neural networks achieving the highest accuracy (R² up to 0.74). Results indicate that data quality, analytical capabilities and alignment between analytics and decision-making are the strongest determinants of LIP. AI adoption alone has limited impact without complementary organisational capabilities. Findings also reveal significant non-linear relationships, threshold effects, and capability complementarities shaping the development of logistics intelligence across firms. This study contributes to the literature by conceptualising and empirically validating LIP as a distinct organisational capability. By integrating organisational capability theory with machine learning methods in the context of an emerging economy, the research moves beyond technology adoption perspectives and provides new insights into how firms convert data and AI investments into intelligence-driven logistics performance.