How Artificial Intelligence Drives Operational Cost Reduction: Evidence from Jordanian Companies

Abstract

This study aims to propose a conceptual framework that links the components of artificial intelligence applications to operational costs. The framework included machine learning, computer vision, planning, task management, Smart thinking as an independent variable, with operational costs in Jordanian companies as a dependent variable. A quantitative research approach was used to evaluste the prposed measurement consisting of 31 items. A total of 250 questionnaires were distributed and 163 valid responses were received. The initial analysis showing that some factors loading and AVE values did not meet the recommended thresholds, requiring refinement of the measurement model. After modification, the model achieved acceptable levels of factor loadings, Composite Reliability, Cronbach’s Alpha, and AVE. Discriminant validity was confirmed using the Fronell-Larcker criterion and the HTMT ratio. The study focused on validating the measurement model, while future research should examine both the measurement and structural model.