Building a Proposed Structural Model to Identify the Most Influential Factors in the Success of Machine Learning Applications in the Iraqi Hotel Sector: A Field Study of Four-Star and Deluxe Hotels in Baghdad

Abstract

This study aims to construct a proposed structural model to identify the most influential factors on the success of Machine Learning (ML) applications in the Iraqi hotel sector, focusing on four- and five-star hotels in Baghdad to address the research gap in unique local environments. The study adopted the descriptive-analytical approach based on primary data collected through a field survey of a sample of (83) individuals from hotel management and staff, reflecting the challenges of sample sizes available in unique local settings. The measurement instrument (questionnaire) was designed to evaluate three main complex constructs: Human factors, Organizational factors, and Technological factors. The Smart PLS V.4 software was utilized for Partial Least Squares Structural Equation Modeling (PLS-SEM), which included validity and reliability analyses and structural path testing. The results demonstrated that the proposed model possesses high statistical robustness, and that Technological and Human factors hold the largest and most significant impact on explaining ML success. This outcome confirms that data quality and technical competencies are the cornerstones for technological projects in this context.