Decoding ERP Training for Non-Technical Users: A Novel Algorithm-Driven Framework for Enhanced Training Effectiveness

This research studies Enterprise Resource Planning (ERP) training for non-technical employees by integrating Machine Learning techniques, specifically Decision Tree algorithm, into the training analysis process. The research methodology comprises a dual approach: a qualitative analysis of 20 key ERP training articles and the application of Decision Tree algorithms for data categorization and prediction. This innovative method allows for an examination of training methodologies and effectiveness. Key findings from the literature underscore the necessity for user-friendly training approaches, particularly for non-technical stakeholders grappling with complex ERP systems. These findings informed the development of a novel training framework, which includes components like simplified system integration, gamified training modules, and AI-powered interactive support. This framework is designed to address common challenges faced by non-technical users, such as difficulties with system integration and the need for ongoing post-training support. A significant contribution of this study is the introduction of a novel algorithm to quantify training effectiveness. This algorithm represents a leap in evaluating ERP training approaches, moving from subjective assessments to a more structured, data-driven evaluation. It systematically assesses the impact of various training components, grounded in empirical evidence from the literature. The study not only contributes to academic research but also provides practical strategies for organizations to optimize ERP system utilization, ensuring comprehensive training for all users.