Publication: Generative AI-Powered Predictive Analytics Model: Leveraging Synthetic Datasets to Determine ERP Adoption Success Through Critical Success Factors
Date
2024
Authors
Hong K.C.
Shibghatullah A.S.B.
Ling T.C.
Sarsam S.M.
Journal Title
Journal ISSN
Volume Title
Publisher
Science and Information Organization
Abstract
Data scarcity is a significant problem in Enterprise Resource Planning (ERP) adoption prediction, limiting the accuracy and reliability of traditional predictive models. This study addresses this issue by integrating Generative Artificial Intelligence (AI) technologies, specifically Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), to generate synthetic data that supplements sparse real-world data. A systematic literature review identified critical gaps in existing ERP adoption models, underscoring the need for innovative approaches. The generated synthetic data, validated through comprehensive statistical analyses including mean, variance, skewness, kurtosis, and the Kolmogorov-Smirnov test, demonstrated high accuracy and reliability, aligning closely with real-world data. A hybrid predictive model was developed, combining Generative AI with Pearson Correlation Coefficient (PCC) and Random Forest techniques. This model was rigorously tested and compared against traditional models such as SVM, Neural Networks, Linear Regression, and Decision Trees. The hybrid model achieved superior performance, with an accuracy of 90%, precision of 88%, recall of 89%, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC) score of 0.91, significantly outperforming traditional models in predicting ERP adoption outcomes. The research also established continuous monitoring and adaptation mechanisms to ensure the model's long-term effectiveness. The findings provide practical insights for organizations, offering a robust tool for forecasting ERP adoption success and facilitating more informed decision-making and resource allocation. This study not only advances theoretical understanding by addressing data scarcity through synthetic data generation but also provides a practical framework for enhancing ERP adoption strategies. ? (2024), Science and Information Organization. All Rights Reserved.
Description
Keywords
Computational complexity , Correlation methods , Decision trees , Enterprise resource planning , Resource allocation , Support vector machines , Auto encoders , Data scarcity , Enterprise resource planning adoption , Enterprise resources planning , Generative artificial intelligence , Pearson correlation coefficients , Predictive models , Random forests , Synthetic data , Variational autoencoder , Predictive analytics