Generative AI-Powered Predictive Analytics Model: Leveraging Synthetic Datasets to Determine ERP Adoption Success Through Critical Success Factors
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 Adver...
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Science and Information Organization
2025
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| author | Hong K.C. Shibghatullah A.S.B. Ling T.C. Sarsam S.M. |
| author2 | 58774564500 |
| author_facet | 58774564500 Hong K.C. Shibghatullah A.S.B. Ling T.C. Sarsam S.M. |
| author_sort | Hong K.C. |
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| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. |
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| id | my.uniten.dspace-37095 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
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| spelling | my.uniten.dspace-370952025-03-03T15:47:26Z Generative AI-Powered Predictive Analytics Model: Leveraging Synthetic Datasets to Determine ERP Adoption Success Through Critical Success Factors Hong K.C. Shibghatullah A.S.B. Ling T.C. Sarsam S.M. 58774564500 24067964300 55804298500 57189574071 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 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. Final 2025-03-03T07:47:26Z 2025-03-03T07:47:26Z 2024 Article 10.14569/IJACSA.2024.0150547 2-s2.0-85195047792 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195047792&doi=10.14569%2fIJACSA.2024.0150547&partnerID=40&md5=8ad496690b5666c8cf84c1502ec105a3 https://irepository.uniten.edu.my/handle/123456789/37095 15 5 469 482 All Open Access; Gold Open Access Science and Information Organization Scopus |
| spellingShingle | 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 Hong K.C. Shibghatullah A.S.B. Ling T.C. Sarsam S.M. Generative AI-Powered Predictive Analytics Model: Leveraging Synthetic Datasets to Determine ERP Adoption Success Through Critical Success Factors |
| title | Generative AI-Powered Predictive Analytics Model: Leveraging Synthetic Datasets to Determine ERP Adoption Success Through Critical Success Factors |
| title_full | Generative AI-Powered Predictive Analytics Model: Leveraging Synthetic Datasets to Determine ERP Adoption Success Through Critical Success Factors |
| title_fullStr | Generative AI-Powered Predictive Analytics Model: Leveraging Synthetic Datasets to Determine ERP Adoption Success Through Critical Success Factors |
| title_full_unstemmed | Generative AI-Powered Predictive Analytics Model: Leveraging Synthetic Datasets to Determine ERP Adoption Success Through Critical Success Factors |
| title_short | Generative AI-Powered Predictive Analytics Model: Leveraging Synthetic Datasets to Determine ERP Adoption Success Through Critical Success Factors |
| title_sort | generative ai-powered predictive analytics model: leveraging synthetic datasets to determine erp adoption success through critical success factors |
| topic | 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 |
| url_provider | http://dspace.uniten.edu.my/ |