Publication:
Generative AI-Powered Predictive Analytics Model: Leveraging Synthetic Datasets to Determine ERP Adoption Success Through Critical Success Factors

Simple item page
dc.citedby0
dc.contributor.authorHong K.C.en_US
dc.contributor.authorShibghatullah A.S.B.en_US
dc.contributor.authorLing T.C.en_US
dc.contributor.authorSarsam S.M.en_US
dc.contributor.authorid58774564500en_US
dc.contributor.authorid24067964300en_US
dc.contributor.authorid55804298500en_US
dc.contributor.authorid57189574071en_US
dc.date.accessioned2025-03-03T07:47:26Z
dc.date.available2025-03-03T07:47:26Z
dc.date.issued2024
dc.description.abstractData 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.14569/IJACSA.2024.0150547
dc.identifier.epage482
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85195047792
dc.identifier.spage469
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85195047792&doi=10.14569%2fIJACSA.2024.0150547&partnerID=40&md5=8ad496690b5666c8cf84c1502ec105a3
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37095
dc.identifier.volume15
dc.pagecount13
dc.publisherScience and Information Organizationen_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleInternational Journal of Advanced Computer Science and Applications
dc.subjectComputational complexity
dc.subjectCorrelation methods
dc.subjectDecision trees
dc.subjectEnterprise resource planning
dc.subjectResource allocation
dc.subjectSupport vector machines
dc.subjectAuto encoders
dc.subjectData scarcity
dc.subjectEnterprise resource planning adoption
dc.subjectEnterprise resources planning
dc.subjectGenerative artificial intelligence
dc.subjectPearson correlation coefficients
dc.subjectPredictive models
dc.subjectRandom forests
dc.subjectSynthetic data
dc.subjectVariational autoencoder
dc.subjectPredictive analytics
dc.titleGenerative AI-Powered Predictive Analytics Model: Leveraging Synthetic Datasets to Determine ERP Adoption Success Through Critical Success Factorsen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS