 Using deep learning methods in detecting the critical success factors on the implementation of cloud ERPBasem Zughoul, Nidhal Kamel Taha El-Omari and Mohammed Al-Refai International Journal of Business Information Systems, 2023, vol. 44, issue 2, 219-248 Abstract: A research implementation of enterprise resource planning (ERP) systems by medium and large industries is one of the alternatives available to consolidate their competitive position by integrating their activities. Deep learning techniques have been used extensively in the ERP systems for identifying the critical factors making an impact on the usability of SaaS. To predict the success rate of cloud-based ERP systems based on several critical factors, this article carries out a systematic review on the serious factors that influencing the cloud-like ERP systems as well as deep learning models, i.e., recurrent neural networks (RNNs), long short-term memory (LSTM), multi-layer perceptron (MLP) and gated recurrent unit (GRU). Furthermore, two categorical feature selection methods linear discriminant analysis (LDA) and chi-square analysis have been applied to filter the most critical factors. The dataset from 741 scholarly articles based on their conclusions with 26 total features. Keywords: artificial intelligence; AI; cloud computing; deep learning; DL; enterprise resource planning; ERP; long short-term memory; LSTM; recurrent neural networks; RNNs; software-as-a-service; SaaS. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbisy:v:44:y:2023:i:2:p:219-248 Access Statistics for this article More articles in International Journal of Business Information Systems from Inderscience Enterprises Ltd |
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