Deep Recurrent Learning Through Long Short Term Memory and TOPSIS
This work addresses cloud-ERP adoption for business owners, but it appears incremental as it applies existing methods (LSTM and TOPSIS) to a new domain-specific problem.
The authors tackled the problem of cloud-ERP adoption by modeling it as a deep recurrent neural network problem, proposing a classification algorithm based on LSTM and TOPSIS to identify and rank adoption features, with validation through a theoretical model and qualitative survey.
Enterprise resource planning (ERP) software brings resources, data together to keep software-flow within business processes in a company. However, cloud computing's cheap, easy and quick management promise pushes business-owners for a transition from monolithic to a data-center/cloud based ERP. Since cloud-ERP development involves a cyclic process, namely planning, implementing, testing and upgrading, its adoption is realized as a deep recurrent neural network problem. Eventually, a classification algorithm based on long short term memory (LSTM) and TOPSIS is proposed to identify and rank, respectively, adoption features. Our theoretical model is validated over a reference model by articulating key players, services, architecture, functionalities. Qualitative survey is conducted among users by considering technology, innovation and resistance issues, to formulate hypotheses on key adoption factors.