Deep Recurrent Learning Through Long Short Term Memory and TOPSIS

TL;DR

This paper proposes a deep recurrent neural network approach using LSTM and TOPSIS to identify and rank ERP cloud adoption features, validated through a theoretical model and user survey.

Contribution

It introduces a novel classification algorithm combining LSTM and TOPSIS for ERP cloud adoption analysis, integrating theoretical validation and user insights.

Findings

Validated model over reference architecture

Identified key adoption factors through survey

Ranked features influencing cloud ERP adoption

Abstract

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.