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.

翻译：企业资源规划（ERP）软件将资源与数据整合在一起，以保持公司业务流程中的软件流转。然而，云计算所承诺的廉价、易用且快速的管理优势，促使企业主从单体架构向数据中心/云端ERP转型。由于云端ERP开发涉及规划、实施、测试和升级的循环过程，因此其采用可被视作一个深度循环神经网络问题。最终，本文提出了一种基于长短期记忆（LSTM）和TOPSIS的分类算法，分别用于识别和排序采用特征。通过阐明关键参与方、服务、架构和功能，我们的理论模型在一个参考模型上得到了验证。考虑技术、创新和阻力问题，我们在用户中进行了定性调查，以形成关于关键采用因素的假设。