Title: A large-scale data management and application analysis based on advanced classifier computing for the ERP system selection and adoption

Authors: You-Shyang Chen; Chien-Ku Lin; Jerome Chih-Lung Chou; Wen-Shan Chen

Addresses: Department of Information Management, Hwa Hsia University of Technology, 111, Gong Jhuan Rd., Chung Ho District, New Taipei City 235, Taiwan ' Department of Business Administration, Feng Chia University, 100, Wenhwa Road, Seatwen, Taichung 40724, Taiwan ' Department of Information Management, Hwa Hsia University of Technology, 111, Gong Jhuan Rd., Chung Ho District, New Taipei City 235, Taiwan ' Department of Information Management, Hwa Hsia University of Technology, 111, Gong Jhuan Rd., Chung Ho District, New Taipei City 235, Taiwan

Abstract: Enterprise resource planning (ERP), promising trend of emerged large-scale data management, has urgent needs to enterprises that are faced with competitions under external environment and globalisation trend. It is an interesting issue to help ERP system vendor selecting a suitable customer through intelligent models. This motivates the study. We compare the empirical results of the decisional feature database constructed by two classification models, Models 1 and 2, and find out the critical factors for ERP system selection summarised from the analytical results and hypothesis. The empirical results include: 1) Model 1: the accuracy of percentage split without featureselection reaches 89.7810% at maximum; 2) Model 2: the accuracy of percentage split with expert feature-selection also reaches 89.7810% at maximum. This study yields the two management implications: 1) ERP vendors can find out hidden potential customers by the proposal models; 2) expert feature-selection of given data is an effective technique used to increase the purpose of classification quality.

Keywords: large-scale data management; enterprise resource planning system; expert feature-selection; classification model.

DOI: 10.1504/IJSOI.2018.097490

International Journal of Services Operations and Informatics, 2018 Vol.9 No.4, pp.312 - 327

Received: 18 Oct 2017
Accepted: 27 Aug 2018
Published online: 24 Jan 2019 *

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