Volume 12 Number 8 (Aug. 2017)
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JSW 2017 Vol.12(8): 664-670 ISSN: 1796-217X
doi: 10.17706/jsw.12.8.664-670

Framework to Enhance ERP Usability by Machine Learning Based Requirements Prioritization

Shamaila Qayyum1*, Almas Abbasi2
1National University of Modern Languages, Department of Computer Science, Islamabad, Pakistan.
2International Islamic University, Department of Computer Science and Software Engineering, Islamabad, Pakistan.


Abstract—With the growing trend of technology and increased business needs, Enterprise Resource Planning (ERP) systems are verily adapted by many organizations. Despite all the promising benefits and use, ERPs cannot always be successful. It has been established that ERP’s success is measured in terms of its’ users’ satisfaction. Different models exist, that show how Information systems’ success can be achieved. This paper focuses on the idea that machine learning helps in flawless prioritization of requirements and thus results in high user satisfaction. The paper proposes an IS success framework that incorporates machine learning based requirements prioritization techniques in order to increase users’ satisfaction for making an ERP, a successful project.

Index Terms—ERP, Machine Learning, Requirements Prioritization, User Satisfaction

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Cite: Shamaila Qayyum, Almas Abbasi, "Framework to Enhance ERP Usability by Machine Learning Based Requirements Prioritization," Journal of Software vol. 12, no. 8, pp. 664-670, 2017.

General Information

ISSN: 1796-217X
Frequency: Monthly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, DOAJ, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jsw@iap.org
  • Aug 02, 2017 News!

    Papers published in JSW Vol. 12, No. 1- Vol. 12, No. 8 have been indexed by DBLP.    [Click]

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  • Sep 27, 2017 News!

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  • Oct 30, 2017 News!

    Vol 12, No. 11 has been published with online version 8 original aritcles from 4 countries are published in this issue.      [Click]

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    The papers published in Vol.12, No. 11 have all received dois from Crossref.