Loughborough University
Browse

Enhancing ERP responsiveness through big data technologies: an empirical investigation

Download (1.83 MB)
journal contribution
posted on 2024-02-05, 10:05 authored by Florie Bandara, Uchitha JayawickramaUchitha Jayawickrama, Maduka Subasinghage, Femi Olan, Hawazen Alamoudi, Majed Alharthi

Organizations are integrating big data technologies with Enterprise Resource Planning (ERP) systems with an aim to enhance ERP responsiveness (i.e., the ability of the ERP systems to react towards the large volumes of data). Yet, organizations are struggling to manage the integration between the ERP systems and big data technologies, leading to lack of ERP responsiveness. For example, it is difficult to manage large volumes of data collected through big data technologies and to identify and transform the collected data by filtering, aggregating and inferencing through the ERP systems. Building on this motivation, this research examined the factors leading to ERP responsiveness with a focus on big data technologies. The conceptual model which was developed through a systematic literature review was tested using Structural equation modelling (SEM) performed on the survey data collected from 110 industry experts. Our results suggested 12 factors (e.g., big data management and data contextualization) and their relationships which impact on ERP responsiveness. An understanding of the factors which impact on ERP responsiveness contributes to the literature on ERP and big data management as well as offers significant practical implications for ERP and big data management practice.

History

School

  • Loughborough Business School

Published in

Information Systems Frontiers

Volume

26

Issue

1

Pages

251 - 275

Publisher

Springer

Version

  • VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Acceptance date

2023-01-13

Publication date

2023-02-18

Copyright date

2023

ISSN

1387-3326

eISSN

1572-9419

Language

  • en

Depositor

Dr Uchitha Jayawickrama. Deposit date: 16 March 2023

Usage metrics

    Loughborough Publications

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC