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Enhancing ERP Responsiveness Through Big Data Technologies: An Empirical Investigation

Author

Listed:
  • Florie Bandara

    (Loughborough University)

  • Uchitha Jayawickrama

    (Loughborough University)

  • Maduka Subasinghage

    (Auckland University of Technology)

  • Femi Olan

    (University of Essex)

  • Hawazen Alamoudi

    (King Abdulaziz University)

  • Majed Alharthi

    (King Abdulaziz University)

Abstract

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.

Suggested Citation

  • Florie Bandara & Uchitha Jayawickrama & Maduka Subasinghage & Femi Olan & Hawazen Alamoudi & Majed Alharthi, 2024. "Enhancing ERP Responsiveness Through Big Data Technologies: An Empirical Investigation," Information Systems Frontiers, Springer, vol. 26(1), pages 251-275, February.
    • Handle: RePEc:spr:infosf:v:26:y:2024:i:1:d:10.1007_s10796-023-10374-w
    DOI: 10.1007/s10796-023-10374-w
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