IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/9auec_v1.html
   My bibliography  Save this paper

Traditional Marketing Analytics, Big Data Analytics, Big Data System Quality and the Success of New Product Development

Author

Listed:
  • Aljumah, Ahmad Ibrahim
  • Nuseir, Mohammed T.
  • Alam, Md. Mahmudul

    (Universiti Utara Malaysia)

Abstract

Objective/Purpose: This study investigates the impact of traditional marketing analytics and big data analytics on the success of a new product. Moreover, it assesses the mediating effects of the quality of big data system. Methodology/Design: This study is based on primary data that were collected through an online questionnaire survey from large manufacturing firms operating in UAE. Out of total distributed 421 samples, 327 samples were used for final data analysis. The survey was conducted from March-April 2020 and data analysis was done via Structural Equation Modelling (SEM-PLS). Findings: It emerges that big data analysis (BDA), traditional marketing analysis (TMA) and big data system quality (BDSQ) are significant determinants of new product development (NPD) success. Meanwhile, the BDA and TMA significantly affect the BDSQ. Results of the mediating role of BDSQ in the relationship between the BDA and NPD as well as TMA and NPD are significant. Implications: There are significant policy implications for practitioners and researchers concerning the role of analytics, particularly big data analytics and big data system quality, when attempting to achieve success in developing new products. Originality/Value: This is an original study based on primary data from UAE.

Suggested Citation

  • Aljumah, Ahmad Ibrahim & Nuseir, Mohammed T. & Alam, Md. Mahmudul, 2021. "Traditional Marketing Analytics, Big Data Analytics, Big Data System Quality and the Success of New Product Development," OSF Preprints 9auec_v1, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:9auec_v1
    DOI: 10.31219/osf.io/9auec_v1
    as

    Download full text from publisher

    File URL: https://osf.io/download/61f160a29f195e08ea2e2fd1/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/9auec_v1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:osfxxx:9auec_v1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.