IDEAS home Printed from https://ideas.repec.org/a/idi/jermbe/v24y2018i1p1-7.html
   My bibliography  Save this article

Research Trends On Big Data In Marketing: A Text Mining And Topic Modeling Based Literature Analysis

Author

Listed:
  • Amado, Alexandra

    (Instituto Universitário de Lisboa (Portugal))

  • Cortez, Paulo

    (University of Minho (Portugal))

  • Rita, Paulo

    (Instituto Universitário de Lisboa (Portugal))

  • Moro, Sérgio

    (University of Minho (Portugal))

Abstract

Given the research interest on Big Data in Marketing, we present a research literature analysis based on a text mining semi-automated approach with the goal of identifying the main trends in this domain. In particular, the analysis focuses on relevant terms and topics related with five dimensions: Big Data, Marketing, Geographic location of authors’ affiliation (countries and continents), Products, and Sectors. A total of 1560 articles published from 2010 to 2015 were scrutinized. The findings revealed that research is bipartite between technological and research domains, with Big Data publications not clearly aligning cutting edge techniques toward Marketing benefits. Also, few inter-continental co-authored publications were found. Moreover, findings show that research in Big Data applications to Marketing is still in an embryonic stage, thus making it essential to develop more direct efforts toward business for Big Data to thrive in the Marketing arena. / 0

Suggested Citation

  • Amado, Alexandra & Cortez, Paulo & Rita, Paulo & Moro, Sérgio, 2018. "Research Trends On Big Data In Marketing: A Text Mining And Topic Modeling Based Literature Analysis," European Research on Management and Business Economics (ERMBE), Academia Europea de Dirección y Economía de la Empresa (AEDEM), vol. 24(1), pages 1-7.
    • Handle: RePEc:idi:jermbe:v:24:y:2018:i:1:p:1-7
    as

    Download full text from publisher

    File URL: http://www.elsevier.es/en-revista-european-research-on-management-business-489-avance-resumen-research-trends-on-big-data-S2444883417300268
    File Function: complete text
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Big data; Marketing; Literature analysis; Research trends; Text mining; 0;
    All these keywords.

    JEL classification:

    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

    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:idi:jermbe:v:24:y:2018:i:1:p:1-7. 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: Tony Crespo Franco (email available below). General contact details of provider: http://www.aedem-virtual.com/ermbe .

    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.