IDEAS home Printed from https://ideas.repec.org/a/ids/ijpmbe/v21y2025i1p106-123.html
   My bibliography  Save this article

Digital frontier as the predictor of buying intention: a recursive model approach

Author

Listed:
  • Saket Ranjan Praveer
  • Naveen Kumar Ranganathan
  • John William Arokiasamy
  • Pavithra Rajendran

Abstract

The evolving consumer landscape, shaped by digital frontiers, has profoundly altered traditional marketing practices, offering new avenues for information acquisition, interaction, and transactions. This study focuses on the digital frontier, representative of cutting-edge advancements in digital technologies, as a key predictor variable influencing consumers' intentions to engage in online purchases. Employing a comprehensive analysis through a recursive model, the research aims to reveal the nuanced dynamics and causal relationships between the digital frontier and buying intention. In the broader context, the study explores the influential factors affecting buying intention on websites, with content, clarity, design, convenience, reviews, and chatbot considered as exogenous variables impacting the endogenous variable of buying intention. Building on prior research by Davenport and Harris (2007), the study emphasises the potency of personalisation and recommendation algorithms in shaping consumer decisions in the digital realm. The empirical investigation, conducted through a recursive model with multivariate analysis using primary data, identifies significant factors such as web content, website clarity, website design, and consumer reviews on web portals as crucial contributors to buying intention, particularly in the domain of consumer electronics.

Suggested Citation

  • Saket Ranjan Praveer & Naveen Kumar Ranganathan & John William Arokiasamy & Pavithra Rajendran, 2025. "Digital frontier as the predictor of buying intention: a recursive model approach," International Journal of Process Management and Benchmarking, Inderscience Enterprises Ltd, vol. 21(1), pages 106-123.
  • Handle: RePEc:ids:ijpmbe:v:21:y:2025:i:1:p:106-123
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=147977
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    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:ids:ijpmbe:v:21:y:2025:i:1:p:106-123. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=95 .

    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.