IDEAS home Printed from https://ideas.repec.org/a/eee/techno/v142y2025ics0166497225000215.html
   My bibliography  Save this article

Exploring consumer intentions to continue: Integrating task technology fit and social technology fit in generative AI based shopping platforms

Author

Listed:
  • Chakraborty, Debarun
  • Troise, Ciro
  • Bresciani, Stefano

Abstract

This study explores the significance of task-technology fit (TTF) and social-technology fit (STF) in generative AI-based shopping platforms. Examination of this integration can assume importance for generative AI-based shopping platforms that can drive consumer intentions to continue using, ensuring long-term engagement and platform success. The study evaluates how these alignments influence users' satisfaction, perceived usefulness, and intention to continue using the platform. A mixed methodology approach was used in the study, and exploratory and confirmatory analyses were conducted. In the exploratory study, 27 respondents provided their responses; in study 2, which is confirmatory, 472 participants answered the questions. Generative AI can handle complex tasks and accommodate various user needs to enhance the platform's efficiency and overall user experience. In addition, the study focuses on social factors such as trust and community engagement, which influence user satisfaction and the effectiveness of the platform being used. Gender differences are also considered in the study by examining how these affect users' interactions with AI features. Gender differences significantly influence satisfaction and continued use of generative AI-based shopping platforms, highlighting the need for personalized and diverse AI features to cater to varied user preferences. These results provide detailed suggestions and worthwhile practices for developing AI-based shopping platforms that would appeal to their users in the long run and satisfy their emerging needs and preferences. Practical implications show the importance of deploying AI tasks that fit most business needs in order to promote scalability, community needs, personalization based on the gender of the user, and ethical considerations in order to promote the proper use of AI in business. These findings offer practical guidance for enhancing user engagement through tailored AI features.

Suggested Citation

  • Chakraborty, Debarun & Troise, Ciro & Bresciani, Stefano, 2025. "Exploring consumer intentions to continue: Integrating task technology fit and social technology fit in generative AI based shopping platforms," Technovation, Elsevier, vol. 142(C).
    • Handle: RePEc:eee:techno:v:142:y:2025:i:c:s0166497225000215
    DOI: 10.1016/j.technovation.2025.103189
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0166497225000215
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.technovation.2025.103189?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:eee:techno:v:142:y:2025:i:c:s0166497225000215. 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/01664972 .

    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.