IDEAS home Printed from https://ideas.repec.org/a/taf/oabmxx/v6y2019i1p1699283.html
   My bibliography  Save this article

Analysis of students’ online shopping behaviour using a partial least squares approach: Case study of Indonesian students

Author

Listed:
  • Heri Kuswanto
  • Wildan Bima Hadi Pratama
  • Imam Safawi Ahmad
  • Mutiah Salamah

Abstract

The emergence of the Internet has influenced business methods in the world, which made online shopping has become popular due to its practical strengths. Students are one of the potential markets of online shopping in Indonesia. This research investigates the factors influencing university students’ online shopping behaviour in Surabaya as one of the fastest-growing cities in Indonesia, an important issue that has never been explored. The survey dataset is analyzed by using Structural Equation Modeling-Partial Least Squares (SEM-PLS) as well as PLS Predictive-Oriented Segmentation (PLS-OLS) to group the students based on their online behaviour. Both methods are applied due to the fact that the sample size is relatively small. The analysis shows that the students’ online shopping behaviour is significantly influenced by enjoyment, perceived risk, and social influence. Clustering with PLS-POS leads to three segments of students based on behaviour: those mostly influenced by social influence and perceived risk, those influenced by enjoyment and website quality, and those influenced by website quality and trust and security. These results can be a meaningful knowledge and input for the online business owners in Indonesia in designing their marketing strategy.

Suggested Citation

  • Heri Kuswanto & Wildan Bima Hadi Pratama & Imam Safawi Ahmad & Mutiah Salamah, 2019. "Analysis of students’ online shopping behaviour using a partial least squares approach: Case study of Indonesian students," Cogent Business & Management, Taylor & Francis Journals, vol. 6(1), pages 1699283-169, January.
  • Handle: RePEc:taf:oabmxx:v:6:y:2019:i:1:p:1699283
    DOI: 10.1080/23311975.2019.1699283
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/23311975.2019.1699283
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/23311975.2019.1699283?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.

    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:taf:oabmxx:v:6:y:2019:i:1:p:1699283. 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: Chris Longhurst (email available below). General contact details of provider: http://cogentoa.tandfonline.com/OABM20 .

    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.