Predicting online buying behaviour - a comparative study using three classifying methods

Sanjeev Prashar, T. Sai Vijay and Chandan Parsad

International Journal of Business Innovation and Research, 2018, vol. 15, issue 1, 62-78

Abstract: Online retailing with its increasing foothold has made India one of the most anticipated destinations for both local and multinational retailers. The success of these online retailers will depend on their ability to attract more and more consumers to shop online. Therefore, it is pertinent to comprehend and understand consumers' attitude and behaviour towards online shopping, besides predicting online buying potential. This empirical study investigates the accuracy of prediction of different classifiers used in determining online buying. We empirically compared the forecasting ability of neural network (NN), linear discriminant analysis (LDA) and k-nearest neighbour (kNN) in the context of consumers' willingness to shop online. Statistical evidence has been provided that neural network significantly outperforms the other two models in terms of the predicting power. This study shall contribute to online retailers in reducing their vulnerability with respect to market demand and improve their preparedness to handle the market response.

Keywords: online buying; predicting buying behaviour; neural network; e-commerce; k-nearest neighbour; kNN; linear discriminant analysis; LDA. (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=88468 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbire:v:15:y:2018:i:1:p:62-78

Access Statistics for this article

More articles in International Journal of Business Innovation and Research from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().