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Predicting Shoppers' Continuous Buying Intention Using Mobile Apps
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Author(s): Sanjeev Prashar (Indian Institute of Management (IIM) Raipur, Raipur, India), Priyanka Gupta (Indian Institute of Management (IIM) Raipur, Raipur, India), Chandan Parsad (Rajagiri Business School, Kochi, India) and T. Sai Vijay (Institute of Management Technology (IMT) Nagpur, Nagpur, India)
Copyright: 2021
Pages: 18
Source title:
Research Anthology on E-Commerce Adoption, Models, and Applications for Modern Business
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-8957-1.ch029
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Abstract
The rapid penetration of smartphones and consumers' increased usage/dependence on mobile applications (apps) has ushered favorable opportunities for retailers as well as shoppers. The traditional brick-and-mortar as well as online retailers must attract shoppers to use mobile shopping apps. For this, it is pertinent for retailers to predict users' continuous intention to buy through apps. To address this question, the present study has applied four prominent binary classifiers - logit regression, linear discriminant analysis, artificial neutral network and decision tree analysis to develop predictive models. Findings of the study shall help the marketers in accurately forecasting shoppers' buying behaviour. Various indices have been used to check the predictive accuracy of four techniques. The outcome of the study shows that the models developed using decision tree analysis and artificial neutral network provide better results in predicting consumers' continuous intention to buy through app. Based on the findings, the paper has also provided implications for the retailers.
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