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Research Article · Open access · Published 29 January 2015

Factors Influencing Online Shopping in Mauritius: An Application of Principal Component Analysis and Binary Logistic Regression

P. Ramtohul1, N. Mamode Khan1 & M. Hosenally1

Journal of Social Sciences, Volume 10, Issue 4, pp. 179–184 · doi.org/10.3844/jssp.2014.179.184

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Abstract

In our modern world, the intensive use of internet has imposed new lifestyles and encouraged new behaviour amongst many across the globe. With the development in Internet technologies, the emergence of online shopping has altered the way businesses operate. While many of them have embraced this platform to present their offerings, many customers on the other hand, are finding it more cost-effective and convenient to carry out their transactions online. Therefore, the present study was undertaken to refine our understanding on consumers' attitudes, perception and behaviour towards online shopping in a Mauritian context. Data was collected among 224 respondents in Mauritius whereby a questionnaire was administered through personal interviews with the aim of achieving a higher response rate. Principle Component Analysis (PCA) was performed to reveal the underlying factors influencing people's perceptions and attitudes towards online shopping and the results uncovered that 'online shopping conveniences', 'security and product risk', 'complexity and waiting time' and 'enjoyment and pleasure' were major contributors to overall attitudes towards online shopping attributes. The binary regression model was also fitted and factors such as marital status and internet at home were the significant factors to contribute towards online shopping.

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Ramtohul, P., Khan, N. M. & Hosenally, M. (2014). Factors Influencing Online Shopping in Mauritius: An Application of Principal Component Analysis and Binary Logistic Regression. Journal of Social Sciences, 10(4), 179–184. https://doi.org/10.3844/jssp.2014.179.184

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Volume 10, Issue 4
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Factors Influencing Online Shopping in Mauritius: An Application of Principal Component Analysis and Binary Logistic RegressionJournal of Social Sciences · Volume 10, Issue 4 · pp. 179–184 · CC BY 3.0Download