Impact of Gender and Product Type on Perceived Risk and Online Shopping Intentions

Impact of Gender and Product Type on Perceived Risk and Online Shopping Intentions

Stuart Dillon (University of Waikato, New Zealand), John Buchanan (University of Waikato, New Zealand) and Kholoud Al-Otaibi (University of Waikato, New Zealand)
DOI: 10.4018/978-1-4666-9787-4.ch116
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Background

Online Shopping

The Internet has transformed how consumers shop for goods and services. Consumers are now able to buy a seemingly unlimited range of products and services through the Internet at any time, from almost any place in the world. Online shopping is a real alternative as some challenges of conventional shopping such as crowding, traffic and parking and limited time for shopping are removed. (Yulihasri, et al., 2011). Companies are using the Internet not only to sell products, but also to reduce marketing costs, communicate with their customers, and collect customer feedback. “…the Internet provides a unique opportunity for companies to more efficiently reach existing and potential customers” Shergill and Chen (2005, p. 80).

Forrester Research predicts that U.S. online retailing will reach $370 billion by 2017 (Mulpuru, 2013). This would be a 50% increase on predicted 2014 figures (Retail Refugees, 2010).

In a 2010 study of the online purchasing habits across 55 different countries/markets, Nielsen (2010) reports that while certain products are universally bought online; others have yet to attain a considerable share of online trade. Their survey showed that books and clothing took the lead as the products most often purchased online where 46% of global consumers purchased books online and 41% purchased clothing online.

Key Terms in this Chapter

B2C: Business to consumer.

Cronbach’s alpha: A numerical coefficient of reliability.

Likert Scale: A five (or seven) point scale which is used to allow the individual to express how much they agree or disagree with a particular statement.

Chi Square: A statistical test commonly used to compare observed data with data we would expect to obtain according to a specific hypothesis.

Beta Coefficient: The estimates resulting from an analysis carried out on independent variables that have been standardized so that their variances are 1.

ANCOVA: Analysis of covariance, a general linear model which blends analysis of variance (ANOVA) and regression.

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