Role of demographics, social connectedness and prior internet experience in adoption of online shopping: Applications for direct marketing

Abstract

is a PhD candidate at Macquarie University. His research focuses on direct and database marketing, empirical model comparison and modelling diffusion of innovations. His research has been published in academic journals and proceedings of various international conferences. Currently Bakher holds managing position at a multinational telecommunication company. Greg Elliottis a professor of business (marketing) at Macquarie University. He has also held academic positions at a number of other Australian and overseas universities. He has published widely and his current research interests are in the fi elds of fi nancial services, international, cross-cultural and social marketing. He currently teaches marketing management, strategic marketing and services marketing. He has also had extensive experience in management education and training in Australian and Asia for major international and local companies.ABSTRACT The use of demographics by researchers in the online shopping literature is common, however, they are typically constructed as either moderators or control factors. Little attention has been given to explicitly modelling the predictive utility of demographics. The present research models the impact of nine demographics, six social connectedness measures and fi ve prior online experience variables on consumers ' actual online purchases. A large and representative data set was used. Our results show that a model on the basis demographic data alone explains 22.6 per cent of the variance in the consumers ' overall online shopping behaviour. The model ' s utility increased to 45.4 per cent once social connectedness and prior internet experience were added to the model. Furthermore, analysing 14 online product categories, we found that the predictive power of demographic variables is product specifi c. Overall, our results strongly support the use by practitioners of demographics as powerful predictors for direct targeting of online shoppers.

The Impact of Opinion Leadership on Purchases through Social Networking Websites

“…Prior research reports increased age to be negatively associated with adoption of online shopping [17], [39], [57]. However, in our research we found increased age to be positively associated with intentions for online purchase.…”

Antecedents of Online Purchasing Intention among MBA Students: The Case of University Utara Malaysia Using the Partial Least Squares Approach

“…According to Naseri (2011) having prior experience on shopping online would significantly decrease the amount of time and cognitive efforts involved in both learning and conducting web shopping.Similarly, Hsieh and Liao (2011) argued that experience reduce the time needed to navigate and search for information therefore increasing the probability of online purchase. However, experimental dimensions of the site is its ability to produce an involving shopping experience, it enables shoppers to further explore their shopping interests or needs.…”

Relating Big Five Factor Model to the Acceptance and Use of On-line Shopping

“…Among the earlier shopping orientation studies, Stone (1954) explained that a personalizing shopper seeks personal relationships with sales-people. Further, it is perceivable that social connectedness facilitates the spread of word of mouth, which has been found to have a strong influence on the adoption of innovation (Naseri & Elliott, 2011). Sultan et al (1990) indicated from their study that word of mouth is the main deriver of diffusion of new products.…”

“…Sultan et al (1990) indicated from their study that word of mouth is the main deriver of diffusion of new products. Naseri & Elliott (2011) studied the relationship between social connectedness and the adoption of online shopping and their results suggested that socially active and extraverted consumers are more likely to purchase online. Gefen & Straub (2003) have found that social presence affects consumers' trust, which in turn influences their purchase intentions.…”

“…Research done to review the role of Demographics on the tendency of consumers to purchase online proves that demographic variables such as gender, education, household income were significantly related to consumers online purchase preference (Girard et al, 2003;Yook & Lauren, 2008;Afizah, Erlane, & Jamaliah, 2009;Ahasanul, Javad, & Ali, 2006). However, though demographics display a predictive power across all product categories, but the degree of predictive power appears to be product specific (Naseri & Elliott, 2011). However, this contention was refuted by Gehrt & Carter (1992) as they suggest that psychographic variables predict shopping orientation rather than demographic variables.…”