Purpose – This study aims to theorize and empirically examine the relationship between “purchase intention and conversion rate”, “website satisfaction and conversion rate” and “purchase intention and conversion rate”. E-Commerce conversion rate represents the percentage of visits to an e-tailer’s website that includes a purchase transaction. Despite the importance of conversion rates for e-tailers, prior research predominantly used purchase intention and website satisfaction as main dependent variables and implicitly assumed that these variables will influence the actual purchase. Design/methodology/approach – Data on 85 US retail websites were used to test the hypotheses. The unit of the analysis is the online retail website. Regression analysis was used to perform the data analysis. Findings – The results indicate that both purchase intention and website satisfaction positively influence conversion rates. It was also found that website satisfaction positively influences purchase intention. Research limitations/implications – Only data from 85 US e-tailers from the top-100 US online retailers are used to test the hypotheses. Also, conversion rate is only one of the several important success metrics used by e-tailers. Originality/value – This study not only examines antecedents of e-commerce conversion rates, but also theorizes and tests if there is a statistically significant relationship between “purchase intention and conversion rate” and “website satisfaction and conversion rate”. This is because, although previous studies used purchase intention and website satisfaction as main dependent variables and proxies for actual purchase behavior, they did not validate this relationship. This study shows that: there is a statistically significant relationship between “purchase intention and conversion rate” and “website satisfaction and conversion rate”, there is also a statistically significant relationship between “website satisfaction and purchase intention” and this study used firm-level data to theorize, measure and analyze the data, whereas prior literature used only individual-level data.

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Preparing undergraduate information systems students to be successful systems analysts is a challenging task. The students require higherorder thinking skills to successfully solve many real life business problems. Traditional lecturing alone is not entirely effective in imparting such skills. The objective of this article is to propose an active learning approach that can be used when teaching an undergraduate systems analysis course. The development of active learning exercises and their implementation in the classroom setting is discussed. Both quantitative and qualitative feedback from students is elicited. The feedback shows that students perceived the active learning based instruction positively. The students also indicate that this approach contributed to their understanding of the material, and their ability to apply the learned material to different contexts.

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