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ItemWhat’s the difference between work and fun? Explaining the difference between utilitarian and hedonic IT use( 2017-01-04)Information systems theory tells us that the deepest going difference between utilitarian and hedonic information technology use is that different sets of motivational factors direct the two types of use. However, recent advances in social psychology and consumer behavior research suggest that there is an even more profound difference: Only utilitarian IT use depends on the self-control mechanism and the limited resources consumed bµ exercise of self-control. This causes the daily and weekly rhythms of utilitarian and hedonic use to be different. Utilitarian information technology use decreases throughout the day and the week while hedonic information technology use does not. In this paper, we test for the first time whether the daily consumption pattern of utilitarian information technology use indeed reflects the hypothesized patterns at the aggregate level. Our data suggests that it does, which means that the self-control mechanism should be integrated in the information systems models that seek to explain information technology use.
ItemPersonalized Product Recommendations: Evidence from the Field( 2017-01-04)Targeting personalized product recommendations to individual customers has become a mainstream activity in online stores as it has been shown to increase click-through rate and sales. However, as personalization becomes increasingly commonplace, customers may feel personalized content intrusive and therefore not responding or even avoiding them. Many studies have investigated advertising intrusiveness and avoidance but a research gap on the effect of degree of personalization on customer responses based on field evidence exists. In this paper, 27,175 recommendation displays from five different online stores are analyzed. The results show that the further the customer is in the purchasing process, the more effective personalization is if it is based on information about the present rather than past browsing session. Moreover, recommendations in passive form are more effective than recommendations in active form suggesting the need to dispel the perception of intrusiveness.
ItemAn Explorative Study on Sales Distribution in M-commerce( 2017-01-04)Despite the proliferation of studies on the sales distribution in e-commerce, little research has been conducted on the sales distribution in the m-commerce channel. This study empirically examines the sales distribution of various product categories in the mobile channel, using the large transaction data from a leading e-marketplace in Korea. Overall, transactions in the mobile channel are more concentrated to head products compared to the PC channel sales, but the pattern is inconsistent across product categories. Transactions in product categories of high average price (e.g., computers) and low purchase frequency rate (e.g., health care products) are less concentrated to head products in the mobile channel than the PC channel. The revenue distribution, however, shows the opposite. Head products generate relatively less revenue in the mobile channel than the PC channel. We provide explanations why the mixing results appear across product categories and between the distribution types.