Online advertising has transformed the advertising industry with its measurability and accountability. Online software and services supported by online advertising is becoming a reality as evidenced by the success of Google and its initiatives. Therefore, the choice of a pricing model for advertising becomes a critical issue for these firms. We present a formal model of pricing models in online advertising using the principal-agent framework to study the two most popular pricing models: input-based cost per thousand impressions (CPM) and performance-based cost per click-through (CPC). We identify four important factors that affect the preference of CPM to the CPC model, and vice versa. In particular, we highlight the interplay between uncertainty in the decision environment, value of advertising, cost of mistargeting advertisements, and alignment of incentives. These factors shed light on the preferred online-advertising pricing model for publishers and advertisers under different market conditions.
We consider a publisher that earns advertising revenue while providing content to serve a heterogeneous population of consumers. The consumers derive benefit from consuming content but suffer from delivery delays. A publisher's content provision strategy comprises two decisions: (a) the content quality (affecting consumption benefit) and (b) the content distribution delay (affecting consumption cost). The focus here is on how a publisher should choose the content provision strategy in the presence of a content pirate such as a peer-to-peer (P2P) network. Our study sheds light on how a publisher could leverage a pirate's presence to increase profits, even though the pirate essentially encroaches on the demand for the publisher's content. We find that a publisher should sometimes decrease the delivery speed but increase quality in the presence of a pirate (a quality focused strategy). At other times, a distribution focused strategy is better; namely, increase delivery speed, but lower quality. In most cases, however, we show that the publisher should improve at least one dimension of content provision (quality or delay) in the presence of a pirate.
Recommendations and consumer reviews are universally acknowledged as significant features of a business-to-consumer website. However, because of the well-documented obstacles to measuring the causal impact of these artifacts, there is still a lack of empirical evidence demonstrating their influence on two important outcome variables in the shopping context: perceived usefulness and social presence. To test the existence of a causal link between information technology (IT)-enabled support for the provision of recommendations and consumer reviews on the usefulness and social presence of the website, this study employs a novel approach to generate the experimental conditions by filtering the content of Amazon.com in real time. The results show that the provision of recommendations and consumer reviews increases both the usefulness and social presence of the website.
Most of the recent research in data visualization has focused on technical and aesthetic issues involved in the manipulation of graphs, specifically on features that facilitate data exploration to make graphs interactive and dynamic. The present research identifies a gap in the existing knowledge of graph construction, namely potential problems in both 3D and 2D graphs that will impede comprehension of information when three or more variables are used in a graphical representation. Based on theories regarding perceptual issues of graph construction (Bertin 1981; Pinker 1991), we evaluate specific cases where 3D graphs may outperform 2D graphs, and vice-versa. Two experiments have been conducted to test these hypotheses, and 3D graphs have been found to consistently outperform 2D graphs in all of our experimental scenarios. A third experiment has been conducted to identify situations where 2D graphs might perform at least as well as 3D graphs, but its results suggest that 3D graphs outperform 2D graphs even for simple tasks, thus leading to the conclusion that 3D graphs perform better than 2D graphs under all task conditions with more than two variables.