A Dynamic Structural Model of User Learning in Mobile Media Content

Consumer adoption and usage of mobile communication and multimedia content services has been growing steadily over the past few years in many countries around the world. In this paper, we develop and estimate a dynamic structural model of user behavior and learning with regard to content generation and usage activities in mobile multi-media environments. Users learn about two different categories of web content – (i) content from regular Internet social networking and community (SNC) sites and (ii) that from mobile portal sites. They can choose to engage in the creation (uploading) and consumption (downloading) of multi-media content from these two categories of websites depending on how well the content matches their preferences. In our context, users have two sources of learning about content match value – (i) direct experience through their own content creation and usage behavior and (ii) indirect experience such as the content creation and usage behavior of their social network neighbors. Our paper seeks to model the underlying mechanism of user content generation and usage behavior in mobile multi-media settings and examine how direct and indirect experiences influence this behavior over time. Towards this, we develop a dynamic structural model and estimate it using a unique dataset of consumers’ mobile media content creation and usage behavior over a 3-month time period. Our estimates suggest that, on an average, the content downloaded from mobile portal sites has the highest level of match value with user preferences. In contrast, the content downloaded from Internet SNC sites has the lowest level of match value. The standard deviation of indirect experience signals is higher than the standard deviation of direct experience signals. That is, learning based on direct experience is more accurate (has less variability) than learning based on indirect experience. We run different counterfactual experiments that provide insights into how changes in different parameters affect user behavior. Potential implications for mobile phone operators and mobile advertisers are discussed.