A Dynamic Structural Model of User Learning in Mobile Media Content

Abstract

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 structural model of user behavior and learning with regard to content generation and usage activities in mobile digital media environments. Users learn about two different categories of content – content from regular Internet social networking and community (SNC) sites and that from mobile portal sites. Then they can choose to engage in the creation (uploading) and consumption (downloading) of multi-media content from these two categories of websites. In our context, users have two sources of learning about content quality – (i) direct experience through their own content creation and usage behavior and (ii) indirect experience through word-of-mouth such as the content creation and usage behavior of their social network neighbors. Our model seeks to explicitly explain how direct and indirect experiences from social interactions influence the content creation and usage behavior of users over time. We estimate this model using a unique dataset of consumers' mobile media content creation and usage behavior over a 3-month time period. Our estimates suggest that when it comes to user learning from direct experience, the content that is downloaded from mobile portals has the highest average quality level. In contrast, content that is downloaded by users from SNC websites has the lowest average quality level. Besides, the order of magnitude of accuracy of signals for each content type from direct experiences is consistent with the order of the quality levels. This finding implies that in the context of mobile media users make content choices based on their perception of differences in both the average content quality levels and the extent of content quality variation. Further we find that signals about the quality of content from direct experience are more accurate than signals from indirect experiences. Potential implications for mobile phone operators and advertisers are discussed.