Advanced Search
MyIDEAS: Login

A Dynamic Structural Model of User Learning in Mobile Media Content

Contents:

Author Info

Registered author(s):

    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 level of quality. In contrast, content that is downloaded by users from SNC websites has the lowest level of quality. Besides, the order of magnitude of signal accuracy for each content type from the direct experience is consistent with the order of true quality level. This finding implies that in the context of mobile media users make content choices based on their perception of differences in both content quality level and 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.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://www.netinst.org/Ghose_Han_09-24.pdf
    Download Restriction: no

    Bibliographic Info

    Paper provided by NET Institute in its series Working Papers with number 09-24.

    as in new window
    Length: 30 pages
    Date of creation: Oct 2009
    Date of revision: Oct 2009
    Handle: RePEc:net:wpaper:0924

    Contact details of provider:
    Web page: http://www.NETinst.org/

    Related research

    Keywords: structural modeling; mobile media; mobile portals; Internet websites; uploading content; downloading content; dynamic programming; simulated maximum likelihood estimation;

    Find related papers by JEL classification:

    This paper has been announced in the following NEP Reports:

    References

    No references listed on IDEAS
    You can help add them by filling out this form.

    Citations

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:net:wpaper:0924

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Nicholas Economides).

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.