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Abstract

We present analytic framework for evidence-based management, design, and engineering of collaborative intranet environments. The analytics target elucidation of essential elements of human-system interactions. Temporal segmentation of human behavior in digital environments permits identification of crucial navigational points as well as higher order abstractions. Explorations of these elements provide fertile grounds for assessment of usability and behavioral characteristics that directly translate to actionable knowledge indispensable for improvements of collaboration portals. We extrapolate the analytic findings from a case study of a large scale collaborative organizational intranet; in order to identify three crucial domains facilitating alignment between observed evidence and best management and engineering practices.

Keywords

Collaborative Intranets Web-based Portals Analytics Logs 

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References

  1. 1.
    Davenport, T.H., Harris, J.G.: Competing on Analytics: The New Science of Winning. Harvard Business School Press, Boston (2007)Google Scholar
  2. 2.
    Géczy, P., Akaho, S., Izumi, N., Hasida, K.: Knowledge worker intranet behaviour and usability. Int. J. Business Intelligence and Data Mining 2, 447–470 (2007)CrossRefGoogle Scholar
  3. 3.
    Huntington, P., Nicholas, D., Jamali, H.R.: Website usage metrics: A re-assessment of session data. International Journal of Information Processing and Management 44(1), 358–372 (2008)CrossRefGoogle Scholar
  4. 4.
    Petre, M., Minocha, S., Roberts, D.: Usability beyond the website: an empirically-grounded e-commerce evaluation for the total customer experience. Behaviour and Information Technology 25, 189–203 (2006)CrossRefGoogle Scholar
  5. 5.
    Park, Y.-H., Fader, P.S.: Modeling browsing behavior at multiple websites. Marketing Science 23, 280–303 (2004)CrossRefGoogle Scholar
  6. 6.
    Moe, W.W.: Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. Journal of Consumer Psychology 13, 29–39 (2003)CrossRefGoogle Scholar
  7. 7.
    Barabasi, A.-L.: The origin of bursts and heavy tails in human dynamics. Nature 435, 207–211 (2005)CrossRefGoogle Scholar
  8. 8.
    Catledge, L., Pitkow, J.: Characterizing browsing strategies in the world wide web. Computer Networks and ISDN Systems 27, 1065–1073 (1995)CrossRefGoogle Scholar
  9. 9.
    Thakor, M.V., Borsuk, W., Kalamas, M.: Hotlists and web browsing behavior–an empirical investigation. Journal of Business Research 57, 776–786 (2004)CrossRefGoogle Scholar
  10. 10.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17, 734–749 (2005)CrossRefGoogle Scholar
  11. 11.
    Symeonidis, P., Nanopoulos, A., Papadopoulos, A.N., Manolopoulos, Y.: Collaborative recommender systems: Combining effectiveness and efficiency. Expert Systems with Applications 34(4), 2995–3013 (2008)CrossRefGoogle Scholar
  12. 12.
    Jin, R., Si, L., Zhai, C.: A study of mixture models for collaborative filtering. Information Retrieval 9, 357–382 (2006)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2009

Authors and Affiliations

  • Peter Géczy
    • 1
  • Noriaki Izumi
    • 1
  • Shotaro Akaho
    • 1
  • Kôiti Hasida
    • 1
  1. 1.National Institute of Advanced Industrial Science and Technology (AIST) Tokyo and TsukubaJapan

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