Abstract
We present a formal approach to analysis of human browsing behavior in electronic spaces. An analysis of knowledge workers’ interactions on a large corporate intranet have revealed that users form repetitive elemental and complex browsing patterns, use narrow spectrum of resources, and exhibit diminutive exploratory behavior. Knowledge workers had well defined targets and accomplished their browsing tasks via few subgoals. The analyzed aspects of browsing behavior exposed significant long tail characteristics that can be accurately modeled by the introduced novel distribution. The long tail behavioral effects present new challenges and opportunities for business information systems.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Schlender, B.: Peter Drucker sets us straight. Fortune (Dec. 2003), http://www.fortune.com
Davenport, T.H.: Thinking for a Living - How to Get Better Performance and Results from Knowledge Workers. Harvard Business School Press, Boston (2005)
Barabasi, A.-L.: The origin of bursts and heavy tails in human dynamics. Nature 435, 207–211 (2005)
Park, Y.-H., Fader, P.S.: Modeling browsing behavior at multiple websites. Marketing Science 23, 280–303 (2004)
Géczy, P., et al.: Navigation space formalism and exploration of knowledge worker behavior. In: Kotsis, G., et al. (eds.) Information Integration and Web-based Applications and Services, pp. 163–172. OCG, Vienna (2006)
Moe, W.W.: Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. Journal of Consumer Psychology 13, 29–39 (2003)
Benbunan-Fich, R.: Using protocol analysis to evaluate the usability of a commercial web site. Information and Management 39, 151–163 (2001)
Norman, K.L., Panizzi, E.: Levels of automation and user participation in usability testing. Interacting with Computers 18, 246–264 (2006)
Bucklin, R.E., Sismeiro, C.: A model of web site browsing behavior estimated on clickstream data. Journal of Marketing Research 40, 249–267 (2003)
Thakor, M.V., Borsuk, W., Kalamas, M.: Hotlists and web browsing behavior–an empirical investigation. Journal of Business Research 57, 776–786 (2004)
Deshpande, M., Karypis, G.: Selective markov models for predicting web page accesses. ACM Transactions on Internet Technology 4, 163–184 (2004)
Wu, H., et al.: Mining web navigaitons for intelligence. Decision Support Systems 41, 574–591 (2006)
Zukerman, I., Albrecht, D.W.: Predictive statistical models for user modeling. User Modeling and User-Adapted Interaction 11, 5–18 (2001)
Jozefowska, J., Lawrynowicz, A., Lukaszewski, T.: Faster frequent pattern mining from the semantic web. In: Intelligent Information Processing and Web Mining. Advances in Soft Computing, pp. 121–130. Springer, Heidelberg (2006)
Géczy, P., et al.: Extraction and analysis of knowledge worker activities on intranet. In: Reimer, U., Karagiannis, D. (eds.) PAKM 2006. LNCS (LNAI), vol. 4333, pp. 73–85. Springer, Heidelberg (2006)
Catledge, L., Pitkow, J.: Characterizing browsing strategies in the world wide web. Computer Networks and ISDN Systems 27, 1065–1073 (1995)
Vazquez, A., et al.: Modeling bursts and heavy tails in human dynamics. Physical Review, E73, 036127(19) (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Géczy, P., Izumi, N., Akaho, S., Hasida, K. (2007). Long Tails and Analysis of Knowledge Worker Intranet Browsing Behavior. In: Abramowicz, W. (eds) Business Information Systems. BIS 2007. Lecture Notes in Computer Science, vol 4439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72035-5_46
Download citation
DOI: https://doi.org/10.1007/978-3-540-72035-5_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72034-8
Online ISBN: 978-3-540-72035-5
eBook Packages: Computer ScienceComputer Science (R0)