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Combining Ethnographic and Clickstream Data to Identify User Web Browsing Strategies

Author

Listed:
  • Lillian Clark

    (HRMM - Portsmouth Business School)

  • I-Hsien Ting

    (Department of Information Management - National Kaohsiung University of Applied Sciences)

  • Chris Kimble

    (CREGOR - Centre de Recherche sur la Gestion des Organisations - UM2 - Université Montpellier 2 - Sciences et Techniques, Euromed Marseille - École de management - Association Euromed Management - Marseille)

  • P. C. Wright

    (CS-YORK - Department of Computer Science [York] - University of York [York, UK])

  • Daniel Kudenko

    (CS-YORK - Department of Computer Science [York] - University of York [York, UK])

Abstract

Introduction: The strategies that people use to browse Websites are difficult to analyse and understand: quantitative data can lack information about what a user actually intends to do, while qualitative data tends to be localised and is impractical to gather for large samples. Method: This paper describes a novel approach that combines data from direct observation, user surveys and server logs to analyse users' browsing behaviour. It is based on a longitudinal study of university students' use of a Website related to one of their courses. Analysis: The data were analysed by using Footstep graphs to categorise browsing behaviour into pre-defined strategies and comparing these with data from questionnaires and direct observation of the students' actual use of the site. Results: Initial results indicated that in certain cases the patterns from server logs matched the observed browsing strategies as described in the literature. In addition, by cross-referencing the quantitative and qualitative data, a number of insights were gained into potential problems. Conclusion: This study shows how combining quantitative and qualitative approaches can provide an insight into changes in user browsing behaviour over time. It also identifies some potential methodological problems in studies of browsing behaviour and indicates some directions for future research.

Suggested Citation

  • Lillian Clark & I-Hsien Ting & Chris Kimble & P. C. Wright & Daniel Kudenko, 2006. "Combining Ethnographic and Clickstream Data to Identify User Web Browsing Strategies," Post-Print halshs-00489627, HAL.
    • Handle: RePEc:hal:journl:halshs-00489627
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00489627
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    Citations

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    Cited by:

    1. Claudia Elena DINUCA, 2011. "An Application for Data Preprocessing and Models Extractions in Web Usage Mining," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 166-176.
    2. Claudia Elena DINUCA, 2011. "Association and Sequence Mining in Web Usage," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 31-36.
    3. Claudia Elena Dinuca & Dumitru Ciobanu, 2011. "On an Algorithm for Identifying Sessions from Web Logs," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 4(4), pages 10-15, August.

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