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    Quantifying Qualitative Data for Electronic Commerce Attitude Assessment and Visualization

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    Author
    Romano, Nicholas C.
    Bauer, Christina
    Chen, Hsinchun
    Nunamaker, Jay F.
    Issue Date
    2000
    Submitted date
    2004-10-13
    Keywords
    Artificial Intelligence
    Informetrics
    Local subject classification
    National Science Digital Library
    NSDL
    Artificial intelligence lab
    AI lab
    
    Metadata
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    Citation
    Quantifying Qualitative Data for Electronic Commerce Attitude Assessment and Visualization 2000, Journal of Management Information Systems
    Journal
    Journal of Management Information Systems
    Description
    Artificial Intelligence Lab, Department of MIS, University of Arizona
    URI
    http://hdl.handle.net/10150/105275
    Abstract
    We propose a methodology to collect, quantify and visualize qualitative consumer data. We employ a Web-based Group Support System (GSS), GSw,b, to elicit free-form comments and a prototype comment analysis support system to facilitate comment classification, categorization and visualization to measure attitudes. We argue that such a methodology is needed due to the proliferation of qualitative data, the limitations of qualitative data analysis and the dearth of methods to measure attitudes contained within free-form comments. We conducted two experiments to compare our methodology with two long-established traditional methods, Likert scale evaluations and first-week box office sales records. We found that our methodology provides equivalent and superior affective and evaluative attitude information, compared to Likert scale ratings. We also found that comment analysis more accurately reflected actual first-week box office sales than did Likert scale ratings. Comment analysis with the prototype tool was seventy-five percent more efficient than manual coding. We designed the prototype to generate visualizations to make sense of multiple attitude dimensions through at-a-glance understanding and comparative presentation. The methodology we propose overcomes drawbacks often associated with qualitative data analysis and offers marketers and researchers a method to measure attitudes from free-form comments. The results indicate that qualitative data in the form of freeform comments may be quantified and visualized to provide meaningful attitude assessment. Finally, we present future research directions to enhance data collection and the comment analysis support system.
    Type
    Journal Article (Paginated)
    Language
    en
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