Quantifying Qualitative Data for Electronic Commerce Attitude Assessment and Visualization

Persistent Link:
http://hdl.handle.net/10150/105275
Title:
Quantifying Qualitative Data for Electronic Commerce Attitude Assessment and Visualization
Author:
Romano, Nicholas C.; Bauer, Christina; Chen, Hsinchun; Nunamaker, Jay F.
Citation:
Quantifying Qualitative Data for Electronic Commerce Attitude Assessment and Visualization 2000, Journal of Management Information Systems
Journal:
Journal of Management Information Systems
Issue Date:
2000
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/105275
Submitted date:
2004-10-13
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
Keywords:
Artificial Intelligence; Informetrics
Local subject classification:
National Science Digital Library; NSDL; Artificial intelligence lab; AI lab

Full metadata record

DC FieldValue Language
dc.contributor.authorRomano, Nicholas C.en_US
dc.contributor.authorBauer, Christinaen_US
dc.contributor.authorChen, Hsinchunen_US
dc.contributor.authorNunamaker, Jay F.en_US
dc.date.accessioned2004-10-13T00:00:01Z-
dc.date.available2010-06-18T23:22:51Z-
dc.date.issued2000en_US
dc.date.submitted2004-10-13en_US
dc.identifier.citationQuantifying Qualitative Data for Electronic Commerce Attitude Assessment and Visualization 2000, Journal of Management Information Systemsen_US
dc.identifier.urihttp://hdl.handle.net/10150/105275-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractWe 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.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectInformetricsen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial intelligence laben_US
dc.subject.otherAI laben_US
dc.titleQuantifying Qualitative Data for Electronic Commerce Attitude Assessment and Visualizationen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalJournal of Management Information Systemsen_US
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