TY - JOUR
T1 - A methodology for analyzing Web-based qualitative data
AU - Romano, Nicholas C.
AU - Donovan, Christina
AU - Chen, Hsinchun
AU - Nunamaker, Jay F.
N1 - Funding Information:
HSINCHUN CHEN is Professor of MIS at the University of Arizona and head of the UA/MIS Artiicifal Intelligence Group. He is also principal investigator of the Illinois Digital Library Initiativproeject funded by NS,AR FPAand ,NASA. Hreseiaschr interests are in semantic retrievl, seaarch algorithms, knowledge discove, anrdycol-laborativcoemputing. He receivd hies Ph.D. in Information Systems from New York Universit. y JAY F.NUANAKEMR JR.i Rsegents Professor of MIS, Computer Science, and Communication at the University of Arizona and Director of the Center for the Management of Information. He has publishedmore than 200 papers and svebeooksndealing with collaborative computing, systems devlopement automation, databases, expert systems, systems analysis and design,and straegitc planning. D.N raakumer renceived a B.S. in from Carnegie Mellon University,a B.S. in Mechanical Engineering, and an M.S. in Industrial Engineering from the Univrsityeof Pittsburgh, and a Ph.D. in Operations Research and Systems Engineering from the Case Institute of Technology.
PY - 2003
Y1 - 2003
N2 - The volume of qualitative data (QD) available via the Internet is growing at an increasing pace and firms are anxious to extract and understand users' thought processes, wants and needs, attitudes, and purchase intentions contained therein. An information systems (IS) methodology to meaningfully analyze this vast resource of QD could provide useful information, knowledge, or wisdom firms could use for a number of purposes including new product development and quality improvement, target marketing, accurate "user-focused" profiling, and future sales prediction. In this paper, we present an IS methodology for analysis of Internet-based QD consisting of three steps: elicitation; reduction through IS-facilitated selection, coding, and clustering; and visualization to provide at-a-glance understanding. Outcomes include information (relationships), knowledge (patterns), and wisdom (principles) explained through visualizations and drill-down capabilities. First we present the generic methodology and then discuss an example employing it to analyze free-form comments from potential consumers who viewed soon-to-be-released film trailers provided that illustrates how the methodology and tools can provide rich and meaningful affective, cognitive, contextual, and evaluative information, knowledge, and wisdom. The example revealed that qualitative data analysis (QDA) accurately reflected film popularity. A finding is that QDA also provided a predictive measure of relative magnitude of film popularity between the most popular film and the least popular one, based on actual first week box office sales. The methodology and tools used in this preliminary study illustrate that value can be derived from analysis of Internet-based QD and suggest that further research in this area is warranted.
AB - The volume of qualitative data (QD) available via the Internet is growing at an increasing pace and firms are anxious to extract and understand users' thought processes, wants and needs, attitudes, and purchase intentions contained therein. An information systems (IS) methodology to meaningfully analyze this vast resource of QD could provide useful information, knowledge, or wisdom firms could use for a number of purposes including new product development and quality improvement, target marketing, accurate "user-focused" profiling, and future sales prediction. In this paper, we present an IS methodology for analysis of Internet-based QD consisting of three steps: elicitation; reduction through IS-facilitated selection, coding, and clustering; and visualization to provide at-a-glance understanding. Outcomes include information (relationships), knowledge (patterns), and wisdom (principles) explained through visualizations and drill-down capabilities. First we present the generic methodology and then discuss an example employing it to analyze free-form comments from potential consumers who viewed soon-to-be-released film trailers provided that illustrates how the methodology and tools can provide rich and meaningful affective, cognitive, contextual, and evaluative information, knowledge, and wisdom. The example revealed that qualitative data analysis (QDA) accurately reflected film popularity. A finding is that QDA also provided a predictive measure of relative magnitude of film popularity between the most popular film and the least popular one, based on actual first week box office sales. The methodology and tools used in this preliminary study illustrate that value can be derived from analysis of Internet-based QD and suggest that further research in this area is warranted.
KW - Attitudes and purchase intentions
KW - Clustering
KW - Coding
KW - Elicitation
KW - Future sales predictions
KW - Information systems (IS)
KW - Qualitative data analysis (QDA) methodology
KW - Reduction
KW - Selection
KW - Visualization
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U2 - 10.1080/07421222.2003.11045741
DO - 10.1080/07421222.2003.11045741
M3 - Article
AN - SCOPUS:0037368017
SN - 0742-1222
VL - 19
SP - 213
EP - 246
JO - Journal of Management Information Systems
JF - Journal of Management Information Systems
IS - 4
ER -