TY - JOUR
T1 - Expertise visualization
T2 - An implementation and study based on cognitive fit theory
AU - Huang, Zan
AU - Chen, Hsinchun
AU - Guo, Fei
AU - Xu, Jennifer J.
AU - Wu, Soushan
AU - Chen, Wun Hwa
N1 - Funding Information:
This research is partly supported by NSF Digital Library Initiative-2, “High-performance Digital Library Systems: From Information Retrieval to Knowledge Management,” IIS-9817473, April 1999–March 2003. We would like to thank Anne Hsu for her help in initial data processing and discussion of research ideas.
PY - 2006/12
Y1 - 2006/12
N2 - Expertise management systems are being widely adopted in organizations to manage tacit knowledge. These systems have successfully applied many information technologies developed for document management to support collection, processing, and distribution of expertise information. In this paper, we report a study on the potential of applying visualization techniques to support more effective and efficient exploration of the expertise information space. We implemented two widely applied dimensionality reduction visualization techniques, the self-organizing map (SOM) and multidimensional scaling (MDS), to generate compact but distorted (due to the dimensionality reduction) map visualizations for an expertise data set. We tested cognitive fit theory in our context by comparing the SOM and MDS displays with a standard table display for five tasks selected from a low-level, domain-independent visual task taxonomy. The experimental results based on a survey data set of research expertise of the business school professors suggested that using both SOM and MDS visualizations is more efficient than using the table display for the associate, compare, distinguish, and cluster tasks, but not the rank task. Users generally achieved comparable effectiveness for all tasks using the tabular and map displays in our study.
AB - Expertise management systems are being widely adopted in organizations to manage tacit knowledge. These systems have successfully applied many information technologies developed for document management to support collection, processing, and distribution of expertise information. In this paper, we report a study on the potential of applying visualization techniques to support more effective and efficient exploration of the expertise information space. We implemented two widely applied dimensionality reduction visualization techniques, the self-organizing map (SOM) and multidimensional scaling (MDS), to generate compact but distorted (due to the dimensionality reduction) map visualizations for an expertise data set. We tested cognitive fit theory in our context by comparing the SOM and MDS displays with a standard table display for five tasks selected from a low-level, domain-independent visual task taxonomy. The experimental results based on a survey data set of research expertise of the business school professors suggested that using both SOM and MDS visualizations is more efficient than using the table display for the associate, compare, distinguish, and cluster tasks, but not the rank task. Users generally achieved comparable effectiveness for all tasks using the tabular and map displays in our study.
KW - Cognitive fit theory
KW - Expertise management
KW - Information visualization
KW - Multidimensional scaling
KW - Self-organizing map
KW - Visualization evaluation
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U2 - 10.1016/j.dss.2006.01.006
DO - 10.1016/j.dss.2006.01.006
M3 - Article
AN - SCOPUS:33750442910
SN - 0167-9236
VL - 42
SP - 1539
EP - 1557
JO - Decision Support Systems
JF - Decision Support Systems
IS - 3
ER -