An empirical investigation of end-user query development: The effects of improved model expressiveness vs. complexity

Bowen, P. L., O'Farrell, R. A. and Rohde, F. H. (2009) An empirical investigation of end-user query development: The effects of improved model expressiveness vs. complexity. Information Systems Research, 20 4: 565-584. doi:10.1287/isre.1080.0181

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Author Bowen, P. L.
O'Farrell, R. A.
Rohde, F. H.
Title An empirical investigation of end-user query development: The effects of improved model expressiveness vs. complexity
Journal name Information Systems Research   Check publisher's open access policy
ISSN 1047-7047
1526-5536
Publication date 2009-12
Year available 2009
Sub-type Article (original research)
DOI 10.1287/isre.1080.0181
Open Access Status
Volume 20
Issue 4
Start page 565
End page 584
Total pages 20
Editor Vallabh Sambamurthy
Place of publication Hanover, MD, United States
Publisher Institute for Operations Research and the Management Sciences (INFORMS)
Collection year 2010
Language eng
Subject C1
150302 Business Information Systems
890205 Information Processing Services (incl. Data Entry and Capture)
Abstract Data models provide a map of the components of an information system. Prior research has indicated that more expressive conceptual data models (despite their increased size) result in better performance for problem solving tasks. An initial experiment using logical data models indicated that more expressive logical data models also enhanced end-user performance for information retrieval tasks. However, the principles of parsimony and bounded rationality imply that, past some point, increases in size lead to a level of complexity that results in impaired performance. The results of this study support these principles. For a logical data model of increased but still modest size, users composing queries for the more expressive logical data model did not perform as well as users composing queries for the corresponding less expressive but more parsimonious logical data model. These results indicate that, when constructing logical data models, data modelers should consider tradeoffs between parsimony and expressiveness.
Keyword Scalability
Expressiveness
Ontology
Ontological clarity
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: 2010 Higher Education Research Data Collection
UQ Business School Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 7 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 17 times in Scopus Article | Citations
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Created: Sun, 10 Jan 2010, 00:05:54 EST