This Article Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Bowen, P. L. Articles by Rohde, F. H. Search for Related Content

An Empirical Investigation of End User Query Development: The Effects of Improved Model Expressiveness vs. Complexity

College of Business, Florida State University, Tallahassee, Florida 32306
School of Business, The University of Queensland, Brisbane QLD 4072, Australia
School of Business, The University of Queensland, Brisbane QLD 4072, Australia
pbowen{at}cob.fsu.edu
r.o'farrell{at}business.uq.edu.au
f.rohde{at}business.uq.edu.au

Data models provide a map of the components of an information system. Prior research has indicated that more expressive conceptual data models (in spite of 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.

Key Words: scalability; expressiveness; ontology; ontological clarity; parsimony; logical data models
History: This paper was received on August 19, 2005.