Modeling End-users’ Acceptance of a Knowledge Authoring Tool

N. C. Hulse; G. Del Fiol; R. A. Rocha

doi:10.1055/s-0038-1634114

Methods of Information in Medicine, Inhaltsverzeichnis

Methods Inf Med 2006; 45(05): 528-535
DOI: 10.1055/s-0038-1634114

Original Article

Schattauer GmbH

Modeling End-users’ Acceptance of a Knowledge Authoring Tool

N. C. Hulse

¹Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA

²Medical Informatics, Intermountain Healthcare, Salt Lake City, Utah, USA

,

G. Del Fiol

¹Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA

²Medical Informatics, Intermountain Healthcare, Salt Lake City, Utah, USA

,

R. A. Rocha

¹Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA

²Medical Informatics, Intermountain Healthcare, Salt Lake City, Utah, USA

› Institutsangaben

Abstract

Summary

Objectives: Knowledge bases comprise a vital component in the classic medical expert system model, yet the knowledge acquisition process by which they are created has been characterized as highly iterative and labor-intensive. The difficulty of this process underscores the importance of knowledge authoring tools that satisfy the demands of end-users. The authors hypothesize that the acceptability of a knowledge authoring tool for the creation of medical knowledge base content can be predicted by an accepted model in the information technology (IT) field, specifically the Technology Acceptance Model (TAM).

Methods: An online survey was conducted amongst knowledge base authors who had previously established experience with the authoring tool software. The Likert-based questions in the survey were patterned directly after accepted TAM constructs with minor modifications to particularize them to the software being used. The results were analyzed using structural equation modeling.

Results: The TAM performed well in predicting end-users’ behavioral intentions to use the knowledge authoring tool. Five out of seven goodness-of-fit statistics indicate that the model represents the behavioral intentions of the authors well. All but one of the hypothesized relationships specified by the TAM were significant with p values less than 0.05.

Conclusions: The TAM provides an adequate means by which development teams can anticipate and better understand what aspects of a knowledge authoring tool are most important to their target audience. Further research involving other behavioral models and an expanded user base will be necessary to better understand the scope of issues that factor into acceptability.

Keywords

Knowledge acquisition - knowledge bases - software acceptance - clinical information systems - Technology Acceptance Model

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