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|Title:||An evaluation of novice end-user computing performance: Data modeling, query writing, and comprehension|
|Authors:||Chan, H.C. |
|Source:||Chan, H.C., Teo, H.H., Zeng, X.H. (2005). An evaluation of novice end-user computing performance: Data modeling, query writing, and comprehension. Journal of the American Society for Information Science and Technology 56 (8) : 843-853. ScholarBank@NUS Repository. https://doi.org/10.1002/asi.20178|
|Abstract:||End-user computing has become a well-established aspect of enterprise database systems today. End-user computing performance depends on the user-database interface, in which the data model and query language are major components. We examined three prominent data models-the relational model, the Extended-Entity-Relationship (EER) model, and the Object-Oriented (OO) model-and their query languages in a rigorous and systematic experiment to evaluate their effects on novice end-user computing performance in the context of database design and data manipulation. In addition, relationships among the performances for different tasks (modeling, query writing, query comprehension) were postulated with the use of a cognitive model for the query process, and are tested in the experiment. Structural Equation Modeling (SEM) techniques were used to examine the multiple causal relationships simultaneously. The findings indicate that the EER and OO models overwhelmingly outperformed the relational model in terms of accuracy for both database design and data manipulation. The associations between tasks suggest that data modeling techniques would enhance query writing correctness, and query writing ability would contribute to query comprehension. This study provides a better and thorough understanding of the inter-relationships among these data modeling and task factors. Our findings have significant implications for novice end-user training and development. © 2005 Wiley Periodicals, Inc.|
|Source Title:||Journal of the American Society for Information Science and Technology|
|Appears in Collections:||Staff Publications|
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