Auditor efficiency and effectiveness with diagrammatic and linguistic conceptual model representations

Dunn, Cheryl L.; Gerard, Gregory J.

doi:10.1016/s1467-0895(01)00022-7

Cited by 25 publications

(20 citation statements)

References 18 publications

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“…They presented participants with a series of directed queries for which the answers could be derived from the models. Query examples include -What is the primary key of X?‖ and -What information is needed to determine Z?‖ Dunn and Gerard (2001) found no difference in accuracy, but they did find an effect for time. Participants receiving the diagrammatic/aggregate ER models were faster than those receiving other treatments, suggesting a difference in computational equivalence.…”

Section: Conceptual Models For Studying Diagrammatic Attention Managementioning

confidence: 98%

“…In Figure 1, note that for the sentential structures, (a) the aggregate structure on the left l ists all entities and then all relationships, and (b) the disaggregate structure on the right partitions the information into a series of separate binary relationships. Dunn and Gerard (2001) investigated user performance on search, inference, and recognition tasks as a function of informationally equivalent diagrammatic/aggregate versus sentential/aggregate ER models. They presented participants with a series of directed queries for which the answers could be derived from the models.…”

Section: Conceptual Models For Studying Diagrammatic Attention Managementioning

confidence: 99%

“…Participants receiving the diagrammatic/aggregate ER models were faster than those receiving other treatments, suggesting a difference in computational equivalence. The directed tasks used in Dunn and Gerard (2001) do not require an exhaustive search through all cardinalities in the model and are facilitated through localization (as predicted by Larkin and Simon, 1987) within the diagram. Their results are also consistent with the contiguity principle, which states that better performance results when corresponding words and images are presented simultaneously (Mayer, 2001).…”

Section: Conceptual Models For Studying Diagrammatic Attention Managementioning

confidence: 99%

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Diagrammatic Attention Management and the Effect of Conceptual Model Structure on Cardinality Validation

Dunn

Gerard²,

Grabski

2011

JAIS

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Diagrams are frequently used to document various components of information systems, from the procedures established for user-system interaction, to the structure of the database at the system's core. Past research has revealed that diagrams are not always used as effectively as their creators intend. This study proposes a theory of diagrammatic attention management to contribute to the exploration of diagram effectiveness. Based upon diagrammatic attention management, this study demonstrates that the type of diagram most commonly used to represent conceptual models is less effective than three other alternatives for validating the models' cardinalities. Most conceptual models are documented using entity-relationship diagrams that include a full transaction cycle or module on a single page, i.e., an aggregate diagrammatic format. Participants in this study using three alternative representations (disaggregate diagrammatic, aggregate sentential, and disaggregate sentential) outperformed users of the aggregate diagrammatic format for cardinality validation. Results suggest that to facilitate effective use of aggregate diagrams, users need a mechanism by which to direct their attention while using the diagrams. If such an attention direction mechanism is not inherent in a diagram, it may need to be applied as an external tool, or the diagram may need to be disaggregated to facilitate use.

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Section: Conceptual Models For Studying Diagrammatic Attention Managementioning

confidence: 98%

Section: Conceptual Models For Studying Diagrammatic Attention Managementioning

confidence: 99%

Section: Conceptual Models For Studying Diagrammatic Attention Managementioning

confidence: 99%

See 1 more Smart Citation

Diagrammatic Attention Management and the Effect of Conceptual Model Structure on Cardinality Validation

Dunn

Gerard²,

Grabski

2011

JAIS

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“…Counterbalancing diagram type (non‐REA or REA) and business process alleviates possible learning and maturation effects. Such effects can still bias the results, for instance, when there are differential learning rates for the two treatments (Bodart et al ., 2001; Dunn & Gerard, 2001). Therefore, the observations were regrouped according to which treatment (non‐REA or REA) was administered first.…”

Section: Data Analysis and Interpretationmentioning

confidence: 99%

“…Dunn & McCarthy (1997) admit, however, that the benefits of having a structuring orientation are only hypothesized and have not been subjected to empirical testing. Empirical evidence for this and related claims such as high semantic expressiveness (Dunn & Grabski, 2000) and the improved readability of REA diagrams due to an indexing mechanism inherent in the Basic REA Template (Dunn & Gerard, 2001; Romney & Steinbart, 2006) is scarce. Dunn & Grabski (2000) demonstrated experimentally that accounting users perceive an enterprise system based on the REA model as more semantically expressive than a system based on the Debit‐Credit‐Account (DCA) model.…”

Section: Introductionmentioning

confidence: 99%

The pragmatic quality of Resources- Events-Agents diagrams: an experimental evaluation

Poels¹,

Maes²,

Gailly³

et al. 2010

Information Systems Journal

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The Resources-Events-Agents (REA) model is a semantic data model for the development of enterprise information systems. Although this model has been proposed as a benchmark for enterprise information modelling, only few studies have attempted to empirically validate the claimed benefits of REA modelling. Moreover, these studies focused on the evaluation of REA-based system implementations rather than directly assessing the REA-modelled conceptual schemas that these systems are based on. This paper presents a laboratory experiment that measured the user understanding of diagrammatic conceptual schemas developed using the REA model. The theoretical foundation for the hypotheses are cognitive theories that explain pattern recognition phenomena and the resulting reduction in cognitive effort for understanding conceptual schemas. The results of the experiment indicate a more accurate understanding of the business processes and policies modelled when users recognize the REA model's core pattern of enterprise information in the diagram. The implication for modelling practice is that the use of the REA model improves the requirements engineering process by facilitating the user validation of conceptual schemas produced by analysts, and thus helps ensuring the quality of the enterprise information system that is developed or implemented

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A Research Program for Studying the Impact of Process Representation on Risk Analysis

Ritchi

Mendling

2013

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Auditor efficiency and effectiveness with diagrammatic and linguistic conceptual model representations

Cited by 25 publications

References 18 publications

Diagrammatic Attention Management and the Effect of Conceptual Model Structure on Cardinality Validation

Diagrammatic Attention Management and the Effect of Conceptual Model Structure on Cardinality Validation

The pragmatic quality of Resources- Events-Agents diagrams: an experimental evaluation

A Research Program for Studying the Impact of Process Representation on Risk Analysis

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