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Does construct overload truly overload the performance? - An experimental study of experienced data modeler.

DC Field Value Language
dc.contributor.advisor박진수-
dc.contributor.author서지혜-
dc.date.accessioned2017-10-27T16:30:47Z-
dc.date.available2017-10-27T16:30:47Z-
dc.date.issued2017-08-
dc.identifier.other000000146091-
dc.identifier.urihttps://hdl.handle.net/10371/136682-
dc.description학위논문 (박사)-- 서울대학교 대학원 경영대학 경영학과, 2017. 8. 박진수.-
dc.description.abstractA principal activity in information systems development involves building a conceptual model of domain that an information system is intended to support. Such models are created using a conceptual-modeling grammar fundamental means to specifying information systems requirement. However, the actual usage of grammar is poorly understood and some issues regarding conceptual grammar such as construct overload still remain unsolved. With regard to construct overload in conceptual modeling, past studies have had some deficiencies in research methods and even have presented contradicting results. In this paper, we experimented to test whether construct overload enables conceptual models users to understand a domain more efficiently. To acquire a more complete and accurate understanding of construct overload, our study focused on three major points-
dc.description.abstractthe evaluation of conceptual modeling grammar semantics, research participants and domain familiarity. This papers key contribution is that it is one of the first studies to investigate practitioners aspects of construct overload employing different degrees of domain familiarity by investigating the cognitive processes of practitioner. In addition, this research reconciles conflicting outcomes by examining practical directions for model variation. The result of study will broaden the perspective on usability in the context of the conceptual model and may serve as an ontological guidance to construct overload when modelers create a conceptual model.-
dc.description.tableofcontents1. Introduction 2
2. Theory and Related Work 5
2.1. Theory 6
Theory of Ontological Clarity 7
Feynman-Tufte Principle 7
Mayers Cognitive Theory of Multimedia Learning 8
Information Processing Theory 8
Theory of Visual Attention 9
2.2. Related Work 9
Ontological Clarity 13
Domain Familiarity 13
3. Proposition Development 14
4. Research Method 18
4.1. Design and Measures 19
4.2. Materials 20
Personal Profile and Training Materials 20
Conceptual Models 21
Understanding Task Materials 27
4.3. Participants 31
4.4. Procedures 32
4.5. Results 33
Data Scoring 33
Quantitative Data Analysis 33
5. Cognitive Process Tracing Study 36
5.1. Design and Measures 36
5.2. Materials 37
5.3. Participants 38
5.4. Procedures 38
5.5. Coding Scheme 39
5.6. Analysis of Protocol Data 40
5.7. Analysis of Eye-tracking Data 45
Scan Path 48
Focus and Heat Map 52
Quantitative Data Analysis of Key Performance Indicators 56
6. Discussion 63
6.1. Conclusion 63
6.2. Implication 63
6.3. Limitations and Future Research Directions 65
Reference 66
Appendix A 73
Summary of Information Processing Coding Typology 73
Appendix B 75
Glossary of Eye Tracking Technique 75
Focus Map of Unfamiliar Domain (Waste Processing System) 75
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dc.formatapplication/pdf-
dc.format.extent4665613 bytes-
dc.format.mediumapplication/pdf-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subjectConceptual Modeling-
dc.subjectConceptual Model Grammars-
dc.subjectConstruct Overload-
dc.subjectDomain Familiarity-
dc.subjectDomain Knowledge-
dc.subjectEmpirical Research-
dc.subjectEntity Relationship Diagram (ERD)-
dc.subjectInformation Systems Development-
dc.subjectOntological Clarity-
dc.subjectProtocol Analysis-
dc.subjectNeuroscience Methods-
dc.subject.ddc658-
dc.titleDoes construct overload truly overload the performance? - An experimental study of experienced data modeler.-
dc.typeThesis-
dc.description.degreeDoctor-
dc.contributor.affiliation경영대학 경영학과-
dc.date.awarded2017-08-
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