Effects of External Conceptual Models and Verbal Explanations on Shared Understanding in Small Groups

  • Wolfgang Maass
  • Veda C. Storey
  • Tobias Kowatsch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6998)


Effective conceptual modeling requires a shared understanding of the concepts that are found in an application domain. Achieving such understanding, especially for large design problems, is a challenging, and long-standing problem. Conceptual models tend to be either subjective representations of individuals that require mutual knowledge sharing between members of a modeling team or externalized normative representations that require knowledge transfer from model preceptors to model receptors. Model preceptors have either created a conceptual model or conceived it by another preceptor. In prior studies, normative conceptual models were used to investigate knowledge transfer between preceptors and receptors. This research, in contrast, investigates knowledge transfer of conceptual models between model owners and receptors. A 2x2 study design with modeling novices was used that varied the type of conceptual modeling language and the type of information system. Further testing investigated whether knowledge transfers were affected by additional verbal explanations given by the preceptor. Each modeler was provided access to two conceptual modeling languages that naturally support structure or process representations. The study investigated whether the use of particular conceptual modeling languages differ in their effects on shared understanding between two persons and whether additional verbal explanations might increase shared understanding. The results of this exploratory empirical study provide useful insights into the use of Conceptual Modeling Language pairs for shared understanding in conceptual modeling in small groups.


Mental Model Knowledge Transfer Information System Mental Rotation Shared Understanding 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Wolfgang Maass
    • 1
    • 3
  • Veda C. Storey
    • 2
  • Tobias Kowatsch
    • 3
  1. 1.Saarland UniversitySaarbrückenGermany
  2. 2.University PlazaGeorgia State UniversityAtlantaUnited States
  3. 3.Institute of Technology Management (ITEM)University of St. GallenSwitzerland

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