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forces (Salancik & Pfeffer, 1978; Weick, 1979), a closer analysis of the impact of social
forces on data modeling (Ram & Ramesh, 1998) is warranted, as was also suggested in
Wand and Weber (2002).
Second, and in addition to research focusing on fundamental psychological and social
psychological processes, rigorous applied empirical research and theory development is
also needed. It is important that this work collectively covers all uses of conceptual data
modeling (see, for example, the Background section of this chapter); much of prior research
has focused on issues most closely associated with the communication between analysts
and designers. In applied research, two important characteristics of the real world modeling
tasks have to be taken into account. First, the process of model building, validation, and
implementation is almost always iterative. Models are not built in a very limited amount
of time and accepted without conceptual and empirical testing, or if they are, at least the
implementation (and the implicit, but not the documented, data model) will be changed if
modeling errors lead to application errors. Second, the elicitation, representation, and vali-
dation phases of the modeling process are normally closely integrated, and the separation
of them in research environments is often artifi cial.
In addition to broader tasks, a richer set of methodologies is also needed. A quantitative
analysis of results obtained in a laboratory environment is not enough. In addition, qualitative
techniques and fi eld data are needed. For example, Batra and Davis (1992) and Srinivasan and
Te'eni (1995) used protocol analysis (Ericsson & Simon, 1993) to gain a deeper understand-
ing of the modeling process. In-depth case studies, observations, and other methods that can
be applied in fi eld environments—for exploratory and later for theory testing purposes—are
also necessary to analyze the real effects of data modeling in organizational environments.
It is also important to continue research that studies how conceptual data modeling is, in
practice, used in the broader context of systems development (see, for example, Batra and
Marakas, 1995; Hitchman, 1995).
CONCLUSIONS
Conceptual data modeling forms an important foundation for systems development.
In this chapter, we have reviewed the existing human factors research on conceptual data
modeling. In addition, we proposed an extended framework and described avenues for
further work in this area. Also, we emphasized the importance of continuing to build a
stronger theoretical foundation based on the work in cognitive science and other relevant
reference disciplines.
ACKNOWLEDGMENTS
Earlier versions of this chapter were published in the Proceedings of the Sixth CAiSE/
IFIP8.1 Intl. Workshop on Evaluation of Modeling Methods in Systems Analysis and Design
(EMMSAD'01) and in the Journal of Database Management (Topi & Ramesh, 2002). We
gratefully acknowledge the highly valuable comments by the EMMSAD'01 participants
and the reviewers and editors of all versions of this chapter.
