Abstract

We live in the age of complexity. Yet, complexity is rarely studied in MIS. The paper studies cognitive complexity in an important IS domain – data modeling. The focus is on novice data modelers. Four major sources of complexity principles are identified: problem solving principles, design principles, information overload, and systems theory. Based on prior literature, the factors that lead to complexity are listed in each category. Each factor is then applied to the context of data modeling to gauge the extent to which it affects data modeling complexity. Redundant factors from different sources are ignored, and closely linked factors are identified. The factors are then integrated to come up with a comprehensive list of factors, which are divided into two categories – those that are intrinsic to data modeling and are difficult to control, and those than can be addressed to minimize data modeling complexity.

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