Creativity in engineering: systems thinking approach to improve understanding

Abstract

On the surface, creativity seems to be an attainable, easy-to-master construct. However, ‎when diving into the literature, the observer will realize that creativity is a complex, ‎multifaceted, highly debatable phenomenon with many definitions, models, and factors ‎attached to it. Given the complexity and the multi-disciplinary nature of creativity, this ‎dissertation took the systems thinking approach to organize its associated landscape and ‎confirm the effect of some factors on engineers. The mission was accomplished by first ‎proposing a data-driven definition for creativity based on the analysis of 166 definitions. ‎Second, a classification of the vast number of models describing creativity was proposed ‎based on the analysis of tens of creativity related papers. The classification resulted in ‎five categories: Level Models, Thematic Models, Process Models, Mental Models, and ‎Ecological Models. Third, a nested model for the five creativity levels: mini-c creativity, ‎little-c creativity, ed-c creativity, Pro-c creativity, and Big-C creativity was proposed based ‎on an extended analysis of four creativity level models: 2C Model, 3C Model, 4C Model, ‎and 5C Model. Fourth, an enhanced thematic model for the seven strands of creativity: ‎person, process, product, press, measure, persuasion, and potential was proposed based on ‎the ‎analysis of five creativity thematic models: 3PM Model, Rhodes 4P Model, Simonton ‎‎4P ‎Model, 5P Model, and 6P Model. Fifth, a simplified process model for creativity ‎consisting of three ‎interconnected steps: problem understanding, divergent thinking and ‎convergent ‎thinking was proposed based on an extended analysis of two creativity process ‎models: Wallas Model and Osborn-Parnes Creative Problem-Solving Model and an overall ‎analysis of six other models. Sixth, a classification of the vast number of factors affecting ‎creativity was proposed based on ‎the analysis of tens of creativity related papers. The ‎classification resulted in three categories: personal ‎characteristics, environmental ‎characteristics, and approaches and tools. Seventh, an ecological model for creativity ‎based on the classification suggested ‎for creativity models as well as the classification ‎suggested for creativity ‎factors was proposed. Eighth, the relationship between a set of ‎personal characteristics (biological ‎factors, knowledge and experience, personality, creative ‎self-efficacy, and creative ‎potential), task engagement, and creative performance was ‎studied to confirm their effects in the field of engineering. Ninth, the effect of near and ‎far cues on the creative performance of engineers was investigated both behaviorally and ‎neurologically and no statistically significant differences were detected.‎