The innovations of creative individuals are regarded as vital for business functioning and survival. To this end, efforts have been made to design measures of creative personality in hopes of predicting creative performance. Current measures of creative personality all reside at the explicit level, yet theory and research both suggest that a large proportion of personality can also be conceptualized at the implicit level. We address this issue by presenting a theoretical basis for creative personality that operates on an implicit level. Using conditional reasoning methodology, we describe five cognitive biases that serve as justification mechanisms for creative personality. Next, we link implicit creative personality to creative abilities through a developmental process. We then test this model and our new measure of creative personality in five different studies. Our results provide evidence in support of an implicit component of creative personality and suggest that it is a substantial predictor of creative performance. Finally, we describe the management and human resources implications of the conceptualization of creative personality as an implicit construct.
Current methods used in the analysis and interpretation of behavioral data tend to ignore a potentially important explanatory component. That component is the joint variance shared between predictors in explaining variance in the outcome variable. The authors provide an example of joint variance and how it could be interpreted. The authors believe ignoring this component has inhibited development of explanatory theories. The authors discuss a method developed by Mood for calculating joint explanatory variance. This method was initially developed to better interpret the unique effects of predictors on a criterion but can also be used to gain a better understanding of joint effects as well. They reanalyze published data to demonstrate the contribution of this approach in analyzing and interpreting behavioral data. They also provide a method for calculating the significance of joint variance components.
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