Implicit measures in leadership categorization theory: the case for using cognitive mapping and verbal protocols to study cross-categorization phenomena

Abstract

PurposeThis paper contributes to leadership categorization theory by advocating a new method to surface people's implicit leadership theories. The purpose of this new approach is to simultaneously capture individual difference in how they conceptualize leadership but within a common framework to allow for comparison of within- and between-person effects.Design/methodology/approachThe authors conduct a narrative review of the implicit leadership theory, leadership categorization theory, cognitive mapping and verbal protocol literature with the purpose of surfacing a research method that will overcome the problems of over-simplification and over-individualization in existing methods.FindingsThe authors argue that using a combination of cognitive mapping and verbal protocols can capture the idiosyncrasies of individual lay theories of leadership while retaining the ability to compare people's responses through a common framework. The authors provide an example of how this method can be used to elicit people's perceptions of one aspect of implicit leadership theories, intelligence.Research limitations/implicationsThis new method will provide a methodology to test the subset propositions advocated by leadership categorization theory. These include the idea that subordinate level implicit leadership theories contain a subset of attributes found in the basic-level implicit leadership theories, that there is attribute integrity in superordinate implicit leadership theories through the levels, and the idea that people define leadership differently depending on the context they are observing.Originality/valueWhereas previous approaches to surfacing people's implicit leadership theories either heavily constrain their responses with a predetermined generic suite of attributes or are totally open-ended and idiosyncratic, the authors advocate an approach that combines the best of both.