Corpus linguistics for leadership studies: Bridging the quantitative-qualitative divide

Our aim in this paper is to add to the methodological toolbox of qualitative research in leadership by illustrating how corpus linguistics (CL) can be used to study large-scale datasets comprised of text or talk. CL differs from other approaches to analysing large-scale textual data, such as topic modeling and sentiment analysis, because it enables detailed quantitative and qualitative analysis of linguistic choices on the level of vocabulary and grammar. CL can be used to study text or talk produced by leaders (such as CEO speeches, letters to shareholders, or interviews) or written about leaders (such as newspaper or magazine articles, social media posts, or biographies). Whilst CL methods can be applied using R or Python, here we demonstrate how a user-friendly proprietary software, Sketch Engine, can be used. We illustrate the relative strengths of the method using a corpus of media texts comprising leader profiles published in The Times (UK) newspaper where senior executives (n = 733) answered the question “What does leadership mean to you?”. We conclude by discussing the potential that CL offers for informing future research and theory development, spanning positivist, interpretivist and social constructionist styles of theorizing. We also outline the practical benefits the method offers for improving leadership practice and for people involved in leadership teaching and training by providing robust evidence about concrete and learnable behaviors.