Abstract
Introduction: Research on MBA student performance typically relies on GPA as the primary indicator of success. However, business schools aim to develop future leaders for diverse career paths, which value multiple forms of performance. We examine whether performance is better understood as multidimensional, testing a longstanding distinction in MBA discourse between “poets” and “quants.” We also examine how different forms of admissions data (i.e. standardized test scores, undergraduate grades, stated interests, and pre-MBA experiences) predict distinct forms of success.
Methods: We report results from two large-N studies using survey and archival data from an elite U.S. MBA program. Study 1 examines whether core course grades reflect multiple dimensions of academic performance and whether admissions-time aptitude measures differentially predict those dimensions. Study 2 replicates these findings using archival academic, extracurricular, and peer-evaluation records and extends the analysis to leadership outcomes. Confirmatory factor analysis and multivariate regression models are used across both studies.
Results: Across both studies, MBA academic performance bifurcates into two weakly correlated dimensions: systematizing (quantitative, analytical success) and social (verbal, interpersonal success). These align with the popular MBA “poet vs. quant” distinction. Quantitative aptitude predicts quantitative academic performance, whereas verbal and writing aptitude predict social academic performance. Beyond grades, social performance is uniquely associated with leadership success, including both objective attainment (e.g., club leadership roles) and peer perceptions (e.g., assertiveness and inclusiveness). Student interests further differentiate outcomes: quant-oriented interests predict quantitative academic success but negatively predict leadership attainment, whereas poet-oriented interests positively predict leadership outcomes.
Discussion: These findings demonstrate that MBA success is fundamentally multidimensional and that different admissions indicators predict different forms of performance, with implications for talent assessment, leadership development, and MBA admissions practice.