Acceso usuarios
Herman Aguinis
We use the metatheoretical principle of cumulative advantage as a framework to understand the presence of heavy-tailed productivity distributions and productivity stars. We relied on 229 datasets including 633,876 productivity observations collected from approximately 625,000 individuals in occupations including research, entertainment, politics, sports, sales, and manufacturing, among others. We implemented a novel methodological approach developed in the field of physics to assess the precise shape of the productivity distribution rather than relying on a normal versus nonnormal artificial dichotomy. Results indicate that higher levels of multiplicity of productivity, monopolistic productivity, job autonomy, and job complexity (i.e., conductors of cumulative advantage) are associated with a higher probability of an underlying power law distribution, whereas lower productivity ceilings (i.e., insulator of cumulative advantage) are associated with a lower probability. In addition, higher levels of multiplicity of productivity, monopolistic productivity, and job autonomy were associated with a greater proportion of productivity stars (i.e., productivity distributions with heavier tails), whereas lower productivity ceilings were associated with a smaller proportion of productivity stars (i.e., productivity distributions with lighter tails). Results serve as a building block for future theory development and testing efforts aimed at understanding why, when, and how the distribution of individual productivity may follow a nonnormal curve—and to what extent. We also discuss implications for organizations and management in terms of the design and implementation of human resource systems (e.g., selection, training, compensation), as well as for individuals interested in becoming productivity stars themselves.
