The Variance of Variance

Chance models – mechanisms that explain empirical regularities through unsystematic variance – have a long tradition in the sciences but have been historically marginalized in management scholarship, relative to an agentic worldview about the role of managers and organizations. An exception is the work of James G. March and his coauthors, who proposed a variety of chance models that explain important management phenomena, including the careers of top executives, managerial risk taking, and organizational anarchy, learning, and adaptation. This paper serves as a tribute to the beauty of these “little ideas” and demonstrates how they can be recombined to generate novel implications. In particular, we focus on the example of an inverted V-shaped performance association centering around the year when executives were featured in a prominent listing, Barron’s annual list of Top 30 chief executive officers. Our recombination of several chance models developed by March and his coauthors provides a novel explanation for why many of the executives’ exceptional performances did not persist. In contrast to the common accounts of complacency, hubris, and statistical regression, the results show that declines from high performance may result from the way luck interacts with these executives’ slow adaptation, incompetence, and self-reinforced risk taking. We conclude by elaborating on the normative implications of chance models, which address many current management and societal challenges. We further encourage the continued development of chance models to help explain performance differences, shifting from accounts that favor heroic stories of corporate leaders toward accounts that favor their changing fortunes.