At the core of my research agenda in strategy is the question: what explains high performance? I am interested in developing rigorous explanations of persistent performance as well as understanding what can be learned from top performers. I am also interested how managers try to learn from top performers and the result of such imitation processes. Will best practices spread as a result of imitation? When does imitation fail or lead to adoption of risky practices? Most important, when and why can strategic opportunities exist despite attempts to imitate best practices?
In a series of papers I have analyzed when top performers may not be the most impressive and when imitation of top performers can lead to adoption of inefficient and risky practices. Consider, for example, sustained performance. Are firms with high and sustained performance the most impressive? In Denrell and Liu (Proceedings of the National Academy of Sciences, 2012) we show that if firm performance is subject to strong self-reinforcing, “rich-get-richer”, processes, sustained performance may be less informative about skill than what less sustained and less extreme performance is. The implication is the highest and most sustained performers may not have the highest expected ability and should not be imitated or praised. Rather firms with moderately high performance have the highest expected level of skill. The intuition is that sustained performance indicates the presence of strong rich-get-rich dynamics rather than exceptional skill. Sustained performance can therefore be an unreliable indicator of superior capabilities; a topic that we explore empirically in (Denrell, Fang, and Zhao, 2013, Strategic Management Journal).
Or consider forecasters. Are the most accurate forecasters the necessarily the best? In Denrell and Fang (2010, Management Science) we show that an accurate prediction about an extreme event (such as the mortgage loan crisis or the Internet Bubble) may indicate inferior rather than superior foresight. The mechanism is that forecasters who frequently make bold predictions are more likely to predict extreme events. Forecasters who often make bold predictions, however, are likely to be those that overreact to weak signals. The implication is that accurate predictions of successful new products will be associated with intuitive forecasting methods, but the reason is that such methods tend to generate extreme predictions.
My work on strategy is informed by my research on learning. In particular, I am interested when and why managers and others may develop inaccurate beliefs about the determinants of performance. When learning fails, a market opportunity could exist. Denrell, Fang, and Winter (2003, Strategic Management Journal) develop this argument in more detail and relate it to research in strategy and, to discussions of coordination failures in incomplete markets in economics. In a recent paper (Denrell, Fang, and Liu, Academy of Management Review, 2019) we explore when cognitive biases give rise to profit opportunities in strategic factor markets. In particular, we examine when failure to understand the role of luck, and regression-to-the-mean, can give rise to profit opportunities and how firms can position themselves to be prepared for such opportunities.
A recurring theme in my research on strategy and performance dynamics is the importance of taking into account chance variation. A survey outlining the importance of such chance mechanisms was recently published in Organization Science (Denrell, Fang, and Liu, 2015). In Denrell (2004, Management Science) I proposed that a random walk model could account for much of the empirically observed persistence in performance. Recent re-analysis of profitability data has confirmed that a random walk model provides a superior fit compared to models assuming heterogeneity in firm averages (Henderson et al., 2012, Strategic Management Journal). The fact that such chance models explain much of the performance variation has important normative implications; an area I am currently working on.
Publications