KU Business faculty experts discuss: star performers
Star performers often drive an outsized share of results in organizations, from companies and startups to digital platforms and teams. While their impact can fuel growth and innovation, it can also create risk, inequality and overreliance. In this KU Business faculty experts blog, researchers unpack what defines star performance, what research reveals about its effects and what leaders should consider when managing exceptional contributors.
Kaushik Gala, is an assistant professor of entrepreneurship, studies star performance and breaks down what research tells us about star performance, why it creates both opportunity and risk, and what leaders should consider when managing exceptional contributors. Gala has a doctorate in entrepreneurship from Iowa State University, an MBA from the University of Texas, Austin, a M.S. in electrical engineering from the University of Minnesota, Twin Cities, and a bachelor's in engineering from the University of Pune in India.
Who are star performers?
Star performers are those who outperform their peers by orders of magnitude. Individuals, teams, entrepreneurial ventures, or firms can be star performers. Depending on who we are talking about, performance may be measured using metrics such as productivity, sales, user growth and profit margins.
What does research tell us about how much of an organization’s output is typically driven by its top performers?
Research consistently shows that star performers account for most of the cumulative output in many organizational contexts. This is commonly referred to as the Pareto or 80/20 Principle, wherein 80% of all output is contributed by 20% of those involved in generating it. In digital environments, this concentration is often even more extreme. For example, a small fraction of star sellers on platforms such as Amazon captures the vast majority of total sales across millions of sellers. Indeed, a single star seller can generate over 1,000 times the output as a typical seller on such platforms.
How can heavy reliance on star performers contribute to inequality within organizations?
Star performance is often generated through winner-take-most or winner-take-all dynamics. Because stars contribute disproportionately to the output, they often capture a disproportionate share of rewards and attention. This creates a positive feedback loop: stars get more legitimacy and resources, which allows them to perform even better, further widening the gap between the stars and non-stars.
What risks do organizations face when too much value is concentrated in a few star performers?
The primary risk from such concentration is systemic fragility. For example, if a digital platform earns a share of the revenue from its constituent star performers, the platform owner-operator can experience a sudden drop in revenue if such stars choose to leave the platform or multi-home (i.e., simultaneously operate on competing platforms). We also see this in the high-tech sector, where the departure of a single AI engineer can derail a startup’s growth trajectory, a reality reflected in the eight-figure compensation packages used to ‘lock in’ or lure such critical human capital.
What are common misconceptions about understanding and rewarding performance?
The biggest misconception is the “fallacy of the average” - the belief that performance follows a Bell curve. Organizations often try to manage star performance using systems designed for average performance. Another misconception is the role of luck versus skill. Research suggests that, beyond a point, extreme performance may be explained as much by luck and randomness as by knowledge, skills, and abilities.
In addition to averages, what metrics should we be tracking?
We must look at the skewness of the performance distribution. First, compare median performance with average performance; these can be substantially different when performance follows a non-normal curve, and the average is ‘pulled’ higher by star performance. Second, we should compare the maximum performance to the median performance to sense the extent of outperformance by stars. Finally, we also define a suitable criterion for star performance and measure the fraction of performers that are stars and their collective contribution vis-à-vis the cumulative output.
What should leaders be thinking about if they want exceptional performance without over‑reliance on a few star performers?
Besides recruiting and retaining stars, leaders should also focus on the generative mechanisms that produce star performers. A key mechanism is the multiplicative effect at work. Exceptional output is rarely the result of being “10 times better” in a single dimension. Instead, star performers are often “good enough” (i.e., above a certain threshold) across several complementary fronts, such as technical proficiency, communication skills, and citizenship behaviors. When these performance drivers interact in a multiplicative rather than additive fashion, the result is an exponential increase in output. Interestingly, research also suggests that stars tend to have exceptional expectations for performance compared to their peers. This suggests that leaders should cultivate an organizational culture that normalizes high-variance expectations and encourages “extreme” outcomes.