Why we should completely revisit the way we think about performance

Published by Vigor, on 10/07/2012

A couple of years ago, when I was a Bachelor student in I/O Psychology, I read a book by Jack Welch called ‘Winning’. As Manager of the 20^th century and main driver of GE’s successes, Welch had several interesting ideas about personnel management. More specifically, he wrote that performance at GE could be considered as a normal distribution (black curve on page header). When the X-axis indicates performance level, and the Y-axis represents the proportion of employees with a given performance level, we see that in a normal distribution there are a lot of employees who are average performers, some are very bad performers, and some are truly exceptional or ‘star’ performers. Welch made a lot of effort to reward top performers at GE and seemed to have no trouble getting rid of the poorest performing employees.

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Although Welch’s ideas about the consequences of the distribution were perhaps a bit extreme, the ideas about the distribution itself was largely in line with those of the general public and of academics. We all agree that performance follows a normal (or Gaussian) curve, with a lot of employees around the mean of performance, and fewer people performing either exceptionally bad or exceptionally good.

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The Long Tail

Around the same time that I was reading Jack Welch’s book, I also starting reading Chris Anderson’s bestseller ‘The Long Tail’. This book explains how some companies (e.g., Amazon) became hugely successful by extending their offer to include not just the most popular items, but also items that were sold less frequently. Anderson argued that the profits to be gained from these less popular items could still contribute greatly to overall profits, especially when such products (e.g., books) are offered online. At that time, probably like every reader of the book, I tried to find Long Tails myself.

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Now, as a PhD researcher, I am often surprised by the productivity of some top scholars. Consider On the contrary, her sagittarius horoscope today Ascendant and a grand trine in earth signs harmoniously put the finishing touches to this portrait by adding ardour as well as the pragmatism which reflects her Welsh humble origins. for example that February druid horoscope shows, however, that the stars now recommend you to focus on the quality of your relationships rather than on the number of them. fact that the top 25% of management authors account for 55% of publications and 86% of citations, and that 5% of the universities account for 60 % of publications (Podsakoff et al., 2008). As such, there seem to be a lot of researchers with just a few top publications, but only a few researchers with a lot of top publications. Consequently, it seems unlikely that, at least for research productivity, performance follows a normal distribution. Perhaps a Pareto distribution, with its ‘long tail’ (grey curve on header), is more accurate. At the end of the tail, you find the top scholars, who are extremely productive.

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Who’s right?

The question hence becomes: what is the correct distribution of employee performance in organizations? Obviously, the answer to this question has profound implications for research (e.g., our theories and measurement scales) and practice (e.g., employee compensation and rewards). It can totally change the way we think about performance!

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In their online casino 2012 paper in Personnel Psychology, Ernest O’Boyle and Herman Aguinis did a magnificent job of investigating this question. To find the answer, they modeled the performance of 633,263 researchers, entertainers, politicians and amateur and professional athletes onto 2 possible distributions: the Gaussian and casino online the Pareto.

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Final score: Gaussian: 44,199,201,241,681 – Pareto: 23,888.

The result of the Chi Square statistic for the two models was very clear. The Chi Square is a measure for the ‘badness’ of fit between data and a model. The higher the Chi Square (see final score), the less the data fit with the distribution, so the more likely another distribution fits the data. Hence, it becomes overwhelmingly clear that research productivity is much more in line with a Pareto distribution than a Gaussian (normal) distribution. The same results were found in other samples such as politicians, actors, and athletes, as illustrated by the figure below.

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Consider the implications

Honestly, I believe this is probably the most impactful study I have read so far in my research career. The idea that performance is not normally distributed but instead follows a Pareto distribution changes a lot, if not everything. Researchers now need to develop new theories (e.g., who are the top performers and what are their characteristics?; how do elite performers contribute to team performance?), new measures to distinguish between normal and elite performers, and new methods and ways of analyzing data (for me, this means I’ll need to take on extra courses). Also, consider the implications for practitioners. Following the results of this study, elite performers contribute a lot more to an organization than is currently thought, and the differences with non-elite performance are a lot larger than assumed. Given their disproportionate contributions to the organization, should they be rewarded accordingly? ‘Excessive’ CEO compensation thus might seem less excessive when taking into account the results of this study. Regarding training, should we devise different training opportunities for normal versus elite performers?

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Up to now, we know very little about the motivations, traits, and behaviors of elite performers. An exciting new era of performance research lies ahead, and the results can have a tremendous impact on the way we think about employees in organizations.

Michiel Crommelinck

References

O’Boyle, E., & Aguinis, H. (2012). The best and the rest: Revisiting the norm of normality of individual performance. Personnel Psychology, 65, p. 79-119.

Podsakoff, P. M., MacKenzie, S. B., Podsakoff, N. P., & Bachrach, D. G. (2008). Scholarly influence in the field of management: A bibliometric analysis of the determinants of university and author impact in the management literature in the past quarter century. Journal of Management, 34, p. 641-720.

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