Growth Science International, LLC

A MICRO-EXPERIMENT

Framework in Question: Great management leadership is predictive of superior firm performance.  (i.e. ‘bet on the jockey, not the horse’)

Framework Background: Venture capitalists and angel investors cited the “quality of the management team” as a top indicator of likely startup success in a 2009 poll.  Indeed, few people would prefer bad, dysfunctional teams to good, effective teams.  There are numerous empirical studies supporting the view that good leadership can, indeed, make a positive difference in company performance.  But is it predictive?

First, it should be noted that there have been many studies around this general topic.  For example, a study of 132 Methodist churches over 20 years found that new ministers with a history of growing membership and donations made similar improvements to new churches after being transferred (Pfeffer & Blake).  A study of major league baseball coaches found that better managers boosted team performance (Kahn).  A 40-year study of labor and capital productivity in the automobile industry found that leadership had an impact on firm performance (Lieberman, Lau & Williams).

Landmark research by psychologist Dean Keith Simonton similarly concluded that approximately 10% of contributors frequently account for 90% of contributions (Simonton).  This is analogous to the “80/20 rule” where 80% of work is typically done by 20% of those involved.  Similarly, one study found that only 16 composers out of 251 had produced roughly 50% of the classical music that is now performed and recorded (Sackman, Erikson & Grant).  The best and brightest have a disproportionate impact.

Our Analysis: Great teams are, by definition, better than bad teams.  This is not in dispute.  However it is quite another thing to assert that great teams are predictive of superior firm performance.  Are startups with great leaders statistically more likely to survive than startups without great leaders?

To test the predictive validity of the “great leadership” variable, 100 startups were separated into two categories (50 that survived, and 50 that were shut down).  A number of leadership-related classification schemes were then tested against the sample.

Conclusion 1:  Highly subjective. The most obvious challenge in attempting to make predictions on the basis of “great” or “bad” leadership is the inherent subjectivity within the classification scheme itself.  After all, what makes a great team “great”?  Experience?  Education?  Track record?  Intellectual horsepower?  Intra-industry expertise?  Extra-industry expertise?  Height?  Weight?  Eye color?  Animal spirit?  Other?  What is “great”?  Leadership can be largely intangible.  Therefore a relatively limited number of easily measured factors were tested in this study.  Considerations beyond these factors were not tested and therefore will not be addressed here.

Conclusion 2:  Not predictive. Acknowledging the challenges of this subjectivity, we tested the following leadership-related hypotheses against the startup leadership teams in our sample.  The results were as follows:

• Total years of higher education held by the leadership team (Bachelor’s degree or higher):  No statistically significant correlation to firm survival or failure
• Years of work experience post-higher education:  No correlation
• Years in same industry:  No correlation
• Years in “big companies” ($500 million in annual revenue or more):  No correlation
• Years in “startups” (0-3 year old firms):  No correlation
• Years in professional consulting industry:  No correlation
• Number of team members with MBA degrees:  No correlation
• Number of team members from Ivey League schools:  No correlation
• Total team age:  No correlation
• Age of CEO/Leader:  No correlation

Secondary Research: As stated earlier, this analysis was a limited attempt to address a much broader question.  To more fully explore the issue, other external studies were reviewed.

An analysis of intelligence quotient (IQ) found that IQ is the most powerful predictor of job performance across studies, but still seldom correlated more than 0.4 with performance.  In other words, IQ did not account for approximately 84% of performance (Pfeffer & Sutton).

The impact of changes in leadership was studied across 167 companies over a 20-year period and concluded that company and industry have far larger effects on sales and profits than leadership (Lieberson & O’Connor).  Leadership studies of large samples of CEOs, University Presidents and Managers of sports teams showed that organizational performance was largely determined by factors beyond a leader’s control (Pfeffer).  Several studies have also shown that changing CEOs has no statistically significant impact on organizational survival or death (Carroll and Hannan).

A wide review of leadership studies as far back as 1977 found that, while leaders have some impact, their actions rarely explain more than 10% of the difference in performance between the best and worst organizations and teams (Pfeffer & Sutton).

Summary: Bad, dysfunctional teams can have an infinite ability to destroy any business.  No argument there.  However this experiment found that the tested factors did not create statistically higher likelihoods of firm survival, and secondary research suggests that teams can have an impact – but less than you might think.

We conclude that teams with the tested qualities may indeed be nice to have, but they were not significantly predictive of survival or failure in our sample.  While a seemingly anticlimactic finding, it does suggest that betting on the “jockey” (leadership team) and not the “horse” (business itself) can easily lead to bad decisions by managers and investors alike.  Good and bad teams – as defined in our samples – failed at the same rate.

Sources:

1. Pfeffer & Blake, Administrative Succession and Organizational Performance:  How Administrative Experience Mediates the Succession Effect, Academy of Managemnet Journal 29 (1986)
2. Kahn, Managerial Quality, Team Success, and Individual Player Performance in Major League Baseball, Industrial and Labor Relations Review 46 (1993)
3. Lieberman, Lau & Williams, Firm-level Productivity and Management Influence: A Comparison of U.S. and Japanese Automobile Producers, Management Science 36 (1990)
4. Simonton, Greatness: Who Makes History and Why, Guilford Press (1994)
5. Sackman, Erikson & Grant (1968), republished by Brooks Jr., The Mythical Man Month, Addison-Wesley (1995)
6. Pfeffer & Sutton, Hard Facts, Dangerous Half-Truths & Total Nonesense; Profiting from Evidence-Based Management, Harvard Business School Press (2006)
7. Lieberson & O’Connor, Leadership and Organizational Performance: A Study of Large Organizations, American Sociological Review 37 (1972)
8. Pfeffer, The Ambiguity of Leadership, Academy of Management Review 2 (1977)
9. Carroll & Hannan, The Demography of Corporations and Industries, Princeton University Press (2000)