Validity of A Clinical Method for Predicting Leadership Performance Part 1
How Clinical Assessment Techniques Improve Predictive Accuracy When Assessing Candidates for CEO
By Leslie S. Pratch
Previously, I presented a psychological model for predicting leadership in challenging or changing conditions. In research funded by the University of Chicago Graduate School of Business, I led the first systematic effort to identify in advance individuals with the psychological resources needed to be successful business leaders. That research established the ability of measures under our hypotheses to predict leadership effects, beyond conventional standards of chance occurrence, among already high-achieving individuals. Subsequently, with my colleague Jordan Jacobowitz, we conducted the first-ever empirical study into the personality characteristics of successful CEOs of private equity-funded ventures.
A central feature of our model is a construct called active coping. Active coping is the readiness to adapt resourcefully and effectively to challenge and change. Active coping predicts the behavior and success of leaders when the setting — the characteristics of the environment to which one is making predictions — cannot be fully specified in advance.
In this series, I examine how well this model predicts the performance of executives functioning in private equity-funded ventures. In my work with private equity firms, I seek to enhance investors’ due diligence by establishing whether operating executives who have a disproportionate influence on firm performance (measured as cash-on-cash returns) will successfully achieve the goals of the enterprise business strategy. The initial formulation of this strategy is a product of investors’ skill combined with their understanding of the executives who will have to implement it. Investors and the portfolio company executives will have to adjust to respond to changing circumstances.
As noted, we assume that an executive’s past performance is partially the result of happenstance, that is, factors extraneous to and uncontrolled by him or her. We do not infer that an executive’s past success automatically implies the capacity to succeed in a new environment. Rather, we disentangle the role of chance and circumstance from the executive’s objective past and ongoing ability to function under conditions of challenging uncertainty.
Just as investors evaluate a company to understand the underlying enterprise bases for earnings growth, we assess an executive to explain and predict individual performance. We measure performance success against the degree to which the executive has met the requirements of his or her role. Those role requirements follow from the investment thesis articulated by investors, and elaborated and accepted by operating management. Our prediction of how the executive will function in the management role emerges from an analysis of the person, the organization, and the environment.
Our clinical method allows us to assess the underlying psychological forces that lead to success or lack of success among already high achieving individuals. By examining the person-organization-environment interplay, we can set forth scenarios in which an executive’s underlying psychological tendencies might present risks — or opportunities — to investors, so that investors can provide appropriate support and incentives. Thus, in addition to helping in the due diligence process, our clinical method also indicates ways to structure and manage the relationship between the investor group and the operating executive.
Our model incorporates a time dimension in the definition of success or failure: the longer the interval between assessing predictor variables and measuring outcome variables, the greater the chance that outcomes could deviate from expectations. Individuals change and conditions change.
In this series, I focus on how closely predictions generated by our system were borne out in reality. My next post will describe our structural psychological approach to assessment. After that, I’ll describe the characteristics of our data. Then, I will report our findings. I will also discuss how characteristics of our model uniquely lend themselves to predicting performance in conditions of complex and possibly dangerous uncertainty.
