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Harnessing Personality Assessments to Predict Current and Future Job Performance

Much has been written recently about the rapidly changing nature of work, and the worker characteristics that will be at a premium in the workforce of the future [1] [2]. These forecasts suggest that certain personality-oriented characteristics, rather than specialized technical competence, will be most critical to successful and enduring performance.

Even though we face uncertainty regarding exactly what various jobs across our global economy will look like in the future, the great news is that we do know a lot about people, and we know how to measure characteristics that are very likely to be critical to performance. In fact, in a recent internal study conducted by the Myers-Briggs Company, we were able to demonstrate that many of the characteristics hypothesized to be critical in the future jobs are already important predictors of performance across a range of existing jobs.

Specifically, our study examined relationships between the California Psychological Inventory (CPI 260; a broad-spectrum measure of personality traits) and 360 degree ratings of a number of aspects of job performance for a diverse group of over 16,000 “on-track” managers and executives participating in a leadership development program facilitated by the Center for Creative Leadership.

The results of our analyses indicated that a core set of CPI 260 traits were meaningfully related to ratings of “human-only” aspects of performance, (i.e., tasks or behaviors that can’t be taken over by technology). These traits included empathy, tolerance, responsibility, flexibility, creativity, leadership potential, amicability, and impression management. Notice the interpersonal nature of many of these characteristics; these are behaviors that allow us to accomplish work with, and through, others. Thus, while the exact nature of the future of work is murky, we have great cause for confidence in our ability to measure those personality characteristics that will be important predictors of performance.