Peak performance : an empirical examination in workplace settings

Abstract

Understanding job performance has been an important and longstanding workplace challenge for over a century. However, continued criterion deficiency has resulted in ongoing disparity between job performance and job performance ratings (Murphy, 2008). In response, this dissertation broadens the traditional focus of job performance to consider the factors that characterize peak performance (i.e., exceptional or optimal performance; Garfield, 1986). Peak performance is a well-established concept in performance psychology that is beginning to emerge in the broader organizational literature (Hays, 2009). However, there is no known empirical work assessing its relation to traditional workplace factors. Therefore, in Study 1, peak performance was conceptualized; exploratory analyses were conducted on a newly-developed measure; and relationships between peak performance and three well-established job performance concepts were examined. They include task performance (Borman & Motowidlo, 1997; Williams & Anderson, 1991); organizational citizenship behavior (Borman & Motowidlo, 1997; Dalal, 2005; Dunlop & Lee, 2004); and counterproductive work behavior (Dalal, 2005; Dunlop & Lee, 2004). Study 2 used confirmatory factor analysis on two independent samples to confirm the peak performance measure. Regression, redundancy, moderator, and relative weights analyses demonstrated the construct and predictive validity of peak performance. Using two-way multivariate analysis of covariance, Study 3 applied the refined measure in an experimental design to demonstrate the individual and combined effects of expected performance and peak performance on ratings of three important workplace outcomes: acknowledgement, rewardability, and promotability. Together, the studies show that it is possible and important to consider peak performance in workplace research and practice. Future research should identify the individual, role, organizational, and external factors that predict peak performance. Having a better understanding of the multidimensional nature of peak performance could improve personnel-related practices, including recruitment, selection, training, performance appraisal, and promotion decisions, resulting in a better-fitting workforce that is more capable and effective.