A multilevel investigation of the impact of culture on the relationship between self-other agreement and job performance

Date of Completion

January 2008


Psychology, Industrial




With the recent growth in popularity of multisource (360-degree) feedback programs, which typically collect behavioral or performance ratings by a focal manager and his/her supervisor, peers, and subordinates, there has been increased research interest in the relationship between self-ratings and ratings by coworkers. Both the magnitude and direction of the discrepancy between self and others' ratings are thought to be indicators of the focal manager's self-awareness and have been linked with personal and organizational outcomes such as motivation to improve, managerial effectiveness, and overall performance in U.S. managerial samples. The present research expanded this research to other cultural settings by examining self-other agreement and its relationship with performance in a multinational population. The study found that self-other (self-peer and self-subordinate) agreement on leadership behaviors was related to both people-related and business-related job performance, with underestimation of leadership associated with higher performance than overestimation. Further, using multi-level modeling, this study found that cultural practices—most notably collectivism and assertiveness—acted as cross-level moderators of self-other agreement and its relationship with performance. Specifically, managers in societies high in collectivism had a greater tendency to overestimate their leadership behaviors compared with managers in more individualistic societies. Further, managers in societies low in assertiveness provided self-ratings that were more relevant to performance outcomes than those by managers in high-assertiveness societies. Societal levels of performance orientation and power distance were less relevant to the self-other agreement-performance relationship. Findings are discussed in terms of their contribution to current theory and practice of multisource feedback programs. ^