CAN MULTI-SOURCE FEEDBACK CHANGE PERCEPTIONS OF GOAL ACCOMPLISHMENT, SELF-EVALUATIONS, AND PERFORMANCE-RELATED OUTCOMES? THEORY-BASED APPLICATIONS AND DIRECTIONS FOR RESEARCH

Authors

  • MANUEL LONDON,

    Corresponding author
    1. State University of New York at Stony Brook
      and requests for reprints should be addressed to Manuel London, 306 Harriman Hall, State University of New York at Stony Brook, Stony Brook NY 11794-3775 or e-mail at MLONDON@CCMAIL.SUNYSB.EDU.
    Search for more papers by this author
  • JAMES W. SMITHER

    1. La Salle University
    Search for more papers by this author

  • The authors express appreciation to Robert Boice, Richard Reilly, Gerrit Wolf, and three anonymous reviewers for their constructive comments on earlier drafts of this manuscript.

and requests for reprints should be addressed to Manuel London, 306 Harriman Hall, State University of New York at Stony Brook, Stony Brook NY 11794-3775 or e-mail at MLONDON@CCMAIL.SUNYSB.EDU.

Abstract

Multi-source feedback extends traditional performance appraisal by collecting information from subordinates, peers, supervisors, and customers. Ratees often receive the results along with normative data and self-ratings. This paper explores how multi-source feedback goes beyond traditional performance appraisal by providing ratees with comparative information. Focusing on person perception and information processing dynamics, this paper develops a model and associated propositions to explain the effects of multi-source feedback on perceptions of goal accomplishment, re-evaluation of self-image, and changes in outcomes such as goals, development, behavior, and performance. Moderators of relationships between the major components in the model include individual difference variables (self-image, feedback seeking, self-monitoring, task-specific self-efficacy, and impression management) and situational conditions (the content and process of multi-source feedback and organizational performance standards). Issues of research and practice intended to improve understanding and effectiveness of multi-source feedback are discussed.

Ancillary