Performance appraisal in the changing world of work: Implications for the meaning and measurement of work performance.

Abstract We critically evaluate performance appraisal research by highlighting how research has traditionally focussed upon the measurement, rather than the meaning of work performance. We propose that maximizing performance appraisal effectiveness requires that we carefully examine the meaning of performance. Next, we consider current trends in appraisal research which have closely followed recent changes in the structure and process of work. The implications of this research for increasing our understanding of the meaning of work performance is considered.In the past decade, we have witnessed a number of profound changes in the workplace which, collectively, have tremendous implications for how individual work performance is assessed. Specifically, with the continuing trends of downsizing, mergers, and decentralization, new performance appraisal practices and research topics have emerged (e.g., 360-degree feedback; electronic performance monitoring). In this paper, we critically examine the "state of the art" in performance appraisal research. Our central thesis is that some of the most current research on the evaluation of individual level performance continues to emphasize ways of measuring performance without sufficiently concentrating upon the meaning of performance. We will argue that our continuing failure to grapple with the "criterion problem" -- the problem of properly defining what is meant by performance -- creates a boundary condition on the utility of this research for the practice of performance appraisal (cf. Austin & Villanova, 1992). In short, we believe that attempting to define performance is a critical precondition for effective performance measurement. Although this might seem obvious and straightforward, we shall soon see that this precondition is often relegated to secondary status or ignored altogether.Over the past three decades, performance appraisal has been one of the most heavily researched topic areas in the fields of Industrial-Organizational (I-O) Psychology and Human Resource Management (Pearce & Porter, 1986). The interest in performance appraisal is understandable inasmuch as organizations appraise employee performance for a variety of purposes. For example, performance appraisals are used for personnel decisions (e.g., promotions, pay increases, identifying employees for remedial training, etc.), employee feedback and development, and personnel research (e.g., selection test validation studies). Historically, performance appraisal research has treated performance measurement as a "psychometric problem", whereby the goal has been to improve the psychometric quality of performance ratings. Given that both important personnel decisions and employee feedback are often based upon performance rating data, this focus makes a great deal of sense. Ever since Thorndike's (1920) seminal paper on halo error, we have been preoccupied with rating errors and ways of eliminating them through the development of optimal rating formats and rarer training programs (Murphy & Cleveland, 1991). Unfortunately, however, relatively lost in our quest to achieve optimal rating systems has been a concomitant search for the meaning of performance. A number of scholars have repeatedly raised the concern that we have not paid sufficient attention to the meaning of performance (e.g., Austin & Villanova, 1992; Smith, 1976; Wallace, 1965). However, there have been some recent attempts to map out theories of work performance (e.g., Borman & Motowidlo, 1993; Campbell, McCloy, Oppler, & Sager, 1993). These theories attempt to provide performance dimensions (e.g., demonstrating effort, helping and cooperating with others) thought to generalize across jobs. Although ambitious, these theories are necessarily limited in that they cannot possibly capture the entire criterion domains for all jobs. Moreover, they do not provide details concerning levels of performance within dimensions -- the performance standards that underlie each dimension. …