Taichi Kimura: Evaluation, Bias, and Its Resolution

2023/11/27

My specialization is management accounting, particularly performance evaluation. In recent years, I have been focusing on the issues surrounding subjective performance evaluation.

The foundation of performance evaluation is what is known as objective performance evaluation. This involves evaluating employee performance using objective metrics such as sales, costs, and profits. The intent is to motivate employees by linking objective performance to incentives. However, drawbacks to objective performance evaluation have also been pointed out. For example, if a sales department employee's performance is evaluated solely based on their sales quota achievement rate, that employee might resort to aggressive sales tactics just to meet the quota. Even if the quota is temporarily met, the reputation of a company engaging in such sales activities will likely decline.

Therefore, subjective performance evaluation has gained attention as a way to compensate for the flaws in evaluation based on objective metrics. By having supervisors make subjective judgments about their subordinates' work, aspects that tend to be overlooked in evaluations based on objective metrics can be incorporated. However, this has the disadvantage of allowing bias to enter the evaluation results. Since evaluations are determined by the evaluator's subjectivity, they may consciously or unconsciously become too lenient (leniency bias) or result in similar evaluations for everyone (central tendency bias).

To eliminate bias, a practice called "calibration" has been attracting attention in recent years. In this process, after an immediate supervisor performs an initial evaluation, a calibration committee composed of multiple managers compares and reviews the various evaluation results to reconsider whether each evaluation is appropriate. Calibration is conducted with an awareness of "aligning perspectives," "reconciliation," and "adjustment" of evaluations; if necessary, the results of the initial evaluation are rewritten.

It is unclear whether calibration will solve the problems of subjective performance evaluation. In fact, empirical research published in recent years points out that while the introduction of calibration mitigates leniency bias, it worsens central tendency bias by adjusting extreme evaluations. Regarding performance evaluation, we are still in the midst of a process of trial and error, aiming for better system design.