The present study compared the effectiveness of two decision-making approaches—statistical judgment and clinical judgment—in selecting employees for job rotation based on the Effort–Reward Imbalance (ERI) model. This applied study used a quasi-experimental pretest–posttest design with a control group. The population consisted of production-line employees of an automobile manufacturing company. Sixty participants were selected through convenience sampling and randomly assigned, after the pretest, to statistical judgment, clinical judgment, or control groups.

Data were collected using the standardized Effort–Reward Imbalance questionnaire developed by Siegrist and semi-structured interviews with managers and supervisors to operationalize clinical judgment. Data were analyzed using SPSS version 27 through paired-samples t-tests, one-way analysis of variance (ANOVA), and the LSD post hoc test.

Results showed that the statistical judgment group experienced significant increases in both effort and reward (p < 0.001). Mean effort increased from 14.00 to 17.35, and mean reward increased from 28.25 to 32.10. Significant differences were also found among the three groups, and post hoc analyses confirmed the superiority of the statistical judgment approach over the clinical judgment and control groups. Additionally, the effort–reward ratio was significantly lower in the statistical judgment group, indicating a more favorable perceived balance.

Overall, the findings suggest that data-driven decision-making in selecting employees for job rotation can improve employees’ perceived effort–reward balance. However, limitations such as the sample size and focus on a single industrial setting require caution in generalizing the results.