<![CDATA[Selecting the best employees at Kopi Twenty relies on subjective managerial assessment. This study compares the convergence, effectiveness, and stability of multi-attribute utility theory (MAUT) and simple multi-attribute rating technique (SMART) in a decision support system for employee selection. A descriptive quantitative approach based on computational simulation was applied to five employees using four criteria: attendance, store arrangement, customer service, and discipline. Sensitivity was assessed in two scenarios involving a change in criterion weights and the addition of an extreme alternative. Under baseline conditions, MAUT and SMART produced identical preference scores and rankings. In the weight variation scenario, the reported rankings changed but remained identical across the methods. When an extreme alternative was added, the reported MAUT scores for the original employees changed with the value range, whereas their SMART scores remained unchanged. The study concludes that computational convergence does not establish the substantive fairness or validity of managerial decisions, which also depend on criterion weights and initial assessments. The system should therefore serve as an explanatory decision support instrument alongside managerial judgment.]]>
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