<![CDATA[The advancement of digital governance requires municipal recruitment processes that are transparent, accountable, and based on measurable criteria. In many local government environments, recruitment remains manual or semi-structured, increasing subjectivity, reducing efficiency, and limiting the traceability of decision outcomes. Although Decision Support Systems (DSS) using the Simple Additive Weighting (SAW) method are widely applied for candidate ranking, prior work often emphasizes technical scoring accuracy with limited attention to Smart City governance needs such as transparency, auditability, and accountable decision justification. This study develops and evaluates a SAW-based DSS to support objective, transparent, and traceable recruitment decisions within a Smart Governance context. Using a quantitative system development approach, candidate attributes were transformed into numerical scores and assessed through weighted criteria: education, work experience duration, English proficiency, age (cost criterion), and relevance of work experience. The SAW computation produced consistent and interpretable rankings, with the highest preference score reaching 98.462, indicating reduced reliance on unstructured subjective judgment. Usability testing using the System Usability Scale (SUS) yielded an average score of 87.6 (“Excellent”), demonstrating strong acceptance and practical feasibility across stakeholder roles. Overall, the proposed system functions as a governance-support tool that strengthens transparency and accountability in public-sector recruitment.]]>
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