<![CDATA[This study examined how operational service evidence could inform governance innovation in end-user device (EUD) services when rapid response did not translate into consistent end-to-end recovery. This issue was practically significant because organizations often treated response SLA compliance as a sufficient indicator of service effectiveness, whereas end-to-end restoration depended on interdependent technical, logistical, and governance mechanisms that were rarely examined together. A sequential explanatory mixed-methods design analyzed 627 comparable tickets from 888 IT service management (ITSM) records and incorporated member-checked evidence from nine service stakeholders to explain the quantitative patterns. This design was selected because ticket records could reveal where performance variation occurred but could not fully explain why such variation emerged within actual service-delivery arrangements. Response SLA compliance reached 97.14%, resolution SLA compliance reached 74.68%, the median baseline recovery time was 72.33 hours, and Core Data Completeness was 72.80%. These indicators revealed a response–recovery governance paradox: strong front-end responsiveness coexisted with greater variation in restoration capability and an informational foundation that was not uniformly auditable. Location and device category showed statistically significant but small differences in recovery time, whereas the backup comparison was statistically inconclusive (p = .058). Cross-informant evidence linked recovery variation to technician readiness, logistics, vendor coordination, continuity arrangements, data quality, and device lifecycle conditions. The integration of quantitative diagnostics and qualitative explanations demonstrated that governance learning depended on sufficiently auditable service information and coordinated capabilities across internal and external actors. The study developed an ITIL 4-informed Governance Optimization Model that transformed operational records into coordinated governance mechanisms rather than treating aggregate SLA compliance as a stand-alone indicator of service effectiveness. The model’s novelty lay in its analytical sequence: service records ? diagnostic variation ? explanatory mechanisms ? governance gaps ? governance redesign. This sequence positioned innovation in the reconfiguration of governance capabilities rather than in technology adoption alone.]]>
