Designing a Collaborative Compliance Framework for Motor Vehicle and Heavy Equipment Tax Validation in a Gated Industrial Zone: Lessons from Bintan Inti Industrial Estate - Kurnianingsih, Fitri; Mayarni; Lamidi (Kemudi, 2026)
The objective of the research is to explain how collaborative tax validation can be structured as a repeatable and auditable mechanism in an industrial-estate environment and to formulate an implementable framework for optimizing local revenue realization. The study employs a qualitative secondary-data design using document-based evidence, including relevant laws and implementing regulations, administrative procedures and SOPs, institutional reports, and publicly accessible industrial-estate documents. Data were analyzed through qualitative content analysis with iterative coding to identify recurring themes and bottlenecks in the validation process, followed by synthesis into a phased implementation model and governance requirements. The results indicate that the main leakage risk is driven less by outright non-payment and more by systematic mismatches between administrative registries and operational reality, compounded by weak evidence standards for confirming asset presence and use inside the estate. Effective collaboration is shown to depend on explicit data governance arrangements purpose limitation, stewardship roles, standardization protocols, audit trails, and dispute-resolution pathways rather than purely technical integration. The study also finds that a phased approach is most feasible: (1) standardizing identifiers and taxable-object categories; (2) piloting data-sharing governance and a minimum viable dataset; (3) implementing a validation workflow with clear exception handling; and (4) scaling through performance metrics and integrity controls. In conclusion, collaborative validation can be strengthened when designed as routine compliance infrastructure that reduces ambiguity, clarifies institutional roles, and produces verifiable evidence for liability confirmation, thereby improving the sustainability and predictability of local revenue collection in industrial regions.