Cloud-Integrated Decision Model for Aligning Recruitment Success Indicators with Compensation Progression

Organizations increasingly rely on cloud-based human resource systems to manage recruitment and compensation decisions; however, alignment between recruitment success indicators and subsequent compensation progression remains fragmented and largely intuition-driven. This study proposes a cloud-integrated decision model that systematically links recruitment performance metrics with structured compensation progression pathways. The model consolidates multi-source recruitment data, including candidate quality indicators, time-to-hire efficiency, onboarding outcomes, and early performance signals within a unified cloud analytics framework. Using a rule-assisted and data-driven decision layer, the framework translates validated recruitment success indicators into compensation adjustment triggers that support transparency, equity, and strategic workforce planning. The proposed approach enhances consistency between hiring outcomes and reward structures while enabling real-time monitoring and adaptive policy refinement. By bridging recruitment analytics and compensation management, the model enables organizations to develop a scalable mechanism for enhancing talent retention, optimizing reward allocation, and supporting evidence-based human capital decisions. The study contributes to cloud HR analytics literature by offering an integrated decision perspective that aligns talent acquisition effectiveness with long-term compensation strategy.