Organizations increasingly rely on digital recruitment processes that collect detailed applicant information, including compensation expectations, scheduling constraints, workload preferences, mobility limits, and other sensitive employment-related preferences. Although this information may support hiring decisions, it also requires applicants to disclose private preference structures before either party knows whether a mutually acceptable employment relationship exists. This study proposes Selective Compatibility Signaling (SCS), a recruitment and matching mechanism that evaluates applicant preferences and employer opportunities without requiring direct disclosure of the underlying preference values. Instead, SCS releases a limited outcome indicating whether an applicant and employer have a potentially viable match. The study evaluates SCS through a simulation spanning student employment, travel-intensive home-infusion nursing, faculty hiring, and executive recruitment. Five recruitment approaches are compared using vacancy fill, applicant placement, false rejection of viable matches, direct disclosure, and preference inferability. The full analysis used 40 paired replications and the original study populations. The findings show that the value of compatibility signaling depends on market structure and preference heterogeneity. In student employment, SCS improves fill and placement and reduces false rejection, although it does not reduce threshold inferability. In nursing, matching outcomes remain unchanged while direct preference disclosure falls substantially. Faculty hiring shows improved matching and lower preference inferability, while executive recruitment shows a substantial reduction in preference inferability with largely preserved matching performance. Across the four markets, SCS preserves or improves matching while eliminating exact preference disclosure in the modeled process. The study contributes to recruitment, labor-market matching, and information-governance research by showing that organizations can assess employment compatibility through selective disclosure rather than routine exchange of detailed applicant preferences. It also identifies market complexity, preference heterogeneity, and operational constraints as boundary conditions affecting the value of compatibility-based recruitment.
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Michael Douglas (2026) studied this question.
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