AI-Driven Compensation Transparency and Human Capital Accounting Disclosure: A Framework for Manufacturing Organizations in Emerging Economies

Abstract

Purpose: This study investigated the relationship between AI integration in compensation systems and the quality of human capital accounting disclosure (HCAD) among manufacturing organizations in Thailand's Eastern Economic Corridor (EEC), and developed and validated an integrated framework that links AI-driven compensation analytics to human capital disclosure practices.
Design/Methodology/Approach: A cross-sectional, exploratory sequential explanatory mixed-methods design was employed, combining quantitative survey data from 400 manufacturing organizations across Chonburi, Rayong, and Chachoengsao with qualitative thematic analysis of three open-ended questions embedded in the same survey instrument. Quantitative analysis used correlation analysis, Cronbach's alpha reliability testing, and subgroup moderation testing through Fisher's z-test. Qualitative analysis followed Braun and Clarke's (2021) six-phase thematic method, with quantitative and qualitative findings integrated through a joint display (Fetters et al., 2013). Confirmatory factor analysis, bootstrapped mediation testing, and inferential moderated regression are committed to the next research phase.
Findings: AI integration in compensation systems demonstrated moderate-to-high levels (M = 4.73), while human capital disclosure quality remained persistently low (M = 2.98), producing a data-to-disclosure gap of 1.75 points. The direct relationship between AI integration and HCAD quality was not supported (H1: r = −0.075, p = .132), nor was mediation by integration protocols (H2) or moderation by organizational size (H4). Pay transparency was confirmed as a significant positive moderator (H3: z = 2.25, p = .024), demonstrating that organizational transparency culture — rather than technological capability alone — conditions disclosure outcomes. Qualitative themes (organizational readiness, governance, ethical legitimacy) provided convergent evidence from an independent methodological lens. A tiered AI–Human Capital Accounting Disclosure (AI-HCAD) implementation framework was developed and validated across organizational segments. Barrier–enabler analysis revealed that technical constraints (M = 3.67) and weak external support structures (M = 2.45) sustain the gap between AI capability and disclosure practice.
Practical Implications: Manufacturing organizations must invest simultaneously in AI infrastructure and in an internal transparency culture to translate data capabilities into stakeholder-accessible disclosures. Policymakers and industry bodies should strengthen external enablers through regulatory guidance and technical assistance frameworks.
Originality/Value: This study provides the first empirically grounded framework that integrates AI-driven compensation systems with human capital accounting disclosure in emerging-economy manufacturing. The findings reframe AI integration as a necessary but not sufficient condition for disclosure quality — institutional readiness, embodied in pay transparency, is the enabling mechanism that translates technological capability into stakeholder-accessible reporting. By demonstrating that institutional readiness, not technological capacity, conditions disclosure outcomes, the study advances theory across AI transparency, human capital accounting, and organizational disclosure behavior.