Technology‐Driven Transformation of Compensation Systems: Mapping the Causal Structure of Digital Reward Systems

Abstract

Traditional reward systems, which are largely salary‐centered and structurally rigid, are no longer sufficient in an era defined by digital transformation. Building on this premise, this study examines how emerging technologies reshape integrated compensation systems by uncovering their causal, mediating, and outcome‐oriented roles. A systematic literature review, following the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) protocol, first identified 15 technology‐related variables across the reward mix. These were subsequently refined through multiple focus group sessions and modeled using neutrosophic fuzzy interpretive structural modeling (NFISM) to map their hierarchical interdependencies. The findings reveal a five‐level structure comprising three overarching roles: (i) foundational drivers, (ii) intermediary tools, and (iii) outcome‐focused influencers. “Revolution in Performance Management” and “Shift from Fixed Pay to Individual” emerged as the principal drivers shaping the entire system. Mid‐level variables, including compensation automation, data‐driven evaluation, retention analytics, and job satisfaction analytics, serve as transmission mechanisms that translate structural reforms into operational improvements. Outcome variables, such as motivation, fairer pay, and simplified reward management, sit at the highest level. Sensitivity analysis demonstrates the hierarchy's substantial stability under varying expert judgments. The study contributes an integrated, technology‐centered framework that clarifies how digitalization restructures modern reward systems and offers a strategic pathway for organizations seeking to implement technology‐enabled compensation reforms. The findings highlight the role of automated reward‐system technologies in reducing payroll costs (dependent power = 6.75), enhancing data‐driven performance evaluation (dependent power = 6.59), and supporting individualized pay structures (dependent power = 5.64). Talent analytics also emerges as a key mediating mechanism (influence score = 7.25), linking foundational drivers to organizational outcomes. Future research could explore cultural, ethical, and legal aspects of technology adoption and undertake comparative studies across industries and regions. Future studies could examine the cultural, ethical, and legal aspects of technology acceptance. In addition, future studies could conduct comparative research across industries and geographic regions with different cultures.