Digitalization is remaking corporate pay: firms are shifting from fixed wages to individualized, performance-linked compensation delivered through automation and analytics, with talent analytics as the crucial conduit for cost and fairness outcomes. The conclusion rests on a PRISMA-based literature synthesis and expert NFISM modeling and requires field-based empirical validation before causal magnitudes can be accepted.

Main Finding

Digital technologies are reorganizing organizational reward systems into a stable five‑level hierarchy with three functional layers: foundational drivers (technology‑enabled structural shifts), intermediary tools (automation and analytics that transmit change), and outcome‑focused influencers (motivation, fairer pay, simplified reward management). The principal structural drivers identified are a “Revolution in Performance Management” and a “Shift from Fixed Pay to Individual” pay models. Mid‑level mechanisms such as compensation automation, data‑driven evaluation, and retention/job‑satisfaction analytics translate these drivers into operational outcomes. Quantitative modeling (NFISM) and sensitivity checks show this hierarchy is robust to changes in expert judgments; talent analytics is a key mediator linking drivers to outcomes.

Key Points

• Study scope: systematic literature review + expert focus groups to identify technology‑related variables in modern reward mixes.
• Variables: 15 technology‑related reward variables were identified and refined.
• Hierarchy: five levels grouped into three roles:
  • Foundational drivers: e.g., revolution in performance management; shift from fixed pay to individualized pay.
  • Intermediary tools: compensation automation, data‑driven evaluation, retention analytics, job‑satisfaction analytics (transmission mechanisms).
  • Outcome influencers: employee motivation, fairer pay, simplified reward management (top‑level outcomes).
• Modeling approach: neutrosophic fuzzy interpretive structural modeling (NFISM) used to map causal and hierarchical interdependencies under uncertainty.
• Stability: sensitivity analysis indicates strong stability of the derived hierarchy under varying expert judgments.
• Quantitative highlights:
  • Automated reward systems reduce payroll costs (dependent power = 6.75).
  • Enhanced data‑driven performance evaluation (dependent power = 6.59).
  • Support for individualized pay structures (dependent power = 5.64).
  • Talent analytics as a mediator (influence score = 7.25).
• Contributions: integrated, technology‑centered framework clarifying how digitalization restructures compensation systems and a strategic pathway for implementing tech‑enabled reforms.
• Future research recommended on cultural, ethical, legal aspects and comparative cross‑industry/region studies.

Data & Methods

• Evidence base: systematic literature review following PRISMA protocols to identify relevant studies and technology variables affecting reward systems.
• Variable refinement: multiple expert focus group sessions to validate and refine the 15 technology‑related variables.
• Modeling technique: neutrosophic fuzzy interpretive structural modeling (NFISM):
  • Rationale: NFISM handles uncertainty and indeterminacy in expert judgments, producing a hierarchical causal map of variables.
  • Outcome: a five‑level structural model with measures of dependent power and influence scores for variables.
• Robustness checks: sensitivity analysis varying expert inputs to assess stability of the hierarchical structure.
• Quantitative indicators reported in the study: dependent power values for key outcomes and an influence score for talent analytics as the principal mediator.

Implications for AI Economics

• Effect on wage structure and payroll costs:
  • Automation and automated reward systems can lower payroll costs but may also shift compensation toward individualized and performance‑linked pay, altering income risk and wage dispersion.
• Incentives, productivity, and measurement:
  • Data‑driven evaluation changes incentive design, monitoring intensity, and measurement of worker productivity—raising questions about measurement error, gaming, and effort responses.
• Role of talent analytics:
  • Talent analytics mediates effects from structural change to outcomes—implying investments in analytics capacity can multiplicatively affect organizational returns to compensation reform.
• Distributional and fairness concerns:
  • Moves to individualized pay and algorithmic evaluations can produce distributional shifts and potential fairness/equity issues that require careful regulatory and governance attention.
• Labor market dynamics:
  • Technology‑enabled reward reforms may affect retention, mobility, and matching in labor markets; heterogeneity across industries and regions likely to matter.
• Policy, legal, and ethical dimensions:
  • Need for regulation on algorithmic transparency, data protection, auditability, and anti‑discrimination when using automated evaluation and reward allocation.
• Research directions for AI economics:
  • Empirically quantify productivity and distributional impacts of automated compensation systems across sectors.
  • Model worker responses (effort, search, sorting) to individualized, algorithmic pay regimes.
  • Study complementarities between AI/analytics investments and human capital, and cross‑country comparisons to capture institutional/cultural moderating effects.
  • Evaluate welfare and labor market equilibrium consequences, and design policy interventions (transparency mandates, fairness constraints, social insurance) to mitigate adverse outcomes.

Limitations to note for interpretation: the model is based on literature synthesis and expert judgment rather than primary panel or field experiments; NFISM captures causal structure under uncertainty but requires empirical validation and calibration across contexts before generalizing quantitative magnitudes.