A Systemic Approach to Evaluate Maturity in Data-Driven Human Resource Analytics: The Multi-Level Perspective Framework

Document Type : Research Article

Authors

Hamidreza Aghaeian ¹

Ali Parannejad ²

Hasan Boudlaie ³

¹ Department of HR management and Organizational behavior, Kish International campus Tehran University, Kish, Iran.

² Department of leadership and Human Capital, Faculty of Management, University of Tehran, Tehran, Iran.

³ Department of Industrial Management, Kish International Campus, University of Tehran, Kish, Iran.

10.22067/jstinp.2025.94182.1159

Abstract

Many maturity models for Human Resource Analytics (HRA) do not capture how capabilities evolve across levels within and outside the organization. We use a systems-thinking framework to assess HRA maturity across five stages, from Initial to Optimized. The study combines the Fuzzy Analytic Hierarchy Process (FAHP) to weight nine key performance indicators (KPIs) and the Multi-Level Perspective (MLP) to interpret how change is shaped by outside pressures, current routines, and local innovations. The nine KPIs cover training, data security, system integration, budgeting, and related enablers. Results show a clear shift in priorities: early stages focus on funding, training, and basic safeguards, while later stages emphasize full integration and continuous improvement. MLP helps connect these internal shifts to wider forces of digitalization and policy. The work offers practical guidance for organizations in Iran that aim to build HRA in resource-constrained settings. We also outline next steps, including simple simulation tools and adaptive roadmaps, so leaders can test “what if” choices and plan long-term alignment between HR strategy and changing socio-technical conditions.

Keywords

Human resource analytics maturity model

Multi-level perspective

Systems thinking

Data-Driven human resource management

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