From Instinct to Intelligence: People Analytics as a Framework for Human-Centred HRM in Nigerian Manufacturing Organizations

Abstract

Purpose — Nigerian manufacturing HRM remains intuition-driven, applying uniform motivational strategies to an occupationally diverse workforce lacking data infrastructure. This article argues that people analytics applied to motivation and satisfaction data provides the methodological foundation for human-centred HRM consistent with Society 5.0's vision of technology serving individual flourishing.
Aims — The article maps motivation–satisfaction evidence onto the four-level people analytics maturity model, develops a phased implementation roadmap for Nigerian industrial contexts, and constructs an ethical risk matrix ensuring analytics serves worker flourishing rather than surveillance.
Design/methodology/approach — A cross-sectional survey of 144 employees across Lagos, Kano, and Port Harcourt employed validated instruments (Cronbach α = .76–.93), hierarchical regression, and moderation analysis (PROCESS macro), mapped as a proof-of-concept across descriptive, diagnostic, predictive, and prescriptive analytics levels. The study demonstrates how conventional survey methodology, when designed with occupational granularity, can populate each tier of the people analytics maturity model without requiring longitudinal or big-data infrastructure.
Findings — Mean job satisfaction was M = 3.14 (SD = 0.86), concealing substantial heterogeneity. Working conditions and recognition were primary drivers (β = .19; β = .15); 34.7% of workers were educationally underemployed (d = 0.58); technical staff (M = 3.42) reported markedly higher satisfaction than non-skilled workers (M = 3.02).