From Readiness to Performance: Modelling AI-Enabled HR Analytics Adoption Capability as a Second-Order Formative Construct in Nigerian SMEs
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Abstract
Objective: This study examines how AI-enabled HR Analytics Adoption Capability (AIHRA), a second-order formative construct, is associated with operational performance among Nigerian SMEs.
Design: Drawing on UTAUT2, the Resource-Based View, and Socio-Technical Systems Theory, a survey was administered to 296 respondents across SMEs in Lagos, Abuja, Port Harcourt, and Kano. PLS-SEM via SmartPLS 4.0.9.9 was used for analysis.
Findings: Technological Readiness (weight = 0.584, p<0.001) and Organisational Support (weight = 0.532, p<0.001) significantly form AIHRA. Perceived Value and Use is non-significant (0.119, p = 0.219) but shows absolute importance. AIHRA is positively associated with operational performance (β = 0.626, R2 = 0.392).
Policy Implications: Managers and policymakers should prioritise digital infrastructure and organisational support before AI procurement.
Originality: This study contributes an empirically tested higher-order formative model linking AI-HR analytics adoption capability to operational performance in Nigerian SMEs, extending prior technology-organisation-environment evidence by showing structural determinants dominate perceptual constructs within a multi-theory, capability-based specification.
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Copyright (c) 2026 Michael Raphael Onenyi, Peter Ugbedeojo Nelson, Muhammed Idris Ogbike
This work is licensed under a Creative Commons Attribution 4.0 International License.
Michael Raphael Onenyi, Department of Business Administration, Prince Abubakar Audu University, Anyigba, Nigeria
The data used in this study are not publicly available due to confidentiality agreements with participating organizations but are available from the corresponding author upon reasonable request.
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