Artificial Intelligence Adoption and Recruitment Efficiency in Malaysian SMEs The Mediating Role of Decision-Making Quality

Abstract

Md Alomgir Hossain and Imam Uddin

Micro, small, and medium enterprises (MSMEs) form the backbone of the Malaysian economy, contributing 39.5% of gross domestic product and 48.7% of total employment in 2024 (Department of Statistics Malaysia, 2025). At the same time, national digital and artificial intelligence (AI) readiness remains uneven, and AI adoption in human resource (HR) functions inside Malaysian SMEs is empirically underexplored. This study examines whether AI adoption improves recruitment efficiency in Malaysian SMEs, and whether decision-making quality mediates that relationship. Grounded in the Technology Acceptance Model (TAM) and the Resource-Based View (RBV), the study conceptualises AI adoption as the independent variable, recruitment efficiency as the dependent variable, and decision-making quality as the mediating variable. A quantitative, cross-sectional, explanatory design is adopted. Primary data are to be collected through a structured online questionnaire distributed to SME owners, HR managers, HR executives, talent acquisition officers, and departmental managers who are involved in recruitment decisions, using a five-point Likert scale. A minimum of 384 valid responses is targeted in line with Krejcie and Morgan (1970). Data are analysed using IBM SPSS Statistics through descriptive statistics, Cronbach's alpha reliability testing, Pearson correlation, multiple regression, and mediation analysis (Hayes PROCESS Model 4 with bootstrapping). Four hypotheses are proposed: AI adoption positively affects recruitment efficiency (H1) and decision-making quality (H2); decision-making quality positively affects recruitment efficiency (H3); and decision-making quality mediates the relationship between AI adoption and recruitment efficiency (H4). The study contributes empirical evidence on AI-enabled recruitment in a developing-economy SME setting, extends the joint application of TAM and RBV, and offers practical guidance for SME owners, HR practitioners, and policymakers on the organisational conditions under which AI creates value in recruitment.

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