Factors Influencing Intention to Use AI in Business Decision Making among Professional Employees in Malaysia's Financial Industry

Gehad Mohammed Ahmed Naji^1,2*Mohammed Alzoraiki³Nagendra Suba Rao²Yulita Hanum P Iskandar²

• ¹Universiti Sains Malaysia, Penang, Malaysia
• ²Universiti Sains Malaysia Pusat Pengajian Sains Matematik, George Town, Malaysia
• ³Gulf University, Sanad, Bahrain

Purpose – This research investigates the factors influencing professional employees’ intentions to use AI in business decision-making within Malaysia's financial industry by integrating the Technology Acceptance Model (TAM) with the Value-Based Adoption Model (VAM). More specifically, the study examines how perceived usefulness, perceived ease of use, perceived enjoyment, perceived risk, perceived value, and attitude jointly influence behavioral intention to use artificial intelligence. Research Design & Methodology – A quantitative approach used structured online questionnaires distributed to 210 professional employees across various Malaysian financial institutions (digital financial services, banking, insurance, investment management, and provident funds). Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4 to assess the measurement model and test the eleven hypothesized relationships. Findings – The integrated TAM–VAM framework explained 74% of the variance in AI use intention, with seven of eleven hypotheses supported. Attitude was the strongest direct predictor of intention (β = 0.773, p < 0.001). Perceived value had a significant positive effect on attitude (β = 0.675, p < 0.001) but did not directly influence intention. Perceived usefulness, perceived ease of use, and perceived enjoyment each positively affected perceived value. Perceived risk negatively influenced attitude. The results highlight perceived value and attitude as key mediators in the AI adoption process. Given the cross‑sectional design, findings should be interpreted as predictive associations rather than causal effects. Research Limitations – This study used convenience and snowball sampling within Malaysia’s financial sector, which may introduce selection bias and limit generalizability. The sample overrepresented FinTech employees (50.5%), who may hold more favorable views toward AI compared to employees in traditional banking or insurance. Moreover, the cross-sectional design captured perceptions at a single point in time and measured behavioral intention rather than actual usage behavior, further limiting external validity. Practical/Theoretical Implications – Theoretically, this research extends technology acceptance theory by validating the integrated TAM-VAM framework in emerging markets, showing that value-based mechanisms mediate utilitarian perceptions' influence on attitudes. Findings offer valuable insights for financial institutions' AI implementation, vendors' user-centered solutions, and policymakers' supportive regulatory frameworks for responsible AI use in Malaysia's financial sector.