Financial Technology Adoption Among SMEs in Klang Valley: A TAM-Based Analytical Approach

International Postgraduate Conference on Accounting and Finance (IPCAF 2025) 2 May 2025 Kuala Lumpur e-ISBN: 978-967-0760-39-1 Publish Date: 14 October 2025 1 Paper ID: IPCAF202520 DOI: Nur Syahirah Lokman, Hazlina Abd-Kadir* , Aza Azlina Md Kassim Graduate School of Management, Management and Science University, Selangor *Corresponding author: [email protected], [email protected] ABSTRACT This study investigates what drives SMEs in Klang Valley, Malaysia, to adopt financial technology (FinTech), using the Technology Acceptance Model (TAM) as a framework. It focuses on two key factors: perceived usefulness and perceived ease of use. Data from 128 SMEs were analyzed using correlation and regression via SPSS. Findings confirm that both factors significantly influence FinTech adoption decisions. While the study is limited to Klang Valley, the results offer practical insights into improving digital financial integration for SMEs. Future research should consider broader samples, perceived risks, and longitudinal approaches to better understand evolving adoption behaviors. Keywords: FinTech adoption, Technology Acceptance Model (TAM), SMEs, perceived usefulness, perceived ease of use, Malaysia 1. INTRODUCTION The rapid proliferation of financial technology (FinTech) has transformed the landscape of financial services globally. In Malaysia, SMEs—comprising 98.5% of all business establishments—are integral to economic growth but have shown varied levels of FinTech adoption. The Klang Valley, a dynamic business hub, presents a unique opportunity to assess FinTech usage due to its urban infrastructure and tech-savvy population. Despite available digital infrastructure, many SMEs remain hesitant to integrate FinTech fully, often due to misconceptions about its utility and usability. Drawing on the Technology Acceptance Model (TAM), this study seeks to explore how perceived usefulness (PU) and perceived ease of use (PEU) influence FinTech adoption in SMEs. The study fills a gap in empirical research specific to Klang Valley and contributes to policy and practical strategies to encourage FinTech integration. The Technology Acceptance Model (TAM), introduced by Davis in 1989, has long been used to understand why people choose to adopt new technologies. At its core, TAM suggests that individuals are more likely to embrace a technology if they find it useful (perceived usefulness or PU) and easy to use (perceived ease of use or PEU). These ideas have proven relevant across many settings, including the growing FinTech space, especially among SMEs in countries like Indonesia and India (Perwitasari, 2022; Sakthi & Balamurugan, 2023). Over time, researchers have added layers to the model—factors like productivity, usability, and controllability help explain users’ decisions more clearly (George et al., 2021; Saadah & Setiawan, 2024). Others, like Helmi et al. (2024), introduced elements such as trust International Postgraduate Conference on Accounting and Finance (IPCAF 2025) 2 May 2025 Kuala Lumpur e-ISBN: 978-967-0760-39-1 Publish Date: 14 October 2025 2 and attitude. Still, gaps remain. Challenges like geographic differences, fast-changing technologies, and overlooked factors like perceived risk call for further exploration. This study revisits TAM to better understand FinTech adoption among SMEs in Klang Valley. 2. METHODOLOGY A descriptive, quantitative survey design was adopted. The target population included SME decisionmakers in Klang Valley. Using Krejcie and Morgan’s sampling table and G*Power analysis, a sample size of 128 respondents was determined. A structured questionnaire, adapted from prior validated studies, was distributed online and in person. Constructs measured included PU (productivity, performance, operational efficiency) and PEU (ease of use, learning, controllability), using a 5-point Likert scale. Data were analyzed using SPSS for reliability, correlation, and multiple regression to test the hypothesized relationships. Ethical protocols such as informed consent and anonymity were upheld throughout. 3. DISCUSSION AND CONCLUSION This section presents the key findings from the quantitative analysis of factors influencing FinTech adoption among SMEs in Klang Valley. Data were gathered from 128 respondents through a structured questionnaire, and analyzed using SPSS. The analysis focuses on descriptive statistics, correlation, and multiple regression to test the proposed hypotheses. Table 1 : Demographic Results Demographic Category Highest Frequency (%) Gender Male (53.9%) Age Group 29–39 years (53.9%) Education Level Bachelor’s Degree (74.2%) Job Position Executive (57.8%) Years of Service > 5 years (40.6%) Company Industry Services/Other (43.8%) Company Size 76–200 employees (59.4%) Reliability Test Cronbach’s Alpha was used to assess the internal consistency of the constructs. All constructs exceeded the 0.7 threshold, indicating strong reliability. Correlation Analysis The Pearson correlation coefficients show significant positive relationships between the independent variables (PU and PEU) and the dependent variable (FinTech Adoption). All relationships were significant at the 0.01 level. Table 2 : Correlation Analysis results Variables FTA PU PEU FinTech Adoption (FTA) 1 0.487** 0.452** Perceived Usefulness 0.487** 1 0.262** Perceived Ease of Use 0.452** 0.262** 1 Regression Analysis A multiple regression analysis was conducted to determine the predictive power of PU and PEU on FinTech Adoption. International Postgraduate Conference on Accounting and Finance (IPCAF 2025) 2 May 2025 Kuala Lumpur e-ISBN: 978-967-0760-39-1 Publish Date: 14 October 2025 3 Model Summary Model R R² Adjusted R² 1 0.593 0.352 0.341 ANOVA Table Table 3 : Anova result Model Sum of Squares df Mean Square F Sig. Regression 189.852 2 94.926 32.534 0.000 Residual 350.127 120 2.918 Total 539.979 122 The F-statistic (32.534, p < 0.001) confirms the model's statistical significance. Both PU and PEU are statistically significant predictors of FinTech adoption. Among the two, Perceived Ease of Use (β = 0.351) has a slightly stronger impact compared to Perceived Usefulness (β = 0.395), supporting TAM’s assumptions. This indicates that SMEs are more likely to adopt FinTech solutions if the tools are intuitive, easy to learn, and integrate well with existing processes. The results confirm TAM’s relevance in explaining FinTech adoption among SMEs. PEU’s stronger effect suggests that user-friendly interfaces, clear guidance, and customisation options are crucial. SMEs value tools that are not only effective but also easy to implement without substantial training. The findings also highlight that productivity and operational gains are important motivators. This aligns with past studies in Southeast Asia and supports the view that FinTech can drive efficiency in emerging markets. The study's contributions lie in contextualising TAM within a Malaysian SME setting and offering evidence-based strategies for FinTech developers and policymakers. 4. 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