Application of the Technology Acceptance Model (TAM) in the Context of NBFC Customers

Abstract: This study investigates how the Technology Acceptance Model (TAM) and its extensions can explain and predict the adoption of digital services offered by Non‑Banking Financial Companies (NBFCs) in India. Central to TAM are two key user perceptions: Perceived Usefulness (PU)—the belief that a technology enhances performance or convenience—and Perceived Ease of Use (PEOU)—the belief that using the system is effort‑free. These constructs drive Behavioral Intention (BI), which precedes actual use.

To contextualize TAM within the NBFC sector—which primarily serves underserved and financially vulnerable populations—this research incorporates additional factors such as Trust, Perceived Risk, Subjective Norms, and Facilitating Conditions. Trust captures users’ beliefs in institutional reliability and data protection; Perceived Risk reflects fears of privacy breaches or hidden charges; Subjective Norms cover the influence of family or social circles; and Facilitating Conditions include factors such as smartphone access, digital literacy, and support systems.

Based on these constructs, we formulate a conceptual framework in which PU, PEOU, Trust, Risk, and Social Influence affect BI, and BI in turn predicts actual usage behavior. Facilitating Conditions are postulated to moderate the translation from intention to action.

Empirically, the framework is validated using a structured survey administered to a representative sample of 400–500 NBFC customers across urban and rural India. Measures are adapted from validated TAM studies and extended-fintech acceptance research. Structural Equation Modeling (SEM) is leveraged to test measurement reliability and the hypothesized relationships.

Findings are expected to show that PU and Trust are strong positive predictors of BI, PEOU influences PU and intention, and that Perceived Risk exerts a negative effect. Subjective Norms and Facilitating Conditions are also anticipated to play significant roles. The research explores how demographic moderators—such as age, education, and digital literacy—shape these relationships.

This study contributes to theory by adapting extended TAM to the unique context of NBFC customers in India, offering a nuanced understanding of digital financial adoption. Practically, it offers actionable insights for NBFCs and regulators seeking to enhance adoption and financial inclusion—emphasizing user‑friendly design, transparent policies, trust‑building mechanisms, and supportive digital ecosystems.

Technology Acceptance Model (TAM), Perceived Usefulness (PU), NBFC Digital Adoption, NBFC customers’ technology acceptance, Behavioral Intention (BI)

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