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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue VII, July 2025
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Application of the Technology Acceptance Model (TAM) in the
Context of NBFC Customers
Vaivaw Kumar Singh1, Kunal Sinha2
1Research Scholar, Faculty of Business Management, Sarala Birla University, Ranchi, Jharkhand, India
2Assistant Professor, Faculty of Commerce, Sarala Birla University, Ranchi, Jharkhand, India
DOI: https://doi.org/10.51583/IJLTEMAS.2025.1407000102
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.
Keywords: Technology Acceptance Model (TAM), Perceived Usefulness (PU), NBFC Digital Adoption, NBFC customers’
technology acceptance, Behavioral Intention (BI)
I. Introduction
Digital financial services have transformed the landscape of credit and payments in India—particularly through rapid proliferation
of alternate credit providers like Non-Banking Financial Companies (NBFCs), fintech lenders, and e-commerce platforms
integrating credit offerings. Between 2012 and 2022, India’s digital lending market expanded from approximately $9 billion to
nearly $270 billion, growing at a staggering compounded annual growth rate of 39.5%. Recently, players like Flipkart gained full
NBFC licensure from the Reserve Bank of India (granted on March 13, 2025), enabling them to offer direct loans via their fintech
app ecosystem, signaling the intensifying convergence of retail and finance technology. Moreover, the rollout of the Unified
Lending Interface (ULI)—an RBI-led initiative—aims to digitize and accelerate credit assessment processes for small borrowers
in rural and MSME sectors, akin to UPI’s role in revolutionizing payments.
Against this backdrop, understanding what drives NBFC customers—especially underserved micro-entrepreneurs, rural borrowers
and gig-economy workers—to adopt digital services becomes critical. The Technology Acceptance Model (TAM), originally
formulated by Davis (1989), offers a robust theoretical lens: per TAM, Perceived Usefulness (PU)—the conviction that using a
digital system improves performance—and Perceived Ease of Use (PEOU)—the belief that the system is user-friendly—shape
Behavioral Intention (BI), which in turn drives actual usage behavior.
While TAM has been extensively employed to study mobile banking and fintech adoption across India—highlighting the roles of
trust, cost perception, and social norms—its application to the NBFC ecosystem, especially digital lending platforms for financially
marginalized users, remains limited. One pilot study in India involving NBFC fintech adoption showed that demographics (such as
higher education and urban residence), trust, PU, and PEOU significantly predicted adoption, but fuller empirical frameworks are
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue VII, July 2025
www.ijltemas.in Page 847
scarce. Likewise, mobile-banking adoption research in India consistently finds that trust and perceived risk are equally critical
alongside usability and usefulness.
To address this gap, the present study proposes an extension of TAM tailored to the NBFC context—especially digital borrowers
among low-income and digitally novice populations. Core constructs include PU and PEOU, but we enrich TAM with:
Trust (in the institution’s data practices and fairness)
Perceived Risk (concerns over hidden fees, data misuse, aggressive debt recovery)
Subjective Norms (influence of peers, family, community)
Facilitating Conditions (such as digital literacy support, smartphone access, multilingual app interfaces)
Drawing from multi-theory literature—such as integrated TAM, TPB, and TAM 3 or UTAUT frameworks—this enriched model
enables a nuanced inquiry into both enabling and inhibiting factors shaping NBFC customers’ digital service uptake.
Empirically, we adopt a structured survey approach with a sizable sample (400–500 respondents) of NBFC customers representing
both urban and rural areas. Standard validated measures from prior TAM and fintech acceptance studies are deployed, and Structural
Equation Modeling (SEM) techniques are used to validate the measurement model and test hypothesized relationships.
By situating TAM within the evolving Indian NBFC-fintech ecosystem—amid regulatory reforms like ULI, rising NBFC market
share, and new entrants like Flipkart Finance—this research contributes both theoretically and practically. Practically, it offers
actionable insights: NBFCs and policymakers should focus on user-friendly onboarding, transparent pricing, trust-building features,
and socio-technical support to accelerate comparative digital adoption among underserved segments.
II. Literature Review
Core TAM and Its Traditional Extensions
The foundational Technology Acceptance Model (TAM) developed by Davis (1989) centers on two core beliefs: Perceived
Usefulness (PU)—the extent to which users feel a system improves their task performance—and Perceived Ease of Use (PEOU)—
the belief that using the system will be effortless. Extended versions like TAM2 and TAM3 incorporate additional variables such
as subjective norms, job relevance, trust, and perceived risk, providing broader explanatory power.
Empirical Applications in Indian Fintech and Mobile Banking
In India, multiple studies have adapted TAM to explore adoption of mobile banking and digital financial services. Kumar et al.
(2020) proposed an extended TAM framework adding trust, self-efficacy, subjective norms, and personal innovativeness to PU and
PEOU, all of which significantly predicted behavioral intention to adopt mobile banking. Another investigation involving 265 users
revealed that perceived trust and perceived risk meaningfully influence usage intention, alongside PU and PEOU.
UTAUT and Integrated Models
Broader adoption frameworks like UTAUT and UTAUT2 have also been widely applied. These integrate constructs such as
Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions, often augmented with trust and risk
factors in fintech research.
Fintech Adoption in Underserved and Rural India
In low-income and rural contexts, traditional TAM constructs alone are insufficient. Adapted models highlight the importance of
digital literacy, language accessibility, institutional trust, and perceived risk to better explain technology usage decisions among
underserved users.
Limited Research on NBFC-Specific TAM Applications
There are few studies directly applying TAM to NBFC customers, despite the NBFC sector accounting for a significant share of
digital loan disbursements in India. Available evidence indicates that demographic factors (like age, education, urban residence),
PU, PEOU, and trust play important roles in adoption—but comprehensive, publicly reported SEM-based models are scarce.
Trust, Privacy, and Perceived Security
Research consistently underscores that trust and perceived security are pivotal in digital finance adoption. While perceived risk
often acts as a deterrent, some extensions of UTAUT2 find its predictive power may diminish once trust and security are explicitly
modeled. This nuance is especially relevant for users wary of data misuse and hidden costs in digital lending.
Rationale for Extending TAM to NBFC Context
Given the nascent state of research focusing on NBFC customers—particularly those from financially marginalised and digitally
novice populations—this literature review supports the rationale for developing an extended TAM framework. It should integrate:
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Perceived Usefulness (PU)
Perceived Ease of Use (PEOU)
Trust (data privacy, institutional fairness)
Perceived Risk (fee transparency, privacy breaches)
Subjective Norms (family/community influence)
Facilitating Conditions (app support, digital access)
Contextualizing TAM to NBFC Customers
The Unique Operating Environment of NBFCs in India
Non-Banking Financial Companies (NBFCs) in India play a vital role in delivering credit to underserved segments such as
micro-entrepreneurs, gig workers, and first-generation borrowers. For example, UGRO Capital reports that 78% of its clients are
first-generation entrepreneurs accessing formal credit for the first time, highlighting the deeply novel nature of these customers’
financial engagement. In response to rising risks in the sector—including aggressive pricing, poor recoveries, and lack of
transparency—the Reserve Bank of India (RBI) has implemented tighter regulatory oversight, recently reinstating limits on Navi
Finserv and other NBFCs until they brought their practices in line with compliance norms.
NBFCs are also rapidly adopting AI-enabled credit underwriting, processing behavioral and financial data in real time to automate
loan decisions—thereby transforming how trust is established with customers during digital interactions.
How TAM Maps onto the NBFC Customer Journey
In light of this, adapting TAM to NBFC digital service contexts requires more than just measuring PU and PEOU. For digital
lending platforms—where loans are disbursed via apps, e-KYC completes digitally, and aggregators rate products—the model must
reflect institutional trust, perceived security, and transparency. Empirical research on digital lending usage has already underscored
these dimensions: perceived usefulness, ease of use, and perceived security positively influence adoption intention, though
perceived risk may not always predict behavior when security is strongly established.
Key Variables in an NBFC-Tailored TAM Framework
Perceived Usefulness (PU)
Customers need to believe that using NBFC digital services—such as instant loan approvals, flexible repayment, and self-service
access—offers tangible advantages over traditional paper-based interactions or informal lending.
Perceived Ease of Use (PEOU)
Given that many NBFC customers may lack prior exposure to formal digital financial services, usability factors (like vernacular
interfaces, simplified application flows, or minimal documentation) become critical for forming positive perceptions of
effortlessness.
Trust & Perceived Security
Trust emerges as a cornerstone in digital finance adoption. Institutional trust—confidence in NBFC’s data practices, fairness of
pricing, and grievance mechanisms—shapes behavioral intention. Security perception mediates perceived risk by assuaging fears
about fraud or data misuse. Research confirms trust reduces perceived risk, enabling higher intention to adopt.
Perceived Risk
Concerns over hidden charges, opaque algorithms, and aggressive debt recovery are major inhibitors. Evidence shows that high
perceived risk weakens the linkage between intention and actual use unless trust and security measures are robustly established.
Subjective Norms (Social Influence)
In collectivist and financially underserved communities, peer or family endorsement of digital platforms strongly influences
adoption. Prior studies in mobile banking contexts confirm that subjective norms positively affect both perceived usefulness and
behavioral intention.
Facilitating Conditions
Elements such as availability of smartphones, reliable internet connectivity, digital literacy programs, and responsive customer
support are vital in enabling the transition from intention to actual use, particularly among novice digital users.
Constructing an Extended TAM for NBFC Context
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Synthesizing the above:
PU, PEOU, trust, perceived security, perceived risk, social influence, and facilitating conditions act as antecedent variables
influencing Behavioral Intention (BI).
PU and PEOU drive BI, per classic TAM logic.
Trust and perceived security reduce the deterrent impact of perceived risk.
Subjective norms influence PU and BI directly.
Facilitating conditions act as enablers, moderating the transition from intention to actual use.
Tailoring Hypotheses for NBFC Digital Adoption
Using the adapted model, plausible hypotheses include:
1. PU positively influences BI—customers adopt platforms they believe deliver time savings, ease, and flexibility.
2. PEOU positively influences PU and BI—ease drives usefulness and motivation to use.
3. Trust and security perceptions positively influence BI and mitigate perceived risk’s negative effect.
4. Perceived risk negatively influences BI or disrupts BI to actual use conversion if not offset by trust.
5. Subjective norms positively affect PU and BI within community-oriented borrower profiles.
6. Facilitating conditions enhance the likelihood that BI results in actual use, especially for low-digital-literacy users.
Moderating Effects & Demographic Controls
In the NBFC customer base, demographic moderators such as age, education level, urban versus rural residence, and previous
exposure to formal credit or digital interfaces may moderate relationships—especially between BI and actual use. For instance,
older or lower-literacy users may rely more heavily on trust and facilitating conditions.
In summary, extending TAM for NBFC customers requires embedding constructs that reflect regulatory and behavioral realities—
trust, risk, social norms, and infrastructure support. This enriched framework provides a solid theoretical foundation for empirical
studies that aim to explain—and ultimately enhance—digital financial inclusion through NBFC platforms.
III. Methodology
Research Design & Study Population
We propose a quantitative, cross-sectional survey design, targeting NBFC customers who have used digital lending platforms.
Drawing inspiration from recent TAM-based fintech studies, such as Yadav & Shanmugam (2024), which focused on digital
lending, this study adopts a survey-based measurement and structural equation modeling (SEM) approach using SmartPLS or CB-
SEM. The intended sample size—similar to other empirical studies in India—is between 400 and 500 respondents, to ensure
sufficient statistical power and model stability in SEM analyses.
Sampling Strategy & Data Collection
Stratified convenience sampling will be employed across multiple geographic zones (urban/rural), income brackets, and
literacy levels.
Data will be collected through structured questionnaires—either in person at branch points or via app–based surveys.
We will adapt and pre-test validated Likert-scale measures from prior TAM and fintech adoption studies (including
constructs such as PU, PEOU, Trust, Risk, Social Influence, Facilitating Conditions).
Measurement Instrument
Items will be drawn from established scales in previous literature: e.g., Kumar et al. (2020) for PU, PEOU, trust, subjective
norms; Yadav & Shanmugam (2024) for digital lending-specific constructs like perceived security and perceived risk.
Questionnaire items will be translated into local vernacular where needed and pre-tested for clarity among a small pilot
group.
Data Analysis Strategy
The analysis will follow a two-step SEM approach:
Measurement Model Assessment
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o Evaluate reliability (Cronbach’s α, composite reliability), convergent validity (factor loadings, Average Variance
Extracted), and discriminant validity (Fornell-Larcker criteria or HTMT).
o Assess common method bias using Harman’s single-factor test (variance explained by a single factor should be <50%).
Structural Model Evaluation
o Test hypothesized paths (e.g. PU → BI, PEOU → PU/BI, Trust & Security → BI, Risk → BI, SN → BI/PU, FC → BI →
Use Behavior).
o Evaluate R² (variance explained), path coefficients (β values and t-values), effect sizes (f²), and model fit indices if
CB-SEM is used.
Moderators & Control Variables
Demographic variables—age, gender, education, digital literacy, urban/rural location, and prior exposure to formal credit—will be
included as moderators to assess differences in construct relationships (e.g. perception → intention, intention → use) across
segments. Moderator effects can be tested via multi-group analysis or interaction terms in SEM.
Ethical Considerations & Data Quality
Participant consent and contributor anonymity will be ensured.
Data screening procedures (missing value treatment, normality checks, outlier analysis) will be applied.
To mitigate common method bias, procedural remedies (e.g. assuring anonymity, varying item order) and statistical checks
(Harman’s test) will be conducted.
Analytical Software & Tools
Primary analysis will use SmartPLS (PLS-SEM), common in TAM and fintech research in developing contexts.
Where appropriate, CB-SEM in software like AMOS or LISREL may be used to confirm model robustness.
Supplementary analyses (e.g. descriptive statistics, correlations) to profile respondents and constructs.
This methodology is aligned with established empirical frameworks in Indian digital-finance research (e.g. Kumar et al., 2020;
Yadav & Shanmugam, 2024; Verma & Shome, 2025). It offers a robust empirical foundation to test the extended TAM model for
NBFC digital adoption and supports drawing actionable insights across customer segments.
IV. Findings
NBFCs Leading India’s Digital Lending Boom
NBFCs are at the forefront of digital credit in India, consistently outpacing banks. As of 2020, digital channels accounted for over
60% of NBFCs’ total loan disbursements, compared with just 5–6% for banks. Highlights include:
Retail lending by NBFCs—which significantly serves first-generation and new-to-credit borrowers—has expanded
rapidly, with unsecured loans growing at a ~32–35% CAGR between 2017 and 2024.
MSME and personal unsecured loans continue to rise sharply, especially via digital channels, driven by fintech-enabled
credit models.
These trends underscore the importance of studying NBFC customer behavior in digital lending contexts.
Expanding Credit for the Underserved
Data shows that 78% of borrowers at MSME-focused NBFCs like UGRO are first-time credit users, indicating limited prior
exposure to formal credit mechanisms and a high degree of novelty in their experience. NBFCs often deploy AI/ML-based
underwriting, incorporating alternative data sources (such as mobile behavior and social indicators) to assess creditworthiness—
enhancing reach among customers lacking traditional financial footprints.
Regulatory Landscape & Trust Implications
The RBI’s Digital Lending Guidelines, issued in September 2022, mandate transparency in disclosures, upfront cost presentation,
responsible lending practices, and enforcement of grievance redressal mechanisms. These steps significantly enhance trust by
promoting responsible conduct and ethical frameworks in digital lending. Evidence from recent fintech analyses indicates that trust
and transparency are key enablers of behavioral intention, helping mitigate perceived risk despite occasional algorithmic opacity.
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Trust, Risk, and Adoption Behavior
Research from UTAUT2-based fintech adoption models shows that perceived institutional trust and perceived security significantly
influence intention to use digital finance tools—whereas perceived risk may not retain significance once trust is well established.
At the same time, studies on financial inclusion caution that data misuse, black-box credit decisions, and aggressive recovery
practices can deter usage and exacerbate debt risks among vulnerable borrowers.
Social Influence and Digital Inclusion
In Indian contexts, subjective norms—peer or family recommendations—play a significant role in shaping digital financial
adoption. Several studies in mobile banking and fintech domains show that community endorsement boosts perceived usefulness
and directly influences intention to adopt.
Facilitating Conditions and Literacy Support
Access to smartphones, internet connectivity, vernacular-based UI, and digital literacy support are essential enabling factors,
especially for NBFC customers with low digital exposure. Hybrid engagement models—combining digital tools with field staff
support—have proven effective for last-mile borrowers.
Implications for Modeling TAM in NBFC Context
Taken together, these studies reinforce that extensions of TAM for the NBFC customer context must incorporate:
1. Trust and perceived security as key influencers of intention.
2. Perceived risk as a potentially negative driver—mediated by trust and institutional reputation.
3. Subjective norms shaping both PU and BI, particularly in community contexts.
4. Strong emphasis on facilitating conditions (digital access, multilingual usability, on-ground support) for enabling actual
usage behavior among new-to-credit and digitally novice users.
This empirical synthesis sets a firm foundation for the empirical testing of the extended TAM model proposed—one that is tailored
to the behavioral, socio-technical, and regulatory realities of NBFC digital customer journeys in India.
V. Discussion
Core Drivers of Adoption: Usefulness, Trust & Ease of Use
Consistent with the bulk of digital finance literature, both Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) emerged
as pivotal drivers of adoption. As studies in Indian M-banking show, PU and PEOU strongly predict behavioral intention across
diverse demographic settings—extending even to rural and low-literacy users. In the NBFC digital lending space, PU translates to
tangible benefits like rapid loan disbursal, flexible tenures, and digital record-keeping—factors that empower underserved users
with limited prior credit exposure. Meanwhile, an easy-to-navigate interface, vernacular support, and streamlined onboarding
amplify PEOU, reinforcing positive perceptions even among digital novices.
The Mediating Role of Trust and Influence of Perceived Risk
In line with broader fintech adoption theory, trust in the NBFC platform—stemming from transparency in pricing, secure
information handling, and grievance redress mechanisms—is a powerful catalyst for intention to adopt. Research indicates that trust
not only acts as a direct antecedent to adoption but also mediates the negative influence of perceived risk—including fears of hidden
charges, data misuse, or algorithmic errors. In digital lending contexts where automated underwriting and alternative data are used,
investors and borrowers alike must rely on reputational cues and institutional credibility. Such findings echo the notion that
perceived risk has a reduced direct impact when institutional trust is robust.
Social Influence in Community and Informal Settings
Studies in Indian digital finance environments consistently highlight the importance of subjective norms—community or familial
approval—as a significant predictor of both perceived usefulness and behavioral intention. Borrowers in rural or collective
ecosystems often rely on word-of-mouth endorsements and peer validation when adopting digital tools, reinforcing the social
pathways through which PU and BI are shaped.
Enabling Actual Usage: Facilitating Conditions & Demographic Moderators
Facilitating conditions—such as smartphone access, multilingual support, functional internet, and on-ground help desks—often
determine whether intention translates into actual usage. Especially for first-generation borrowers with low digital literacy, hybrid
support models (combining field agents with digital tools) have shown to materially increase onboarding and retention. Moderating
these relationships, factors like age, education level, urban/rural status, and prior exposure to formal credit shape how much trust
or perceived usefulness matter across segments. Older or less literate users, for instance, may rely more on institutional trust and
support, while younger urban users may lean more on interface design and speed.
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Regulatory Context as a Trust Builder
RBI’s Digital Lending Guidelines (issued September 2022) mandate transparency in interest and fee disclosure, responsible creditor
practices, and institutional accountability in digital lending. Such regulatory measures serve as vital safeguards that help reduce
perceived risk and bolster trust within extended TAM frameworks. In tandem with cybersecurity mandates—such as RBI’s call for
“zero-trust” AI-aware defence strategies—these developments elevate customers’ confidence in institutional frameworks governing
NBFC platforms.
Implications for Theory, Practice & Policy
Theoretical Contributions
The synthesis reinforces that an extended TAM—featuring trust, risk, social influence, and facilitating conditions—provides a far
more robust explanatory framework than classic PU/PEOU alone, particularly when applied to NBFC digital adoption. Trust
emerges not only as an essential direct determinant but also as a mediator that mitigates perceived risk’s dampening effects—
confirming and extending models from fintech studies across Africa and Asia.
Managerial Implications
NBFCs looking to drive digital adoption among customers should:
Prioritize transparency: Upfront cost disclosure, easy-to-understand terms, and open communication channels.
Design user-centric platforms: Simple UI flows, language/voice support, minimal documentation, and visible onboarding
success stories.
Build trust infrastructure: Publicize grievance redressal systems, secure data practices, and fair debt collection approaches.
Leverage social proof and endorsement: Employ peer testimonials, community ambassadors, or referral schemes to harness
social influence.
Invest in support ecosystems: Provide helpline support, digital literacy workshops, and local field integration for bridging
digital divides.
Policy Insights
Regulators play a critical role in legitimizing digital lending. Policy mandates for disclosure, algorithmic fairness, and platform
accountability help to institutionalize trust and moderate risk—making extended TAM models practically implementable and
theoretically sound.
Limitations & Future Directions
While existing studies provide rich insights, most are cross-sectional and rely on self-reported intention, which risks common-
method bias and limits behavioral inference. Future research should explore longitudinal designs, track actual use patterns over
time, and test interventions—for example A/B testing of different UI elements or trust-building messages. Experimental or quasi-
experimental studies could validate causal effects of facilitating conditions, social nudges, or transparency interventions. Segment-
specific investigations—e.g. comparing rural versus urban, first-generation versus repeat borrowers—would further broaden
theoretical precision.
This discussion underscores that adopting digital services via NBFC platforms is shaped by a holistic constellation of cognitive
perceptions, social influences, institutional assurances, and infrastructural support—elements that must be systematically integrated
into extended TAM frameworks tailored for India’s underserved markets.
A Comprehensive Extended TAM Framework to Accelerate Digital Adoption in NBFCs
Non-banking financial companies (NBFCs) confront unique hurdles in adopting digital technologies—from legacy systems and
regulatory compliance, to low digital literacy and customer trust concerns. An Extended Technology Acceptance Model (TAM),
customized for the NBFC context and enriched by organizational and environmental factors, offers a holistic roadmap for driving
meaningful digital adoption.
1. Core TAM Constructs Reimagined for NBFCs
Perceived Usefulness (PU): Users are more inclined to embrace digital tools (such as mobile lending apps or CRM
platforms) when they believe these tools will boost efficiency and service effectiveness. This belief is a powerful driver of
adoption.
Perceived Ease of Use (PEOU): Simplicity in design is critical—particularly for rural agents and clients with limited digital
experience. Interfaces must minimize friction to foster sustained usage.
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Trust & Perceived Risk: In the NBFC space, trust in security, transparency, and fair practices significantly shapes adoption
decisions. Users weigh potential risks carefully before embracing technology.
2. Behavioral & Social Influences
Self-Efficacy & Innovativeness: When users are confident in their ability to navigate digital tools—thanks to tailored
training or micro-learning modules—they’re more likely to engage actively.
Subjective Norms & Peer Influence: Recommendations and encouragement from managers, peers, or community leaders
can substantially elevate adoption rates, especially in semi-rural markets.
Government & Regulatory Support: Regulatory mandates (such as digital lending norms by the RBI) and supportive policy
frameworks serve as both facilitators and motivators in NBFC digital transformation.
3. Integrating Organizational, Technological, and Environmental Contexts (TOE Framework)
To enrich the extended TAM, the TOE framework considers broader factors:
Technological Context: The availability of digital support systems—like in-app help, AI-guidance, or walkthroughs—
helps ease adoption in real time.
Organizational Context: Leadership-driven change management, multilingual design, localized hiring, and performance-
linked adoption metrics reinforce internal readiness.
Environmental Context: Competitive pressures, rural market dynamics, and compliance requirements further shape the
urgency and direction of digital adoption.
4. How It All Works
Core Drivers: PU, PEOU, social norms, self-efficacy, and innovation drive Behavioral Intent (BI) to use digital tools.
Checks and Balances: Trust, risk perception, regulatory conformity, and support conditions moderate both intention and
actual use.
Organizational Backbone: Training programs, onboarding tools, and leadership support transform intent into real usage—
especially in tier-2/3/4 regions.
5. Implementation Roadmap
1. Embed Assisted Onboarding: Incorporate in-app walkthroughs and real-time AI support to guide agents during onboarding
and usage phases.
2. Prioritize Trust & Compliance: Align systems with RBI’s digital lending norms, ensure data privacy, and establish
grievance redressal mechanisms to reduce perceived risk.
3. Localize Experience: Provide interfaces in local languages, design culturally appropriate interfaces, and hire locally to
deepen community engagement.
4. Track Adoption Metrics: Monitor usage, onboarding velocity, error rates, and user feedback to assess progress—and
involve leadership in adoption oversight.
5. Iterate Based on Feedback: Use insights from users, regulatory updates, and field realities to continuously refine usability
and support mechanisms.
VI. Conclusion
The application of an extended Technology Acceptance Model (TAM) to the context of digital adoption among NBFC customers
in India underscores that Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) remain foundational predictors for
intentions to adopt—especially when borrowing involves formal credit delivery. However, in a setting characterised by first-
generation borrowers, low digital literacy, and minimal prior exposure, Trust, Perceived Security, Perceived Risk, Subjective Norms
(SN), and Facilitating Conditions (FC) become essential components of the adoption framework.
Trust emerges as both a direct enabler of behavioral intention and a buffer against perceived risk associated with opaque algorithms,
aggressive recovery tactics, or hidden costs. As seen in lending contexts employing AI-based underwriting, trust-building
practices—such as transparent disclosures and grievance mechanisms—increase the likelihood of digital adoption.
Meanwhile, perceived risk—though a potential barrier—can be moderated effectively when institutional regulations such as the
RBI’s Key Fact Statement, cooling-off provisions, and explicit data consent mechanisms are in place. The recent Digital Lending
Guidelines (2022) and 2025 Master Directions significantly enhance product transparency and strengthen consumer safeguards,
thereby boosting institutional trust and reducing risk perception among borrowers.
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue VII, July 2025
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Subjective Norms (SN)—reflecting community or family endorsement—continue to influence both perceived usefulness and
behavioral intention. In collectivist rural contexts, peer validation often catalyzes adoption more than individual perceptions alone.
Facilitating Conditions (FC)—such as smartphone access, vernacular interface, and on-ground support—play a crucial role in
converting intention into actual usage, especially for digitally novice segments and older borrowers relying on hybrid outreach
models.
Regulatory reforms — for example, mandated fund flow segregation, audit protocols for Lending Service Providers (LSPs), and
certification requirements — further solidify trust and clarify institutional accountability within the digital lending ecosystem.
Theoretical & Practical Implications
Theoretical: This adapted TAM framework—integrating trust, risk, social influence, and infrastructure support—offers
broader explanatory power for populations with limited exposure to formal credit or digital experience.
Managerial: NBFCs must emphasise product clarity, intuitive onboarding, transparent data usage policies, and community-
based trust-building practices to drive digital adoption.
Policy: Regulators can reinforce digital inclusion by supporting hybrid onboarding models and mandating algorithmic
transparency and customer grievance mechanisms.
Limitations and Future Research
Most findings are based on cross-sectional, self-reported intention data, limiting inference about actual usage. Longitudinal or field-
experimental designs would better reveal causal effects and behavioral patterns—such as how trust interventions or app
modifications influence real user behavior over time. Examining specific borrower segments (e.g. rural vs urban, low literacy vs
high education) can uncover moderation effects and tailor policies more effectively.
Final Takeaway
By tailoring TAM to the NBFC digital context—anchored in cognitive, social, institutional, and technological nuances—this
research offers a comprehensive roadmap for understanding and promoting digital finance adoption among underserved borrowers.
Strategic integration of usability, trust, peer influence, regulatory clarity, and support conditions will not only increase adoption but
also foster inclusive and responsible lending ecosystems across India's digital lending era.
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