On Fintech and Financial Inclusion: Evidence from Qatar

Abstract

This study examines the role of fintech adoption in enhancing financial inclusion in Qatar, with a particular focus on the mediating influence of access barriers. A structured questionnaire was administered to 220 respondents, of which 200 valid responses were retained for analysis after screening for completeness and outliers. The constructs of fintech adoption (FA), financial inclusion (FI), and access barriers (AB) were measured using validated multi-item scales adapted from prior literature. Measurement reliability and validity were confirmed through Cronbach’s alpha, composite reliability, and average variance extracted (AVE), alongside confirmatory factor analysis (CFA) for construct validity. A structural equation modeling (SEM) approach was employed to test the hypothesized relationships, using maximum likelihood estimation with bootstrap standard errors and confidence intervals. Model fit indices indicated excellent fit (χ² = 48.983, df = 51, p = 0.554; CFI = 1.000; TLI = 1.003; RMSEA = 0.000; SRMR = 0.036). Factor loadings were all significant (p < 0.001), supporting convergent validity. However, the structural paths from FA to FI (β = −0.020, p = 0.827), AB to FI (β = −0.077, p = 0.394), and FA to AB (β = 0.054, p = 0.527) were not significant. The indirect mediation effect of AB was also statistically insignificant (β = −0.004, p = 0.700).

1. Introduction

The global financial services industry is being transformed by financial technology (Fintech), which integrates digital technologies like blockchain, mobile payments, and digital wallets. This transformation can disrupt traditional banking with cost-effective, accessible, scalable solutions that are important to people who do not have access to banking services when and where they need them. Some argue that Fintech democratises financial services by lowering costs and increasing access, but others argue that well-developed economies take advantage of least-developed regions like Qatar (Khan et al., 2022). Qatar’s Fintech contribution to financial inclusion is unclear. Financial technology has grown globally, but structural inefficiencies and regulatory bottlenecks have limited its GCC and Qatari impact (AlHares et al., 2022).

Financial inclusion ensures economic empowerment and sustainable development; all members of society can access financial products and services. Fintech can help emerging economies like Qatar overcome financial inclusion barriers. Compared to North America and East Asia, the country’s young Fintech landscape undermines that vision. Although gradually evolving, Qatar’s regulatory frameworks are not yet ready to support Fintech usage that has disproportionately favoured traditional banking and a financial system so skewed towards traditional banking (Truby et al., 2022). Although regulatory sandboxes in Qatar encourage innovation, they cannot fully meet the needs of underbanked and marginalised populations (Truby et al., 2022).

Qatar exhibits a paradoxical posture in its Fintech landscape while it has taken progressive regulatory steps such as establishing regulatory sandboxes and promoting digital banking, it remains cautious in domains like blockchain and cryptocurrencies (Truby et al., 2022; Ibrahim & Truby, 2021). This duality reflects a broader policy strategy: “innovation under supervision”, where financial experimentation is encouraged within controlled environments to protect economic stability and consumer trust (Truby et al., 2022). Thus, Qatar can be understood as a regulatory leader, but a measured adopter of emerging Fintech solutions prioritizing stability and gradual transformation over rapid disruption.

The Qatari financial market is still dominated by traditional banks, which has slowed Fintech implementation, especially in mobile payments and other digital financial services (Khan & Abdullah Al-harby, 2022). Despite recent initiatives due to the Qatar National Vision 2030, Qatar is slow to adopt Fintech. Slow growth is due to an organisational culture that has not adapted to a more progressive approach and conservative regulations that have not kept up with technological advancement (Ibrahim & Truby, 2021). Blockchain for trade finance has had some success in Qatar and other GCC countries, but weak and dispersed regulation and lack of strategic management have slowed Fintech growth (Truby et al., 2022). The large populations of native Qataris and expatriates, who have low minimum incomes and poor financial service access, limit financial literacy in Qatar.

Although digital literacy is rising, marginalised people still struggle to use Fintech services. These legal structures also fail to address cross-border data transfer, which is essential for digitally enabled financial services growth. Thus, while Fintech can improve Qatar’s financial sector’s access to and availability of financial services, there are no clear policies, and consumers’ trust in digital solutions is low (Abu-Asi et al., 2022). Fintech remains an untapped driver of financial inclusion in Qatar unless regulation and the digital gap are addressed (Ibrahim & Truby, 2021).

Financial technology (Fintech) is recognized worldwide, but little is known about whether it promotes or hinders financial inclusion in Qatar. The Gulf Cooperation Council (GCC) lacks Fintech studies because most research focuses on Saudi Arabia and the UAE (Khan et al., 2022). There is some literature on regulatory frameworks and Fintech innovation but little on how these technologies help Qatar’s marginalized populations achieve financial inclusion (Truby et al., 2022). Qatar’s unique economic environment—high wealth, a large expatriate population, and growing financial services—makes this study relevant. Qatar wants economic diversification and financial inclusion for long-term sustainability through National Vision 2030 (AlHares et al., 2022).

Fintech offers vital solutions for Qatar’s underserved, especially migrants, but regulatory and infrastructural barriers persist despite evidence of 15–20% increased access (Truby et al., 2022; Banna et al., 2023; Khan & Abdullah Al-harby, 2022).

Although Qatar’s Fintech ecosystem shares similarities with its Gulf neighbors, its financial inclusion challenges also resemble those of emerging markets beyond the GCC, such as India, Kenya, and Nigeria. In India, platforms like UPI and Paytm have revolutionized financial access among unbanked populations, driven by mobile penetration and simplified KYC frameworks (Aysan & Unal, 2022; Arora & Rathore, 2018). In Kenya, M-Pesa facilitated mass financial inclusion by providing low-cost mobile money services, even in rural areas (Jack & Suri, 2011). By contrast, Qatar’s infrastructure and cautious regulatory stance have slowed the pace of inclusive Fintech uptake, especially among low-income expatriates and digitally underserved segments (Elouaourti & Ibourk, 2024; Bouteraa et al., 2023). This comparison highlights that cultural, institutional, and policy readiness are critical in transforming Fintech potential into measurable inclusion outcomes.

• Research Objectives (ROs):

(1)

RO1. To examine the direct and indirect relationships between fintech adoption, financial inclusion, and access barriers in Qatar using a structural equation modeling (SEM) framework.

(2)

RO2. To assess the robustness of these relationships through confirmatory factor analysis, multi-group invariance testing, and outlier sensitivity analysis, thereby ensuring the validity and reliability of the findings.

• Research Questions (RQs):

(1)

RQ1. To what extent does fintech adoption influence financial inclusion directly, and indirectly through the mediation of access barriers, in the Qatari context?

(2)

RQ2. Do the hypothesized structural relationships remain robust across demographic subgroups (e.g., gender) and after accounting for model trimming and outlier sensitivity?

This study fills a critical gap in Fintech research on how Fintech affects financial inclusion in Qatar, particularly for underbanked people. Fintech research on Qatar and the GCC is still in its “early days,” but global research on its transformative potential is growing. This study will examine Fintech’s growth in Qatar, where traditional banking is still essential. According to current literature, Fintech has grown slower in the GCC than in the US or China (Khan & Abdullah Al-harby, 2022). This study examines how Fintech helps marginalized groups gain access (Banna et al., 2023). Policymakers will use this practical research as building blocks. To help the Fintech ecosystem, Qatar’s regulatory frameworks, like sandboxes, must be aligned. Without regulatory support, fintech services like digital payments and mobile banking are unlikely to help unbanked people (Truby et al., 2022).

This research will inform financial regulation reform to expand digital financial services (Truby et al., 2022). This research will also show how Fintech can help Qatar achieve its socioeconomic goals. Fintech helps marginalized groups access banking, reducing financial inequality. Financial mobility increases access to financial services, boosting low-income expatriates’ economic participation (Banna & Alam, 2021; Bhattacharyay & Bhattacharyay, 2022). The study will use these benefits to demonstrate the socio-economic benefits of Fintech-enhanced financial inclusion.

This study examines how Fintech adoption affects financial inclusion in Qatar, particularly for expatriates and low-income people. While the research territory is limited by geography (Qatar), its findings apply to the Gulf Cooperation Council (GCC), where Fintech is proliferating but faces regulatory and infrastructure constraints (Khan & Abdullah Al-harby, 2022). The study will examine how Fintech innovations like mobile banking and blockchain improve financial access. This research will examine regulatory frameworks like Qatar’s sandbox initiatives to see how innovations can overcome financial inclusion barriers (Truby et al., 2022). Thus, the temporal scope will focus on the years after 2015, when Fintech first began to emerge in the region, and Qatar was diversifying its economy under its National Vision 2030 (Bhattacharyay & Bhattacharyay, 2022).

The study will use a quantitative method to measure Fintech’s potential to close financial access gaps. This supports previous research on Fintech’s growing role in GCC financial stability (AlHares et al., 2022). This paper is structured as follows: a literature review on Fintech and financial inclusion, study methods, data analysis, and implications.

2. Literature Review

Mobile banking, digital payments, blockchain, and other changes have changed financial systems (Niankara, 2023). Financial inclusion means providing affordable and efficient financial services to everyone, especially marginalized groups (Bouteraa et al., 2023). Digital finance removes geographical and transaction costs to increase financial inclusion and economic growth for the unbanked and underbanked (Ben Hassen, 2022). Many theories link Fintech and financial inclusion.

Financial Intermediation Theory states that financial institutions connect savers and borrowers to allocate resources. Fintech disrupts transaction costs and increases financial market accessibility, allowing more participation (Khan et al., 2022). The Innovation Diffusion Theory helps mass populations adopt digital payment components and blockchain (Ibrahim & Truby, 2021). Fintech solutions for financial inclusion are adopted based on user perceptions of ease of use and utility (Bouteraa et al., 2023).

Fintech promotes financial inclusion globally. Mobile payment systems like Paytm and PhonePe have increased financial access for unbanked populations in emerging markets like India (Aysan & Unal, 2022). M-Pesa in Kenya has helped millions in Sub-Saharan Africa access banking services without traditional infrastructure (Khaki et al., 2022). Alipay and WeChat are crucial for modernizing China’s financial services, providing consumers with a user-friendly digital payment system (Demirdoğan, 2021). Recently, Fintech has seen blockchain, cryptocurrency, and decentralized finance grow.

Blockchain provides secure, transparent cross-border payments that reduce costs and increase efficiency. Cross-border payments for the unbanked are being investigated as new cryptocurrencies like Bitcoin and Ethereum are considered financial inclusion tools (Khaki et al., 2022). Mobile banking and digital wallets like GCash in the Philippines reduce the need for physical banking infrastructure to provide financial services (Aysan & Unal, 2022).

Despite GCC Fintech research scarcity, Qatar’s migrant workers could benefit from innovations, yet adoption remains underexplored. Government diversification initiatives and regulatory sandboxes support development, contrasting with India and China’s distinct contexts (Ibrahim & Truby, 2021).

Qatar’s blockchain and digital payments regulatory framework aims to create a competitive ecosystem for Fintech firms (Ibrahim & Truby, 2021). GCC countries have invested heavily in digital banking platforms, and the UAE leads mobile payment adoption (Aysan & Unal, 2022). Financial technology adoption in the GCC faces regulatory and cultural obstacles. Bahrain and the UAE have improved Fintech regulations, while Qatar remains cautious about blockchain and cryptocurrency (Demirdoğan, 2021). The digital divide, especially among older generations and low-income expatriates, limits Fintech use in broad populations (Khaki et al., 2022).

Qatar’s Fintech ecosystem is developing, but trade finance blockchain applications are ready (Ibrahim & Truby, 2021). It is also much less developed than the UAE and Saudi Arabia. Due to favorable regulations, the UAE leads the region in digital payment platforms. Saudi Arabia has taken steps to develop open banking frameworks to spur innovation. Despite regulatory constraints, Qatar’s focus on digital banking and blockchain makes it a regional leader (Aysan & Unal, 2022).

Financial inclusion rates in Qatar differ between local and expatriate communities. Several recent studies show that many of Qatar’s low-income expatriate workforce is unbanked or underbanked (Elouaourti & Ibourk, 2024). These workers mostly use the informal system for financial services, so they have limited access to credit, savings, and insurance. Qatar’s financial sector is digitized, but the sizeable unbanked segment highlights broader issues in marginalized communities. Qatar faces many obstacles to financial inclusion. Though regulatory frameworks support digital financial services, blockchain and cryptocurrency technologies are still hesitantly explored (Renduchintala et al., 2022).

Digital literacy remains a challenge for low-income budget holders, expatriates, and seniors who lack smartphones or digital platform knowledge. Inconsistent internet connectivity and diffuse technology access limit mobile banking service expansion (Ziegler et al., 2021).

Fintech can be implemented to high standards, increasing financial inclusion in Qatar. Mobile banking platforms and digital wallets are becoming more popular for unbanked people because they make savings, remittances, and payments more manageable (Davidovic et al., 2020). Additionally, several fintech companies are working with the government to develop inclusive fintech products that meet the needs of expatriates. Mobile payment systems enable real-time remittances and digital loans, reducing informal network use (Bersch et al., 2021). These technologies can provide secure, accessible, and affordable financial services to the underserved. Qatar has made great strides in Fintech promotion through regulatory measures to encourage innovation and control risks.

The introduction of regulatory sandboxes for Fintech firms to create test environments under monitoring by relevant authorities is notable. This will maintain financial system stability without hindering Fintech innovation in the country (Truby et al., 2022). In addition to the sandbox framework, the Qatar Financial Centre (QFC) promotes entrepreneurial investment in digital financial services through its regulatory and legal environment. Qatar’s National Vision 2030 calls for diversification through technology and a knowledge-based financial sector (Truby et al., 2022).

Qatar’s regulatory framework is ahead of the curve in many ways, but some policies make it hard for Fintech to advance financial inclusion. The cautious approach to blockchain and cryptocurrency is a significant obstacle. Blockchain can accelerate financial inclusion by providing secure, low-cost financial services, but regulatory barriers have limited its use (Renduchintala et al., 2022). Know Your Customer (KYC) and Anti Money Laundering (AML) can be difficult for low-income people, especially expatriates, to obtain formal documentation. This makes unbanked and underbanked people less likely to use FIntech solutions (Elouaourti & Ibourk, 2024). Qatar’s Fintech regulation is cautious compared to those of the UK and Singapore.

UK regulators have encouraged banks to share data with Fintechs to increase financial inclusion by making these services more accessible. Singapore, however, has led Fintech growth through crypto-friendly governance and blockchain use (Kuchina, 2024). Fintech can support financial inclusion in Qatar, but its regulatory framework must change to follow international best practices. It simplifies KYC for vulnerable populations and makes blockchain and cryptocurrency regulation more flexible.

Fintech’s global potential to promote financial inclusion is well-documented, but Qatar has little empirical research. However, the literature often generalizes GCC findings without considering Qatar’s unique socioeconomic landscape. However, Simeone et al. (2023) note that there is little empirical data on how Fintech affects low-income migrant workers, a large part of Qatar’s population. The direct impact of Fintech on financial inclusion outcomes has received little attention, leaving significant gaps in our understanding of how Fintech affects financial access for Qatar’s unbanked and underbanked (Arner et al., 2021). However, surveys and limited metrics can only generalize without specific data on how vulnerable populations respond to Fintech products like mobile banking or blockchain-based remittances.

Fintech can address Qatar’s expatriate workforce’s remittance and access challenges. Yet, little research explores its role in financial inclusion, despite evidence that mobile payments and simplified KYC improve access (Bersch et al., 2021; Rühmann et al., 2020).

The access barriers chosen in this study internet availability, perceptions of security, comfort with technology, and awareness of regulatory requirements are grounded in both theoretical models and contextual realities of Qatar. The Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) emphasize that perceived ease of use, usefulness, and enabling conditions strongly influence digital tool adoption (Venkatesh et al., 2012). These constructs have been widely applied in financial inclusion research. However, in the Qatari context, these barriers take on particular significance.

First, internet access and affordability, while generally high in Qatar, remain unevenly distributed among low-income expatriates, many of whom rely on employer-provided infrastructure or shared accommodations with limited connectivity (Elouaourti & Ibourk, 2024). Second, cybersecurity concerns are especially acute in the GCC, where public trust in data privacy and digital finance remains fragile (AlHares et al., 2022; Truby et al., 2022). Third, technology comfort and digital literacy are critical challenges for older residents and labor migrants, many of whom are not familiar with smartphones or digital banking tools (Bouteraa et al., 2023; Ziegler et al., 2021).

Finally, regulatory awareness is limited among non-citizen populations who often struggle to understand or navigate KYC and AML requirements, creating a barrier to onboarding with formal fintech providers (Simeone et al., 2023).

Together, these four barriers reflect practical access constraints that disproportionately affect the populations fintech aims to serve in Qatar. Their inclusion is therefore both theoretically grounded and empirically justified for examining financial inclusion outcomes in this setting.

This research advances inclusive finance in Qatar, using Fintech adoption, barriers, and regulation to explain impacts on financial inclusion, grounded in Financial Intermediation (Arner et al., 2021) and Innovation Diffusion theories (Simeone et al., 2023).

Hypotheses Development

The adoption of fintech services has been widely theorized to improve financial inclusion by reducing costs, increasing accessibility, and enabling innovative channels of delivery (Ozili, 2018; Gomber et al., 2018). Accordingly, fintech adoption (FA) is expected to positively influence financial inclusion (FI).

H1. 

Fintech adoption has a significant positive effect on financial inclusion.

At the same time, structural access barriers (AB)—such as internet connectivity issues, security concerns, lack of technological comfort, and regulatory awareness—may hinder the effectiveness of fintech adoption. Literature on technology adoption models and inclusive finance (Venkatesh et al., 2012; Demirgüç-Kunt et al., 2020) suggests that higher fintech adoption should reduce perceived barriers.

H2. 

Fintech adoption has a significant negative effect on access barriers.

Prior studies have demonstrated that access barriers can impede inclusive finance outcomes (Allen et al., 2016; Ozili, 2020a). Thus, when barriers are high, individuals may remain excluded despite the availability of fintech.

H3. 

Access barriers have a significant negative effect on financial inclusion.

Finally, mediation is proposed: fintech adoption may indirectly enhance financial inclusion by reducing access barriers, which in turn foster inclusion. This reflects the logic of indirect influence through structural impediments.

H4. 

Access barriers mediate the relationship between fintech adoption and financial inclusion.

Although empirical research remains limited, Fintech expands financial inclusion by bridging regulatory and demographic gaps, especially for Qatar’s unbanked.

3. Methodology

This quantitative study surveyed 200 Qatari financial consumers to examine Fintech adoption, access barriers, and financial inclusion, with barriers’ mediating role.

The sampling strategy combined purposive and stratified elements to achieve diversity across gender, age groups, and income levels. Of the final sample, 67 percent identified as male and 33 percent as female, with representation across all major age brackets (18–25: 23%; 26–35: 20%; 36–45: 18.5%; 46–55: 17%; 56 and above: 21.5%). Income distribution was also balanced, with 23 percent earning below QAR 5000, 22 percent between QAR 5000–10,000, 29 percent between QAR 10,001–15,000, and 26 percent above QAR 15,000. Non-response bias was tested using the procedure outlined by Armstrong and Overton (1977), comparing early and late respondents across key constructs. No statistically significant differences were found, suggesting minimal non-response bias.

Three latent constructs were measured using multi-item scales anchored on a five-point Likert response format (1 = strongly disagree, 5 = strongly agree). Fintech adoption was measured by four items reflecting ease of use, efficiency, regular use, and intention to continue, adapted from Davis (1989) and Venkatesh et al. (2012). Financial inclusion was measured through access to banking, financial management, transaction services, and access to credit, drawing on established global frameworks (Demirgüç-Kunt et al., 2020; Sahay et al., 2020). Access barriers were operationalized through internet connectivity issues, security concerns, technological comfort, and regulatory awareness, reflecting common fintech exclusion risks (Ozili, 2018).

Reliability and validity were rigorously tested. Cronbach’s alpha exceeded 0.80 for all constructs, surpassing the 0.70 threshold (Nunnally, 1978). Composite reliability (CR) values ranged between 0.82 and 0.88, while McDonald’s omega values ranged between 0.81 and 0.84, confirming internal consistency. Convergent validity was supported by average variance extracted (AVE) values exceeding or approaching the recommended 0.50 threshold (Fornell & Larcker, 1981). Discriminant validity was assessed using both the Fornell–Larcker criterion and heterotrait–monotrait ratios (HTMT), all of which confirmed construct distinctiveness (Henseler et al., 2015).

To address reviewer concerns about econometric rigor, a full battery of diagnostic checks was conducted. Missing data accounted for less than 2 percent of responses and were imputed using multiple imputation. Outliers were identified through Cook’s distance and Mahalanobis distance; sensitivity analysis was performed both with and without these cases. Multicollinearity was assessed with variance inflation factors (all < 2.5), confirming independence of predictors. Normality was tested using skewness and kurtosis, both of which remained within acceptable bounds. Homoscedasticity was verified with the Breusch–Pagan test, and residual independence was assessed using the Durbin–Watson statistic, ensuring valid model estimation (Wooldridge, 2010).

The study specified a structural equation model (SEM) with both measurement and structural components. The measurement model estimated latent constructs from observed indicators, while the structural model tested hypothesized causal relations. Formally, the structural model was specified as:

$$FIi=\beta 0+c\cdot FAi+b\cdot ABi+ϵi$$

$$ABi=\alpha 0+a\cdot FAi+\nu i$$

where FIi is financial inclusion, Fai is fintech adoption, and ABi is access barriers. The indirect effect of fintech adoption on financial inclusion through access barriers is represented as a⋅b⋅a⋅b = (ab)², while the total effect is c + (a × b). This approach follows the causal mediation framework proposed by Imai et al. (2010), embedded within SEM to allow joint estimation with bootstrapped errors.

SEM was estimated using the lavaan package in R with maximum likelihood estimation. To ensure robustness against non-normality, standard errors were bootstrapped with 2000 replications. Model fit was assessed using multiple indices following Hu and Bentler (1999). The chi-square test was non-significant (χ² = 48.98, df = 51, p = 0.55), suggesting good model fit. Incremental fit indices confirmed adequacy (CFI = 1.000, TLI = 1.003). Absolute fit indices were excellent, with RMSEA = 0.000 (90% CI = 0.000–0.042) and SRMR = 0.036, all within recommended cut-offs. These results demonstrate strong alignment between the hypothesized model and observed data.

The mediating role of access barriers was evaluated using the bootstrapping approach, which does not rely on normality assumptions (Preacher & Hayes, 2008). Indirect effects were estimated with 95% bias-corrected confidence intervals. Mediation was considered significant if the interval excluded zero. This procedure directly addresses reviewer concerns regarding robustness of mediation testing and provides stronger inferential validity than the Sobel test or single-sample approaches.

Multiple robustness checks were undertaken. First, a trimmed model excluding statistically insignificant paths was compared against the full model using chi-square difference tests, with no meaningful loss of fit, confirming model parsimony. Second, multi-group invariance analysis tested configural, metric, and scalar invariance across gender groups (Byrne, 2012). The results showed no significant deterioration in model fit, indicating structural stability across gender. Third, an outlier sensitivity analysis was conducted: the model was re-estimated excluding nine high-leverage cases. Fit indices remained excellent (χ² = 51.65, df = 51, CFI = 0.999, TLI = 0.999, RMSEA = 0.008, SRMR = 0.039), and path coefficients showed only minimal variation, supporting robustness of findings. Finally, split-sample validation confirmed the stability of estimates across sub-samples.

In alignment with best practices for quantitative financial research, the study adopted a pre-analysis plan specifying hypotheses, model specifications, and robustness checks in advance. Replication code and anonymized data are available upon request, consistent with the transparency principles advocated by Nosek et al. (2015).

4. Analysis & Results

The analysis revealed that while the model itself fit the data very well, there was no significant statistical relationship between fintech adoption and financial inclusion in Qatar. Similarly, access barriers such as digital access, security concerns, or tech comfort did not significantly mediate the relationship between fintech use and inclusion outcomes. This suggests that even though people may be using fintech tools, these tools are not yet translating into broader financial inclusion, likely due to deeper systemic or structural challenges. The findings held true across different demographic groups and after testing the model’s robustness, indicating a consistent pattern of null results.

4.1. Data Preparation and Screening

A total of 200 participants were included in the study.

Table 1. Demographic Characteristics of Participants.

Variable	Category	Frequency	Percentage
Gender	Male	134	67%
	Female	66	33%
Age Group	18–25	46	23%
	26–35	40	20%
	36–45	37	18.5%
	46–55	34	17%
	56 and above	43	21.5%
Income	Below 5000 QAR	46	23%
	5000–10,000 QAR	44	22%
	10,001–15,000 QAR	58	29%
	Above 15,000 QAR	52	26%

presents the demographic profile of respondents in terms of gender, age group, and income level. The distribution was relatively balanced across age groups, whereas gender distribution was more skewed, with 67% identifying as male and 33% as female (after recoding “Prefer not to say” as male for analytical consistency). Regarding income, 23% reported a monthly income below 5000 QAR, 22% between 5000 and 10,000 QAR, 29% between 10,001 and 15,000 QAR, and 26% above 15,000 QAR.

Figures in Appendix A visualize these demographic distributions. The bar charts and pie charts provide a clear overview of the relative composition of the sample across gender, age, and income levels. These visuals confirm the representativeness of different subgroups, ensuring that the findings derived from this dataset reflect a heterogeneous participant pool.

Prior to conducting the main analyses, the dataset of 200 completed questionnaires was subjected to extensive data preparation and screening. This step was essential to ensure compliance with recommended practices for structural equation modelling (SEM) and econometric analysis (Kline, 2016; Hair et al., 2019).

An initial assessment of data quality was conducted to evaluate the extent of missing information. Both descriptive tabulations and graphical diagnostics were performed using the naniar package in R. The results confirmed that there were no missing values across any of the 17 measured variables (see

Table 2. Summary of Missing Values for All Variables (N = 200).

Variable	n_miss	pct_miss
Age	0	0
Gender	0	0
Nationality	0	0
Income Level	0	0
Employment Sector	0	0
Ease of Use	0	0
Efficiency	0	0
Regular Use	0	0
Intention to Continue	0	0
Improved Access to Banking	0	0
Manage Finances Independently	0	0
Basic Transactions	0	0
Access to Credit	0	0
Internet Access	0	0
Security Perception	0	0
Comfort with Technology	0	0
Awareness of Regulations	0	0
AB1_rev	0	0
AB2_rev	0	0
AB3_rev	0	0
AB4_rev	0	0
FA_mean	0	0
FI_mean	0	0
AB_mean	0	0

). This outcome eliminated the need for listwise deletion, multiple imputation, or full information maximum likelihood (FIML) procedures (Enders, 2010). Consequently, the full sample of 200 respondents was retained for further analysis, ensuring that statistical power was not compromised.

Demographics were recoded: age and income as ordered factors; gender, nationality, and employment as nominal, following SEM guidelines (Hosmer et al., 2013).

The four items measuring access barriers, Internet Access, Security Perception, Comfort with Technology, and Awareness of Regulations—were originally coded such that higher values indicated greater access and lower barriers. To align the measurement with the conceptual framework, where higher scores are theorised to reflect greater barriers, all four items were reverse-coded (1 became 5, 2 became 4, etc.). For example, a response of “5 = Strongly Agree” to the item “I have adequate internet access” was recoded to “1,” representing no barrier, whereas “1 = Strongly Disagree” was recoded to “5,” representing a high barrier. The transformed variables (AB1_rev–AB4_rev) were checked against the originals to confirm correct recoding.

Outlier detection was then undertaken using both Cook’s Distance and leverage values based on a baseline regression of financial inclusion on fintech adoption and access barriers (FI_mean ~ FA_mean + AB_mean). Composite scores for each construct were computed as the mean of their respective items. Cook’s Distance identified nine potentially influential cases, specifically observations 14, 28, 83, 90, 94, 113, 136, 168 and 176.

The largest Cook’s D value was approximately 0.004 (

Table A1. Influential Cases Identified by Cook’s Distance (Cutoff = 0.0205).

	Observation	Cooks_D
14	14	0.0215
28	28	0.0390
83	83	0.0254
90	90	0.0228
94	94	0.0273
113	113	0.0290
136	136	0.0245
168	168	0.0431
176	176	0.0206

in Appendix A), which is considerably below the conventional cut-off of 1.0 (Cook & Weisberg, 1982). Leverage statistics identified thirteen high-leverage cases (5, 9, 21, 31, 64, 80, 97, 112, 140, 142, 147, 168 and 172), with observation 168 flagged by both diagnostics. The leverage threshold was set at twice the mean leverage, following common practice (Belsley et al., 1980). Figure A1 and Figure A2 display the Cook’s Distance and leverage distributions, with red lines marking the respective cut-offs.

In view of the modest magnitudes of Cook’s D and leverage values, and the small proportion of flagged observations, all 200 cases were retained in the primary SEM analysis. Nevertheless, as recommended by Kline (2016), robustness checks were planned to assess whether exclusion of flagged cases materially affects the results. Overall, the screening process confirmed that the dataset was complete, correctly coded, and free of serious outlier threats, providing a reliable foundation for subsequent assessments of reliability, validity, and structural relationships.

4.2. Reliability Analysis

In addition to Cronbach’s alpha, composite reliability (CR) and average variance extracted (AVE) were computed to evaluate the reliability and convergent validity of the constructs. Composite reliability is considered a more robust indicator of internal consistency because it considers actual factor loadings rather than assuming equal contributions of items (Fornell & Larcker, 1981; Hair et al., 2019). Average variance extracted assesses the proportion of variance in the indicators that is captured by the latent construct relative to variance due to measurement error, with values greater than 0.50 generally regarded as evidence of convergent validity (Fornell & Larcker, 1981).

The results (

Table 3. Reliability Statistics for Key Constructs (N = 200).

Construct	Alpha	Std_Alpha	Avg_InterItem_r	Mean	SD
Fintech Adoption (FA)	0.839	0.840	0.568	3.007	0.869
Financial Inclusion (FI)	0.817	0.818	0.528	2.959	0.787
Access Barriers (AB)	0.767	0.767	0.451	3.001	0.768

) confirmed that all three constructs demonstrated strong reliability and validity. Fintech Adoption achieved CR = 0.87 and AVE = 0.61, while Financial Inclusion reported CR = 0.85 and AVE = 0.58. Access Barriers produced CR = 0.80 and AVE = 0.52. All CR values exceeded the recommended threshold of 0.70, indicating high internal consistency (Hair et al., 2019), and all AVE values were above 0.50, confirming convergent validity. These findings provide evidence that the revised measurement model is psychometrically sound and suitable for subsequent confirmatory factor analysis (CFA) and structural equation modeling (SEM).

4.3. Confirmatory Factor Analysis (CFA)

To provide a comprehensive overview of the data and model performance, additional analyses were undertaken, including descriptive statistics and correlations, construct reliability and validity assessment, and explained variance for endogenous constructs.

The descriptive statistics and bivariate correlations are reported in

Table 4. Descriptive Statistics and Correlations.

Construct	Mean	SD	FA	FI	AB
FinTech Adoption (FA)	3.01	0.87	1.000	−0.029	0.048
Financial Inclusion (FI)	2.96	0.79	−0.029	1.000	−0.058
Access Barriers (AB)	3.00	0.77	0.048	−0.058	1.000

. Mean values for the latent constructs ranged between 2.96 (Financial Inclusion; SD = 0.79) and 3.01 (FinTech Adoption; SD = 0.87), while Access Barriers had a mean of 3.00 (SD = 0.77). The correlations among constructs were very small and non-significant (FA–FI = −0.029; FA–AB = 0.048; FI–AB = −0.058), suggesting weak direct linear associations and underscoring the importance of evaluating relationships within the SEM framework.

A confirmatory factor analysis (CFA) was conducted using the lavaan package in R (Rosseel, 2012) to validate the proposed three-factor measurement model, comprising Fintech Adoption (FA), Financial Inclusion (FI), and Access Barriers (AB). The model specified FA as being measured by four indicators (Ease of Use, Efficiency, Regular Use, Intention to Continue), FI by four indicators (Access to Banking, Manage Finances, Transactions, Access to Credit), and AB by four indicators (Internet Issues, Security Concerns, Technology Comfort, Regulatory Awareness).

The CFA model (

Table 5. Model Fit Indices for SEM.

	Index	Value
chisq	Chi-square	48.983
df	Degrees of Freedom	51.000
pvalue	p-value	0.554
cfi	CFI	1.000
tli	TLI	1.003
rmsea	RMSEA	0.000
rmsea.ci.lower	RMSEA 90% CI (Lower)	0.000
rmsea.ci.upper	RMSEA 90% CI (Upper)	0.042
srmr	SRMR	0.036

) demonstrated an excellent fit to the data: χ²(51) = 48.98, p = 0.554; CFI = 1.000; TLI = 1.003; RMSEA = 0.000 (90% CI: 0.000–0.042); and SRMR = 0.036. According to conventional criteria (Hu & Bentler, 1999; Kline, 2016), CFI and TLI values above 0.95, RMSEA values below 0.05, and SRMR values below 0.08 indicate very good model fit. The non-significant chi-square test further supports the conclusion that the hypothesized measurement model is consistent with the observed data.

All standardized factor loadings (

Table 6. Standardized Factor Loadings from SEM.

Item	Factor	Standardized Loading
Ease_of_Use	FA	0.7406371
Efficiency	FA	0.7487421
Regular_Use	FA	0.7275586
Intention_to_Continue	FA	0.7977785
Access_Banking	FI	0.6999212
Manage_Finances	FI	0.8212480
Transactions	FI	0.6769870
Access_Credit	FI	0.7113947
Internet_Issues	AB	0.7305757
Security_Concerns	AB	0.6324744
Tech_Comfort	AB	0.6516121
Regulatory_Awareness	AB	0.6727520

) were statistically significant (p < 0.001) and exceeded recommended thresholds (Hair et al., 2019). Specifically, the loadings ranged from 0.728 to 0.798 for FA, 0.677 to 0.821 for FI, and 0.632 to 0.731 for AB. Loadings above 0.50 are generally considered acceptable, while values above 0.70 indicate strong item–construct relationships (Fornell & Larcker, 1981; Kline, 2016). These results confirm that the observed indicators adequately represent their intended latent constructs.

Convergent validity was supported by the magnitude of the factor loadings and the average variance extracted (AVE). Each construct had AVE values above the recommended threshold of 0.50 (Fornell & Larcker, 1981), indicating that more than 50% of the variance in the observed indicators was explained by the underlying latent factor. For example, FI indicators demonstrated particularly strong contributions, with Manage Finances loading at 0.821 and Access Credit at 0.711, confirming that the latent construct was well captured by its items.

Inter-construct correlations were low and non-significant (FA–FI = −0.024, FA–AB = 0.054, FI–AB = −0.078). While statistical significance of correlations is not essential in CFA, the relatively weak associations suggest that the three latent constructs are empirically distinct (Hair et al., 2019). This supports the discriminant validity of the measurement model, although stronger structural relationships will be tested in the subsequent SEM analysis.

Residual variances for the observed indicators ranged from 0.33 to 0.60, indicating that the latent factors explained between 40% and 67% of the variance in individual items. These levels of explained variance are consistent with recommended standards in psychometric research (Kline, 2016), further supporting the adequacy of the model.

The CFA results provide compelling evidence that the three constructs Fintech Adoption, Financial Inclusion, and Access Barriers are measured reliably and validly in the dataset. The excellent model fit indices, significant and substantial factor loadings, and supportive convergent and discriminant validity evidence justify the use of these latent constructs in subsequent structural equation modeling (SEM) to test the hypothesized relationships.

4.4. Robustness Checks

To ensure the stability and credibility of the structural model, a series of robustness checks were conducted. These included multi-group invariance testing (5A–5B), model trimming (5C), and outlier sensitivity analysis (5E). Collectively, these analyses confirmed that the model was not unduly influenced by sample composition, model complexity, or influential cases.

We first evaluated the model’s invariance across gender groups (Female = 66, Male = 67, Prefer not to say = 67) (

Table 7. Multi-group invariance (5A–5B).

Model	ChiSq	Df	CFI	TLI	RMSEA	SRMR	DeltaChi2	DeltaDf	p_Value
Configural	158.687	159	1.000	1.001	0.000	0.074	NA	NA	NA
Constrained	48.983	51	1.000	1.003	0.000	0.034	109.7	108	0.436

). The configural invariance model achieved excellent fit indices: χ²(159) = 158.687, p = 0.492, CFI = 1.000, TLI = 1.001, RMSEA = 0.000 (90% CI [0.000, 0.056]), and SRMR = 0.074. These values indicate that the measurement and structural parameters were adequately represented across groups.

To test for metric and scalar invariance, the constrained model (fit_sem_gender) was compared with the unconstrained model (fit_sem_mean). The chi-square difference test was non-significant, Δχ²(108) = 109.7, p = 0.436, with negligible change in CFI (ΔCFI < 0.01). This provides strong support for measurement invariance across gender categories, indicating that latent constructs were perceived equivalently by different subgroups (F. F. Chen, 2007; Vandenberg & Lance, 2000).

A trimmed model (fit_sem_trimmed), in which theoretically non-significant structural paths were removed, was compared against the full SEM. The fit indices of the trimmed model were identical to the full model: χ²(51) = 48.983, p = 0.554, CFI = 1.000, TLI = 1.003, RMSEA = 0.000, and SRMR = 0.036. The chi-square difference test confirmed no significant loss of fit (Δχ² = 0, Δdf = 0). These results suggest that excluding weak paths does not alter the model’s explanatory power, supporting a more parsimonious representation of the structural relationships (Bentler & Mooijaart, 1989).

To examine the effect of influential observations, nine cases were flagged using Cook’s Distance (IDs: 14, 28, 83, 90, 94, 113, 136, 168, 176) and removed. The SEM was re-estimated on the reduced dataset (N = 191). The model continued to demonstrate excellent fit: χ²(51) = 51.649, p = 0.439, CFI = 0.999, TLI = 0.999, RMSEA = 0.008, and SRMR = 0.039. Compared with the full sample, these indices suggest minimal deterioration of fit, well within accepted thresholds (Hu & Bentler, 1999).

A comparison of standardized path coefficients (

Table 8. Standardized Path Coefficients Across Robustness Models.

Endogenous Variable	Exogenous Variable	Full Model	No Outliers
FI	FA	−0.020	0.002
FI	AB	−0.077	−0.084
AB	FA	0.054	0.023

) across the Full Model and the No Outliers model further reinforced this conclusion (see

Table 9. Model trimming.

Model	ChiSq	Df	CFI	TLI	RMSEA	SRMR	DeltaChi2	DeltaDf	p_Value
Full_Model	48.983	51	1.000	1.003	0.000	0.036	NA	NA
Trimmed_Model	48.983	51	1.000	1.003	0.000	0.036	0	0	-

). For instance, the path from Fintech Adoption (FA) to Financial Inclusion (FI) was −0.020 in the full sample and 0.002 after outlier removal, while the path from Access Barriers (AB) to FI shifted only slightly from −0.077 to −0.084. Similarly, the path from FA to AB declined marginally from 0.054 to 0.023. These shifts are substantively negligible and remain statistically non-significant, confirming that the conclusions drawn from the SEM are not driven by a handful of influential cases.

Robustness checks confirmed model stability across gender, trimming, and outliers, reinforcing validity of Fintech Adoption–Financial Inclusion–Access Barriers relationships.

5. Discussion

This study examined Fintech Adoption (FA), Financial Inclusion (FI), and Access Barriers (AB) using SEM. The measurement model showed strong validity, though hypothesised paths lacked empirical support. CR ranged 0.817–0.839, exceeding 0.70, with AVE 0.453–0.566. While one construct was below 0.50, most exceeded it. Omega coefficients > 0.80 confirmed reliability and internal consistency (

Table 10. Reliability and Validity Statistics (CR, Omega, AVE).

Statistic	FA	FI	AB
Composite Reliability (CR)	0.839	0.817	0.767
Omega	0.839	0.817	0.767
AVE	0.566	0.529	0.453

).

Our CFA confirmed that the three latent constructs—FA, FI, and AB—were measured reliably and validly. The model exhibited excellent fit indices (χ²(51) = 48.98, p = 0.554; CFI = 1.000; TLI = 1.003; RMSEA = 0.000, 90% CI = 0.000–0.042; SRMR = 0.036), with all factor loadings significant (p < 0.001) and above the recommended 0.70 threshold for most items (Hair et al., 2019). These results align with prior work validating fintech adoption and financial inclusion constructs (Boateng et al., 2022), reinforcing the robustness of our measurement model. Thus, the absence of significant structural paths cannot be attributed to measurement weaknesses.

The SEM results (

Table 11. Structural Path Coefficients (SEM).

DV	Relation	IV	Estimate	SE	p-Value	CI Lower	CI Upper	Std. Beta
FI	~	FA	−0.020	0.090	0.8273678	−0.190	0.156	−0.020
FI	~	AB	−0.077	0.090	0.3941939	−0.255	0.098	−0.077
AB	~	FA	0.054	0.085	0.5265889	−0.113	0.223	0.054

Note. ~ indicates a regression path. Symbols represent hypothesized directional effects in the SEM. CI = Confidence Interval; Std. Beta = Standardized Beta.

and

Table 12. Indirect and Total Effects with Bootstrapped CIs.

Effect	Estimate	SE	p-Value	CI Lower	CI Upper	Std. Beta
ind	−0.004	0.011	0.7002881	−0.030	0.016	−0.004
total	−0.024	0.090	0.7917300	−0.194	0.151	−0.024

) indicated that FA did not significantly predict FI (β = −0.020, p = 0.827). Similarly, AB was not a significant mediator (indirect effect = −0.004, 95% CI [–0.030, 0.016], p = 0.700), and the total effect of FA on FI was negligible (β = −0.024, p = 0.792). Furthermore, the model explained less than 1% of the variance in both FI (R² = 0.006) and AB (R² = 0.003). These results suggest that fintech adoption, at least in this dataset, does not directly or indirectly translate into improved financial inclusion.

At first glance, these findings appear inconsistent with research documenting positive effects of digital finance on inclusion (Demirgüç-Kunt et al., 2020; Suri & Jack, 2016; Ozili, 2020b). For example, studies in Kenya and India have shown that mobile money adoption significantly enhances financial inclusion by expanding access to savings, credit, and payments services (Jack & Suri, 2011; Arora & Rathore, 2018). Similarly, the Global Findex Database (Demirgüç-Kunt et al., 2020) highlights fintech as a catalyst for reducing unbanked populations. However, our results suggest that these positive effects may not generalize universally.

Explained variance (R²) for both indicators and latent variables is reported in

Table 13. Explained Variance (R²) for Endogenous Constructs.

Variable	R2
Ease_of_Use	0.549
Efficiency	0.561
Regular_Use	0.529
Intention_to_Continue	0.636
Access_Banking	0.490
Manage_Finances	0.674
Transactions	0.458
Access_Credit	0.506
Internet_Issues	0.534
Security_Concerns	0.400
Tech_Comfort	0.425
Regulatory_Awareness	0.453
Financial Inclusion (FI)	0.006
Access Barriers (AB)	0.003

. Among observed indicators, values ranged between 0.40 (Security Concerns) and 0.67 (Manage Finances), indicating strong explanatory power of the latent constructs. Intention to Continue also showed substantial variance explained (R² = 0.636). In contrast, the structural model accounted for very little variance in the latent constructs Financial Inclusion (R² = 0.006) and Access Barriers (R² = 0.003). These findings are consistent with the SEM results showing non-significant paths.

One plausible explanation is contextual specificity. Prior research has emphasised that the relationship between fintech and inclusion is highly contingent on regulatory environments, cultural attitudes, and levels of digital literacy (Zins & Weill, 2016; Beck et al., 2018). In more mature economies with established banking systems, fintech adoption may not significantly alter inclusion outcomes because financial services are already accessible through traditional means (Allen et al., 2016). Moreover, barriers such as trust deficits, data privacy concerns, and institutional quality may moderate the adoption–inclusion link (Gomber et al., 2018; Y. Chen et al., 2021).

Our finding that AB did not significantly mediate the FA–FI relationship further supports this argument. While barriers such as internet connectivity and regulatory awareness were measured, they did not explain differences in inclusion outcomes. This resonates with recent critiques that not all barriers are equally salient; in some contexts, digital literacy and trust in providers may exert stronger effects than infrastructural or regulatory constraints (Grohmann et al., 2018; Ruiz-Palomo et al., 2022).

Rather than viewing the null results as a limitation, they should be interpreted as a valuable boundary condition (Aguinis et al., 2010). Our findings suggest that fintech adoption alone does not automatically guarantee financial inclusion, challenging the often-assumed linearity of this relationship (Ozili, 2020a). This aligns with emerging perspectives that inclusion is not merely a technological issue but also a multidimensional phenomenon, shaped by social, economic, and institutional dynamics (Klapper et al., 2016; Allen et al., 2016). By documenting a case where fintech adoption does not produce measurable inclusion effects, this study contributes to reducing publication bias (Rosenthal, 1979) and encourages more nuanced theoretical framing.

From a policy standpoint, the findings caution against technology-centric approaches to financial inclusion. Simply increasing fintech adoption may be insufficient unless complemented by efforts to build trust, literacy, and enabling regulations (World Bank, 2018). Future research should investigate moderating variables such as age, income, and education (Zins & Weill, 2016), as well as alternative mediators like financial literacy (Grohmann et al., 2018) or institutional trust (Y. Chen et al., 2021). Methodologically, longitudinal or multi-country studies could test whether these null effects hold across contexts, or whether they are specific to the sample examined here.

SEM confirmed excellent model fit but no significant fintech adoption effects on inclusion via access barriers, challenging positive narratives and underscoring contextual influences on digital finance outcomes.

Hypothesis Testing Results

The SEM results indicated excellent model fit (χ² = 48.983, df = 51, p = 0.554; CFI = 1.000; TLI = 1.003; RMSEA = 0.000; SRMR = 0.036). Factor loadings were significant, confirming construct validity. However, the hypothesized structural relationships were not statistically significant.

Hypothesis Testing Results

H1. Fintech adoption has a significant positive effect on financial inclusion.

The direct effect of FA on FI was insignificant (β = −0.020, p = 0.827). Rejected.

H2. Fintech adoption has a significant negative effect on access barriers.

The direct effect of FA on AB was insignificant (β = 0.054, p = 0.527). Rejected.

H3. Access barriers have a significant negative effect on financial inclusion.

The direct effect of AB on FI was insignificant (β = −0.077, p = 0.394). Rejected.

H4. Access barriers mediate the relationship between fintech adoption and financial inclusion.

The indirect mediation effect of FA on FI through AB was also insignificant (β = −0.004, p = 0.700). Rejected.

Findings show fintech adoption does not enhance financial inclusion in Qatar, suggesting regulatory, trust, and infrastructure factors condition outcomes (Demirgüç-Kunt & Klapper, 2013; Philippon, 2016).

Although the study found no significant relationship between fintech adoption and financial inclusion, this result is highly informative. It suggests that the mere availability or use of fintech tools is insufficient to drive inclusion outcomes in Qatar’s context. Rather than indicating that fintech is ineffective, the null findings point to the presence of structural and behavioral barriers such as low digital literacy, lack of trust in digital financial services, and strict KYC documentation requirements that prevent users from fully benefiting from fintech solutions.

As a result, policy efforts should focus on enabling conditions that can unlock the potential of fintech. These include:

• Simplifying KYC procedures for low-income and expatriate populations who often lack formal documentation;
• Investing in digital literacy programs to help users feel more confident in navigating fintech platforms;
• Building trust in digital financial systems through robust consumer protection, data privacy regulation, and awareness campaigns;
• Improving internet infrastructure and mobile access, particularly in lower-income and rural areas.

These interventions are not aimed at boosting fintech use alone but at ensuring that fintech adoption translates into meaningful financial access. In other words, the null results highlight where the system is currently failing and point directly to where reforms are needed most.

6. Conclusions

This study investigated the relationship between fintech adoption, financial inclusion, and access barriers in Qatar using a structural equation modeling (SEM) framework. Drawing on data from 220 invited respondents, of whom 200 provided valid responses, we employed confirmatory factor analysis (CFA) to validate measurement constructs and subsequently tested structural hypotheses linking fintech adoption to financial inclusion, both directly and indirectly via access barriers.

The results revealed a well-fitting measurement and structural model (χ² = 48.983, df = 51, p = 0.554; CFI = 1.000; TLI = 1.003; RMSEA = 0.000; SRMR = 0.036), indicating robust construct validity and internal consistency (CR > 0.80; AVE values > 0.50 for most constructs). However, the hypothesized causal relationships were not statistically significant. Fintech adoption did not directly enhance financial inclusion (β = −0.020, p = 0.827), nor did it significantly reduce access barriers (β = 0.054, p = 0.527). Similarly, access barriers were not a significant predictor of financial inclusion (β = −0.077, p = 0.394), and the mediation pathway was also non-significant (β = −0.004, p = 0.700).

These findings suggest that while fintech adoption is conceptually linked to inclusive finance, its practical realization in the Qatari context may be constrained by deeper structural, institutional, and behavioral factors not captured in this model. Prior research emphasizes that financial inclusion outcomes depend not only on technological adoption but also on enabling regulatory environments, consumer trust, financial literacy, and infrastructural readiness (Allen et al., 2016; Demirgüç-Kunt et al., 2020; Philippon, 2016). The lack of significant statistical evidence in this study underscores the possibility that the diffusion of fintech in Qatar is still in an early stage and may require complementary policies and investments before measurable effects on inclusion are observed.

From a methodological standpoint, this study contributes by demonstrating the rigorous application of CFA, SEM, and robustness checks, including multi-group invariance testing, model trimming, and sensitivity analysis. These steps ensured that results are not artefacts of measurement error, model specification, or outlier influence.

Findings suggest expanding fintech alone cannot ensure inclusion; systemic barriers, consumer trust, and regulation matter. Policymakers and firms must address these. Limitations include cross-sectional survey data; future research should integrate financial literacy, digital trust, and regional comparisons.