For decades, The Gambia’s economy has operated predominantly on cash. Physical dalasi notes change hands in market stalls, coins are stored in domestic savings boxes, and money is physically carried to relatives in rural areas (Mashaqbeh, 2025). This purely corporeal system has excluded large segments of the population from formal financial security and opportunities. Because bank branch networks remain concentrated in the Greater Banjul urban area, rural communities, women, youth, and micro-entrepreneurs have relied on informal, insecure, and inefficient processes (Adeosun et al., 2023). The history of financial inclusion in The Gambia has been one of geographical and socioeconomic inequality. Therefore, the history of financial inclusion in The Gambia has been one of geographical and socioeconomic inequality.
The introduction of mobile money has provided a disruptive digital alternative. Within less than a decade, services such as Africell Money and QMoney have fundamentally challenged the conventional meaning of “being banked” (Mananyetso & Dehinbo, 2025). By turning the ubiquitous mobile phone, a device present in most households, into a channel for financial transactions, these platforms have democratized access. The number of registered accounts has risen sharply, and a network of agents has turned corner shops into functional bank branches (Muzeya & Hamadziripi, 2025). This numerical advancement in access is often celebrated by regulators and development partners alike (Mohamed, 2025). However, beneath the surface of rapid adoption lies a more pertinent question: does the proliferation of mobile money accounts lead to meaningful financial inclusion, or does it merely add a new layer of transactional activity that fails to build long term economic resilience or stimulate business growth?
International literature highlights both the promise and ambiguity of this digital transition. Foundational studies in pioneering markets such as Kenya have demonstrated that mobile money can lead to quantifiable poverty reduction and consumption smoothing among vulnerable households as M-Pesa spreads (Nonvide, 2025). This body of work positions mobile money as an instrument for developmental leapfrogging. Concurrently, technology adoption theories, especially the Unified Theory of Acceptance and Use of Technology (UTAUT), offer a robust framework for explaining first-time user acceptance, focusing on constructs such as performance expectancy, social influence, and facilitating conditions (Fokides & Giagiakou, 2025). In the West African context, these lenses have been appropriately applied to identify obstacles, ranging from network trust to agent liquidity.
Despite the richness of this literature, a critical gap remains, which this study addresses (Kelly, 2025). Existing research tends to follow two separate paths: the first examines the antecedents of adoption (why people start using mobile money), and the second quantifies its final socioeconomic effects (what changes as a result of mobile money use). What remains unexplored, particularly in the Gambian case, is the critical mediating process of how. Integrated models that treat the intensity and diversity of mobile money use as a key mediating variable through which initial drivers are transformed into concrete financial inclusion benefits are scarce (Mohamed, 2025). Existing research tends to follow two separate paths: the first examines the antecedents of adoption (why people start using mobile money), and the second quantifies its final socioeconomic effects (what changes as a result). What remains unexplored, particularly in the Gambian case, is the critical mediating process the how. Integrated models that treat the intensity and diversity of mobile money use as a key mediating variable through which initial drivers are transformed into concrete financial inclusion benefits are scarce (Isaeva, 2024). This discontinuity limits our understanding of how to move from elementary access to transformational financial deepening.
The scientific novelty of this study lies in its addressing of this conceptual gap. We argue that the effects of mobile money are not automatic outcomes of its availability; rather, they depend critically on how users interact with the technology itself. In other words, mobile money creates value only when it is used actively and meaningfully. Based on this premise, this study develops and empirically tests a detailed structural research framework (presented graphically in Figure 1) that views mobile money not as an end goal but as a mediating mechanism for change (Cheruyot et al., 2024). In the proposed model, two key outcome areas, household financial resilience and small business operational efficiency, are influenced by the intensity of mobile money use, which is shaped by external factors such as perceived usefulness, social norms, and the enabling environment.
Accordingly, our central research question is: What is the structural interaction between the determinants of mobile money adoption, the intensity of its use, and the financial inclusion outcomes achieved by different user groups in The Gambia? To address this question, the following hypotheses were tested:
H1: Perceived utility, positive social influence, and strong facilitating conditions are positively and significantly associated with a greater intensity of mobile money adoption.
H2: The financial resilience of households and the operational efficiency of small businesses are positively correlated with the intensity of mobile money adoption, and adoption intensity mediates the drivers’ effects.
H3: The strengths of these mediation pathways are moderated by context, specifically urban rural location, and enterprise size.
This research aims to move policy and academic debate beyond simple access metrics and towards a path of impact analysis. By validating this structural framework, we provide a granular map of the mobile money ecosystem in The Gambia (Kelly, 2025). This is essential for shifting the focus from celebrating how many accounts have been opened to strategically nurturing the quality of lives improved through finance so that the digital financial revolution can realize its full potential for inclusive economic empowerment.

The nature of this study, which examines the role of mobile money in financial inclusion, necessitates a framework that goes beyond linear cause-and-effect relationships. This study is guided by an integrated conceptual framework that assumes that mobile money adoption is not an end state but a crucial mediating mechanism (Voptia & Stukalina, 2025). The framework integrates ideas from the UTAUT with the essential dimensions of financial inclusion to map the theorized relationships between user perceptions, environmental factors, and socioeconomic performance (Ojiaku et al., 2024). Figure 1 presents this framework and indicates the precise relationships that are to be tested.
The conceptual framework specifies three exogenous constructs that influence the central mediating variable, Mobile Money Adoption Intensity (MMA). Perceived Utility and Trust (PUT) combine performance expectancy, effort expectancy, and system security. Social Influence (SI) captures normative pressures from family, friends, and community leaders. Facilitating Conditions (FC) include agent network accessibility, coverage, cost-effectiveness, and customer support. MMA is measured on a continuous scale of frequency, regularity, and variety of use (transfers, payments, savings, airtime), not as a binary status.
The MMA in turn affects two endogenous outcomes: Household Financial Resilience (HFR), the ability to manage shocks, smooth consumption, and save digitally, and Small Business Operational Efficiency (SOE), reduced transaction costs, improved cash flow, digital record keeping, and efficient payments. The framework also includes moderating variables (geographic location and enterprise size), which are theorized to alter the strength of the associations between MMA and outcomes. This structure provides a testable roadmap for analyzing how mobile money adoption translates into financial inclusion in The Gambia.

Figure 1. Proposed Structural Model of Mobile Money Adoption and Financial Inclusion Outcomes

Source: Author’s elaboration based on UTAUT

This study employed a sequential explanatory mixed methods design, prioritizing quantitative data collection to test the hypothesized structural model, followed by qualitative interviews to contextualize the results. A cross-sectional survey was conducted from March to July 2025 using a multistage sampling strategy. Four administrative regions were purposely selected to reflect The Gambia’s socioeconomic diversity: Kanifing, Banjul, the West Coast, and Lower River.
Households (n=384) were selected via cluster random sampling from enumeration areas, with the sample size determined using Cochran’s formula (95% confidence, 5% margin of error). Small businesses (n=152) were stratified by size (micro: 1–4 employees; small: 5–19 employees) and randomly selected from Chamber of Commerce and market vendor listings. A structured questionnaire measured six latent constructs (PUT, SI, FC, MMA, HFR, and SOE) using five-point Likert scales.
The questionnaire was translated into Mandinka and Wolof, back-translated, and achieved a Content Validity Index of 0.89 after expert review and pilot testing (n=50). Face-to-face interviews were conducted by trained enumerators, followed by 28 semi-structured interviews with purposively selected users, agents, and business owners.
Data analysis proceeded in four stages using SPSS 27.0 and AMOS 28.0: preliminary data cleaning and descriptive profiling; assessment of the measurement model for reliability (Cronbach’s alpha, composite reliability), convergent validity (AVE > 0.50), and discriminant validity (Fornell-Larcker criterion); structural model testing with bootstrapping (5,000 samples) for direct and indirect effects, using fit indices (χ²/df < 3, CFI > 0.90, TLI > 0.90, RMSEA < 0.08, SRMR < 0.08); and multi group analysis to test moderation by location and enterprise size. Qualitative transcripts were analyzed using reflexive thematic analysis to explain the quantitative findings.

4.1. Results
In this section, the empirical results of the survey and interview data analysis are reported to first indicate the quantitative findings that test the hypothesized model and then provide a synthesis of these findings and interpretations in the context of the general scientific and policy environment.

4.1.1. Descriptive Characteristics of the Respondents
The total sample comprised 536 respondents (384 households and 152 small-business operators). Gender distribution was even (51% female, 49% male), and the mean age of the household sample was 38.7 years old. Geographically, 58% were urban (Banjul/Kanifing) and 42% were peri-urban or rural. The SME sample consisted of 76 micro enterprises (1-4 employees) and 76 small enterprises (5-19 employees), mostly in retail trade (45%), services (30%), and light agro-processing (25%).
A key finding was the near saturation of access: 94% of participants had registered a mobile money wallet. However, the usage intensity varies greatly (Cho et al., 2025). While 88% of the respondents used mobile money to receive remittances, only 34% actively used the savings feature, and only 12% had ever used a digital loan or credit facility. This gap between registration and diversified use underscores the importance of measuring adoption as a continuum rather than a binary state.

4.1.2. Measurement Model and Psychometrics Assessment
Before testing the structural model, the reliability and validity of the six latent constructs were evaluated. Table 1 shows that all constructs exceeded the recommended thresholds for internal consistency and convergent validity.

Table 1. Reliability and Convergent Validity of Constructs

Source: Author’s survey data (2026)

Cronbach’s alpha and composite reliability values were well above 0.70. Convergent validity was established as each construct exceeded 0.50. Discriminant validity was verified using the Fornell-Larcker criterion: the square root of each construct’s AVE (diagonal values in Table 2) was larger than its correlations with all other constructs.

Table 2. Discriminant Validity Assessment (Fornell Larcker Criterion)

Source: Author’s survey data (2026)

Note: Diagonal elements (in bold) are the square root of the AVE

4.1.3. Structural Model and Hypothesis Testing
Structural equation modelling was used to evaluate the hypothesized model. The model showed good fit with the data: χ²/df = 2.38, CFI = 0.94, TLI = 0.93, RMSEA = 0.051 (90% CI: 0.046 0.056), SRMR = 0.047. These values were within the accepted limits, allowing for the meaningful interpretation of the path coefficients. Table 3 summarizes the results of the path analysis for H1 and H2.

Table 3. Structural Path Coefficients and Hypothesis Testing

Source: Author’s survey data (2026)

First, finding 1, primacy of practical utility and enabling infrastructure. Hypothesis H1 was fully supported. Perceived Utility and Trust were the strongest predictors of MMA (β = 0.41, p < .001). This indicates that Gambian users are primarily pragmatic: they intensify usage when they see tangible benefits in terms of time saving, cost reduction, and system reliability (Valentine et al., 2026). FC also had a substantial effect (β = 0.29, p = .001), showing that poor agent network quality or unreliable connectivity can stifle adoption, even when the perceived utility is high. SI had a smaller but still significant effect (β = 0.17, p = 0.008), suggesting that while community norms may encourage initial trials, continued and diversified use depends more on personal experience and system reliability.
Second, finding 2, adoption as a potent mediator of outcomes. Hypothesis H2 was strongly supported. MMA was a powerful mediator, with strong direct relationships with both Household Financial Resilience (β = 0.53, p < .001) and SME Operational Efficiency (β = 0.61, p < .001). The strength of these paths validates the core assumption of the conceptual model: the technology’s effects are not automatic but are realized through active usage by the user. Bootstrapping (5,000 samples) confirmed that the indirect effects of PUT, SI, and FC on HFR and SOE via MMA were significant (p < .01), with no remaining significant direct effects, indicating full mediation (see Table 4).

Table 4. Specific Indirect (Mediation) Effects

Source: Author’s survey data (2026)

Third, finding 3, usage plateau and broken credit pathway. An exploratory analysis that was not originally hypothesized revealed a critical fissure. Valentine et al. (2026) Although MMA strongly predicted transactional efficiency and resilience, its relationship with access to formal credit was weak and non-significant (β = 0.08, p = .152). This suggests a usage plateau: users progress from non-use to active use for transfers and payments, but the ladder to advanced financial products such as credit remains broken (Iheme, 2022). Qualitative data explain this paradox in the following ways. As one tailor in Brikama stated, “I have a history; I use my mobile money every day. However, when I went to take a small loan to purchase a new machine, they requested a bank statement and a land title, he said. My online history did not exist to them.” Thus, mobile money has successfully brought users into the payment ecosystem but not into the credit ecosystem.
Fourth, finding 4, moderation by enterprise size, but not location. The multigroup analysis for H3 showed a significant moderating effect of enterprise size on the MMA → SOE relationship. The effect was much stronger for small enterprises (β = 0.71, p < .001) than for micro-enterprises (β = 0.48, p < .001), with a critical ratio difference of 2.89 (p < .01). This indicates that larger small businesses gain exponentially higher operational benefits, presumably because they have more complex financial logistics that technology can simplify. Contrary to expectations, urban rural location did not significantly moderate the paths to HFR and SOE. This implies that once adopted, the perceived benefits are similar across different locations. However, qualitative data nuance this finding: even when the path slopes are identical, rural users more often noted that the absolute level of benefit was limited by lower agent liquidity and network instability.

4.2. Discussion
These findings provide a nuanced explanation of financial inclusion in The Gambia. The strong support for our theoretical claim that technology adoption for development should be viewed as a process, not an event, validates the proposed model. The predominance of perceived utility is consistent with the UTAUT but further shows that in a low-trust, informal context, utility cannot exist without trust that the system will not fail and leave users without their money (Linh & Huyen, 2025).
The most critical discovery was the usage plateau. These findings echo recent critiques of the financial inclusion agenda that warn against over-celebrating access. We suggest that while The Gambia has built a digital payments infrastructure, the roads and pathways to savings, insurance, and credit remain underdeveloped. Therefore, even with high adoption levels, many users remain financially vulnerable (Saidyjeng & Sawaneh, 2026). The system facilitates daily liquidity management but has not yet met long-term capital accumulation or risk mitigation needs (Sattorova, 2024).
The moderation finding related to enterprise size highlights a digital divide in the business community. Micro enterprises use mobile money primarily as a cash replacement for security and convenience, whereas small enterprises use it as a management information system for decision-making based on digital records (Khan, 2025). This implies a need for increasingly sophisticated business support solutions that transform simple transactional platforms into analytical dashboards for small businesses.
Overall, this discussion affirms that mobile money is a radical but imperfect solution. It has considerably improved the efficiency and stability of financial transactions for a large number of people (Mohammed & Yakubu, 2025). However, its power to achieve profound systemic financial inclusion will not be fully realized until policy and targeted market innovations connect mobile money data to credit scoring, trigger formal savings products, and ensure equitable infrastructure quality. The model not only validates the success trajectories but also explicitly identifies remaining roadblocks.

This study aimed to examine the structural pathways through which mobile money influences financial inclusion in The Gambia, moving beyond simple access measures to identify key channels of influence. The main hypotheses were supported in this study. H1 is accepted: users’ perception of practical utility and trust in the system, together with the quality of FC such as agent networks, are significant determinants of MMA, while social norms play a smaller role. H2 is confirmed: mobile money use is a powerful mediating factor, translating the initial drivers into concrete changes that make households more financially resilient and small business operations more efficient. H3 is partially confirmed: enterprise size significantly moderates the benefits small firms achieve much greater operational efficiency gains than micro-enterprises, whereas urban-rural location does not significantly alter the strength of the adoption-outcome correlations, although access difficulties at the base remain.
Overall, the study finds that mobile money is a highly effective means of enabling digital participation in transactions in rural areas. It has largely solved the old problem of financial access for households and businesses through the transformation of payments and transfers of funds. However, the more important conclusion is that this bridge now leads to a usage plateau in the future. The system is very capable of moving money but poor at converting transactional information into added financial richness, especially in the provision of formal credit. Thus, mobile money has accelerated inclusion but has not yet deepened it.
The scientific value of this study lies in the empirical validation of a process-based model. This shows that the socioeconomic influence of a financial technology is not a direct product of its mere presence but depends on the density and diversity of its use, which, in turn, is shaped by rational utility assessments, social context, and system reliability.
The policy implications are clear. Policymakers and service providers in The Gambia require a strategic pivot from increasing access to promoting qualitative usage and product bundling to ensure sustainability. Specific recommendations include: (1) mobilizing agent liquidity and reliability as a foundation of trust; (2) creating tiered financial products that use transaction data to offer microloans or insured savings, thereby linking the mobile money network to formal credit markets; and (3) designing targeted financial literacy programmes that help users move beyond simple transfers to the strategic use of digital savings and credit services.
Future research should employ longitudinal designs to trace causation and adoption patterns over time to address this limitation. A major direction is to explore interoperability and data portability frameworks that would allow mobile money transaction histories to serve as collateral or credit records that are accepted by formal institutions. Finally, a comparative analysis across similar economies could identify the most successful regulatory and market innovations to transform an effective payment platform into a broad‑scale financial deepening engine.