Exploring long-term impact of digital financial inclusion on humanitarian crises in Africa: evidence from dynamic common correlated estimation technique Open Access

Humanitarian crises (HC) pose significant challenges to global progress by disrupting lives, undermining economies, straining essential services, including reversing years of developmental gains (Ajiboye et al., 2024). These crises have a profound influence on the SDGs (i.e. Sustainable Development Goals), exacerbating poverty, hunger and inequality, while impeding access to quality education, healthcare and clean water (Besiou et al., 2021; Barakat et al., 2023). Additionally, HC divert resources from long-term development initiatives to immediate relief efforts, thereby hindering the achievement of global targets (Shehu and Abba, 2020).

In 2024, a record 299.4 million people required assistance due to escalating crises. Among these, East and Southern Africa accounted for 74.1 million, West and Central Africa 65.1 million, the Middle East and North Africa 53.8 million, Asia and the Pacific 50.8 million, Latin America and the Caribbean 38.9 million and Eastern Europe 16.8 million people [United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA), 2024a]. While previous budgets (to alleviate human sufferings) saw a decline from $24bn in 2022 to $20bn in 2023, dwindling global aid and growing demands have intensified the need to reduce delivery costs and ensuring aid reaches intended beneficiaries directly (Callen et al., 2024).

HC in Africa are particularly acute, driven by conflicts, climate change and socio-economic challenges. They exacerbate food insecurity, malnutrition, gender-based violence and health emergencies, among others. Consequently, Africa accounted for $33.1bn (71.3%) of the $46.4bn requested by the UN and partner-organisations to provide aid to 180.5 million people across 72 countries (UNOCHA, 2024a). These funds aimed to improve access to clean water, healthcare, education, social programmes and other essential services. However, Africa’s humanitarian needs have grown significantly, rising from 146 million in 2023 to 193 million in 2024 (UNOCHA, 2024b) due to growing crises (Chehade et al., 2020; Chen, 2021; Ajiboye et al., 2024).

Persistent escalation in humanitarian needs, coupled with diminishing aid, underscores the necessity for innovative, cost-effective and transparent approaches to delivering relief materials to beneficiaries. One such approach is digital financial inclusion (DFII). Transferring physical cash to vulnerable individuals is costly, logistically challenging, prone to diversion and requires in-person contact (Akbari et al., 2023). In contrast, DFII leverages accessible technology to expedite transactions and streamline processes (Perdomo and Buzurukova, 2023; Wormald et al., 2021). Also, digital financial tools make it easier to transfer aid across borders, ensuring that international donors can support crises-affected people anywhere without the delays associated with physical transfers (Suri et al., 2023).

In addition, digital aid delivery reduces co-ordination costs, increases transparency and preserves beneficiaries’ privacy (De Waal, 2023; Maghsoudi et al., 2023). It reduces manual errors associated with traditional aid delivery methods, including incorrect entries, mismanagement and/or duplication of beneficiaries (Maghsoudi and Abakar, 2024), and enabling faster need detection and responses (Bemo et al., 2017; Namagembe and Ntayi, 2024). Where limited humanitarian workers exist, digital humanitarianism empowers recipients to take ownership of their recovery, including offering a remote, cost-effective and self-help solution (Holloway et al., 2021). It can also reach out to larger number of beneficiaries in a short period, and promotes participation in economic activities beyond receiving aid (Duffield, 2016). Moreover, unlike traditional aid methods constrained by logistical challenges, high costs, limited transparency and risk security, digital solutions offer the potential to enhance efficiency, scalability and transparency in delivering aid.

Despite the growing recognition of DFII as a transformative tool in humanitarian responses, empirical evidence on its efficacy in ameliorating HC is scanty, particularly from multi-country perspectives in Africa, thus, leaving significant gaps in understanding its applicability and outcomes across diverse contexts. Moreover, existing research (including Gurung and Perlman, 2018; Pinna, 2020; Abdelgawad et al., 2023; Namagembe and Ntayi, 2024; Guliyeva et al., 2025) primarily focus on individual-country, while multi-nation studies that provide a richer foundation for crafting robust, context-sensitive and globally informed policies which are impactful for addressing complex and transnational issues, are limited.

Ten African countries (i.e. Angola, Burkina Faso, Burundi, Cameroon, the Central African Republic, Mauritania, Senegal, Uganda, Zambia and Zimbabwe), for the second year running, represent the most under-reported HC areas deeply affected by conflicts, climate change and socio-economic challenges (Elbagir, 2024). For example, Angola faces significant challenges, with 7.3 million people requiring humanitarian aid. In Zambia, 1.35 million people lack sufficient food, while Burundi grapples with 5.6 million children suffering from chronic malnutrition. Senegal and Mauritania are deeply affected by food-insecurity and poverty, with 1.4 million and one in four people impacted, respectively. The Central African Republic endures the world’s sixth-highest child mortality rate, and Cameroon has one in six people needing humanitarian support. Burkina Faso and Zimbabwe face extreme economic hardship, with 8.8 million living in poverty and nearly 8 million in extreme poverty, respectively. Uganda’s maternal mortality rate remains alarmingly high at 284 per 100,000 live births (UNOCHA, 2023; Elbagir, 2024; World Bank, 2024a).

The severity of these crises notwithstanding, they have failed to garner significant attention amongst researchers, possibly due to limited access to the affected regions and insufficient visibility. Thus, our research aims to bridge the gap by evaluating DFII’s role in humanitarian outcomes across the ten nations, contributing valuable insights for policymakers, humanitarian organisations and stakeholders in developing workable and sustainable solutions. It also enriches the limited body of cross-national studies, including providing evidence to support advocacy for digital solutions in humanitarian interventions, moving beyond traditional cash-based aid models to reduce costs, increasing transparency and empowering beneficiaries. Specifically, the research aims to answer the following research questions (RQs):

To ensure robust and consistent research’s outcomes for policymaking, the dynamic common correlated estimation (DCCE) technique was employed to account for possible cross-sectional dependence, while accommodating dynamic long- and short-term relations between/amongst variables. Furthermore, the Dumitrescu and Hurlin (2012) heterogeneous panel non-Granger causality test was used, to provide insights into the causal nexuses amongst the variables.

Also, our research (unlike prior studies) develops indexes for both DFII and HC using the PCA (i.e. principal component analysis). Finally, we used a stepwise approach based on different measures of HC including mortality rates, refugee population and clean water access. The outcomes/findings of the research are anticipated to form the basis for developing more effective financial inclusion policies and/or programmes. Following the introduction, Section 2 provides literature review, Section 3 details the methodology, Section 4 presents results and discussion and Section 5 concludes the paper.

This study is anchored on the dynamic capabilities view (DCV) and the technology acceptance model (TAM). Each provides a distinct lens to explain how DFII influences HC. The DCV hypothesised that an organisation’s survival and success in violent environments is rooted in its ability to integrate, build and re-configure internal and external resources (Teece et al., 1997). In the humanitarian context, DCV helps to explain how technology strengthens the ability of organisations to respond to crises by enhancing operational efficiency, building institutional capacities and supporting long-term resilience (Singh, 2025). Applied to DFII, it suggests that digital financial tools can be conceptualised as dynamic capabilities, since they enable humanitarian organisations and governments to reconfigure resource allocation, enhance transparency, lower coordination costs and adapt aid delivery systems to highly unstable environments. By facilitating faster and more accountable distribution of resources, reducing leakage and mismanagement and empowering beneficiaries, DFII contributes to mitigating crises such as mortality, refugee displacement and limited access to basic services over the long-term.

The TAM, on the other hand, is based on the assumption that individuals’ adoption of technology is primarily driven by perceived usefulness (the belief that the technology improves performance), and perceived ease of use (the belief that it requires minimal effort) (Davis et al., 1989). It premised that perceptions shape attitudes, intentions and actual usage of technology. In the context of DFII, TAM explains why beneficiaries, humanitarian workers and local actors are more likely to embrace digital financial services, such as mobile banking, electronic payments and digital wallets. These technologies are perceived as highly useful because they provide faster, safer and more reliable access to humanitarian aid than traditional cash-based or in-person delivery methods, thereby, reducing risks of diversion, corruption and insecurity during aid distribution (Maghsoudi and Abakar, 2024). At the same time, their ease of use, enabled by widespread mobile phone penetration, simple interfaces and minimal literacy requirements, lowers barriers to adoption among vulnerable populations, including, those in remote or resource-constrained environments (Perdomo and Buzurukova, 2023). Moreover, TAM highlights that when users perceive DFII as both beneficial and easy to adopt, it fosters greater trust in digital aid systems (Callen et al., 2024), encourages continuous use and promotes financial inclusion beyond immediate crisis response (De Waal, 2023; Maghsoudi et al., 2023). Thus, TAM helps to explain not only the behavioural mechanisms behind the uptake of DFII, but also how these mechanisms amplify the overall effectiveness of humanitarian responses by ensuring broader acceptance, accessibility and sustainability of digital solutions (Dhawan and Zollmann, 2023).

Together, DCV highlights DFII as an organisational capability for resilience, while TAM explains the behavioural mechanisms through which end-users embrace DFII, both converging to show how digital solutions mitigate HC.

HC encompass both man-made and natural disasters: armed conflicts, forced displacement, refugee crises, natural disasters like floods, hurricanes, earthquakes, droughts, including major infectious disease outbreaks (Kohrt et al., 2019). DFII implies access to and use of technology, like the internet and electronic devices, to achieve equitable financial services (Aziz and Naima, 2021). DFII plays a transformative role in addressing HC via leveraging technology to enhance economic stability and resilience between/amongst displaced persons, refugees and affected communities (Dhawan and Zollmann, 2023; Perdomo and Buzurukova, 2023; Callen et al., 2024). This enables individuals and/or families to secure their livelihoods, manage risks and rebuild their lives amidst HC challenges. For instance, mobile banking facilitates direct remittances, and cash transfers, thus, reducing reliance on physical cash and enhances financial security in unstable environments. In addition, it boosts access to clean water, sanitation and other basic services via targeted financial interventions, thus, enabling faster recovery and an improved quality of life. Moreover, DFII can reduce mortality rates via ensuring timely access to funds for healthcare and food during crises (Bemo et al., 2017). For example, mobile money transfers help displaced persons afford medical treatment and essential supplies. Table 1 below discusses how the DFII can address countries’ HC peculiarity.

Despite limited DFII and escalating HC in Africa, empirical research on their linkage is scanty. In addition, related researches focused on individual-country and HC indicator(s) like refugees, food security, internally displaced persons (IDPs) or overall well-being. For instance, Guliyeva et al. (2025) analysed HC in the digital age via a qualitative approach, and the study’s findings underscored growing importance of digital tools in monitoring, coordinating and delivering assistance during crises. Similarly, Callen et al. (2024) evaluated digital payments’ role in mitigating HC in Afghanistan, using an experimental approach. They found improved humanitarian conditions following digital payments adoption. On their part, Bruder and Baar (2024) uncovered improvements in humanitarian assistance in the Netherlands, owing to innovation efforts.

Furthermore, Abdelgawad et al. (2023) assessed the use of digital delivery mechanisms for cash-based assistance during Norway’s refugee crises. They disclosed that, whereas digital mechanisms offered improved efficiency and effectiveness in aid distribution, there was no consensus on when, where and how they should be applied. Also, Kurdi (2021) examined cash transfers’ influence on nutritional outcomes in Yemen within the Cluster Randomised Controlled Trial framework. He revealed that non-staple foods’ purchases and child dietary diversity scores improved significantly due to cash transfers for nutritional programmes. Moreover, Gurung and Perlman (2018) analysed digital financial services’ role in HC responses in Colombia. Their descriptive study exposed that digital financial services boosted financial inclusion and provided tailored interventions for specific crises. From a broader perspective, Madianou (2019) investigated digital innovation and data practices in global humanitarian responses. While digital innovations were noted for their transformative potential, the study highlighted challenges such as dependency and inequality in humanitarian efforts.

In Africa, studies have provided valuable insights. For instance, Namagembe and Ntayi (2024) explored organisational culture and its influence on the adoption of digital cash-based assistance in Uganda, using the SEM (i.e. structural equation modeling) approach. They found positive organisational culture enhancing the use of digital tools during HC. Similarly, Maghsoudi and Abakar (2024) used a qualitative approach to evaluate impacts of digital technologies including mobile money and electronic vouchers on the distribution of humanitarian cash-based assistance in Uganda. The authors’ findings highlighted benefits like cost-effectiveness, timeliness, safety and improved financial inclusion. More so, Maghsoudi et al. (2023) used a case study approach to examine digital technologies for cash and voucher assistance during disasters, and reported enhanced cost-efficiency, timeliness and quality of aid delivery in selected cases.

Contrasting the findings above, Dhawan and Zollmann (2023) analysed the role of DFII in building refugees’ resilience and self-reliance in Kenya and Jordan. Using in-depth interviews, they revealed that digital financial tools did not enhance refugees’ resilience or self-reliance significantly, thus, highlighting the complexity of these interventions.

The reviewed studies reveal certain gaps in understanding the DFII and HC nexus. Firstly, prior research (including Maghsoudi et al., 2023; Guliyeva et al., 2025) adopted qualitative approaches, focusing on individual aspect/indicator of DFII or HC rather than their combined/aggregated effects. Also, whereas Callen et al. (2024) and Abdelgawad et al. (2023) explored digital payments’ and delivery mechanisms’ impacts, they did not use comprehensive measurements which aggregate individual indicator. Additionally, related studies like Kurdi (2021) and Namagembe and Ntayi (2024) either examined specific outcomes (e.g. nutritional improvements or organisational culture), or concentrated on individual-country like Kenya and Uganda (e.g. Dhawan and Zollmann, 2023). These divergent approaches/methods expose conceptual tensions, between efficiency and equity, short-term relief and long-term resilience and technological optimism versus contextual limitations. They also point to unresolved theoretical debates about whether DFII primarily functions as a humanitarian delivery mechanism or as a transformative development tool.

Overall, while most studies recognise DFII’s short-term benefits in improving humanitarian operations, there remains limited longitudinal and cross-country analysis to determine whether these effects persist over the long-term. Therefore, this study seeks to fill this empirical gap by assessing:

• whether DFII alleviates humanitarian crises across ten African countries over the long-term; and

• how cross-country evidence can inform policies aimed at building sustainable and resilient humanitarian systems in Africa.

Thus, our research’s goal is to explore DFII’s influence on HC in ten under-reported African countries, using the DCCE technique and measurements (or composite indexes) of DFII and HC developed via the PCA procedure.

The research adopts the TAM to establish DFII and HC link. According to TAM, two key factors influence individuals’ decisions to adopt new technologies. Firstly, is perceived usefulness, which enhances performance. The other is perceived ease of use, that minimises efforts required (Davis et al., 1989). DFII aligns with these principles, as it leverages technologies including internet, mobile phones and electronic payment systems to improve delivery efficiency (Guliyeva et al., 2025), in addition to lowering administrative costs, and safeguarding beneficiaries’ privacy (Sakanko and David, 2019; De Waal, 2023). Existing evidences underscore the significant role of DFII in enhancing humanitarian access and mitigating HC (Belliveau, 2016; Kurdi, 2021; Maghsoudi et al., 2023; Maghsoudi and Abakar, 2024; Guliyeva et al., 2025). Based on these, a model of HC and DFII nexus is written as:

Besides, factors like insecurity (IS) and political instability (PS) have been established to worsen HC (Collinson et al., 2010; Panter‐Brick et al., 2018; Stoddard et al., 2017; Debroy et al., 2023). Rising IS can worsen HC via disrupting economic activities, displacing communities and undermining individuals’ ability to meet basic needs. Besides, IS can lead to conflicts which in turn destroy infrastructure, disrupt livelihoods and hinder the delivery of aid (Panter‐Brick et al., 2018; Debroy et al., 2023). Also political instability (PS) can deteriorate HC if the government (for example, a military leadership in attempt to hold on power and stave off opposition) prioritise military related spending to expenditures on citizens’ welfare (Nafziger and Auvinen, 1997). In addition, high poverty rate (PR) contributes to HC, as it amplifies vulnerability by leaving people without resources to withstand shocks (Dagne, 2004; Kandeh and Kumar, 2015; Cuesta and Leone, 2020). Furthermore, limited access to domestic credit by the private sector (DCPS) increases HC via preventing individuals and businesses from securing financial resources needed to withstand shocks and/or invest in efforts to remove trapped communities from cycles of deprivation and dependency (Eckert, 2021).

Based on the foregoing, the HC−DFII relation is re-specified as:

where:

HC = Humanitarian Crises;

DFII = Digital Financial Inclusion;

IS = Insecurity;

PS = Political Stability;

PR = Poverty Rate; and

DCPS = Domestic Credit to Private Sector.

Furthermore, IS, measured as the share of military expenditure in GDP (with higher values indicating greater insecurity and lower values suggesting reduced insecurity), was obtained from the SIPRI Military Expenditure Database (2023). PS, scaled from −2.5 (indicating instability) to +2.5 (indicating stability), and DCPS measured as domestic credit to the private sector as a percentage of GDP, were obtained from the World Bank (2024a). Finally, data on PR, represented by the proportion of people living in poverty, was sourced from the Development Initiative (2023) database.

$β0$ is the common constant term (or entity-specific constant in fixed-effects model), $β1, β2,β3,β4,β5, and β6$ are long-run coefficients, and $ϵit$ = error term for entity $i$ at time $t$⁠.

Our research develops indexes for both DFII and HC, using PCA to comprehensively and systematically measure these complex phenomena. The HC index combines indicators like infant mortality rate (per 1,000 live births), access to clean drinking water (% of population using basic water services), IDPs (new displacements from disasters) and refugee population (by country or territory of asylum). Similarly, the DFII index integrates mobile money transactions (per 1,000 adults), internet usage (% of population), mobile phone subscriptions (per 100 people) and broadband subscriptions (fixed). The PCA enables better comparability across regions, simplifies analysis and minimises bias from single variables (Sakanko et al., 2024; Nguyen, 2020). Additionally, these indexes offer a holistic representation of conditions, aiding policymakers and stakeholders through intuitive and actionable measures (Shah et al., 2021; Li et al., 2022).

We analyse the HC-DFII relation using the DCCE technique of Chudik and Pesaran (2015). The DCCE extends the common correlated estimation (CCE) via incorporating lagged dependent variable(s) to estimate dynamic panel models with cross-sectional dependence. It assumes weak cross-sectional dependence, stationarity and homogeneous slope coefficients across panels (Faheem et al., 2021). Consequently, the bi-variate unrestricted error-correction representation of the autoregressive distributed lag (ARDL) (⁠$p$⁠) model is:

$ECTit−1$= error-correction term (lagged residual), the speed of adjustment parameter is $α1$⁠, indicating how quickly the system corrects deviations from the long-run equilibrium. Δ is the difference operator, representing short-term changes. $γ1,γ2,γ3,γ4andγ5$ = coefficients capturing short-run effects, and $μt$= error term for the short-run dynamics, while equation (2) captures the long-run impacts.

The descriptive statistics (Table 2) summarise the variables’ distribution and variability. The mean HC = 2.851, DFII = −3.681, IS = 1.663 and PS = −0.797. In addition, PR and DCPS have high average at 40.512 and 21.178, respectively. Standard deviations portray the extent of variations, with PR (21.071) exhibiting the highest variability, followed by DCPS (3.409), DFII (1.487) and HC (1.236). In addition, IS (0.987) and PS (0.697) revealed moderate variability.

The range (of values) signifies the spread in each variable. HC spans from −1.554 to 6.197 and DFII from −1.590 to 5.333, reflecting substantial variability. Also, IS and PS have narrow ranges, with IS values between 0 and 4.903 and PS between −2.699 and 0.661. PR shows the widest range, from 5.103 to 81.322, indicative of significant disparity. DCPS ranges from 15.094 to 25.633, suggesting a more stable distribution compared to PR.

The correlation results (Table 1) portray weak negative correlations between DFII and most variables, except for a moderate positive association with DCPS (0.265). IS shows weak negative correlations with DFII (−0.219) and DCPS (−0.078), while its relation with PS (−0.042) and PR (−0.091) is negligible. PS is largely uncorrelated with the other variables except for a slight positive relation with DFII (0.150). PR exhibits weak negative correlations with DFII (−0.459) and PS (−0.288), while its relation with IS and DCPS are weakly negative. DCPS demonstrates a negligible/weak correlation with all other variables except DFII.

The cross-sectional dependence test’s results (Table 3) reveal strong dependence for HC, DFII and DCPS, while IS, PS and PR exhibit moderate to weak dependence. Overall, the results confirm significant cross-sectional dependence in most variables, thus, rejecting the null hypothesis of “no cross-sectional dependence”. This interdependence among the 10 countries (representing Africa’s most under-reported humanitarian crises) justifies the use of estimation techniques that account for cross-sectional dependence, i.e. the DCCE.

The second-generation unit root test’s results accounting for cross-sectional dependence (Table 4) portray HC and DCPS to be stationary at level, while DFII, IS, PS and PR become stationary after first-differencing. This mix of I(0) and I(1) series justifies examining the long-term nexus between the variables using cointegration method.

The borderline unit root test’s results of I(0) and I(1) raise concerns about traditional panel cointegration tests. Hence, Kao, Pedroni and Westerlund’s cointegration tests were applied. The cointegration test’s results (Table 5) reject the null hypothesis of no cointegration, thus, confirming a long-term nexus among the variables at 1% and 5%, respectively. The outcomes highlight the need for estimation techniques which capture long- and short-run effects.

The results of DCCE (Table 6) portray that DFII coefficient (−0.894) is significant at 5%, signifying that increasing DFII by a unit reduces HC by 0.894 unit over the long-term. Also, less insecurity (IS) shows negative influence on HC at 5% level, with a 1% rise in IS lowering HC by 0.959 unit over the long-term. However, political instability (PS) and poverty rate (PR) coefficients are insignificant, implying that both factors are not core drivers of HC during the long-term. Conversely, access to credit by private sector (DCPS) coefficient (−0.239) is significant at 10% level, implying that a 1% increase in DCPS lowers HC by 0.239 unit over the long-term.

The error correction mechanism (ECM) coefficient (−0.559) confirms model stability at 1% level, indicating that around 60% of disequilibrium is corrected annually. The significance of F-statistic (6.17) at 1% level, portrays that the regressors adequately explain HC. The diagnostic tests reveal model’s validity, with an MGR² of 68% explaining substantial variation in HC, while a root MSE of 60% suggests moderate prediction accuracy. The significant cross-sectional dependence (CD) underscores interdependencies among the nations, emphasising the importance of using appropriate methods for panel data analysis.

To evaluate both robustness and consistency of the results, we explore DFII impact on individual humanitarian indicator (including mortality rates, access to clean water and refugees). The results (Table 7) portray that improvements in DFII significantly lower mortality rates and refugee populations, but increase the access to clean water over the long-term. This highlights its (DFII) transformative role in improving health, relief and infrastructure.

In addition, less IS and PS including improvements in DCPS lower mortality rates over the long-term, at 5% level or 1% level. However, PR does not influence HC significantly. The short-run results (although not the research’s focus) disclose that improvements in DFII reduce mortality rates and refugees, but boost clean water access. Also, rising IS and increases in DCPS raise mortality rates, while PS contribute to mortality rates and clean water access marginally, over the short-term.

The error-correction term (ECM) is significant across all models, confirming strong adjustments towards equilibrium. Diagnostic tests portray strong model performance for mortality rates and clean water access, with high R² (93% and 99%), significant F-statistics (34.91 and 264.97) and low root mean square errors (4.31 and 1.43). Cross-sectional dependence is significant in the first and last models, indicating spill-over effects.

The Dumitrescu and Hurlin (2012) causality test’s results (Table 8) show significant bi-directional causality between DFII and HC, indicating mutual influence. In addition, a one-way causality is observed from IS, PS, PR and DCPS to HC, suggesting these factors drive changes in humanitarian crises without feedback effects.

The empirical findings provide valuable insights and implications. The findings portray that greater DFII significantly lowers humanitarian crises in African economies over the long-term. In addition, disaggregating humanitarian crises into mortality rates, access to clean water and refugees, we find evidence that greater DFII lessens mortality rates and refugee populations, while enhancing clean water access. These outcomes align with Belliveau (2016), Kurdi (2021), Maghsoudi et al. (2023), Maghsoudi and Abakar (2024) and Guliyeva et al. (2025) findings which emphasised the potentials of DFII in enhancing governments’ and non-governmental organisations (NGOs’) capacity to deliver humanitarian aid. Thus, our study has provided answers to the following research questions (RQ):

RQ1. Does DFII alleviate HC across African countries over the long-term?

Findings:DFII was shown to be an effective tool in mitigating HC impacts in Africa by improving resilience and reducing human vulnerability over the long-term. The empirical evidence confirms a long-term significant positive effect of DFII on humanitarian crises. Specifically, greater DFII is associated with lower mortality rates, reduced refugee populations and improved access to clean water. The long-term influence suggests that the benefits of DFII accumulate over time as adoption deepens, systems mature and institutional capacity to leverage digital tools improves:

RQ2. Can cross-country evidence inform policies for sustainable humanitarian interventions?

Findings: The cross-country evidence suggests that DFII across African nations can optimise resource allocation, lower aid delivery costs and build more sustainable and transparent humanitarian systems.

Less IS shows a negative influence on HC in both the short- and long-term, suggesting that reductions in insecurity contribute significantly to alleviating humanitarian challenges. This outcome aligns with the findings of Panter‐Brick et al. (2018) and Debroy et al. (2023), who reported that improved security conditions foster stability, enhance humanitarian access and facilitate the effective delivery of aid to vulnerable populations. Thus, the current study’s findings reinforce the argument that enhancing peace and security is a critical prerequisite for long-term humanitarian resilience in Africa.

However, PS exerts a significant negative effect on HC during both periods of analysis, indicating that unstable political environments exacerbate humanitarian crises. This finding corroborates the conclusions of Spiegel et al. (2023) and Debroy et al. (2023), who observed that political instability undermines governance capacity, disrupts essential services and heightens population vulnerability during crises. The consistency between these studies and the present results suggests that political stability is essential for effective crisis management and sustainable humanitarian outcomes.

Conversely, DCPS exhibits a significant negative effect on HC (captured by mortality rates) in the long term, implying that increased private sector credit availability is associated with a reduction in humanitarian crises over time. This finding supports Eckert (2021), who argued that improved access to credit enhances livelihood opportunities, promotes business resilience and reduces vulnerability to crises, particularly by lowering mortality and poverty rates. Therefore, the current study’s results suggest that fostering financial sector development and expanding private sector credit could serve as long term strategies for mitigating the intensity of humanitarian crises in Africa.

The research’s findings suggest that greater DFII significantly reduces HC in African economies, particularly by lowering mortality rates, improving access to clean water and reducing refugee populations. These results climax the transformative role of DFII in humanitarian operations and provide useful implications for policymakers, NGOs and other stakeholders.

Firstly, humanitarian organisations and governments need to invest more in DFII infrastructure, such as mobile money platforms, digital wallets and electronic transfer systems. This would reduce transaction costs, minimise diversion of resources and enable funds and services to reach crisis-affected populations directly, even in remote areas with limited physical banking infrastructure.

Secondly, collaborative partnerships must be enhanced between NGOs, donor agencies, governments and local communities to integrate DFII into humanitarian strategies. Formal agreements and shared frameworks can ensure that digital systems are scaled efficiently, resources are pooled and aid delivery becomes more transparent and equitable.

Thirdly, government and NGOs should train humanitarian staff and beneficiaries on the use of digital financial tools, covering skills in mobile transactions, digital literacy, data management and real-time decision-making. Such trainings would maximise adoption and ensure that the benefits of DFII are widely distributed, especially among vulnerable groups.

Fourthly, emerging technologies should be embraced alongside DFII to build resilient humanitarian systems. AI can help predict aid demand and optimise distribution, blockchain can enhance transparency and accountability in fund transfers and big data analytics can support real-time monitoring of aid delivery. Integrating these tools with DFII can greatly improve efficiency, traceability and trust in humanitarian operations.

Finally, policymakers should prioritise the development of agile digital infrastructures capable of adapting to the unpredictable nature of humanitarian crises. This requires strategic investment not only in technology but also in institutional structures and cultures that foster flexibility, collaboration and accountability. By doing so, DFII can move beyond short-term relief to become a long-term solution for reducing vulnerability and promoting resilience in crisis-affected communities.

The theoretical implications of this study are framed within both the DCV and TAM. DCV emphasises that an organisation’s ability to adapt and thrive in turbulent environments depends on its capacity to integrate, reconfigure and build resources and competencies (Teece et al., 1997). By applying this perspective to HC in Africa, this research extends the DCV by demonstrating that DFII can be conceptualised as a critical tool that enhances resilience and long-term crisis management.

Therefore, this study advances the DCV framework by showing that DFII enables humanitarian organisations and governments to adapt more efficiently to unpredictable environments. By lowering mortality rates, reducing refugee populations and improving access to clean water, DFII demonstrates how technological and financial innovations can be mobilised as dynamic capabilities to address systemic disruptions.

Secondly, the findings provide theoretical evidence that DFII supports the formation of complementary capabilities, such as transparency, agility and inclusiveness, in humanitarian settings. These capabilities not only improve the efficiency of aid delivery but also contribute to sustainability by reducing costs and resource leakages. In doing so, the study clarifies the mechanisms through which digital innovations translate into enhanced operational outcomes in crisis contexts.

Thirdly, the research contributes to the literature on humanitarian operations by theorising DFII as both a direct enabler of resilience and an indirect driver of institutional learning and adaptation. This dual role reinforces the DCV argument that capabilities evolve through continuous integration of new tools, processes, and knowledge.

Fourthly, the TAM explains the DFII−HC nexus. According to TAM, perceived usefulness and perceived ease of use are the two factors that determine the adoption of new technologies (Davis et al., 1989). DFII aligns with these principles by leveraging mobile phones, internet platforms and electronic payment systems to improve delivery efficiency, reduce administrative costs and safeguard beneficiaries’ privacy. This reinforces the theoretical claim that technology adoption in humanitarian contexts is not only driven by institutional capacity (as argued by DCV), but also by user perceptions that encourage widespread uptake.

Finally, by jointly applying DCV and TAM, this study deems the theoretical significance of DFII as both an organisational dynamic capability and user-driven technological innovation. By demonstrating that the digitalisation of humanitarian interventions fosters agility, collaboration and trust, critical dynamic capabilities for responding to rapidly changing crisis environments, support the prior works (Dubey et al., 2020).

This research aimed at examining long-term DFII’s impact on humanitarian crises in ten African countries during 2004–2023 period, using DCCE method. We found evidence portraying a long-term relation between DFII and humanitarian crises based on the cointegration tests. The results of DCCE show DFII as an effective tool to reduce humanitarian crises over the long-term. Specifically, DFII lowers humanitarian crises, including mortality rates, refugee populations, while improving clean water access over the long-term. Other long-term significant drivers of humanitarian crises are insecurity, political instability and domestic credit to private sector.

Thus, efforts should focus on reducing access barriers, and ensuring that digital financial services are inclusive, particularly in regions prone to instability or economic shocks. Expanding digital financial inclusion can be a powerful tool for managing humanitarian crises, enabling faster and more efficient emergency responses. To this end, policymakers are advised to adopt and implement strategies that promote digital financial inclusion, encourage investment in digital infrastructure and integrate these systems into disaster management frameworks to enhance the effectiveness of humanitarian responses. Furthermore, addressing underlying issues like insecurity, access to credit, political instability and poverty alleviation can help prevent and mitigate crises.

This study is limited to ten African economies from 2004 to 2023, while it provides robust evidence on the long-term role of DFII in mitigating humanitarian crises. Notwithstanding its contribution to knowledge, the findings may not capture the full diversity of humanitarian crises across Africa. For instance, data constraints restricted the inclusion of other potential variables such as technological infrastructure depth and social trust, which may further shape DFII’s effectiveness. Thus, future research can extend the analysis to a broader set of countries, incorporate additional variables and/or indicators, and explore micro-level household data to capture the experiences of beneficiaries. In addition, further investigation into the interplay between DFII and structural drivers such as insecurity, poverty and political instability would provide deeper insights into how digital tools can be integrated into sustainable crisis management strategies.