Impact of Mobile Financial Services on Financial Inclusion: Empirical Insights from Kenya

doi:10.21203/rs.3.rs-3343586/v1

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Research Article

Impact of Mobile Financial Services on Financial Inclusion: Empirical Insights from Kenya

https://doi.org/10.21203/rs.3.rs-3343586/v1

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published 20 May, 2024

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The study employed a cross-sectional design, collecting data in Nairobi County's Kasarani constituency. The participants consisted of entrepreneurs and customers present at the stalls during the interviews. While previous research has shown the positive impact of mobile money on financial inclusion in Africa, there has been a lack of investigation into the potential role of demographic variables in the continuous intention to use mobile money applications. This study aims to address this research gap. The results indicate that in East Africa, mobile money applications are primarily used for borrowing loans rather than for saving purposes. Additionally, the study revealed that gender plays a reinforcing role in the positive relationship between confirmation, perceived satisfaction, and the extended post-acceptance model of mobile money application usage.

Continuous intention to use

financial inclusion

mobile money

Financial inclusion refers to the concept of having access to affordable and useful financial products and services that cater to one's financial needs. It stands in contrast to financial exclusion, which is defined as the inability to access necessary financial services in an appropriate manner (Carbo et al., 2005). Financial exclusion poses a major global challenge, closely tied to social deprivation, and has been recognized by the United Nations as a significant barrier to poverty reduction worldwide (Warsame, 2009).

The economic, societal, and developmental implications of enhanced financial inclusion are undeniable. The primary goal of financial inclusion is to integrate the unbanked population into the formal financial system, encouraging them to save and contribute to the economic growth of their countries.

Financial exclusion disproportionately affects vulnerable segments of society, including youth and the elderly. Therefore, governments and other authorities in these countries must pay special attention to the needs of these groups.

A recent United Nations report highlights some striking statistics:

- Approximately 2.5 billion working adults, more than half of the global workforce, lack access to financial services. Financial exclusion is particularly severe among low-income populations in emerging and developing economies, with around 80% of poor individuals being financially excluded.

- Young people are heavily affected, as they are 33% less likely to have a savings account compared to adults and 44% less likely to save in a bank.

- Youth savings account penetration rates vary by region, ranging from 12% in Africa to 50% in East Asia and the Pacific.

Young people are more prone to exclusion from formal financial services due to factors such as legal restrictions, high transaction costs, and negative stereotypes about youth.

Regardless of socioeconomic, demographic, or geographical factors, youth face significant challenges during their transition to adulthood. However, in developing countries, where approximately 87% of the global youth population resides, these challenges are particularly pronounced. Adolescent girls and young women in these regions face the most significant obstacles. For instance, the AIDS epidemic in sub-Saharan Africa has already orphaned a generation of youth, with estimates indicating that 15 to 25 percent of children in several sub-Saharan African countries have been orphaned by AIDS. This trend is expected to persist unless drastic measures are taken in the region.

The situation worsens as this disadvantaged generation transitions into adulthood, often assuming household responsibilities at a younger age compared to their less vulnerable peers. Projections suggest a dramatic increase in the youth population over the next 30 years. While this demographic growth presents an opportunity, the current economic conditions characterized by poverty and limited opportunities for youth pose a significant threat to their future if their needs are not adequately addressed.

In Kenya, nearly 43% of the population is under 15 years old, and this age group faces considerable challenges, including high unemployment rates. Unemployment among youth is double the rate of the general population. Additionally, 23% of young people in Kenya lack access to appropriate financial products and services, exacerbating their situation.

Sub-Saharan African regions perform well in terms of mobile money account ownership, with 11% of adults having a mobile bank account, compared to 6% in advanced economies and 6.5% in non-advanced economies (Mengistu & Saiz, 2018). This underscores the potential role of mobile money services in promoting financial inclusion in sub-Saharan Africa. Despite low internet penetration in the region, most mobile money services offer the option of operating through a USSD (unstructured supplementary service data) protocol.

A study conducted in sub-Saharan Africa by Ohiomu and Ogbeide-Osaretin (2019) found that access to financial services had a greater impact on reducing gender inequality than the level of financial inclusion usage. The study also noted that higher levels of

education among females could improve financial literacy and reduce financial exclusion. Another study by Akileng et al. (2018) in Uganda demonstrated a significant positive relationship between income level, age, financial literacy, and financial inclusion. However, gender and education did not show a significant association. Munyegera and Matsumoto (2016) conducted a study in Uganda on mobile money remittances within households and confirmed the positive impact of mobile money usage. They noted that mobile money services in Uganda had significant welfare benefits, providing access to affordable financial services and helping to reduce poverty and vulnerability among rural populations, thereby promoting financial inclusion. In their study, Tembo and Okoro (2021) reported that Uganda and Rwanda ranked second after Kenya in terms of high mobile money account penetration rates, at 67% and 57% respectively. These percentages directly contribute to financial inclusion. The authors concluded that increased adoption of fintech, particularly through mobile money, offers greater access to financial services and the opportunity to integrate savings into the mainstream economy.

According to Museba et al. (2021), mobile money service adoption in Uganda has had a positive impact by increasing access to affordable financial services and facilitating payment for various services. The study also noted that mobile money services prompted banks to change their marketing approach and develop a complementary agency banking business model. Agency banking is popular in Kenya, where most banks and microfinance institutions have banking agencies in shopping centers, offering convenience and time savings for users.

A study by Hamdan et al. (2021) identified barriers to the contribution of mobile money to financial inclusion among business owners in Uganda, including the spatial distribution of mobile money agents, high fees, and a lack of financial education. The authors suggest that mobile money policies can improve financial inclusion. Another study by Bongomin et al. (2021) among micro, small, and medium enterprises (MSMEs) reported that hedonism plays a significant role in mediating the relationship between mobile money adoption and usage and financial inclusion in Uganda. They found that hedonism enhances the effect of mobile money adoption and usage on financial inclusion.

In Kampala central, Uganda, high transaction costs of mobile money services, network breakdowns, and security issues are major barriers to financial inclusion (Luswata, 2021).

Mobile money has proven to be a valuable tool for African women, enabling them to maintain secret savings and achieve some degree of financial independence (Ahmad et al., 2020). The study further highlights that mobile money strengthens social networks, although this effect diminishes when it becomes purely transactional, limited to text messages and mobile transfers.

While mobile phone services have increased access to formal financial services in Tanzania, women still lag behind in terms of access and utilization of these services (Were et al., 2021). The study identifies several factors contributing to the gender gap in financial inclusion, including lack of income, limited financial literacy, lack of access to smartphones, and other digital facilities.

A study by Ndanshau and Njau (2021) among adults in Tanzania found that the most common barriers to financial inclusion were insufficient funds and a lack of awareness about financial services. The study identified income level, gender, age, place of residence, employment status, and education as factors influencing financial inclusion, with formal employment having a larger marginal effect.

Despite significant economic growth in Tanzania, the financial system has been unable to reach the majority of the population, which is considered a significant reason for the unexpected link between economic growth and financial inclusion (Lotto, 2022). The study suggests that financial inclusion in Tanzania is positively related to income and education level, while negatively associated with gender.

A study by Fanta and Mutsonziwa (2021) on financial inclusion in Kenya and Tanzania reported

that gender does not influence financial inclusion in Kenya. The study also noted that mobile money services in Kenya have helped narrow the gender gap in financial inclusion. However, in Tanzania, women are more likely to be financially excluded compared to men.

In Rwanda, the domestication of mobile money faces barriers such as limited learning opportunities, high and non-transparent costs, and difficulties in accessing network agents with sufficient liquidity (Uwamariya et al., 2021). The study suggests that increasing the adoption of mobile money services can reduce the social exclusion of the unbanked population.

A study by Lichtenstein (2018) in Rwanda revealed that although mobile money services were associated with financial inclusion, their usage was heavily dependent on households with access to a phone. Families without a phone were the least likely to use mobile money services or any other financial services. The lack of mobile phone ownership was a strong indicator of financial exclusion among the surveyed population.

According to the FinScope report (2020), about 82% of adults in Rwanda had access to mobile phones, although females had lower access compared to men. Among adults, 3 in 5 individuals use mobile money, with more males having mobile money accounts than females. The report also identified a lack of product knowledge and a lack of interest as key barriers to the uptake of mobile money. In addition, formal borrowing in Rwanda is driven by mobile money and savings and credit cooperatives (SACCOs), each with a 9% penetration rate, enabling financial inclusion for both banked and unbanked populations.

Umurenge SACCOs in Rwanda have been a success story in promoting financial inclusion, attracting over 1.6 million customers in three years (Ozili, 2020).

Mobile money services have contributed to increased access to banking services in Rwanda, with potential to improve the sustainability of financial institutions (Byukusenge & Muiruri, 2021). A study on mobile money for financial inclusion in Rwanda (Maniriho, 2021) found that it significantly contributes to saving promotions, thereby boosting financial inclusion and socioeconomic growth. The study identified education, marital status, phone ownership, income surplus, account possession, and tracking of sources and uses of money as predictors of mobile money savings promotion in Rwanda. Mobile money is recognized as a powerful tool for financial inclusion in sub-Saharan Africa.

Based on the previous studies mentioned, this current study focuses on the use of mobile money lending applications in Kenya as a savings platform. The research questions are:

1. Do users of mobile money services in Kenya significantly utilize savings opportunities on mobile money wallets, or do they prefer to use them mainly as platforms to acquire microloans?
2. Do demographic factors such as age, the number of mobile money lending applications owned, marital status, gender, and saving using mobile applications, along with being blacklisted by the credit reference bureau (CRB), have significant moderating roles on the extended post-acceptance model (EPAM)?

The next section of the study will provide a literature review focusing on the demographic factors that influence financial inclusion through mobile money, based on the conceptual framework of the extended post-acceptance model (EPAM) as depicted in Fig. 1.

This section examines the potential moderating role of demographics in the current study. Although there is limited existing literature on the topic, this study aims to address the gaps by building upon the available research.

2.1 Age

Zins and Weill (2016) discovered that in Africa, advancing age increases the likelihood of financial inclusion, whether through formal or informal means, with wealthier and older individuals showing a preference for financial inclusion. Yaokumah et al. (2017) conducted a study in Ghana and found no significant differences between males and females regarding satisfaction with e-payment services. Interestingly, older customers were more satisfied than younger ones. Amoah et al. (2020) also conducted a study in Ghana and identified young age as a key determinant of mobile money usage. In a study conducted in the Kingdom of Eswatini (formerly Swaziland) by Myeni et al. (2020), it was found that higher education and being female positively influenced the likelihood of using mobile money for financial inclusion. Soumaré et al. (2016) identified age as a crucial determinant of financial inclusion in Africa's central and western sub-regions. Lotto (2018), in a study conducted in Tanzania, reported that individuals in higher age groups were more likely to be financially included, although this probability decreased after a certain age.

2.2 The number of mobile money applications installed by an individual

No literature has yet explored the relationship between the number of mobile money applications installed by an individual and its potential role in moderating the significant relationships within the EPAM model and continuous intention to use. This study aims to fill this research gap.

2.3 Marital status

In a study by Demirguc-Kunt and Klapper (2012), it was found that the likelihood of formal savings was higher for individuals who were wealthier, more educated, older, urban, employed, or married/separated. Soumaré et al. (2016) also identified marital status as a key determinant of financial inclusion in Africa's central and western sub-regions.

2.4 Gender

Regarding the likelihood of using informal financial services, Aterido et al. (2013) confirmed that women in Botswana, Kenya, Tanzania, and Uganda were less likely to be excluded from financial services and more likely to rely on informal financial services. Venkatesh and Morris (2000) revealed that men placed a greater emphasis on perceived usefulness when making decisions about adopting new technology, both in the short and long term. Studies by José Liébana-Cabanillas et al. (2014) and Shin (2009), focused on mobile money and mobile wallet respectively, reported that when males perceived a service as helpful, their intention to use it increased more than that of females. Demirguc-Kunt and Klapper (2012) highlighted the importance of gender in financial inclusion in developing countries, where a significant gender gap exists in terms of account ownership, formal saving, and formal credit. Being a woman increases the likelihood of financial exclusion. In an examination of the impact of financial inclusion, Swamy (2014) reported that Indian women significantly contribute to increased savings levels in poor households. Gender moderates the adoption and usage of mobile money transfer services in Kenya, as found by Waitara et al. (2015). Similarly, the study found that facilitating conditions significantly predicted the adoption and use of mobile money transfer services. Barriers to financial inclusion in low-income countries disproportionately affect the poor, women, youth, rural populations, informal workers, and migrants, as observed by Mashayekhi and Branch (2015).

According to Zins and Weill (2016), being an African man, wealthier, and older favors financial

inclusion, with a stronger influence on education and income. Being a woman increases the likelihood of informal saving while decreasing the probability of saving at a formal financial institution. In a study conducted in Ghana by Yaokumah et al. (2017), investigating the influence of demographic characteristics on customer attitudes, no significant differences were found between males and females regarding satisfaction with e-payment services. Women's membership in table banking groups in Kenya can easily influence awareness and thus increase the adoption of mobile payment services, as noted by Gichuki and Mulu-Mutuku (2018). The study by Amoah et al. (2020) in Ghana reported statistically significant gender differences in the use of mobile money, with continuous use having the potential to promote financial inclusion. A study from Niger by Aker et al. (2016) revealed that mobile money increased women's intra-household bargaining power and led to other welfare improvements. In the Kingdom of Eswatini (formerly Swaziland), Myeni et al. (2020) found that being female positively influenced the likelihood of using mobile money for financial inclusion. Gender was identified by Soumaré et al. (2016) as a key determinant of financial inclusion in Africa's central and western sub-regions. In a study on mobile money and individual savings in Uganda conducted by Lwanga Mayanja and Adong (2016), it was reported that male Ugandans exhibited higher knowledge about mobile money and had a higher rate of registration as mobile money users. Lotto (2018) reported a negative relationship between gender and mobile banking services, with a significant reduction in the probability of financial inclusion among females.

2.5 Education

Zins and Weill (2016) found that the presence of education contributes to increased financial inclusion, although it does not have an impact on informal savings. Fanta et al. (2016) conducted a study in the SADC region and identified perceived usefulness as one of the key drivers of mobile money usage, with education playing a crucial role in enabling mobile money usage. Lack of education was found to hinder the adoption of mobile money services. Myeni et al. (2020) reported that promoting mobile money among females positively influenced the likelihood of using such services, thereby enhancing financial inclusion. Soumaré et al. (2016) highlighted education as the primary determinant of financial inclusion in the central and western sub-regions of Africa. Lwanga Mayanja and Adong (2016) conducted a study in Uganda and found that individuals with higher levels of education exhibited higher knowledge about mobile money and were more likely to be registered mobile money users. Additionally, Lotto (2018) conducted a study in Tanzania and reported that education influences financial inclusion, with well-educated individuals having better access to financial opportunities. The study also revealed that education has an impact on the utilization of mobile banking services.

2.6 Mobile phones as saving devices

Suri and Jack (2016) conducted a study on mobile money services in Kenya and observed that the increased consumption levels facilitated by such services have lifted many female-headed households out of poverty. Kaffenberger (2014) highlighted the slow uptake of M-Shwari services in Kenya, with only 30% of users utilizing the platform for accessing mobile loans, and a mere 14% using it for saving purposes. Yenkey et al. (2014) found that saving money through M-Pesa services is perceived as less risky and more convenient compared to traditional saving methods at home. Fanta et al. (2016) conducted a study on the role of mobile money in financial inclusion in the SADC region and found that mobile money is predominantly used for transactions and remittances rather than saving purposes. Lwanga Mayanja and Adong (2016) conducted a study on mobile money and individual savings in Uganda and reported that saving through mobile money is relatively low. In Ghana, Narteh et al. (2017) found that savings via mobile money are popular among the unbanked population. Most respondents perceived managing savings through mobile money services as risky, and thus preferred to use the platform for airtime purchases, receiving and transferring money.

2.7 Blacklisting by financial lending institutions (CRB)

There is currently no literature investigating the impact of blacklisting by mobile money applications on users and its potential role in moderating the significant relationships in the EPAM model and continuous intention to use. This study aims to address this research gap.

To achieve the objectives of our study, we propose to utilize the Extended Post-Acceptance Model (EPAM) and examine the significant paths identified in the study by Warsame and Ireri (2021), which was initially tested using the dataset available at https://data.mendeley.com/datasets/f3722v4pg9/1 (Ireri & Warsame, 2019b). The present study aims to investigate the moderating role of selected demographics on the significant paths identified in the previously published study by Warsame and Ireri (2021).

3.1 Description of the Questionnaire and data variables

Section A of the questionnaire comprises demographic variables such as age, gender, marital status, employment status, level of education, number of mobile money lending apps installed on one's phone, utilization of mobile apps for saving money, previous instances of blacklisting by the credit reference bureau (CRB), and current blacklisting status by the CRB at the time of the study.

Section B focuses on six main constructs, all of which are measured using a 5-point Likert scale: fintech service knowledge, perceived security, perceived usefulness, satisfaction, continuous intention, and confirmation.

3.2 Study design

The study design was cross-sectional, and data collection took place in Nairobi County, specifically in Kasarani constituency, from May 2019 to June 2019.

3.3 Target population

The study participants included entrepreneurs and customers present at the stalls during the interviews. Only individuals over the age of 18 were interviewed after providing voluntary consent to participate in the study. A total of 351 questionnaires were collected, but nine incomplete questionnaires were excluded, resulting in 342 final usable questionnaires.

3.4 Statistical method

The analysis in this study primarily focuses on the significant paths identified in the EPAM model described by Warsame and Ireri (2021). Descriptive statistics, cross-tabulations, and chi-square statistics were computed using the summary tools package in R (Dominic, 2021). The primary analytical approach involved hierarchical multiple linear logistic regression. The adjusted odds ratios were used to interpret the significant p-values with a 95% confidence level as the reference. The tables were plotted using the Stargazer package in R (Hlavac, 2018), and bar plots were created using ggplot2 (Wickham, 2011). The statistical analyses were performed using R (R Core Team, 2021), and pseudo-R-squared values were calculated using the fmsb package (Nakazawa, 2021). Data manipulation was conducted using the dplyr package (Wickham et al., 2021), while moderation testing was performed using the psych package (Revelle, 2020) and psychTools package (Revelle, 2021).

4.1 Inferential statistics

The minimum confidence level of 95% was set as the baseline for interpreting the current study’s findings.

4.1.1 The Effect of knowledge on perceived security

Hierarchical binary logistic regression results showed knowledge had a significant positive effect on the perceived security of using mobile money lending services (adjOR = 4.546, 99% CI: 2.070–9.932; p = 0.0002). The other demographic analyzed in the study had no significant effect (see Table 1). Probit regression showed knowledge had a significant positive effect on the perceived security of using mobile money lending services (adjOR = 2.355, 99% CI: 1.505–3.676; p = 0.0002). Current blacklisting on CRB had a negative effect on perceived security (adjOR = 0.443, 95% CI: 0.193–0.971; p = 0.048). The remaining demographic analysed in the study had no significant effect (see Table 2).

4.1.12 The effect of perceived security on perceived usefulness

Hierarchical binary logistic regression results showed that perceived security had a significant positive effect on the perceived usefulness of mobile money lending services (adjOR = 7.029, 99% CI: 2.952–16.797; p = 0.00001). None of the demographics studied had a significant result, as shown in Table 1. Probit regression showed that perceived security had a significant positive effect on the perceived usefulness of mobile money lending services (adjOR = 2.930, 99% CI: 1.830–4.691; p = 0.00001). Similar logistic regression, none of the demographic studied had a significant result, as shown in Table 2.

4.1.3 The effect of perceived security on confirmation

Hierarchical binary logistic regression results showed perceived security had a significant positive effect on confirmation of using mobile money lending services (adjOR = 6.707, 99% CI: 2.943–15.330; p = 0.00001). No demographic variable had a significant effect, as shown in Table 1. Probit regression indicates that perceived security had a significant positive effect on confirmation of mobile money lending services (adjOR = 2.883, 99% CI: 1.818–4.571; p = 0.0001). No demographic variable had a significant effect, as shown in Table 2.

4.1.4 The effect of perceived usefulness on perceived satisfaction

Hierarchical binary logistic regression results showed perceived usefulness had a significant positive effect on perceived satisfaction of using mobile money lending services (adjOR = 2.859, 95% CI: 1.079–7.062; p = 0.027). Gender had significant positive effect on satisfaction (adjOR = 2.433, 95% CI: 1.226–5.065; p = 0.014), while saving using the mobile money lending apps had a significant negative effect on perceived satisfaction (adjOR = 0.331, 95% CI: 0.128–0.750; p = 0.013) as shown in Table 1. Probit regression results showed perceived usefulness had a significant positive effect on perceived satisfaction of using mobile money lending services (adjOR = 1.804, 95% CI: 1.046–3.530; p = 0.032). Gender had significant positive effect on satisfaction (adjOR = 1.634, 95% CI: 1.128–2.401; p = 0.011), while saving using the mobile money lending apps had a significant negative effect on perceived satisfaction (adjOR = 0.538, 95% CI: 0.337–0.827; p = 0.007) as shown in Table 2.

A further logit regression probe on significant demographic variables revealed males had a significant negative influence on perceived satisfaction of using mobile money lending app (adjOR = 0.406, 95% CI: 0.195–0.803; p = 0.012). They acknowledged that saving money using mobile money lending applications positively and significantly influenced the perceived satisfaction of mobile money lending apps (adjOR = 3.029, 95% CI: 1.331–7.866; p = 0.014) (see Table 3).

Probit regression probe on significant demographic variables revealed males had a significant negative influence on perceived satisfaction of using mobile money lending app (adjOR = 0.610, 99% CI: 0.417–0.881; p = 0.010). They acknowledged saving money using mobile money lending positively and significantly influenced the perceived satisfaction of using mobile money lending apps (adjOR = 1.824, 99% CI: 1.186–2.911; p = 0.008) (see Table 4).

4.1.5 The effect of confirmation on perceived satisfaction

Hierarchical binary logistic regression results showed confirmation had a significant positive effect on perceived satisfaction of using mobile money lending services (adjOR = 6.365, 99% CI: 2.673–15.185; p = 0.00003). Gender had significant positive effect on satisfaction (adjOR = 2.642, 99% CI: 1.303–5.643; p = 0.009), the number of mobile lending apps owned on perceived satisfaction (adjOR = 1.750, 95% CI: 1.000-3.036; p = 0.047), while saving using the mobile money lending apps had a significant negative effect on perceived satisfaction (adjOR = 0.293, 99% CI: 0.108–0.688; p = 0.009) as shown in Table 1. Probit regression results showed confirmation had a significant positive effect on perceived satisfaction of using mobile money lending services (adjOR = 2.773, 99% CI: 1.685–4.555; p = 0.0001). Gender had significant positive effect on satisfaction (adjOR = 1.683, 99% CI: 1.150–2.503; p = 0.009), while saving using the mobile money lending apps had a significant negative effect on perceived satisfaction (adjOR = 0.513, 99% CI: 0.314–0.802; p = 0.005) as shown in Table 2.

A further logit regression probe on significant demographic variables revealed males had a significant negative influence on perceived satisfaction of using mobile money lending app (adjOR = 0.376, 99% CI: 0.176–0.763; p = 0.009). Equally, having no mobile money lending app had a significant negative influence on continuous intention to use mobile money lending app (adjOR = 0.297, 95% CI: 0.093–1.067; p = 0.048). However, acknowledging saving using installed applications positively influenced perceived satisfaction while using mobile money lending applications (adjOR = 3.377, 95% CI: 1.436–9.110; p = 0.009) (see Table 3).

A further probit regression probe on significant demographic variables revealed males had a significant negative influence on perceived satisfaction of using mobile money lending app (adjOR = 0.595, 99% CI: 0.401–0.869; p = 0.009). However, acknowledging saving using the mobile money lending apps had a significant positive influence on perceived satisfaction while using mobile money lending apps (adjOR = 1.897, 99% CI: 1.213–3.092; p = 0.007) (see Table 4).

4.1.6 The effect of perceived satisfaction on continuous intention to use

Hierarchical binary logistic regression results showed that perceived satisfaction had a significant positive effect on the continuous intention to use mobile money lending services (adjOR = 4.248, 99% CI: 2.008–8.970; p = 0.0002). The number of mobile lending apps owned on continuous intention to use (adjOR = 2.167, 99% CI: 1.342–3.523; p = 0.002), while blacklisting at the CRB on continuous intention to use (adjOR = 3.647, 95% CI: 1.296–39.668; p = 0.011), as shown in Table 1. Probit regression results showed that perceived satisfaction had a significant positive effect on the continuous intention to use mobile money lending services (adjOR = 2.306, 99% CI: 1.480–3.583; p = 0.0002). The number of mobile lending applications installed on continuous intention to use (adjOR = 1.549, 99% CI: 1.180–2.038; p = 0.002), while blacklisting at the CRB on continuous intention to use (adjOR = 2.085, 95% CI: 1.159–3.691; p = 0.013), as shown in Table 2.

Further logit regression probe on significant demographic variables revealed that not having a mobile money lending app, had a significant negative influence on continuous intention to use mobile money lending app (adjOR = 0.133, 99% CI: 0.045–0.383; p = 0.002). Equally, being previously blacklisted by CRB had a significant negative influence on the continuous intention to use mobile money lending apps (adjOR = 0.281, 95% CI: 0.109–0.780; p = 0.011) (see Table 3).

Probit regression probe on significant demographic variables, revealed having no mobile money lending app, had a significant negative influence on continuous intention to use mobile money lending app (adjOR = 0.308, 99% CI: 0.164–0.578; p = 0.003). Equally, having been previously blacklisted by CRB had a significant negative influence on continuous intention to use mobile money lending apps (adjOR = 0.491, 95% CI: 0.281–0.875; p = 0.014) (see Table 4).

4.2 Moderation testing

According to Hair et al. (2011), multicollinearity is a concern if the variance inflated factor (VIF) is greater than 5. The findings presented in this section show that none of the significant paths had a VIF greater than 2. Thus it was assumed that the data used in the current study was not suffering from multicollinearity issues.

4.2.1 Confirmation and perceived satisfaction

The moderation model shows a significant interaction between confirmation and gender on perceived satisfaction (β = 0.112, SE = 0.054, p = 0.037) of using mobile money lending apps, as shown in Table 5. Gender had a significant negative effect on satisfaction (β= -0.138, SE = 0.052, p = 0.008). However, its interaction with confirmation significantly affected satisfaction (see Table 5) for more statistics. There was no significant moderation interaction between confirmation and saving on perceived satisfaction. Equally, there was no significant moderation interaction between confirmation and the number of mobile money lending applications on perceived satisfaction.

The interaction between mobile money lending applications and the continuous intention was significant. However, the interaction between CRB blacklisting and continuous intention to use was non-significant. There was no significant moderation interaction between perceived satisfaction and mobile money lending applications on continuous intention. Equally, there was no significant moderation interaction between perceived satisfaction and CRB blacklisting on continuous intention (see Table 6) for the actual statistics.

Table 6: moderation testing between perceived satisfaction and continuous intention to use

Figure 2 shows that perceived satisfaction with mobile money lending applications was higher among the participants with more substantial social influence. The finding shows that more males than females who were not satisfied with mobile money lending applications had low social influence. Equally, confirmation was slightly higher among males than females with strong social influence and likewise among those with low social influence. Gender showed a significant association with perceived satisfaction findings shows χ² (1) = 4.927, p = 0.026.

Mobile users who were satisfied and had installed more than two mobile lending applications on their phones were influenced the most by social influence, followed by those who had installed one application, and lastly, those with none. The construct confirmation replicated the same scenario (see Fig. 3). The number of mobile money applications installed had a significant association with continuous intention to use χ² (2) = 20.932, p < 0.001.

Mobile users who were satisfied with saving money on their mobile lending applications experienced a stronger social influence than those who did not. The construct confirmation replicated the same scenario (see Fig. 4). Saving had a significant association with perceived satisfaction χ² (1) = 6.752, p = 0.009.

4.2.2 Perceived usefulness and perceived satisfaction

The interaction between gender and perceived usefulness and between saving and perceived usefulness were significant. However, there was no significant moderation interaction between perceived usefulness and saving on perceived satisfaction. Equally, there was no significant moderation interaction between perceived usefulness and gender on perceived usefulness (see Table 7) for the actual statistics.

Table 7: moderation testing between perceived usefulness and perceived satisfaction

4.3 Mobile money review in East Africa

The financial access survey data FAS Latest Data - IMF Data 2010–2020 using the G20 financial inclusion indicators was analysed. These datasets support policymakers in measuring and monitoring financial inclusion and benchmarking against peers. The mean differences were determined using Flech ANOVA function ggbetweenstats function in (Patil, 2021) ggstatsplot package in R.

4.3.1 Number of registered mobile money accounts

The mean differences in registered mobile money account accounts were significant. Kenya led in the number of registered mobile accounts, followed by Tanzania, Uganda, and Rwanda (Fig. 6).

4.3.2 Value of mobile money transactions

The mean differences were significant regarding the value of mobile money transactions (Fig. 7). Uganda had the highest number of mobile transactions, followed by Tanzania. Kenya and Rwanda had the least.

4.3.3 Value of mobile money transactions (% of GDP)

The mean differences were not significant between the four countries regarding percentage GDP. Kenya had the highest number, followed by Uganda, Tanzania, and Rwanda (Fig. 8).

4.3.4 Outstanding balances on active mobile money accounts

There were significant mean differences between the countries regarding the outstanding balances on active mobile money accounts. Uganda had the highest number, followed by Tanzania and then Rwanda. There was no data for Kenya (Fig. 9).

4.3.5 Number of registered mobile money agent outlets per 100,000 adults

The differences in the number of registered mobile money agent outlets per 100,000 adults in the four countries were insignificant (see Fig. 10). However, Uganda had the highest mean, followed by Rwanda, Kenya, and Tanzania.

4.3.6 Number of registered mobile money accounts per 1000 km²

The mean differences in the number of registered mobile money accounts per 1000 km² in the four countries were significant (see Fig. 11). However, Rwanda had the highest mean, followed by Uganda. Kenya and Tanzania had the least means.

4.3.7 Number of registered mobile money agent outlets per 1000 adults

The mean differences in the number of registered mobile money outlets per 1000 adults in the four countries were insignificant (see Fig. 12). However, Rwanda had the highest mean, followed slightly by Uganda, Kenya and Tanzania.

This is statistical point. Explain what you mean by mean differences here!

This paper examines the impact of demographics on the continuous intention to use mobile money applications and its role in promoting financial inclusion in East Africa. The empirical study conducted in Kenya is applicable to other Eastern African countries, namely Uganda, Rwanda, and Tanzania, as they share similar social and economic characteristics. The introduction of M-Pesa, the first mobile financial service in Africa, in Kenya in 2007 by Vodafone on behalf of Safaricom and Vodacom marked a significant milestone. It quickly gained popularity among Kenyans, primarily for remittances, and greatly enhanced financial inclusion, considering the limited access to formal financial institutions at the time (Financial Sector Deepening Kenya, 2007).

By 2019, Kenya alone had 23 million mobile money users, inspiring competitors across Africa, especially in East Africa, to enter the market due to the initial success of M-Pesa. As a result, M-Pesa was launched in Tanzania by Vodafone in 2008, and MTN Money Mobile was introduced in Uganda and Tanzania by MTN in 2009. Today, all East African countries have multiple mobile financial platforms offering various services, contributing to the financial inclusion of their populations. This trend is evident in most East African countries, where financial inclusion has significantly increased over the years. For example, in Kenya, financial inclusion rose from 29% in 2006 to 75% in 2016. Similar improvements were observed in Rwanda (from 21% in 2006 to 68% in 2016) and Tanzania (from 11% in 2006 to 65% in 2017).

Numerous studies have highlighted the competitive advantage of mobile money account ownership in sub-Saharan African countries and its positive impact on financial inclusion, including narrowing the gender gap. The adoption of mobile money services has not only improved financial inclusion but has also compelled banks to revise their marketing strategies and financial service offerings, further contributing to financial inclusion and economic growth. However, certain barriers to financial inclusion persist, such as the lack of financial education, inadequate spatial distribution of mobile money agents, high transaction fees, network reliability concerns, and security issues.

Nevertheless, mobile money services have undeniably played a crucial role in the financial inclusion of unbanked populations, particularly women in East Africa. These services have enabled financially excluded women to engage in secret savings, thereby enhancing their financial inclusion and independence. However, studies indicate that women in Tanzania still lag behind in terms of financial inclusion due to inherent gender disadvantages such as limited income, low financial literacy, and limited access to digital facilities.

While mobile phone ownership positively influences financial inclusion, there are variations among East African countries. Kenya has the highest mobile phone ownership, while Rwanda has the lowest. Similarly, Rwanda has the lowest value of mobile money transactions and percentage of GDP, likely due to its lower mobile phone ownership rates. Other barriers to financial inclusion in East African countries include acute poverty, lack of awareness about available financial services, and poor product knowledge.

Understanding the role of demographics is essential for formulating and implementing effective policies to address financial exclusion. Policy makers and economic planners must comprehend the key drivers and hindrances to financial inclusion. Once the impact of demographics is fully understood, appropriate policies and procedures can be established to ensure disadvantaged cohorts within a country or region benefit from financial inclusion initiatives. Financial inclusion serves as an economic tool for policymakers to achieve their welfare and sustainable development goals (Grohmann et al., 2018).

Enhanced financial inclusion yields significant positive economic and social outcomes, particularly by eliminating barriers to access for disadvantaged individuals residing in rural areas. This is particularly important for East African regions with a substantial rural population, where mobile financial services have become instrumental in reaching neglected rural communities.

The security of mobile payment systems is a crucial factor affecting user confidence and intention to use mobile money applications. Security issues and perceived risks can decrease usage intention, emphasizing the importance of understanding the financial behavior and trust of potential users.

In the current study, blacklisting by the credit reference bureau was found to negatively influence the perceived security of using mobile money applications due to potential privacy infringements. Privacy concerns can lead to low uptake and repayment of loans. Countermeasures, such as using applications to block spam calls from blacklisted mobile money service providers, are adopted by many Kenyans, particularly the youth.

Perceived usefulness, confirmation, and satisfaction play significant roles in determining users' adoption and continued use of mobile money services. The study found that demographics had no significant effect on perceived usefulness and confirmation. However, gender positively influenced perceived satisfaction, although the relationship between males and perceived satisfaction was negative. Additionally, saving money using mobile money applications negatively affected perceived satisfaction, as a considerable portion of users did not utilize the applications for saving purposes.

The number of mobile money applications installed by users had a positive influence on continuous intention to use, suggesting that satisfied users are more likely to install multiple mobile money applications. However, being blacklisted by the credit reference bureau overrides the effect of the number of installed applications, as defaulters face difficulties in obtaining loans from any mobile money service provider.

Strategies to promote financial inclusion through mobile money services should include incorporating saving services, investing in application security, and increasing awareness campaigns on saving money using mobile money applications. Governments should develop policies and regulations to encourage savings through mobile money applications, while also addressing predatory applications and ensuring the inclusion of unbanked residents in hard-to-reach areas.

this study highlights the preference for mobile money applications as borrowing platforms rather than saving platforms among users in Kenya. Gender was found to strengthen the positive relationship between confirmation and perceived satisfaction. However, further research is needed to explore other demographics that may influence the continuous intention to use mobile money applications.

Conflict of interest

There is no conflict of interest to declare

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Impact of Mobile Financial Services on Financial Inclusion: Empirical Insights from Kenya

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Abstract

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1. Introduction

2. Literature Review

2.1 Age

2.2 The number of mobile money applications installed by an individual

2.3 Marital status

2.4 Gender

2.5 Education

2.6 Mobile phones as saving devices

2.7 Blacklisting by financial lending institutions (CRB)

3. Materials and methods

3.1 Description of the Questionnaire and data variables

3.2 Study design

3.3 Target population

3.4 Statistical method

4. Results

4.1 Inferential statistics

4.1.1 The Effect of knowledge on perceived security

4.1.12 The effect of perceived security on perceived usefulness

4.1.3 The effect of perceived security on confirmation

4.1.4 The effect of perceived usefulness on perceived satisfaction

4.1.5 The effect of confirmation on perceived satisfaction

4.1.6 The effect of perceived satisfaction on continuous intention to use

4.2 Moderation testing

4.2.1 Confirmation and perceived satisfaction

4.2.2 Perceived usefulness and perceived satisfaction

4.3 Mobile money review in East Africa

4.3.1 Number of registered mobile money accounts

4.3.2 Value of mobile money transactions

4.3.3 Value of mobile money transactions (% of GDP)

4.3.4 Outstanding balances on active mobile money accounts

4.3.5 Number of registered mobile money agent outlets per 100,000 adults

4.3.6 Number of registered mobile money accounts per 1000 km2

4.3.7 Number of registered mobile money agent outlets per 1000 adults

5. Discussion

6. Practical implications and recommendations

7. Conclusion

Declarations

References

Tables

Additional Declarations

Supplementary Files

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4.3.6 Number of registered mobile money accounts per 1000 km²