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Malawi University of Business and Applied Sciences

Malawi University of Business and Applied Sciences

* Corresponding author

Financial Technology (FinTech) refers to the technological developments that have the potential to

revotionalize how financial services are provided and inspire the creation of new business models, apps,

workflows, and products (World Bank, 2021). It involves leveraging technology to offer different services

including Blockchain, Data Analysis, Insurance, Personal Finance, Wealth Management, Financial

Services Lending, Payment solutions, Real Estate, and Regulatory Tech (Nanduri 2021). FinTech offers

ways of easily accessing banking and financial services, and promoting financial inclusion, especially in

developing countries like Malawi (Evans 2018). It has changed how financial services and products are

produced, delivered, and consumed (Allen et al. 2021). For instance, instead of going to the bank or Savings

and Credit Cooperative (SACCO) or any financial institution office physically to access services, one may

access the services using Unstructured Supplementary Service Data (USSD) or online, using the Internet

spared Malawi, a country in Sub-Saharan Africa. It has been reported that access to at least a formal financial

product increased from 34% in 2015 to 45% in 2018 (FinMark Trust 2020). This was necessitated by

investments being made in financial technologies like mobile money services. Besides, the same investments

have also been made in banking digital payment solutions.

Savings and Credit Cooperatives (SACCOs) have also been adopting FinTech. Recently, the

Financial Cooperative (FINCOOP) SACCO launched ‘Fin Mobile’, which is a digital banking application

specifically designed for SACCOs. Generally, the innovations in the banking industry have pushed other

financial institutions including SACCOs to adopt innovative means of serving their members. To the extent

that SACCOs are partnering with banks and other FinTech suppliers to help them incorporate FinTech

Services in their operations. Accordingly, some SACCOs have adopted electronic banking and mobile

since 2019. According to the records, in 2019, MUSCCO had Thirty-eight affiliates that were serving one

hundred fifteen thousand one hundred twenty-one members (FinMark Trust 2020).

Despite the benefits associated with the adoption and use of FinTech products and services, the

uptake of these products and services is still very low in Malawi (World Bank 2021). In particular, FinMark

Trust (2020) found that most payments are done in cash and cheques in many Micro Finance Institutions

(MFI) and Village Savings and Loan Associations (VSLAs) (FinMark Trust 2020). Further, loan

applications were all still done manually despite innovations. However, there are hardly any studies to

examine this problem. Besides, there is a dearth of studies on Fintech in general in Malawi; most extant

studies focus on specific products and services like Internet banking and mobile money, leaving other

products or services within the financial technology area (Chirwa 2022). In addition, existing studies from

LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK

Unified Theory of Acceptance and Use of Technology (UTAUT)

Several theories attempt to explain why one adopts or does not adopt a particular technology. These

include the Theory of Reasoned Actions (TRA), Theory of Planned Behaviour (TPB), Technology

Acceptance Model (TAM), Technology Acceptance Model 2 (TAM 2), Technology Acceptance Model 3

(TAM 3), Innovation Diffusion Theory (IDT), Unified Theory of Acceptance and Use of Technology

(UTAUT) among others (Makongoro 2014). However, this study uses the UTAUT model. The model was

adopted due to its predictive power and existing empirical evidence of its reliability of results it produces

(Papagiannidis 2022).

were developed in different fields. For example, TRA, TPB, and MM were developed in the Social

Psychology fields whilst TAM, CTPB-TAM, and MPCU were developed in the social field. Social Cognitive

Theory and IDT were developed in social sciences fields. By integrating these models and theories, UTAUT

took into account all their limitations and worked on them whilst taking advantage of their merits.

Thirty-two variables were used to develop the UTAUT model and these were fused into four variables

which are social influence, expected performance, facilitating conditions, and expected effort (Aziz et al.,

2020). Williams et al. (2015) pointed out that these four constructs directly influence behavioral intention

to use as well as usage itself. Numerous prior studies on the adoption of technologies, innovations, or

systems have used the theory (Papagiannidis 2022). Accordingly, this study uses the four constructs to

examine the determinants of FinTech adoption in SACCOs.

Venkatesh et al. (2012) define expected effort as the degree of use associated with a particular

technology. Expected effort is associated with convenience (Makongoro 2014). It explains which

applications are likely to be adopted and used by a user. Existing studies have produced contrasting results

on the effect of expected effort on the adoption of FinTech. For example, studies by Kurniasari et al. (2023);

Najib et al. (2021); Tun-Pin et al. (2019); Makongoro (2014), and Yan et al., (2021) found that expected

effort had a positive significant influence on the adoption of FinTech. Essentially, a high degree of ease of

use was among the main factors influencing the adoption of FinTech (Kurniasari et al. 2023; Rosnidah et

al. 2019; Tun-Pin et al. 2019). On the other hand, Hassan et al. (2022); Urus et al. (2022); and Rahim et al.

(2023) found that expected effort had no significant influence on the adoption of FinTech. The studies

observed that simplicity in using a system cannot be enough reason to influence one to adopt a system. Thus,

Facilitating conditions

Facilitating conditions entails the degree to which a person thinks that the technological and

organizational infrastructure is available to facilitate the use of a specific technology (Khalili 2011).

According to Venkatesh et al. (2012), facilitating conditions are perceptions of the existence of resources as

well as support to perform a behavior. Thus, facilitating conditions are circumstances or elements that make

it easier or more favorable for a certain outcome or action to occur.

Studies have found contrasting results on the effect of facilitating conditions on the adoption of

FinTech. For instance, Hassan et al. (2022),Rahim et al. (2023), Kadim and Sunardi (2021), Angelina et al.

( 2021), Kurniasari et al. (2023), Rosnidah et al. (2019), Hu et al. (2019), and Najib et al. (2021) found that

facilitating conditions do have a positive significant effect on FinTech adoption. Conversely, Hasyim (2022),

Data analysis

The dependent variable was binary, where 0 indicated non-adoption of FinTech and 1 indicated the

adoption of FinTech, as such, a probit regression model was employed to analyze the data. The following

probit regression model was used:

– the Social Influence

– the Expected Performance

– the Facilitating Conditions

Statistical testing was done to ascertain the relationship between the dependent and the independent

variables in the model with the help of Stata software.

RESULT AND DISCUSSION

than 20,000. The remaining 3% has a membership of more than 20,000.

Descriptive statistics

The questionnaire had questions whose responses were measured using the Likert scale of 1 – 5, with

1 representing strongly disagree, 2 disagree, 3 neutral, 4 agree and 5 strongly agree except for FinTech

adoption which had a binary measure of yes (1), or no (0). All four constructs of the UTAUT model had

questions that were collectively answering their level of influence. Table 1 provides the descriptive results

for the variables.

Table 1. Descriptive statistics results

Source: Data processed (2024)

The results indicate that 87.5% of the sampled SACCOs had adopted FinTech. The high rate of

adoption is not surprising as the world is going digital, as such, organizations even in developing countries

are going along in adopting relevant technologies to remain competitive. In terms of the independent

variables, the minimum mean score on the constructs was 3.5 whilst the maximum average was 4.1. This

meant that most of the responses were positive as they were above the neutral value of 3. On the expected

effort, the mean score was 3.820, which is above 3 representing a neutral stance as it is slightly lower than

agree (4). On social influence, the average score was 4.089, which is somewhat above agreement (4).

Regarding expected performance, the mean score was 4.203 which is slightly above 4 which represents

facilitating conditions. The results from this test are presented in Table 2.

Table 2. Cronbach’s alpha results

Source: Data processed (2024)

The overall alpha result of 0.77 suggests that there was a strong internal consistency in the variables

(Taber 2018). The alphas of the variables ranged between 0.660 and 0.792, as such, they fall in the acceptable

zone. This means that the whole set of variables were measuring the same underlying dependent variable.

Multicollinearity

The researcher tested for multicollinearity. In this process, correlation was tested first (Zakariyah et

al. 2023). It is worth noting that some scholars argue that a value of 0.80 or higher is a sign of

multicollinearity (Shrestha, 2020), while others state that a correlation of 0.70 or higher should be a course

Source: Data processed, 2024

Table 2. Variance Inflation Factor (VIF) results

The results presented in Table 3 show that all the correlation coefficients were less than 0.70

suggesting that the data was free of multicollinearity problems. This was confirmed in Table 4 as VIF values

for all variables were less than 3.

Heteroscedasticity

undertaken to examine whether variances were constant or not (Gujarati, 2004). The result of the test

produced a chi-square statistic of chi2(1) being equal to 23.55 with a probability (Prob > chi2) of 0.0000.

Based on these results the null hypothesis was rejected as it was proved that heteroscedasticity was present.

Accordingly, robust standard errors were used when estimating the model as they tend to violate statistical

model assumptions (Mansournia et al., 2021).

Model specification

Before deciding on the variables to be included in the model, a model specification test was

Source: Data processed, 2024

Based on the results, both hat and hat squares were insignificant. This shows that the model was fit

for the analysis. This was also verified with the joint p-value of the model which at less than 0.01 (see Table

Table 4. Summary of inferential statistics results

Source: Data processed, 2024

The results for the regression model show that expected effort significantly influences FinTech

adoption in SACCOs in Malawi. With a p-value of 0.009, any positive percentage change in expected effort

significantly increases the probability of adoption by 9.18%. The results are consistent with Kurniasari et al.

(2023), Makongoro (2014), Najib et al. (2021), Rosnidah et al. (2019), and Tun-Pin et al. (2019). As regards

social influence, the results of the probit regression model also show that it significantly influences adoption.

can never be refuted. However, this study's results suggest that the benefits may not be realizable in Malawi.

This may be due to low IT literacy in the country as such the users of the technology may not be in a position

to use it efficiently.

Lastly, on the facilitating conditions, probit regression results showed that they significantly influence

the adoption of FinTech in SACCOs. With a p-value of 0.005, any positive percentage change in facilitating

conditions increases the probability of adoption by 27%. The results are in agreement with Angelina et al.

(2021), Hassan et al. (2022), Hu et al. (2019), Najib et al. (2021), Rosnidah et al. (2019), and Tun-Pin et al.

(2019). Besides, it is worth noting that the results showed that facilitating conditions were the most positive

and significant variable in influencing FinTech adoption in SACCOs followed by social influence and

expected effort. The results echo the calls of the World Bank (2021) and Hornuf et al. (2025) for more

The objective of the study was to determine the factors that influence the adoption of FinTech in

SACCOs in Malawi using the UTAUT model. The results indicate that expected effort, social influence,

and facilitating conditions were the significant factors whereas expected performance was not. Further,

facilitating conditions were found to be more influential followed by social influence and expected effort.

The results indicate the areas that need focusing in promoting FinTech adoption in the SACCOs. Further,

the results indicate that the promotional efforts may need to prioritize ensuring the development of a robust

Technological Forecasting and Social Change, 187(February).

Policy, Regulation and Governance, 20(6): 568-581. https://doi.org/10.1108/DPRG-04-2018-0018

literature review and meta-analysis. Research in International Business and Finance, 73(Part A)