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Finance, Accounting and Business Analysis 
Volume 7 Issue 1, 2025 

http://faba.bg/       
ISSN  2603-5324 

DOI: https://doi.org/10.37075/FABA.2025.1.01 

 

Determinants of FinTech adoption in Savings and Credit Cooperatives: 

Evidence from Malawi 

 

Reuben Bereckia Chipeta1 , Andrew Munthopa Lipunga2*  

 

Malawi University of Business and Applied Sciences1 

Malawi University of Business and Applied Sciences2 

* Corresponding author 

 

Info Articles   Abstract 

 
 

History Article: 

Submitted 14 October 2024 

Revised 12 January 2025 

Accepted 11 February 2025 
 

 Purpose: The study examines factors influencing the adoption of 

FinTech in Savings and Credit Cooperatives (SACCOs) using the 

Unified Theory of Use and Acceptance of Technology. 

Methodology: The study adopted a quantitative research design and 

used a survey method to collect data from SACCOs in Malawi. It 

employed a probit regression model to analyze the data. 

Findings: 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. 

Practical Implications: Efforts to promote FinTech adoption in 

SACCOs need to prioritize the development of a robust digital 

ecosystem, that is, the facilitating conditions.  

Originality/Value: This contributes to the discourse on the 

determinants of FinTech adoption that has so far provided 

contrasting results. Further, this is the first empirical study on 

FinTech adoption determinants in Malawi's SACCOs.  

Paper Type:  Research Paper 

 

Keywords:  

FinTech, Malawi, Savings 

and Credit Cooperatives  
 

 

JEL: G2, G3, M0, M1  

* Address Correspondence:   

E-mail: reuben.chipeta@gmail.com1 

   alipunga@mubas.ac.mw2 

 

 
 
 
 
 
 

http://faba.bg/
https://doi.org/10.37075/FABA.2025.1.01
https://orcid.org/0009-0004-9821-656X
https://orcid.org/0000-0003-1712-1311


Reuben B. Chipeta, Andrew M. Lipunga / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 

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INTRODUCTION 
 

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 

provided they have a phone or a computer. 

Worldwide, the rate of adoption of FinTech has gone up to 64% and 96% of consumers are aware of 

these products and services (Hassan et al. 2022). The World Economic Forum projects that 70% of the 

world’s new value will be digitally enabled in the next ten years (World Economic Forum, 2020). Africa has 

demonstrated acceptance in the use of FinTech products. For example, it is noted that as of 2023, 68% of 

global mobile money transactions by value were done in Africa (AfCFTA 2023). These evolutions have not 

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 

banking services (UNDP Malawi 2023). The adoption of these technologies is reforming how members 

access and the cooperatives offer financial products and services (Feyen et al. 2023).  

As of September 2023, in Malawi, forty-four SACCOs were affiliated with the Malawi Union of 

Savings and Credit Cooperative (MUSCCO) the mother body for the SACCOs in the country. The forty-

four SACCOs were serving Two hundred twenty-two thousand nine hundred and eleven members 

(MUSCCO 2023). The number represents an increase in both the number of SACCOs and membership 

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 

other countries provide contrasting results regarding the factors that influence FinTech adoption (Kurniasari 

et al. 2023; Najib et al. 2021;  Rosnidah et al. 2019; Hassan et al. 2022; Rahim et al. 2023; Sebastián et al. 

2023; Hasyim 2022). Therefore, this study seeks to contribute to filling these gaps by assessing the factors 

that drive the adoption of FinTech in the SACCOs. It is worth noting that FinTech and SACCOs both play 

a role in improving financial inclusion in developing countries like Malawi.  

 
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). 



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The theory was developed by Venkatesh et al. (2003). It was developed to get a holistic understanding 

of what influences the adoption of technologies.  It was developed after integrating eight theories which 

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

Acceptance Model(TAM), the Motivational Model (MM), the Combined Theory of Planned Behaviour and 

Technology Acceptance Model (CTPB-TAM), Model of PC Utilisation (MPCU), Innovation  Diffusion 

theory (IDT) and Social Cognitive Theory (SCT) (Williams, 2015; Venkatesh et al. 2003). These theories 

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. 

 

Expected effort  

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, 

this study hypothesized that: 

H1: Expected effort has a positive effect on FinTech adoption in SACCOs. 

 

Social influence 

Social influence is the degree to which influential people think a certain technology is appropriate for 

people to employ (Venkatesh et al. 2012). Since organizations such as SACCOs operate in an open 

environment where technological advances are the norms of the day, they may be forced to adapt and adopt 

FinTech to remain competitive and to serve well their members (Daft 2016). Different researchers have 

studied the impact of social influence on the adoption of FinTech such as  Zakariyah et al. (2023), Kurniasari 

et al. (2023), Hassan et al. (2022), Chan et al. (2022), Rahim et al. (2023), Najib et al. (2021), Nawayseh, 

(2020), Rosnidah et al. (2019) and Tun-Pin et al. (2019). These studies found that social influence had a 

positive and significant influence on the adoption of FinTech. However, Urus et al. (2022) found contrasting 

results. The study found that social influence had a negative significant influence on the adoption of FinTech 

in Indonesia, whilst in Malaysia, the study found that it had no influence at all. This study hypothesized 

that: 

H2: Social influence has a positive effect on FinTech adoption in SACCOs. 

 

Expected performance 

Expected Performance refers to the degree to which people believe that using a specific technology 

would enhance their ability to perform their job (Venkatesh et al. 2012). Since the adoption of a particular 

technology is usually associated with costs, there must be an economic justification for the technology to be 

adopted. In other words, the benefits must outweigh the costs. Thus, expected performance measures the 

degree to which adoption of the technology will provide recognizable benefits to users (Rosnidah et al. 2019). 

It is worth noting that expected performance is one of the factors that have been heavily used in studies on 

mobile and Internet banking (Oliveira et al. 2014; Tarhini et al. 2016; Yu 2012). 

Several studies have also looked into how performance expectancy influences FinTech adoption. 

They include Kurniasari et al. (2023), Rahim et al. (2023), Yan et al. (2021), Rosnidah et al. (2019), and 

Najib et al. (2021) who found that expected performance influences FinTech adoption to a greater extent. 

On the other hand, studies by Sebastián et al. (2023), Hasyim (2022), Pasaribu and Rabbani (2022), Kadim 

and Sunardi (2021), Maharani (2021), Sankaran and Chakraborty (2021), and Angelina et al. (2021) found 

that expected performance does not influence the adoption of FinTech. Accordingly, this study hypothesized 

that: 



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H3: Expected performance has a positive effect on FinTech adoption in SACCOs. 

 

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), 

Pasaribu (2022) and  Sebastián et al. (2023) found that facilitating conditions do not have a significant effect 

on the adoption of FinTech. Accordingly, the study hypothesized that: 

H4: Facilitating conditions have a positive effect on FinTech adoption in SACCOs. 

Based on the extant literature, Figure 1 presents the conceptual framework for the study. The four 

constructs of the UTAUT theory are employed as independent variables whereas FinTech adoption is the 

dependent variable. 

 

Independent Variables     Dependent Variables 
 
 
 
 
 
 
 
 
 
 
Source: Derived from a literature review by researchers (2024).  

Figure 1. Conceptual framework 

 

METHODS 

 

Research design 

This study took a positivist research philosophy, employing quantitative data collection and analysis 

methods. It was cross-sectional and explanatory in nature. A deductive research approach was adopted as 

such the study used an existing theory to determine the effects of UTAUT variables on FinTech adoption in 

SACCOs in Malawi.  

 

Sampling and data collection 
The population of the study was the forty-four SACCOs that are affiliated with the Malawi Union of 

Savings and Credit Cooperatives (MUSCCO) as of September 2023 (MUSCCO, 2023). Considering the 

manageable number of the SACCOs, a census approach was adopted, as such, all the SACCOs were 

sampled. Primary data was collected using a questionnaire. The questionnaire was administered using 

Google Forms and in cases where there were challenges to collecting the data, the researcher physically 

delivered the questionnaire. One questionnaire was sent to each SACCO. Accordingly, forty-four 

questionnaires were sent out of which thirty-three were returned out of the returned questionnaires, thirty-

two were usable. This represented a 72% response rate. 

To determine whether SACCOs had adopted FinTech, the questionnaire (see the appendix) requested 

the participants to tick on the options given on the FinTech product they were using or indicate any other 

product being used if not on the list of options. The other questions were grouped according to the variables 

being studied. For instance, questions on expected effort focused on how respondents could rate on a scale 

of 1-5 how easy it is to understand the FinTech system or product, its user-friendliness and convenience, and 

trust when one is using the system. For social influence, participants rated members’ and competitors’ 

influence, and the desire to improve corporate image. For the expected performance, the questions centered 

on time savings, efficiency, and effectiveness. Lastly, regarding facilitating conditions, questions assessed 

the availability of government support, regulations, resources to use or adopt the systems, technical know-

Expected Performance 

 

Expected Effort 

Social Influence 

 

Facilitating Conditions 

 

FinTech Adoption 



Reuben B. Chipeta, Andrew M. Lipunga / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025 

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how, and support to use the system.   

    

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: 

 

 𝑝𝑟𝑜𝑏[𝑦𝑖 = 1|𝑥𝑖] = Φ(𝛼 + 𝛽0 + 𝛽1𝑥1 +  𝛽2𝑥2 + 𝛽3𝑥3 + 𝛽4𝑥4) (1) 

 

Where Φ is a normal cumulative density function,  𝑦
𝑖
 is a dummy variable taking value 1 if the 

SACCO adopts FinTech and 0 otherwise while the regression parameters were 𝛽
𝑖

= (𝛽
1

, 𝛽
2

, 𝛽
3

, 𝛽
4
 ) being 

the coefficient on the first, second, third and fourth predictor variables. The 𝑥𝑖’s represent independent 

variables denoted as follows:  

𝑥1 – the Expected Effort 

𝑥2 – the Social Influence 

𝑥3 – the Expected Performance 

𝑥4 – 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 

 

Demographic characteristics 

As it has already been noted, 33 responses were received out of which 32 were usable representing a 

72% response rate. In terms of years of existence, 34% of the SACCOs were less than 10 years old. The same 

was observed for those with years of existence between 10-20 years. The remaining 31% were found to have 

existed for more than 20 years. With regards to membership, 97% of SACCOs have a membership of less 

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 

Variables Sample size Mean Std. Deviation Minimum Maximum 

Fintech Adoption 32 0.875 0.336 0.000 1.000 

Expected Effort 32 3.820 0.670 2.000 5.000 

Social influence 32 4.089 0.576 2.500 5.000 

Expected performance 32 4.203 0.610 2.333 5.000 

Facilitating conditions 32 3.526 0.618 1.833 4.667 

 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 

agreement. Likewise, the mean score for facilitating conditions was 4.667 which is somewhat lower than 

strongly agree (5) but above agree (4).  



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Diagnostic tests 
Reliability tests 

Cronbach’s alpha was computed to assess reliability. The computed Cronbach’s was based on 

variables which included Fintech adoption, expected performance, expected effort, social influence, and 

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

 

Table 2. Cronbach’s alpha results 

    Item-test Item-rest 
Average  

inter-item 
 

Item Observations Sign correlation correlation correlation alpha 

Fintech adopted 32 + 0.763 0.600 0.375 0.706 

Expected effort 32 + 0.623 0.404 0.459 0.772 

Social influence 32 + 0.842 0.724 0.327 0.660 

Expected performance 32 + 0.803 0.662 0.350 0.683 

Facilitating conditions 32 + 0.574 0.340 0.488 0.792 

Test scale     0.3999 0.769 

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 

of concern (Pallant 2010; Tarhini et al. 2016). However, multicollinearity is detected through the use of 

Variance Inflation Factor (VIF). VIF of less than 3 is deemed acceptable which indicates that 

multicollinearity is minimal or non-existent. Tables 3 and 4 show the correlation and VIF test results. 

 

Table 1. Correlation coefficients results 

  

Fintech 

adopted 

Expected 

effort 

Social 

influence 

Expected 

performance 

Facilitating 

conditions 

Fintech adopted 1     
Expected effort 0.291 1    
Social influence 0.560 0.476 1   
Expected performance 0.521 0.392 0.691 1  

Facilitating conditions 0.378 0.087 0.3111 0.293 1 

Source: Data processed, 2024 

 

Table 2. Variance Inflation Factor (VIF) results  

Variable VIF 1/VIF 

Social influence 2.18 0.458626 

Expected performance 1.96 0.510071 

Expected effort 1.32 0.760272 

Facilitating conditions 1.13 0.884962 

Source: Data processed, 2024 

 

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. 

 

 



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Heteroscedasticity 

Being a cross-sectional study data is usually affected by heteroscedasticity, accordingly, the test was 

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 

undertaken. Basically, independent variables have to be fit so that errors or biases that may affect the model 

and later the results are avoided. A link test model was used to test the model specification. Table 5 presents 

the results. 

 

Table 3. Model specification test 

Fintech  Coefficient Std. err. z P>z [95% conf. interval] 

_hat  1.015295 0.66119 1.54 0.125 -0.280614    2.311203 

_hatsq -0.0547229 0.066392 -0.82 0.413 -0.1848485  0.0754028 

_cons 0.0365464 0.557426 0.07 0.948 -1.055989    1.129081 

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 

6) indicating the fitness of the model for the analysis.  

 
Inferential statistics 

The inferential statistics were run using the probit regression model and the results, which used robust 

standard errors are presented in Table 6. It is worth noting that the joint p-model was found to be significant 

(p-value = 0.008). This shows that the model was fit, accordingly, its results can be relied on with greater 

confidence.  

 

Table 4. Summary of inferential statistics results 

Variables Coefficients P value 
Robust 

std. errors 

Marginal 

effects 

[95% Confidence 

Interval] 

Expected Effort 1.486 0.009 0.571 0.0918 0.367633 2.603631 

Social influence 3.341 0.032 1.567 0.206 0.2829072 6.401008 

Expected performance 1.119 0.159 0.797 0.069 -0.4389227 2.676093 

Facilitating conditions 4.386 0.005 1.6 0.271 1.289449 7.481799 

constant -33.211 0.001 10.261 n/a -53.15967 -13.26207 

Joint p-value 0.008       
  

Sample size 32       

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. 

With a p-value of 0.032, any positive percentage change in social influence significantly increases the 

probability of adoption by 21%.  Johnson et al. (2017) explained that organizations, like living organisms, 

continue to adapt to their environment if they are to survive. Moreover, in the current competitive and open 

environment, organizations need to actively interact with the environment and the organizations (Daft 2016) 

and try at a minimum to remain at par with fellow organizations. In this study, some SACCOs took a leading 

role in the adoption and usage of Fintech, others adopted the technology as they felt they need not be left 

behind.  



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On the other hand, the results of the expected performance show that it is insignificant as its p-value 

was 0.159 thus greater than the maximum threshold of 0.05. The results, to some extent, were surprising 

because the efficiency brought by the usage of technology is expected to benefit the organization. This is the 

case because operational costs tend to be minimized as less effort or time is spent working on one activity. 

McKillop et al. (2020) observed that FinTech has managed to change business fortunes from loss-making to 

profit-making businesses. They noted that the positive impact of FinTech on the performance of business 

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 

investment in digital infrastructure and making sure that the digital ecosystem is robust and in human capital 

by incorporating Information Communication Technology (ICT) and financial lessons in education such 

that the citizens' expertise and knowledge in the ICT and finance are increased.  

 

CONCLUSION 
 

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 

digital ecosystem to enhance the facilitating conditions. The major limitation of the study is that it is cross-

sectional, in the future, a longitudinal study may be necessary to evaluate the evolution of the subject matter 

over time. 

 

 

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APPENDIX: QUESTIONNAIRE 

 

SECTION A: FINTECH PRODUCTS/SERVICES IN USE 

 (Please tick the relevant box according to your choice) 

Which FinTech products/services do you use?(Vasenska et al. 2021) 

i. Mpamba                    □ 

ii. Airtel-Money                   □ 

iii. Electronic Banking. i.e., Electronic Funds Transfer, Online account access  □ 

iv. Digital loan application and approval                 □ 

v. Others (please specify): …………………………………………………………………… 

vi. We do not use any                   □ 

 

SECTION B: MEASURING CONSTRUCTS 

(Please tick the relevant box according to your choice) 

Expected Effort 

1. It is easy to understand the operation of FinTech Services/products (Hassan et al. 2022) 

i.  Strongly agree □ 

ii.  Agree  □ 

iii. Neutral  □ 

iv. Disagree □ 

v. Strongly disagree □ 

 

2. The operation interface of FinTech services or products is user-friendly (Hassan et al. 2022). 

i.  Strongly agree □ 

ii.  Agree  □ 

iii. Neutral  □ 

iv. Disagree □ 

v. Strongly disagree □ 

 

3. Conducting transactions through FinTech products or Services is convenient (Hassan et al. 2022). 

i.  Strongly agree □ 

ii. Agree  □ 

iii. Neutral  □ 

iv. Disagree □ 

v. Strongly disagree □ 

 

4. There are no doubts about what is being done when using FinTech products or services (Oliveira et 

al. 2014) 

i. Strongly agree □ 

ii. Agree  □ 

iii. Neutral  □ 



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iv. Disagree □ 

v. Strongly disagree □ 

 

Social influence  

5. Our members want us to use or adopt FinTech Services/Products (Hassan et al. 2022). 

i. Strongly agree □ 

ii. Agree  □ 

iii. Neutral  □ 

iv. Disagree □ 

v. Strongly disagree □ 

 

6. Our fellow SACCOs influence us to adopt and use FinTech Services/Products (Hassan et al. 2022). 

i. Strongly agree □ 

ii. Agree  □ 

iii. Neutral  □ 

iv. Disagree □ 

v. Strongly disagree □ 

 

7. Our members prefer that we should use or adopt FinTech Services/Products to traditional banking 

methods (Hassan et al. 2022). 

i. Strongly agree □ 

ii. Agree  □ 

iii. Neutral  □ 

iv. Disagree □ 

v. Strongly disagree □ 

 

8. We find the use of FinTech products or services to be fashionable (Oliveira et al. 2014). 

i. Strongly agree □ 

ii. Agree  □ 

iii. Neutral  □ 

iv. Disagree □ 

v. Strongly disagree □ 

 

9. The use of mobile banking improves our brand and corporate image (Oliveira et al. 2014). 

i. Strongly agree □ 

ii. Agree  □ 

iii. Neutral  □ 

iv. Disagree □ 

v. Strongly disagree □ 

 



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Expected performance 

10. Usage of FinTech products/services saves us time (Hu et al. 2019) 

i. Strongly agree □ 

ii. Agree  □ 

iii. Neutral  □ 

iv. Disagree □ 

v. Strongly disagree □ 

11. Using FinTech help us meet our service needs (Hu et al. 2019). 

i. Strongly agree □ 

ii. Agree  □ 

iii. Neutral  □ 

iv. Disagree □ 

v. Strongly disagree □ 

 

12. FinTech services can improve efficiency (Hu et al. 2019) 

i. Strongly agree □ 

ii. Agree  □ 

iii. Neutral  □ 

iv. Disagree □ 

v. Strongly disagree □ 

 

13. FinTech products/services usage reduces traffic in our offices. 

i. Strongly agree □ 

ii. Agree  □ 

iii. Neutral  □ 

iv. Disagree □ 

v. Strongly disagree □ 

 

14. FinTech products/services allow us to make our payments quicker (Oliveira et al. 2014) 

i. Strongly agree □ 

ii. Agree  □ 

iii. Neutral  □ 

iv. Disagree □ 

v. Strongly disagree □ 

  



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15. Loan applications are made quicker when using FinTech products/services (Oliveira et al. 2014) 

i. Strongly agree □ 

ii. Agree  □ 

iii. Neutral  □ 

iv. Disagree □ 

v. Strongly disagree □ 

 

16. Overall, the FinTech products/services usage provides us with value for money(Yan et al. 2021) 

i. Strongly agree □ 

ii. Agree  □ 

iii. Neutral  □ 

iv. Disagree □ 

v. Strongly disagree □ 

 

Facilitating conditions  

17. Government supports and improves the use of FinTech products/services (Hu et al. 2019). 

i. Strongly agree □ 

ii. Agree  □ 

iii. Neutral  □ 

iv. Disagree □ 

v. Strongly disagree □ 

 

18. FinTech products/services are well-regulated in Malawi (Hu et al. 2019) 

i. Strongly agree □ 

ii. Agree  □ 

iii. Neutral  □ 

iv. Disagree □ 

v. Strongly disagree □ 

  



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19. Necessary resources to use FinTech Services/products exist (Hassan et al. 2022) 

i. Strongly agree □ 

ii. Agree  □ 

iii. Neutral  □ 

iv. Disagree  □ 

v. Strongly disagree □ 

 

20. FinTech Services/products are compatible with other systems that we use (Hassan et al. 2022) 

i. Strongly agree □ 

ii. Agree  □ 

iii. Neutral  □ 

iv. Disagree □ 

v. Strongly disagree □ 

21. Help is available when we get problems in using these services (Yu 2012). 

i. Strongly agree □ 

ii. Agree  □ 

iii. Neutral  □ 

iv. Disagree □ 

v. Strongly disagree □ 

 

22. We have the knowledge of how to use FinTech products/services (Oliveira et al.  2014) 

i. Strongly agree □ 

ii. Agree  □ 

iii. Neutral  □ 

iv. Disagree □ 

v. Strongly disagree □ 

 

Thanks for participating and answering these questions. 

 
 

 

 

 

 

 

 

 

 


