Mobile financial services (MFSs) are perceived to be a potential solution in addressing emerging economies’ challenges. Through MFS, benefits associated with economic financial inclusion and economic growth and opportunities could be realised. Yet, not all emerging economies fully experience these benefits, especially in sub-Saharan Africa and more specifically the bottom of the pyramid (BoP) group of individuals who are characterised as the world’s poorest consumers, living primarily in the informal sector. This study sought to examine the factors contributing to the BoP group’s behavioural intention to adopt MFS in South Africa. The study was positivist in nature, using survey-based questionnaires to garner information from BoP users on MFS adoption. Data were tested for validity and analysed by using structured equation modelling to assess the hypotheses. The findings show that habit, performance expectancy and uncertainty avoidance are strong predictors of behavioural intent. These findings can have significant implications on how MFS service providers operate in the South African context and can be used by government to drive technology as a basis for financial inclusion.
Mobile financial services (MFSs) describe a set of mobile-based financial tasks consumers can execute or access by using devices such as cell phones or tablets (Dass & Pal
Financial inclusion of the unbanked is generally a well-researched area, partly because of the significant uptake of mobile banking in developing countries. For example, it is estimated that the number of bank accounts is significantly lower than the instance of mobile money accounts across nine African countries thus offering otherwise underbanked or unbanked customers a payment solution (Lowry
The BoP grouping is a socio-economic concept that groups the vast segment of the world’s poorest consumers, who live primarily in the informal sector (Prahalad & Hart
Mobile financial services refer to mobile-based financial services provided through devices such as smartphones or tablets to the unbanked or underbanked to perform finance-related functions (Bourreau & Valletti
Adoption of technology has traditionally been studied by using one or a combination of the innovation diffusion theory (Rogers
Social influence refers to the social pressure to use a technology. Given that SI can be seen to be meaningfully and positively correlated with intention to use m-payments in South Africa (Killian & Kabanda
Hedonic motivation is a measure of the pleasure resulting from the use of technology (Brown & Venkatesh
Habitual usage or habit reproduces the various outcomes of past experiences (Venkatesh et al.
Perceived risk (PR) is understood to be a consumer’s individual expectation of suffering a loss in pursuit of a desired outcome (Lee, Warkentin & Johnston
Khan, Hameed and Khan (
Hofstede (
Hypothesis development.
UTAUT2 constructs | Hypothesis development | Source of questionnaire |
---|---|---|
H1: Performance expectancy (PE) | Behavioural intention to use m-payments is positively influenced by PE. | Venkatesh et al. ( |
H2: Effort expectancy (EE) | Behavioural intention to use m-payments is positively influenced by effort expectancy. | |
H3: Social influence (SI) | Behavioural intention to use m-payments is positively influenced by social influence. | |
H4: Facilitating conditions (FC) | a) Behavioural intention to use m-payments is positively influenced by facilitating conditions. | |
b) Use of m-payments is positively influenced by facilitating conditions. | ||
H5: Hedonic motivation (HM) | Use of m-payments is positively influenced by hedonic motivation. | |
H6: Price value (PV) | Use of m-payments is positively influenced by PV. | |
H7: Habit (HA) | a) Habit has a positive effect on the behavioural intention to use m-payments. | |
b) Habit has a positive effect on usage behaviour of m-payments. | ||
H8: Behavioural intention (BI) and use behaviour (UB) | Behavioural intent has a positive effect on use behaviour of m-payments. | |
H9: Perceived risk | a) Behavioural intention to use m-payments is negatively influenced by perceived risk. | Forsythe et al. ( |
b) Use of m-payments is negatively influenced by perceived risk. | ||
H10: Trust | a) Behavioural intention to use m-payments is positively influenced by trust. | Forsythe et al. ( |
b) Use of m-payments is positively influenced by trust. | ||
H11: Individualism/Collectivism | Individualism/Collectivism (I/C) moderates between usage behaviour and behavioural intention such that the relationship strength is greater in the presence of collectivist values. | Srite and Karahanna, ( |
H12: Uncertainty avoidance | Uncertainty avoidance (UA) moderates between usage behaviour and behavioural intention such that the relationship strength is weaker in the presence of collectivist values. | Hofstede ( |
This study was driven by a positivism stance and used the hypotheses constructed in
Researchers in the developing financial technologies landscape have noted that the customers who are most likely to download and use m-payments are millennials, SMMEs and the underbanked. These three segments are particularly sensitive to costs and to the improved consumer experience that the MFS delivery and distribution afford them (Dietz et al.
To gain a statistical significant sample at a 95% confidence level, a target of 300 people equitably distributed across the 3 mentioned segments was set (Wagner & Shimshak
Sampling was conducted randomly, with the first question of the survey testing whether the participant has access to a smartphone device. This eliminated the need for a non-probability sampling approach as this will regulate eligible respondents and yield a stratified sample. The questionnaire that was applied for the survey questions was based on the hypothesis development of
Prior to data collection, permission to conduct the study was requested from the University’s ethics committee. Once permission was granted, the objectives, purpose and motivation for this research were explained to all participants, as it was indicated on the cover letter and any questions that they had regarding confidentiality were addressed. Participants were informed prior to commencing with the questionnaire that their participation was voluntary, and they can at any point decide to withdraw their participation for this research. The voluntary nature of the study was also explained to the participants. Respondents were not obliged to complete the entire process if any level of uneasiness arose. Respondents’ identities were strictly confidential and remained anonymous throughout the study.
The research instrument made use of a Likert Scale of 5 (strongly agree) to 1 (strongly disagree) linked to the identified hypotheses. Demographic factors of age, race, gender, education and access to smartphones were included. Data collection commenced in June 2018 and was completed in September 2018. All data were collected by using a hardcopy survey because of the consciousness that the respondents are from BoP and are not able to easily access the Internet.
Prior to the survey being conducted, respondents were given a short demonstration of the most popular mobile payments app in South Africa. Respondents were shown the mobile payments app’s UI (user interface) and a UX (use experience) demonstration of its functionality. The most inexpensive NFC-enabled smartphone currently found in the South African market was used for the demonstration. This demonstrated the minimum system requirements for downloading and using a mobile payments app. This system demonstration helped to inform the respondent of the usability and ease of use of the mobile payments app being used in the survey.
Two field agents who were former recruitment agents for WIZZIT online bank were contracted to perform the field work. A total of 316 respondents were approached, and the final valid set after exclusions was 311. The data gathered were analysed by using Statistica for the descriptive statistics and to determine the skewness and kurtosis scores. Factor and reliability analyses, as well as a correlation analysis, were conducted. Smart PLS 3 was used for structured equation modelling and factor analysis.
Discriminant validity and internal reliability were employed to verify the measurement model (Chin
Internal and External Consistency and Validity Testing.
Constructs | Cronbach’s Alpha | rho_A | Compositive Reliability | Average Variance Extracte |
---|---|---|---|---|
Behavioural Intention | 0.849 | 0.862 | 0.856 | 0.665 |
Effort Expectancy | 0.915 | 0.920 | 0.917 | 0.735 |
Facilitating conditions | 0.884 | 0.910 | 0.890 | 0.675 |
Habit | 0.779 | 0.834 | 0.753 | 0.457 |
Hedonic Motivation | 0.948 | 0.948 | 0.948 | 0.859 |
Individualism/Collectivism | 0.853 | 0.892 | 0.869 | 0.632 |
Perceived Risk | 0.946 | 0.946 | 0.946 | 0.854 |
Performance Expectancy | 0.941 | 0.942 | 0.941 | 0.801 |
Price Value | 0.962 | 0.965 | 0.962 | 0.895 |
Social influence | 0.963 | 0.963 | 0.963 | 0.898 |
Trust | 0.942 | 0.943 | 0.942 | 0.804 |
Uncertainty Avoidance | 0.586 | 0.745 | 0.625 | 0.337 |
Use Behaviour(UB) | 0.892 | 0.905 | 0.892 | 0.678 |
A key part of statistical analysis is to explain both the location and variability of a data set using normal distribution as a base. This explanation is best done by referring to the skewness and kurtosis of the data set. Skewness measures the symmetry or lack thereof of the distribution of the data. Kurtosis on the other hand measures whether the data are either heavy-tailed (having many outliers) or light-tailed (lacking outliers) relative to a normal distribution. A uniform distribution tends to be a rare occurrence (Heckert et al.
Skewness and kurtosis analyses.
Constructs | Kurtosis | Skewness |
---|---|---|
Performance expectancy | 0.44049322 | −0.770435685 |
Effort expectancy | 0.36242027 | −0.531337904 |
Social influence | −0.128381048 | −0.516287569 |
Facilitating conditions | 1.880697516 | −0.766265445 |
Price value | 1.043190996 | −0.866662052 |
Hedonic motivation | 0.30219461 | −0.75319896 |
Habit | −0.123082734 | −0.029468421 |
Behavioural intention | 1.868081275 | −1.042257597 |
Trust | 0.727748647 | −0.331415432 |
Perceived risk | 1.943578166 | −0.745215774 |
Individualism/collectivism | 1.884126667 | −1.072256357 |
Uncertainty/avoidance | 0.338636387 | 0.075937542 |
The structural equation model was developed to identify the associations between the constructs in the research model (Hair et al.
Structural equation modelling.
Correlation analysis.
Constructs | Latent Variable Correlations |
||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Bl | EE | FC | HAB | HM | l/C | PR | PE | PV | SI | Trust | U/A | UB | |
Behavioral Intention (Bl) | 1.000 | 0.675 | 0.546 | 0.585 | 0.332 | 0.430 | 0.571 | 0.699 | 0.197 | 0.507 | 0.549 | 0.472 | 1.088 |
Effort Expectancy | 0.675 | 1.000 | 0.629 | 0.407 | 0.271 | 0.471 | 0.498 | 0.808 | 0.192 | 0.512 | 0.488 | 0.339 | 0.638 |
Facilitating conditions | 0.546 | 0.629 | 1.000 | 0.451 | 0.265 | 0.320 | 0.407 | 0.597 | 0.279 | 0.387 | 0.345 | 0.220 | 0.534 |
Habit | 0.585 | 0.407 | 0.451 | 1.000 | 0.678 | 0.235 | 0.405 | 0.450 | 0.284 | 0.239 | 0.364 | 0.139 | 0.600 |
Hedonic Motivation | 0.332 | 0.271 | 0.265 | 0.678 | 1.000 | 0.115 | 0.227 | 0.261 | 0.269 | 0.113 | 0.277 | 0.165 | 0.356 |
Individualism/Collectivism | 0.430 | 0.471 | 0.320 | 0.235 | 0.115 | 1.000 | 0.334 | 0.420 | 0.152 | 0.482 | 0.331 | 0.449 | 0.373 |
Perceived Risk | 0.571 | 0.498 | 0.407 | 0.405 | 0.227 | 0.334 | 1.000 | 0.478 | 0.179 | 0.340 | 0.768 | 0.237 | 0.551 |
Performance Expectancy | 0.699 | 0.808 | 0.597 | 0.450 | 0.261 | 0.420 | 0.478 | 1.000 | 0.242 | 0.538 | 0.419 | 0.358 | 0.688 |
Price Value | 0.197 | 0.192 | 0.279 | 0.284 | 0.269 | 0.152 | 0.179 | 0.242 | 1.000 | 0.126 | 0.214 | 0.059 | 0.173 |
Social influence | 0.507 | 0.512 | 0.387 | 0.239 | 0.113 | 0.482 | 0.340 | 0.538 | 0.126 | 1.000 | 0.325 | 0.312 | 0.461 |
Trust | 0.549 | 0.488 | 0.345 | 0.364 | 0.277 | 0.331 | 0.768 | 0.419 | 0.214 | 0.325 | 1.000 | 0.198 | 0.523 |
Uncertainty Avoidance | 0.472 | 0.339 | 0.220 | 0.139 | 0.165 | 0.449 | 0.237 | 0.358 | 0.059 | 0.312 | 0.198 | 1.000 | 0.439 |
Use Behaviour (UB) | 1.088 | 0.638 | 0.534 | 0.600 | 0.356 | 0.373 | 0.551 | 0.688 | 0.173 | 0.461 | 0.523 | 0.439 | 1.000 |
All ethical conditions were met and approved by the Commerce Faculty Ethics in Research Committee. This was cleared using 2018_ MKHCLE003 as reference number for the study. The objectives, purpose and motivation for this research was explained to all participants as it was indicated on the cover letter and any questions that they had regarding confidentiality were addressed. They were informed prior to commencing with the questionnaire that their participation was voluntary, and they can at any point decide to withdraw their participation for this research.
The survey was conducted in three South African townships over a period of 3 months between June 2018 and September 2018. The results show that 65% of the total sample identified themselves as male, whilst the remaining 35% identified themselves as female. Four age brackets were used: 16–24 at 20%; 25–34 at 41%; 35–50 at 36%; and lastly 50 and over at 3%, in categorising the sample’s age distribution. More than half of the sample (56%) classified themselves as working and 48% earning R5000 or more. The majority of the study’s sample had some level of education, the largest group of which being ‘technical artisan’ at 43%, followed by ‘some high school or matriculated’ at 27%, ‘college, university or post matric’ at 23%, ‘no formal education’ at 4% and ‘primary school’ at 3%.
South Africa displays a relatively high percentage of households with bank accounts, estimated to be between 51% and 80% (Chigada & Hirschfelder
The study tested the relationship between BI and cultural dimensions of UA and I/C, as well as PR and trust on the willingness to adopt and use MFS within the BoP group. The Bootstrap method was used to perform the
Hypothesis testing.
Hypothesis | Path Coefficients | Coefficients | STDEV | T Statis | Comments | |
---|---|---|---|---|---|---|
H8 | Behavioral Intention (Bl) → Use Behaviour (UB) | 1.156 | 0.047 | 24.846 | 0.000 | |
H2 | Effort Expectancy → Behavioral Intention (Bl) | 0.136 | 0.104 | 1.299 | 0.194 | |
H4a | Facilitating conditions → Behavioral Intention (Bl) | 0.047 | 0.088 | 0.536 | 0.592 | Rejected |
H4b | Facilitating conditions → Use Behaviour (UB) | −0.079 | 0.033 | 2.429 | 0.015 | Rejected |
H7a | Habit → Behavioral Intention (Bl) | 0.377 | 0.106 | 3.561 | 0.000 | |
H7b | Habit → Use Behaviour (UB) | −0.041 | 0.037 | 1.094 | 0.274 | Rejected |
H5 | Hedonic Motivation → Behavioral Intention (Bl) | −0.127 | 0.085 | 1.490 | 0.137 | |
H11 | Individualism/Collectivism → Behavioral Intention (Bl) | −0.044 | 0.063 | 0.687 | 0.492 | Inconclusive |
H9 | Perceived Risk → Behavioral Intention (Bl) | 0.051 | 0.089 | 0.578 | 0.564 | Rejected |
H1 | Performance Expectancy Behavioral Intention (Bl) | 0.206 | 0.087 | 2.364 | 0.018 | |
H6 | Price Value → Behavioral Intention (Bl) | −0.033 | 0.046 | 0.715 | 0.475 | Rejected |
H3 | Social influence → Behavioral Intention (Bl) | 0.105 | 0.057 | 1.851 | 0.065 | |
H10 | Trust → Behavioral Intention (Bl) | 0.176 | 0.083 | 2.127 | 0.034 | |
H12 | Uncertainty Avoidance → Behavioral Intention (Bl) | 0.252 | 0.076 | 3.298 | 0.001 |
Despite the assumption from previous literature, this study found that SI did not have a significant impact of intention to adopt MFS. The results show a slight significance with BI (β = 0.105). The same findings are reported regarding FCs – with low-income earners placing minimum emphasis on the significance of FCs on BI with β = 0.047. This leads to the rejection of hypothesis H4. Other hypotheses not supported were PV (H6, with β = 0.033) and PR (H9 with β = 0.051). The implications of these findings are that both price and PR did not have any significant impact on BI. Finally, the relationship between the cultural factor of I/C and BIs to adopt MFS was inconclusive. Hypothesis 11 is therefore neither rejected nor accepted.
This study sought to examine the factors contributing to the BoP group’s BI to adopt MFS in South Africa. Factors perceived to influence adoption of MFS amongst BoP group include performance and EE, habitual usage, trust and UA. Further, if BoP consumers perceive enjoyment in using MFS they could potentially adopt MFS although this was not a significant factor.
Empirical findings of factors influencing bottom of the pyramid’s adoption of mobile financial services in South African townships.
Many individuals in South African BoP tend to be dependents because of their low economic and literacy levels and other macroeconomic factors such as high rates of unemployment, male labour migration and premature death brought on by acquired immune deficiency syndrome (AIDS; Schatz, Madhavan & Williams
Factors perceived not to impact adoption of MFS were SI, FCs, hedonic value, PR and price value. The BoP group did not perceive being pressured to adopt MFS by their peers or significant other, and the availability of FCs to adopt MFS does not seem to influence their decision to adopt MFS. These findings echo prior studies in similar contexts in South Africa such as San Martín and Herrero (
Bottom of pyramid consumers in this context did not see PV as having an impact on their intentions to adopt MFS. This observation can be explained by the fact that ‘most mobile users in this country are on prepaid plans, and … users have developed various strategies to optimise data and data costs, including disconnecting themselves regularly from the mobile Internet’ (Mathur, Schlotfeldt & Chetty
This study’s main aim was to understand the factors contributing to the BoP group’s BI to adopt MFS in South Africa. Factors perceived to influence adoption of MFS amongst the BoP group include performance and EE, habitual usage, trust, UA and perceived enjoyment. An interesting finding in this study is that the BoP group is very optimistic with regard to how they respond to uncertainty. They have low UA implying that despite the lack of favourable conditions for MFS adoption, the BoP group did not consider these as significant to influence their intention not to adopt MFS. This observation and the fact that most of these findings resonate with prior studies are an indication that despite the low socio-economic background of the BoP group they are ready to partake in the adoption and use of MFSs. Mobile financial institutions as well as policy interventions from the public sector are therefore encouraged to expand their provision of services to include customers at the BoP.
This study was focussed on the low-income earners’ perspective in the South African context. Therefore, the inferences on culture could be different from other countries as well as the large population of immigrants and asylum seekers. In addition, the demographic sample was not fully inclusive in terms of people from other races and geographical locations in South Africa who may be low-income earners, as the field work was conducted in historical black townships.
Much research has been conducted in the m-payments and mobile banking space in the past decade. Newer studies could focus on the adoption of MFSs with the expressed intention of solving the issues around the financial inclusion of low-income individuals. A study on the technology readiness of low-income individuals for mobile-driven investments, innovations such as applying the power of the blockchain on traditional community savings schemes and studies on further financial products such as micro-insurance focussed on BoP consumers would add value to the body of knowledge.
The authors declare that they have no financial or personal relationships, that may have inappropriately influenced them in writing this research article.
C.M., A.B. and S.K. contributed equally to this research article.
This research received no special grant from any funding agency in the public, commercial or not-for-profit sectors.
Data available from the corresponding author, upon request.
The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any affiliated agency of the authors.