AN EMPIRICAL ASSESSMENT OF PROBABILITY RATES FOR FINANCIAL TECHNOLOGY ADOPTION AMONG AFRICAN ECONOMIES: A MULTIPLE LOGISTIC REGRESSION APPROACH

Tochukwu Timothy Okoli1+--- Devi Datt Tewari2

1Postgraduate Student, Faculty of Commerce, Administration and Law, Department of Economics, University of Zululand, South Africa.
2Professor of Financial Economics, Faculty of Commerce, Administration and Law, Department of Economics, University of Zululand, South Africa.

ABSTRACT

The extent of financial exclusion in Africa drives the adoption of fintech across the continent, but the disruption it can cause hinders progress. This study therefore assesses both the probability and actual rates of fintech adoption in 32 African economies between 2002 and 2018. Based on the information spill-over and rank theories, multiple logistic regression analysis revealed that the average probability of fintech adoption for all, emerging and frontier African economies to be 50.9%, 83.1%, and 23.1%, respectively, whereas the actual rates are 27%, 40%, and 29%, respectively. The fragile economies, however, had no reasonable probability or actual rates of fintech adoption. Further, odds ratios of 1 or more- suggest a one-unit change in the predicators will exert no impact on these rates. Thus, it is concluded that emerging economies and mobile phone banking drive fintech adoption in Africa, and is largely dependent mainly on structural changes rather than economic and financial factors. The current study consequently recommends improved literacy, ICT training, and structural changes to promote fintech across the continent.

Keywords:Africa, Financial technology, Internet banking, Mobile phone banking, Multiple logistic regression.

JEL Classification: O55; G00; C24; A11.

ARTICLE HISTORY: Received:22 June 2020, Revised:20 August 2020, Accepted:9 November 2020, Published:24 November 2020

Contribution/ Originality: This study is one of very few studies that empirically investigate the probability and actual rate of fintech adoption in African economies. The findings reveal that the quality of human capital and the dissemination of information, particularly in emerging economies are the major driving force behind the adoption of fintech in Africa.

1. INTRODUCTION

Financial technology (fintech hereafter) is an emerging field in the world of finance, combining both financial models and information technology to extend financial services to the general public faster and at a lower cost (Arner, Barberis, & Buckley, 2015). Due to this unique quality, its adoption is inevitable especially in regions with high rates of financial exclusion such as Africa; however, its disruptive impact on conventional business etiquette, especially in the banking sector, raises both prospects and problems in Africa, hindering progress (Jugurnath, Bissessur, Ramtohul, & Mootooganagen, 2018). As a result, some African economies doubt its future potential and reliability (Ernest & Young, 2017)- and prefer to pursue raditional means of delivering and accessing financial services. Nevertheless, increasing global commercialization alongside customers’ ever-growing demands on banks to meet their needs means the adoption of fintech is inevitable. This is evident in the rate of fintech adoption in the recent years globally from 16% in 2015 to 33% in 2017 (Ernest & Young, 2017) with a predicted rise to over 70% in the near future. In fact, of particular relevance to Africa, South Africa’s adoption beyond the 33% global average at 35% was probably due to the extensive financial exclusion across the continent (Mihasonirina & Kpodar, 2012). It is essential to examine the rate and determining factors of fintech adoption in Africa which has previously been neglected by researchers.

Moreover, the variation in adoption among different economies suggests that the process could be country’s or region’s specific, implying that fintech will thrive in some areas but fail in others. The success of the mobile phone money transfer service, M-Pesa, in Kenya and Tanzania but not South Africa is an example (Alexander, Shi, & Solomon, 2017). Therefore, this study aims to identify not only the aforementioned rates and factors in Africa, but also the various economic groups across the continent to be able to predict future fintech adoption rates according to the unique attributes of different areas.

2. LITERATURE REVIEW

As a recent innovation in finance, there are few empirical studies of fintech. Most of those are limited in terms of scope and/or measurement tools. This section presents recent studies in this area such as Khatimah and Halim (2016). They adopted the theory of planned behavior, assessed the factors influencing the adoption of e-money in Indonesia, and found social influences could positively impact users’ intentions. Their findings corroborated those of Abdulkadir, Galoji, and Razak (2013), who reported that not only social influences such as peer group pressures, but also perceived usefulness can greatly affect the adoption of mobile banking. Meanwhile, Oliveira and Martins (2011) employed the diffusion of innovation theory when assessing an organization’s adoption of information and communication (ICT). They demonstrated individual external and internal characteristics of organizational structure were important factors influencing innovativeness. This suggests that a firm’s or country’s unique attributes are major determinants in the decision on adoption.

Khalifa (2016) supported this conclusion in their assertion that a firm’s absorptive capacity, structural features, information spill-over characteristics and environmental factors were key to Tunisian firms adopting ICT. However, this comprehensive study suffers two major drawbacks; first, it is based on a single country’s survey and ICT in general, meaning it cannot be used to generalize about the adoption of fintech among African economies; second, despite examining robust determinants, the rate of was not explored through a binary discrete choice. This current study attempts to address these problems by investigating the rates of both the probability of and actual adoption of fintech in a broad representative sample of 32 African economies. Furthermore, from an international perspective, Glass and Saggi (2002) believed that the transfer/adoption of technology can be diffused through various channels; meaning that more than the usual determinants of fintech acceptance exists.

In Africa, the rate at which the use of mobile phones has spread and been exchanged for smartphones capable of financial transactions is extraordinary, bridging the digital-divide and enhancing financial inclusion in developing countries. According to Gough and Grezo (2005), the average penetration rate for mobile phones in Africa was 6.2%, with more recent studies reporting a higher rate (Ernest & Young, 2017).

According to a study using binary logistic regression Jugurnath et al. (2018) found that marital status and occupational group played a major role in the use of mobile banking in Mauritius; those from higher socioeconomic classes, married, and with no children were more likely to use mobile banking than those lower down the socioeconomic ladder (Jugurnath et al., 2018). These findings confirmed those of Kweyu and Ngare (2014) in Kenya and Fall, Ky, and Birba (2015) in Senegal, where personal income was very highly significant in the use of mobile banking. Likewise, Bhatt and Bhatt (2016) asserted that high-income earners who were married were more likely to use mobile banking. As this further suggests that economic and social attributes determine the adoption of new innovations, this current study will test the hypothesis the adoption of fintech in Africa depends on psychological, demographic and socioeconomic factors more than financial indicator variables.

3. THEORETICAL MODELS

The theoretical model is based on two distinct theories: information spill over and rank. The former theory states that as information about an innovation or new technology spreads (spills over) from users to non-users, the rate of adoption increases (Mansfield, 1961). Hollenstein (2004) and Battisti, Canepa, and Stoneman (2009) assert that information spillover is currently the principal driver of adoption in both developed and developing countries; the more frequently new and existing users come into contact and find out about new technology, the greater the number who will adopt it. This assertion implies that the adoption of new technology is directly correlated with the previous (lagged) level of adoption within a given social group; hence adoption is a function of previous users’ experience. In this study, information spill-over model can be presented as Equation 1, taking the lag of previous users (fintech)-the dependent variable-in a first order autoregressive (AR 1) model.

Fintech Adoptiont  =  f (Fintech Adoptiont-1)                                                                                           (1)

Where:  Fintech Adoptiont represents a vector of fintech adoption in the current period.

               Fintech Adoptiont-1 represents a vector of fintech adoption in the previous period.

Equation 1 reveals that the level of adoption of fintech in the current period depends on previous levels of adoption or information spillover.

The second, rank theory states that a firm’s/country’s specific attributes or heterogeneities determine its adoption levels of technology. These attributes include psychological, demographic, and socioeconomic factors: the quality of human capital/literacy rate, population growth rate, extent of its financial openness, among others. This suggests that factors other than macroeconomic ones could be important drivers of fintech in Africa. Thus, as the quality of a country’s human capital improves through educational achievement, potential users will perceive the usefulness of modern devices more easily and be more likely to adopt them for accessing financial services. Likewise, a rapidly growing population in countries with a high level of financial openness is more likely to lead to adopting fintech than in those with a declining population and strict financial repression. This model is therefore, expressed as:

Fintech Adoption t = f (TSEt, POPGt, FOt)                                                                                                        (2)

Where:  Fintech Adoptiont represents a vector of fintech adoption in the current period.

TSEt represents tertiary school enrolment in the current period (a measure for literacy rate).

POPGt represents population growth rate in the current period.

FOt represents financial openness in the current period.

Equation 2 models the heterogeneities that affects the adoption of fintech in a particular country, which is expressed as a function of the quality of human capital/literacy rate, population growth and financial openness in the current year.

Although other theories such as the technology acceptance model (TAM: Davis, Bagozzi, and Warshaw (1989) and the unified theory of acceptance and use of technology (UTAUT: Venkatesh, Morris, Davis, and Davis (2003) are widely used, information spill over and rank models are the dominant theories for explaining the adoption of new technologies (Canepa & Stoneman, 2004). Moreover, these theories are consistent with the objective to investigate binary discrete choice based on socioeconomic drivers for adopting new innovations (Mercer, 2004). Consequently, both theories were combined to form a single unique model to investigate this relationship.

3.1. Model Specification

The two models expressed in Equations 1 and 2 were merged to re-express a final model on Equation 3; however, due to the data on financial openness not being available for all countries, as well as for simplicity, the FO predicator was removed. The final model is expressed as:

Fintech Adoption t = f (Fintecht-1, TSEt, POPGt)                                                                               (3)

This empirical logistic model estimated in its econometric form, expressed as:

Transforming the binary model in Equation 5 means the probability of African economies adopting fintech can be calculated:

Equation 6 is used to solve  for each of the fintech proxy measures- internet banking (INTB), mobile phone banking (MPB), and automated teller machines (ATM) - for all the economic groups - emerging, frontier and fragile African economies. Moreover, the odds ratios were reported to verify whether a one unit increase in the average value of the explanatory (independent) variables affected the level of fintech adoption. The assumption is that – if the confidence intervals of the odds ratio crosses 1, then the explanatory variables do not affect the level of adoption.

3.2. Methodology and Data

A multiple logistic regression (MLR) analysis was conducted to predict the logit of fintech adoption (the event outcome) from the set of predictors. The logit - the natural logarithm of the odds (probability/[1 - probability]) was then transformed into a measure of probability with which to validate that high probability is associated with a high level of adoption, and vice versa, using the actual outcome variables as specified in Equation 3. This model can be estimated as follows:

Taking the anti-log of each side of Equation 7 and the model can be transformed to calculate the probability of African economies adopting fintech.

Equations (7) and (8) are the closed-form models of Equations 5 and 6, respectively, using the predictors selected for investigation.

In the MLR analysis, the outcome variable, “(Y)”, is a binary/dichotomous variable; taking value of 1 (if fintech is adopted and 0 (zero) otherwise. The probability of cases in which Y = 1 is defined as π = P(Y=1), with Y = 0 as

1 - π = P(Y = 0), hence, based on theory and empirical reviews, the set of predictors, relate to and determine Y. In addition, a comparative probability assessment was conducted for the three economic groups used in this study (see Appendix A, Table A.1). Furthermore, time periods selected for the study were consolidated by a principal component analytical (PCA), conducted to generate fintech indices for the three proxy measures that showed the negative values before 2008 became positive afterwards for most countries, especially those with emerging economies (see Appendix B. Tables B.1 and B.2).

The three proxy measures of fintech were the automated teller machines (ATM), also used by Nina (2007) the number of mobile phone subscriptions as a proxy for mobile phone banking (MPB), previously used by Gough and Grezo (2005) as a proxy for mobile banking (Jugurnath et al. (2018); and the number of individuals using internet banking (INTB). Therefore, the current study treats the concepts of fintech in a limited sense. This study is a panel analysis of 32 African economies over the period 2002 - 20181 , disaggregated into 3 emerging, 24 frontier and 5 fragile economies based on the Financial Times Stock Exchange FTSE (2017) classification of countries, which takes into account the heterogeneity of these economic groups. Finally, the data were extracted from the online World Bank Data Base (2018) and the International Financial Statistics Database (2018).

4. RESULTS AND DISCUSSION

The results of the analyses are presented in two parts in the section. The first subsection discusses the descriptive analysis that ascertained the rate at which the economic groups adopted fintech, while the second subsection explores the empirical results from the MLR analysis.

4.1. Descriptive Statistics for Fintech Adoption Rate

The summary of the descriptive statistics are shown in Table 1. The overall weighted average rate for all African economies adopting fintech is approximately 27%, below the global average of 33% (Ernest & Young, 2017), but that for emerging African economies is above it at 40%; however, frontier and fragile African economies show averages only 29% and 12%, respectively. These results suggest that the rate of adopting fintech in Africa is undermined by fragile economies; thus, further analysis using advanced estimation methods are needed.

These results further reveal that, with an average of 57%, mobile phone banking is the most used fintech by all economic groups, which implies that about 71% of the total African population are using this fintech. Moreover, with a 40% rate of fintech adoption, emerging African economies constitute about 49% of total African population using mobile phone banking. This corroborates the findings of Ernest and Young (2017) that around 46% of fintech adoption occurs in emerging economies. Such success could be attributed to high income levels, greater financial development and openness, high quality human capital, as well as significant funding in research and development compared with other African economies.

Table-1. Percentage adoption rate of fintech among African economies
Economies
ATM
Internet Banking
Mobile Phone Banking
Average
Percentage of Total
Emerging
23.85
25
70.7
40
49 units
Frontier
6.56
12.1
68.1
29
36 units
Fragile
1.33
2
32.22
12
15 units
All
10.58
13.03
57.01
27
Percentage
13
16
71
100 units

The results shown in Table 1 are further represented on a bar chart in Figure 1, which provides more comprehensive view of the average rate of fintech adoption across the different economic groups. It is evident once more that mobile phone banking is the fastest growing therefore strongest driver for fintech in Africa. It can also be seen that emerging economies are experiencing the highest growth rate for all three proxy measures, although followed closely by frontier economies. On average, the rates of adoption in Africa measured by ATM, INTB and MPB are 10.58%, 13.03%, and 57.01%, respectively.

Figure-1. Average percentage change in fintech adoption among African economies.

4.2. Multiple Logistic Regression Results

It should be recalled that the dependent variable, - Fintech, in Equation 3 is a vector of three unknown proxies, ATM, INTB, and MPB, which the probability of adoption in all 32 African economies was calculated using Equation 8. This calculation involved inserting the coefficient estimates (β’s) of the significant variables and evaluating the resultant probabilities against the average values shown in Table 1. The MLR results are presented in Tables 2, 3 and 4.

4.2.1. Multiple Logistic Regression Results for African Economies

The results for all 32 African economies shown in Table 2, reveal that the major drivers for adopting fintech were information spillover and the literacy rate of potential adopters. This implies that the more users interact and discuss their experiences with non-users, the higher the rate of adoption, while the quality of human capital also positively drives fintech; this is called it perception of ease-of-use in the TAM (Davis et al. (1989). This finding explains why the population growth exerts no significant impact, although there is a simultaneous increase in the quality of human capital. Thus, despite Africa’s large population, the adoption rate is still very low (Muzari, Gatsi, & Muvhunzi, 2012).

Table 2 also demonstrates that an odds ratio of 1 represents a one-unit increase in that particular predictor that will not significantly change the probability of fintech adoption if the confidence interval crosses 1 or the odds ratio includes a whole number, then a one-unit increase in the explanatory variable will exert no effect on the independent variable; however, an odds ratio of 0 or less than one means that a one-unit change in the predictor will change the probability of adoption. As can be seen, except for population growth rate, the odds ratios of the predictors were greater than one and thus cannot significantly change the probability of fintech adoption.

Table-2. Multiple logistic regression result and estimated probability of fintech adoption among African Economies (N=32).
 
Adoption of ATM
Adoption of INTB
Adoption of MPB
Predictors
Coefficient
Odds Ratio
Coefficient
Odds Ratio
Coefficient
Odds Ratio
Information Spillover
5.378
216.480
3.830
46.058
0.0000013
1.000
 
(8.910)***
(8.910)***
(8.76)***
(8.760)***
(5.15)***
(5.15)***
Population Growth (POPG)
6.414
610.242
0.771
2.161
-0.397
0.672
 
(1.81)
(1.810)
(0.300)
(0.300)
(0.200)
(0.200)
Literacy Rate (TSE)
1.999
7.379
0.554
1.741
0.973
2.646
 
(4.820)***
(4.820)***
(1.61)
(1.610)
(3.90)***
(3.900)***
constant
-75.531
7.85e-35
-51.700
3.52e-23
-26.947
1.98e-12
 
(5.930)***
(5.930)***
(6.69)***
(6.690)***
(6.69)***
(6.690)***
Observation
425
425
425
No in Group
32
32
32
LR (Chi Squared)
89.470***
87.170***
65.630***
Eq. 3.7 (sig variables only)
0.576
-2.102
1.257
Probability of Adoption
(Eq 3.8)
0.6402 or 64.02%
0.1090 or   10.9%
0.779 or 77.9%
Note: Absolute value of z-statistics in parentheses *** significant at 1%; ** significant at 5%;  LR=Long-run.

Finally, the estimated probability of fintech adoption in Africa comprises 64.02%, 10.9%, and 77.85% for ATM, INTB and MPB, respectively; meaning that mobile phone banking and ATMs are more likely to be used than internet banking. Specifically, the current population of Africa totals about 1.3billion (United Nations Organisation, 2019) 832,260,000 (0.6402*1.3billion) people will use ATMs, 141,700,000 internet banking, and 1,012,050,000 mobile phone banking.

Furthermore, with reference to Table 1, since 49% of fintech users in Africa reside in emerging, 36% in frontier, and 15% in fragile economies, of those 1,012,050,000 mobile banking users, 495,904,500 come from emerging economies, 364,338,000 frontier and 151,807,500 from fragile economies. Therefore, it can be concluded that African economies will adopt ATM and MPB but probably not internet banking in the near future, which can now be compared with the rates of fintech adoption in emerging and frontier economies, identifying in which fintech adoption in more probable.

4.2.2. Multiple Logistic Regression Results for Emerging African Economies

The current study studied the hypothesis that emerging economies drive fintech adoption and promote financial integration in Africa by investigating the probability of adoption in Egypt, Morocco, and South Africa. Emerging economies are defined as having established financial system, a relatively knowledgeable population or work force, and a high inflow of foreign direct investment (Latif et al., 2018) which previous studies (Ernest & Young, 2017) have confirmed by demostrating a higher rate of adoption than the 33% global average.

The MLR results for the three emerging economies presented in Table 3 are consistent with those for all the African economies, except that the probability of fintech adoption is reasonably high for all proxy measures – 79.81%, 73.49%, and 95.89% for ATM, INTB and MPB, respectively; thus it appears that Egypt, Morocco, and South Africa are the major driving force behind fintech on the Africa economies. In particular, information spillover  and the literacy level of potential adopters were once more the major drivers of fintech adoption, however, the odds ratios reveal that a one-unit increase in these predictors will again not significantly affect the probability of adoption decision. In contrast, while the population growth rate does not necessarily exert a positive impact on fintech adoption, an odds ratio less than 1 implies that a one-unit change in this predictor can improve the probability of adoption.

Furthermore, the high probability rate among the emerging economies reveals that they have a very high propensity for adoption than the other economies under consideration. The analysis further strengthens the earlier assertion that the information spill over and quality of human capital had positive impact across the three models, whereas population growth rate does not.

Table-3. Multiple logistic regression result and estimated probability of fintech adoption among emerging African economies (N=3).

 
Adoption of ATM
Adoption of INTB
Adoption of MPB
Particulars
Coefficient
Odds Ratio
Coefficient
Odds Ratio
Coefficient
Odds Ratio
Information Spillover
0.1319***
1.2482**
0.087***
2.3428**
0.00000019***
1.000**
Population Growth (POPG)
-146.5
0**
-1.1989***
0**
0.601***
0**
Literacy Rate (TSE)
1.835***
2.0155***
0.011***
1.9310
1.023***
5.873**
Constant
-45.718***
0.1930**
2.042
3.4152**
-31.386***
0.121**
Observations
37
37
37
No in Group
3
3
3
LR (Chi Squared)
25.35**
25.35***
25.35**
Eq. 3.7 (sig variables only)
1.375
1.020
3.149
Probability of Adoption (Eq 3.8)
0.7981 or 79.81%
0.7349 or 73.49%
0.9589 or 95.89%

Note: *** significant at 1%; ** significant at 5%;  LR= Long-run.

4.2.3. Multiple Logistic Regression Results for Frontier African Economies

The 24 frontier African economies, with generally less liquidity than their emerging counterparts are those with an increasing lower middle-income class, rapid economic growth leading to rising living standards, low levels of internal and foreign debts, poorly developed stock markets, and a low level of urbanization, but implementing economic reforms to promote further economic growth (Broome & Seabrooke, 2007). Although, the population growth rate again exerted no significant impact on fintech adoption as can be seen from Table 4, neutral technical changes as a result of fintech can in theory, improve the quality of human capital, the workforce. This assertion was supported by Hicks (1932) when he theorized that a technical change that arises from innovations is capable of improving labour productivity and quality. It is possible to test this hypothesis empirically.

Once more, it is more likely that frontier economies will use mobile phone banking than internet banking or ATMs, although in this case, the ATMs are least to be adopted. This inconsistent result could be attributed to certain phenomena or specification errors; since certain factors fintech adoption differently in frontier and emerging economies, the same predicators cannot be assumed for both types of economy. This could explain the reason for MPESA, a mobile phone-based money transfer service, proving more popular in Kenya, Tanzania, and other frontier economies but failing to launch in the emerging economy of South Africa (Alexander et al., 2017). These heterogeneous factors that affect the adoption of fintech in emerging and frontier economies is therefore a unique area for further research.

Table-4. A Multiple logistic regression result and a probability table for fintech adoption among frontier African economies (n=24).

 
Adoption of ATM
Adoption of INTB
Adoption of MPB
Predictors
Coefficient
Odds Ratio
Coefficient
Odds Ratio
Coefficient
Odds Ratio
Information Spillover
3.319
27.642
2.887
17.942
0.000001
1
 
(1.890)**
(1.890)**
(5.510)***
(5.510)***
(4.790)***
(4.790)***
Population Growth (POPG)
6.939
1031.300
3.895
49.159
-0.219
0.803
 
(1.200)
(1.20)
(1.460)
(1.460)
(0.130)
(0.130)
Literacy Rate (TSE)
1.635
5.131
0.943
2.569
1.223
13.398
 
(2.770)***
(2.77)***
(2.780)***
(2.780)***
(4.740)***
(4.740)***
Constant
-60.718
4.27e-27
-52.042
2.50e-23
-27.386
1.28e-12
 
(4.270)***
(4.270)***
(4.380)***
(4.380)***
(7.040)***
(7.040)***
Observations
314
314
314
No in Groups
23
23
23
LR(chi-squared)
14.510***
33.140***
73.970***
Eq. 3.7 (sig variables only)
-8.152
-7.175
-2.784
Probability of Adoption (Eq 3.8)
0.030 or 3%
0.080 or 8%
0.582 or 58.2%

Note: Absolute value of z statistics in parentheses; *** significant at 1%;** significant at 5%; LR = Long-run.

Finally, the non-convergence of the MLR results for the fragile African economies could be attributed to their poor infrastructural and financial development.

5. CONCLUSION AND POLICY IMPLICATIONS

This study examined both the probability and actual rates of fintech adoption in 32 African economies by means of MLR and descriptive analyses, respectively. These analyses were based on the information spillover and rank theories applied to emerging, frontier and fragile African economies. The results revealed that the overall average probability of fintech adoption in Africa to be 50.9%: 64.02%, 10.9% and 77.85% for ATM, INTB, and MPB, respectively. In particular, the average probability in the emerging economies was 83.1% compared with 23.1% in the frontier economies, indicating that a higher level of fintech adoption, mainly mobile phone banking will be witnessed in the emerging African economies. In fact, the mobile phone banking is widespread across Africa facilitating economic growth. Therefore, the adoption of fintech poses no economic challenges in Africa because it can be predicted and explored for its benefits.

These results further revealed that fintech adoption in Africa is driven by mobile phone banking and the emerging economies, whereas ATMs and fragile economies inhibited it. With 27% average rate of fintech adoption in Africa, it will be about 3.5 years for saturation point to be reached and financial exclusion resolved, if the adoption rate is maintained. However, the rate is below the global average of 33% (Ernest & Young, 2017); although this could be attributed to the low rate of adoption in fragile economies at 12% that weaken the predictors’ effects on all economies, the rate of adoption in emerging African economies at 40% is above the global rate. The average adoption rates of 40% in emerging and 29% in frontier economies also implies that it will be 2.5 years emerging, 3.5 in frontier, but 9 years in fragile economies before fintech can end financial exclusion. These findings carry serious implications for not only Africa financial market development but also its macroeconomic stability.

Finally, the adoption of fintech across Africa is mainly influenced by information and the literacy rate, or quality of human capital, according to the levels of significance shown in the analyses; this is consistent with Khalifa (2016), who reported that these two predictors were the major determinants in whether firms adopted ICT. The odds ratio analysis emphasized the MLR results, because where it is below 1, Table 2, POPG, 0.6723, any change in this predictor will, over time, change the adoption rate. It is therefore inferred that with an overall average probability of 50.9%, the rate of fintech adoption in Africa will be higher in future than its current rate of 27%. Consequently, this study recommends improvements in literacy/education and ICT training should be the way forward.

Funding: This study received no specific financial support.  

Competing Interests: The authors declare that they have no competing interests.

Acknowledgement: Both authors contributed equally to the conception and design of the study.

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APPENDICES

Appendix A: Classification of African Economies.

Table-A.1. Classification of African economies in this study.
Emerging Economies Frontier Economies Fragile Economies
Egypt Algeria Ethiopia Mauritania Senegal Chad
Morocco Angola Ghana Mauritius Seychelles Cote d'Ivoire
South Africa Botswana Kenya Mozambique Swaziland Niger
  Burkina Faso Madagascar Namibia Tanzania Sudan
Burundi Malawi Nigeria Tunisia Togo
  Cameroon Mali Rwanda Zambia  

Source: World economic groupings under Standard & Poor (S&P) and FTSE (2017).

Appendix B. Principal component analysis and fintech indices.

Table-B.1. Principal component result: fintech index for the 32 African economies.
Component
Eigenvalue
Difference
Proportion
Cumulative
Comp1
1.9084
1.0686
0.6361
0.6361
Comp2
0.8398
0.5880
0.2799
0.9161
Comp3
0.2518
…..
0.0839
1.0000
Principal components (Eigenvectors)
Variables
Component 1
Component 2
Component 3
Unexplained
ATM
0.6086
-0.4784
0.6330
0
INTB
0.6682
-0.1213
-0.7341
0
MPB
0.4279
0.8697
0.2458
0
Components: ATM, Internet Banking and Mobile Phone Banking
Number of observations = 544; Number of components = 3;  Trace = 3

Table-B.2. Financial technology index for 32 African Economies.
CtryN
Year
Fnth
CtryN
Year
Fnth
CtryN
Year
Fnth
Algeria
2002
-1.1753
Botswana
2002
-1.098
Burundi
2002
-1.2477
Algeria
2003
-1.1307
Botswana
2003
-1.0978
Burundi
2003
-1.2438
Algeria
2004
-0.9116
Botswana
2004
-0.5175
Burundi
2004
-1.2346
Algeria
2005
-0.6913
Botswana
2005
-0.4755
Burundi
2005
-1.2252
Algeria
2006
-0.4574
Botswana
2006
-0.3087
Burundi
2006
-1.2193
Algeria
2007
-0.1956
Botswana
2007
0.2451
Burundi
2007
-1.2162
Algeria
2008
-0.154
Botswana
2008
0.1774
Burundi
2008
-1.2068
Algeria
2009
0.0388
Botswana
2009
0.1306
Burundi
2009
-1.1867
Algeria
2010
0.1193
Botswana
2010
0.1931
Burundi
2010
-1.1594
Algeria
2011
0.2832
Botswana
2011
0.2419
Burundi
2011
-1.1431
Algeria
2012
0.4679
Botswana
2012
0.5292
Burundi
2012
-1.1216
Algeria
2013
0.7058
Botswana
2013
1.1951
Burundi
2013
-1.0926
Algeria
2014
1.1222
Botswana
2014
1.5324
Burundi
2014
-1.0774
Algeria
2015
1.5363
Botswana
2015
1.684
Burundi
2015
-0.8937
Algeria
2016
1.8317
Botswana
2016
1.764
Burundi
2016
-0.8738
Algeria
2017
2.0485
Botswana
2017
1.9182
Burundi
2017
-0.8234
Algeria
2018
1.9401
Botswana
2018
1.8411
Burundi
2018
-0.8486
Angola
2002
-1.2394
Burkina-F.
2002
-1.243
Cameroon
2002
-1.2253
Angola
2003
-1.2312
Burkina-F.
2003
-1.2331
Cameroon
2003
-1.2085
Angola
2004
-1.1864
Burkina-F.
2004
-1.1648
Cameroon
2004
-1.1703
Angola
2005
-1.1172
Burkina-F.
2005
-1.157
Cameroon
2005
-1.1276
Angola
2006
-1.013
Burkina-F.
2006
-1.144
Cameroon
2006
-1.0804
Angola
2007
-0.9161
Burkina-F.
2007
-1.1215
Cameroon
2007
-1.0074
Angola
2008
-0.8022
Burkina-F.
2008
-1.0974
Cameroon
2008
-0.9445
Angola
2009
-0.6771
Burkina-F.
2009
-1.0652
Cameroon
2009
-0.879
Angola
2010
-0.5496
Burkina-F.
2010
-0.9923
Cameroon
2010
-0.8369
Angola
2011
-0.3976
Burkina-F.
2011
-0.896
Cameroon
2011
-0.7509
Angola
2012
-0.1374
Burkina-F.
2012
-0.8194
Cameroon
2012
-0.5836
Angola
2013
0.0483
Burkina-F.
2013
-0.545
Cameroon
2013
-0.4041
Angola
2014
0.1661
Burkina-F.
2014
-0.4875
Cameroon
2014
-0.0873
Angola
2015
0.2845
Burkina-F.
2015
-0.3546
Cameroon
2015
0.122
Angola
2016
0.2832
Burkina-F.
2016
-0.2144
Cameroon
2016
0.2479
Angola
2017
0.3543
Burkina-F.
2017
-0.073
Cameroon
2017
0.2712
Angola
2018
0.3187
Burkina-F.
2018
-0.1437
Cameroon
2018
0.2595
Chad
2002
-1.2459
Egypt
2002
-1.0528
Ghana
2002
-1.2102
Chad
2003
-1.2385
Egypt
2003
-0.9711
Ghana
2003
-1.1868
Chad
2004
-1.2338
Egypt
2004
-0.4875
Ghana
2004
-0.9958
Chad
2005
-1.2294
Egypt
2005
-0.3167
Ghana
2005
-0.9695
Chad
2006
-1.2168
Egypt
2006
-0.1558
Ghana
2006
-0.8883
Chad
2007
-1.1963
Egypt
2007
0.2039
Ghana
2007
-0.7951
Chad
2008
-1.1614
Egypt
2008
0.5325
Ghana
2008
-0.7062
Chad
2009
-1.1332
Egypt
2009
0.9195
Ghana
2009
-0.5892
Chad
2010
-1.1103
Egypt
2010
1.3016
Ghana
2010
-0.4413
Chad
2011
-1.0854
Egypt
2011
1.7301
Ghana
2011
-0.316
Chad
2012
-1.0664
Egypt
2012
2.048
Ghana
2012
-0.1106
Chad
2013
-1.032
Egypt
2013
2.258
Ghana
2013
0.2307
Chad
2014
-0.9952
Egypt
2014
2.4221
Ghana
2014
0.74
Chad
2015
-0.9559
Egypt
2015
2.6353
Ghana
2015
1.1667
Chad
2016
-0.877
Egypt
2016
2.9074
Ghana
2016
1.406
Chad
2017
-0.7917
Egypt
2017
3.247
Ghana
2017
1.5357
Chad
2018
-0.8344
Egypt
2018
3.0772
Ghana
2018
1.4708
Cote d'Iv
2002
-1.2134
Ethiopia
2002
-1.2498
Kenya
2002
-1.1791
Cote d'Iv
2003
-1.1973
Ethiopia
2003
-1.2483
Kenya
2003
-1.0951
Cote d'Iv
2004
-1.0299
Ethiopia
2004
-1.2434
Kenya
2004
-1.0103
Cote d'Iv
2005
-1.0089
Ethiopia
2005
-1.236
Kenya
2005
-0.9711
Cote d'Iv
2006
-0.9576
Ethiopia
2006
-1.2234
Kenya
2006
-0.8466
Cote d'Iv
2007
-0.8824
Ethiopia
2007
-1.2142
Kenya
2007
-0.6703
Cote d'Iv
2008
-0.8284
Ethiopia
2008
-1.1962
Kenya
2008
-0.4955
Cote d'Iv
2009
-0.7672
Ethiopia
2009
-1.1534
Kenya
2009
-0.3505
Cote d'Iv
2010
-0.7083
Ethiopia
2010
-1.086
Kenya
2010
-0.1369
Cote d'Iv
2011
-0.6373
Ethiopia
2011
-0.9393
Kenya
2011
-0.0026
Cote d'Iv
2012
-0.5367
Ethiopia
2012
-0.7393
Kenya
2012
0.1361
Cote d'Iv
2013
-0.1542
Ethiopia
2013
-0.5749
Kenya
2013
0.27
Cote d'Iv
2014
0.2575
Ethiopia
2014
-0.3494
Kenya
2014
0.4632
Cote d'Iv
2015
1.171
Ethiopia
2015
0.1344
Kenya
2015
0.5432
Cote d'Iv
2016
1.3326
Ethiopia
2016
0.3615
Kenya
2016
0.5447
Cote d'Iv
2017
1.5513
Ethiopia
2017
0.2967
Kenya
2017
0.7161
Cote d'Iv
2018
1.4419
Ethiopia
2018
0.3291
Kenya
2018
0.6304
Madagascar
2002
-1.2359
Mali
2002
-1.243
Mauritius
2002
-0.7938
Madagascar
2003
-1.23
Mali
2003
-1.2357
Mauritius
2003
-0.7061
Madagascar
2004
-1.2138
Mali
2004
-1.1139
Mauritius
2004
0.578
Madagascar
2005
-1.1967
Mali
2005
-1.1039
Mauritius
2005
0.7608
Madagascar
2006
-1.1789
Mali
2006
-1.0813
Mauritius
2006
0.8729
Madagascar
2007
-1.146
Mali
2007
-1.0582
Mauritius
2007
1.19
Madagascar
2008
-1.0459
Mali
2008
-1.0108
Mauritius
2008
1.2269
Madagascar
2009
-1.0166
Mali
2009
-0.9772
Mauritius
2009
1.3157
Madagascar
2010
-0.9853
Mali
2010
-0.9251
Mauritius
2010
1.6291
Madagascar
2011
-0.9526
Mali
2011
-0.8351
Mauritius
2011
2.0241
Madagascar
2012
-0.9272
Mali
2012
-0.735
Mauritius
2012
2.0836
Madagascar
2013
-0.8985
Mali
2013
-0.5875
Mauritius
2013
2.314
Madagascar
2014
-0.8353
Mali
2014
-0.3536
Mauritius
2014
2.53
Madagascar
2015
-0.7875
Mali
2015
-0.2144
Mauritius
2015
2.79
Madagascar
2016
-0.8014
Mali
2016
-0.215
Mauritius
2016
2.839
Madagascar
2017
-0.5535
Mali
2017
-0.1159
Mauritius
2017
2.949
Madagascar
2018
-0.6775
Mali
2018
-0.1654
Mauritius
2018
2.894
Malawi
2002
-1.2428
Mauritania
2002
-1.2333
Morocco
2002
-1.0376
Malawi
2003
-1.2391
Mauritania
2003
-1.2288
Morocco
2003
-0.9734
Malawi
2004
-1.2053
Mauritania
2004
-1.0559
Morocco
2004
-0.2452
Malawi
2005
-1.1721
Mauritania
2005
-1.0437
Morocco
2005
0.2191
Malawi
2006
-1.169
Mauritania
2006
-1.0241
Morocco
2006
0.4127
Malawi
2007
-1.1343
Mauritania
2007
-0.9982
Morocco
2007
0.6171
Malawi
2008
-1.1283
Mauritania
2008
-0.9657
Morocco
2008
1.259
Malawi
2009
-1.0607
Mauritania
2009
-0.9481
Morocco
2009
1.7439
Malawi
2010
-0.9832
Mauritania
2010
-0.8569
Morocco
2010
2.3928
Malawi
2011
-0.8882
Mauritania
2011
-0.8338
Morocco
2011
2.28
Malawi
2012
-0.8119
Mauritania
2012
-0.7816
Morocco
2012
2.7962
Malawi
2013
-0.7571
Mauritania
2013
-0.661
Morocco
2013
2.9362
Malawi
2014
-0.7068
Mauritania
2014
-0.4123
Morocco
2014
3.041
Malawi
2015
-0.5308
Mauritania
2015
-0.1586
Morocco
2015
3.0652
Malawi
2016
-0.4266
Mauritania
2016
-0.0107
Morocco
2016
3.1199
Malawi
2017
-0.3122
Mauritania
2017
0.1093
Morocco
2017
3.3286
Malawi
2018
-0.3694
Mauritania
2018
0.0493
Morocco
2018
3.2243
Mozambique
2002
-1.2378
Niger
2002
-1.2472
Rwanda
2002
-1.2394
Mozambique
2003
-1.2275
Niger
2003
-1.2455
Rwanda
2003
-1.2357
Mozambique
2004
-1.1368
Niger
2004
-1.2151
Rwanda
2004
-1.2308
Mozambique
2005
-1.1031
Niger
2005
-1.2108
Rwanda
2005
-1.2162
Mozambique
2006
-1.0479
Niger
2006
-1.2051
Rwanda
2006
-1.1532
Mozambique
2007
-1.0175
Niger
2007
-1.1926
Rwanda
2007
-1.1374
Mozambique
2008
-0.9426
Niger
2008
-1.1626
Rwanda
2008
-1.0157
Mozambique
2009
-0.8471
Niger
2009
-1.1441
Rwanda
2009
-0.8376
Mozambique
2010
-0.7204
Niger
2010
-1.1284
Rwanda
2010
-0.7807
Mozambique
2011
-0.6749
Niger
2011
-1.0929
Rwanda
2011
-0.7571
Mozambique
2012
-0.5626
Niger
2012
-1.0741
Rwanda
2012
-0.6144
Mozambique
2013
-0.4141
Niger
2013
-1.028
Rwanda
2013
-0.5331
Mozambique
2014
-0.1645
Niger
2014
-1.0009
Rwanda
2014
-0.4287
Mozambique
2015
0.2606
Niger
2015
-0.926
Rwanda
2015
-0.0829
Mozambique
2016
0.2075
Niger
2016
-0.8681
Rwanda
2016
0.0132
Mozambique
2017
0.2299
Niger
2017
-0.5849
Rwanda
2017
0.0863
Mozambique
2018
0.2187
Niger
2018
-0.7265
Rwanda
2018
0.0497
Namibia
2002
-1.1346
Nigeria
2002
-1.2115
Senegal
2002
-1.1994
Namibia
2003
-1.1011
Nigeria
2003
-1.1726
Senegal
2003
-1.1468
Namibia
2004
-0.7093
Nigeria
2004
-1.0329
Senegal
2004
-0.8777
Namibia
2005
-0.6933
Nigeria
2005
-0.7366
Senegal
2005
-0.8487
Namibia
2006
-0.6811
Nigeria
2006
-0.3587
Senegal
2006
-0.7905
Namibia
2007
-0.6426
Nigeria
2007
-0.0537
Senegal
2007
-0.7207
Namibia
2008
0.1599
Nigeria
2008
0.5697
Senegal
2008
-0.6822
Namibia
2009
0.6951
Nigeria
2009
0.945
Senegal
2009
-0.6318
Namibia
2010
1.2284
Nigeria
2010
1.2623
Senegal
2010
-0.5967
Namibia
2011
1.302
Nigeria
2011
1.5334
Senegal
2011
-0.4732
Namibia
2012
1.2657
Nigeria
2012
1.9334
Senegal
2012
-0.3796
Namibia
2013
1.4578
Nigeria
2013
2.3972
Senegal
2013
-0.2497
Namibia
2014
1.6
Nigeria
2014
2.8045
Senegal
2014
-0.018
Namibia
2015
2.1222
Nigeria
2015
3.1732
Senegal
2015
0.1844
Namibia
2016
2.7005
Nigeria
2016
3.3088
Senegal
2016
0.3712
Namibia
2017
2.7879
Nigeria
2017
3.2127
Senegal
2017
0.5541
Namibia
2018
2.7442
Nigeria
2018
3.2607
Senegal
2018
0.4627
Seychelles
2002
-0.6199
Sudan
2002
-1.231
Tanzania
2002
-1.2331
Seychelles
2003
-0.6071
Sudan
2003
-1.2206
Tanzania
2003
-1.2006
Seychelles
2004
1.1664
Sudan
2004
-1.2
Tanzania
2004
-1.0757
Seychelles
2005
1.2
Sudan
2005
-1.1639
Tanzania
2005
-1.0454
Seychelles
2006
1.7152
Sudan
2006
-1.0049
Tanzania
2006
-0.9933
Seychelles
2007
1.9142
Sudan
2007
-0.6801
Tanzania
2007
-0.9241
Seychelles
2008
2.2013
Sudan
2008
-0.4683
Tanzania
2008
-0.8426
Seychelles
2009
2.2982
Sudan
2009
-0.2815
Tanzania
2009
-0.7064
Seychelles
2010
2.3216
Sudan
2010
-0.0535
Tanzania
2010
-0.5941
Seychelles
2011
2.4558
Sudan
2011
0.114
Tanzania
2011
-0.4707
Seychelles
2012
2.8337
Sudan
2012
0.3331
Tanzania
2012
-0.3915
Seychelles
2013
3.4997
Sudan
2013
0.4141
Tanzania
2013
-0.3502
Seychelles
2014
3.6053
Sudan
2014
0.5092
Tanzania
2014
-0.151
Seychelles
2015
3.7324
Sudan
2015
0.6119
Tanzania
2015
0.1361
Seychelles
2016
4.0739
Sudan
2016
0.6853
Tanzania
2016
0.2685
Seychelles
2017
4.4635
Sudan
2017
0.8397
Tanzania
2017
0.4033
Seychelles
2018
4.2687
Sudan
2018
0.7625
Tanzania
2018
0.3359
South Afr.
2002
-0.7111
Swaziland
2002
-1.1722
Togo
2002
-1.2066
South Afr.
2003
-0.6412
Swaziland
2003
-1.1445
Togo
2003
-1.1964
South Afr.
2004
0.6311
Swaziland
2004
-0.8334
Togo
2004
-1.0677
South Afr.
2005
0.6467
Swaziland
2005
-0.6257
Togo
2005
-1.0524
South Afr.
2006
0.8142
Swaziland
2006
-0.5273
Togo
2006
-1.039
South Afr.
2007
1.0359
Swaziland
2007
-0.4743
Togo
2007
-1.0207
South Afr.
2008
1.6089
Swaziland
2008
-0.2173
Togo
2008
-1.0071
South Afr.
2009
2.0285
Swaziland
2009
-0.1196
Togo
2009
-0.9834
South Afr.
2010
2.8954
Swaziland
2010
0.0796
Togo
2010
-0.965
South Afr.
2011
3.6571
Swaziland
2011
0.4157
Togo
2011
-0.9277
South Afr.
2012
4.04
Swaziland
2012
0.6667
Togo
2012
-0.8511
South Afr.
2013
4.4731
Swaziland
2013
0.8828
Togo
2013
-0.7932
South Afr.
2014
4.9207
Swaziland
2014
1.1639
Togo
2014
-0.7625
South Afr.
2015
5.3347
Swaziland
2015
1.1269
Togo
2015
-0.7329
South Afr.
2016
5.3342
Swaziland
2016
1.254
Togo
2016
-0.5082
South Afr.
2017
5.491
Swaziland
2017
1.1904
Togo
2017
-0.4434
South Afr.
2018
5.4126
Swaziland
2018
1.2222
Togo
2018
-0.4758
Tunisia
2002
-1.011
Zambia
2002
-1.2302
Tunisia
2003
-0.9322
Zambia
2003
-1.2061
Tunisia
2004
-0.4801
Zambia
2004
-1.1223
Tunisia
2005
-0.3448
Zambia
2005
-1.0589
Tunisia
2006
-0.1203
Zambia
2006
-0.9602
Tunisia
2007
0.1928
Zambia
2007
-0.8825
Tunisia
2008
0.7242
Zambia
2008
-0.7775
Tunisia
2009
1.1276
Zambia
2009
-0.6584
Tunisia
2010
1.379
Zambia
2010
-0.4527
Tunisia
2011
1.5456
Zambia
2011
-0.3297
Tunisia
2012
1.6854
Zambia
2012
-0.1487
Tunisia
2013
1.823
Zambia
2013
-0.0281
Tunisia
2014
2.0078
Zambia
2014
0.1687
Tunisia
2015
2.1029
Zambia
2015
0.3131
Tunisia
2016
2.2769
Zambia
2016
0.5293
Tunisia
2017
2.5178
Zambia
2017
0.6641
Tunisia
2018
2.3973
Zambia
2018
0.5967

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Footnote:

1. See’ classification in Aappendix A, Table A 1.