Augmenting Bank Credit Flow to Agro-Processing SMEs through

Financial Technology (FinTech): Evidence from Tanzania

JUSTUS MWEMEZI

Department of Finance,

Universiti Tunku Abdul Rahman,

31900 Kampar, Perak,

MALAYSIA

ABDELHAK SENADJKI

Department of Economics,

Universiti Tunku Abdul Rahman,

31900 Kampar, Perak,

MALAYSIA

LAU LIN SEA

Faculty of Business and Finance,

Universiti Tunku Abdul Rahman,

31900 Kampar, Perak,

MALAYSIA

Abstract: -The drivers of bank credit flow of transaction costs, credit risk management, information asymmetry,

and institutional lending structure are extensively examined. Previous studies have assessed how SMEs might

address their financing issues from a demand side. This study is inclined toward the supply side of financing.

We aimed to determine how FinTech can counteract the effects of lending costs, information asymmetry, and

credit risk management to influence the flow of bank credit to agro-processing SMEs and other entrepreneurs.

A total of 399 questionnaires were collected for statistical analysis using partial least square structural equation

modeling (Smart PLS). We demonstrate that FinTech as a moderator reduces the negative effects of

information asymmetry and credit risk management to allow agro-processing SMEs to obtain more loans.

Policymakers can use the findings of this study to improve banks' financial technology in lending activities for

the sustainability of entrepreneurial activities.

Key- Words: - Financial technology, FinTech; Information Asymmetry, Agro-processing SMEs, Credit flow,

Credit Risk management, institutional lending structures, commercial banks.

Received: June 16, 2022. Revised: October 13, 2022. Accepted: November 9, 2022. Published: November 28, 2022.

1 Introduction

Tanzania has identified agro-processing SMEs

(Ap-SMEs) as the cornerstone of transforming an

economy from an agricultural-based to an

industrial-based economy. With 65.31% of its

people employed in agriculture, the agro-

processing industry is the best-prospect sector

designated by the country as the icon of the

industrial economy by 2025, [1]. Globally, Ap-

SMEs are essential for generating income and

creating jobs, especially in underdeveloped

countries, Tanzania included. Trade credit and

loans from commercial banks are SMEs' primary

funding sources. According to a vast body of

research, banks are the main source of loan funding

for SMEs in both developed and developing

nations, [2], [3], [4], but often, SMEs struggle to

secure loans from them. Due to the credit gap

between SMEs and commercial banks, credit

availability to SMEs has attracted the focus of most

researchers. Despite several attempts to address this

issue, the results have been conflicting and

inconclusive. In [5] and [6] state that the below

factors frequently prevent SMEs from obtaining

bank credit: their informality,

SMEs' opaqueness, the conditions imposed by

lenders, ineffective financial technology for loan

processing, and a lack of collateral to offer loan

insurance. Our study looks at how bank financial

technology (FinTech) impacts credit availability for

small- and medium-sized enterprises (SMEs)

engaged in agro-processing in Tanzania. Due to

flaws in the credit market, such as high loan costs,

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2022.19.172

Justus Mwemezi, Abdelhak Senadjki, Lau Lin Sea

E-ISSN: 2224-2899

1914

Volume 19, 2022

information asymmetry, credit risk, and unpleasant

bank lending structures, small and medium-sized

firms (SMEs) typically experience bank financing

constraints, [7]. Banks have adopted new business

models in response to the recent ten-year

technological acceleration to serve customers better

and increase process effectiveness, [8]. The

financial sector is undergoing a global

transformation due to technological advancements

like artificial intelligence, blockchain, cloud

computing, big data analytics, and the internet of

things. FinTech's fast growth is generating much

academic interest. Many studies have appreciated

FinTech's rise, suggesting that newer technologies

can dramatically revolutionize financial services by

making transactions more affordable, convenient,

and secure, [9], [10].

The drivers of bank credit flow are examined in

this study to see if FinTech might play a

moderating influence on the supply side. We want

to see if fintech can help Ap-SMEs overcome

impediments to credit, such as bank lending costs,

information asymmetry, and credit risk

management. The study examines whether FinTech

can broaden financial intermediation theory and act

as a reliable, secure, and quick solution for

increasing loans to Ap-SMEs. Most of the research

mentioned above focuses on the effects of outside

FinTech, although certain publications, [11], [12],

examine the influence of FinTech on the banking

industry. We are unaware of any research exploring

the role of bank FinTech as a mediator in

increasing loans to Ap-SMEs. Therefore, our study

focuses on filling this academic gap because

fintech simplifies and smoothens service offerings.

Specifically, it will add to the current body of

knowledge in which fintech is often applied in

providing other banking services compared to loan

offerings. We suggest that bank FinTech impacts

loan flow in three ways: First, it mitigates

information asymmetry by using big data analytics

to capture borrowers' information in 360 degrees,

preventing banks from avoiding SME financing

due to their opaqueness. Second, FinTech reduces

credit risk by enhancing bank internal governance

and controls, which increases lending appetite

among small businesses. Finally, bank FinTech can

potentially expand bank loans to Ap-SMEs and

other small businesses by reducing lending costs.

2 Literature Review and Hypotheses

Development

2.1 Theoretical Underpinnings of the Study

Agro-processing SMEs are enterprises converting

agricultural produce from agriculture to final

products with capital in machinery not exceeding

TZS 800 million and with employees from 1 and

not exceeding 99, [13]. The agro-processing sector

is an inevitable means of economic development

via increasing employment and improving

agricultural productivity as the sector strongly links

all industries, from the extraction of raw materials

to the tertiary sector. On the other side, we use

Cheng's concept to define bank financial

technology. Bank FinTech refers to applying

emerging technologies in the financial industry,

such as artificial intelligence, blockchain, cloud

computing, big data, and internet technology, [14].

Tanzania's fintech sector has grown fast in the

recent ten years. Fintech services were initially

limited to airtime purchases, money transfers, and

cash deposits and withdrawals. Fintech companies

in Tanzania have facilitated banks to offer various

services based on data and financial technologies,

such as remittances, digital savings, digital lending,

and microinsurance. The launch of government

policies and efforts focusing on information and

communication technology is favorably connected

with regulatory reforms in the payment sector and

the transformation in the fintech start-up scene

(ICT). This study uses the financial intermediation

theory because it aims to justify the motivations for

banks adopting alternative banking channels. The

financial intermediation theory concentrates on the

transaction cost theory and information asymmetry

by examining the existence of financial

intermediaries and how they can supply financial

services, including bank credit, [15], [16], [17], [7].

Fintech's entry is considered to broaden the

financial intermediation theory, boosting financial

intermediaries' potential to achieve long-term

growth, liquidity maintenance, and sustainability.

2.2 Bank Transaction Costs

Transaction costs (TC) are the total direct and

indirect costs incurred by banks when extending

loans to SMEs, including travel time and costs,

local authority and lawyer fees, meeting

facilitation, business viability measuring costs,

security evaluation, contract breaching costs (case

filing fees, debt collectors hiring fees), and other

fees, [18]. Researchers generally agree that

transaction costs increase lending rates, lower loan

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2022.19.172

Justus Mwemezi, Abdelhak Senadjki, Lau Lin Sea

E-ISSN: 2224-2899

1915

Volume 19, 2022

amounts by subtracting administrative costs, and

occasionally lead lenders to deny credit

applications, [19], [20]. However, based on [14],

recent modernization in the banking sector has

reportedly reduced transaction costs to the extent

that banks no longer have a reason to limit loans to

SMEs. This study reasonably investigates the

implications of transaction costs in agro-processing

in a developing country like Tanzania. It is

expected that high transaction costs on the banks'

side limit bank credit flow to Ap-SMEs, and as

such, it is hypothesized that:

Hypothesis 1 (H1). Transaction costs have a direct

and negative effect on the flow of bank credit to

Ap-SMEs

2.3 Information Asymmetry

Asymmetry between banks and SMEs refers to a

gap or mismatch in information [21]. Information

asymmetry negatively and significantly influences

bank credit flow from the lender's side, [22]. Even

though bank credit is widely regarded as the most

essential and comprehensive external source of

SME financing, SMEs have been left unfunded due

to information gaps.

Other works of literature, such as, [14] and [9],

argue that the information asymmetry problem is

dwindling due to technological developments in the

banking industry. Financial and technological

improvements, such as electronic banking, big data

and big data analytics, and credit reference bureaus,

have made it easier for bankers to get the

information needed to provide loans to SMEs.

According to this study, if bankers have all

essential information on Ap-SMEs, credit supply

will likely increase because banks prefer to approve

loans to SMEs with more transparent information

and the contrary will limit the flow of loans from

them. Based on this fact, this study predicts that:

Hypothesis 2 (H2). Information asymmetry

directly and negatively affects the flow of bank

credit to Ap-SMEs.

2.4 Credit Risk Management

[15] defines credit risk management (CRM) as a

collection of integrated duties and actions for

regulating and directing credit risks encountered by

commercial banks. It is important to remember that

risk management processes are not meant to

eliminate risks; instead, they are meant to manage

the possibilities and threats that can lead to risk.

Research, [2], found that in Egypt, risk

management has a detrimental impact on bank

credit supply to SMEs and other borrowers in

general. Their results are congruent with the

financial intermediation theory.

Most SMEs' credit risk management has limited the

number of bank loans they have received, as most

banks avoid lending to SMEs due to the danger of

default. The bank's opinion of a significant risk of

default in SMEs is reflected in high lending rates

and the introduction of stringent collateral

requirements. According to [23] the requirement

for collateral looked like a substantial barrier to

SME financing because banks saw it as a risk

buffer. Most SMEs cite the lack of collateral as a

primary reason for banks' rejection of their loan

proposals. Plenty of empirical studies, [24], [25],

show a significant relationship between collateral

and borrowers' risk. Before extending loans,

commercial banks use a variety of credit risk

evaluations. Because most SMEs do not qualify for

loans, the assessments have had a detrimental

impact on their credit flow. Based on this fact, this

study predicts that:

Hypothesis 3 (H3). Credit risk management has a

direct and negative effect on the flow of bank credit

to Ap-SMEs

2.5 Institutional Lending Structure

Studies of bank credit, [26], [27], found that

institutional lending structure, such as a bank's

lending culture, credit rules, lending principles, and

procedures, has a considerable beneficial impact on

bank credit supply. In [28] argues that variations in

credit regulations, bank organizational structures,

credit staff training, internal banking policies, the

upward recommendation system, authorization

limits, and head office instructions all impact the

flow of credit to SMEs.

The supply side of credit has been one of the

critical reasons for SMEs' credit constraints to a

greater extent. According to [26], lenders cause

credit constraints in three ways: (1) prospective

SMEs may not apply for credit because they are

discouraged by a particular bank's available

processes and criteria; (2) SMEs can be declined

due to a particular bank's credit risk assessments;

and (3) accepted SMEs may receive unfavorable

credit terms as expected. Banks have consistently

marginalized SME sectors in their lending

strategies in certain conditions. The AP-SMEs will

take control if the agro-processing industry is given

priority in the banking policies and guidelines.

Therefore, the study hypothesizes that:

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2022.19.172

Justus Mwemezi, Abdelhak Senadjki, Lau Lin Sea

E-ISSN: 2224-2899

1916

Volume 19, 2022

Hypothesis 4 (H4). The institutional lending

structure has a direct and positive effect on the flow

of bank credit to Ap-SMEs.

2.6 Financial Technology (FinTech) as a

Moderator

FinTech applications such as internet information

technology, big data, blockchain technology, and

artificial intelligence, according to [29] are being

used to provide SME loans. Various professions

have recently scrutinized the evolution of financial

technology and in the financing sector, FinTech has

propelled the supply of finance to SMEs to new

heights, [14], [30], [9]. FinTech impacts banks'

ability to manage SMEs' credit risks. Because

commercial banks only have limited time to

investigate and evaluate loans, bank financial

technology has improved business models that have

increased bank loans. FinTech decreases the

number of both bank and SME visits during the

lending procedure. Banks are more willing to lend

to small and medium-sized businesses, [31].

Furthermore, because FinTech requires less

documentation, fewer workers, and fewer physical

branches, banks' loan revenues increase as

transaction costs decrease, [32]. By allowing

potential borrowers to do much of the work

themselves, e-banking technology, for example,

can substantially reduce the time it takes to

complete loans. As a result of the lower transaction

costs associated with FinTech, providing e-lending

services to SMEs allows banks to expand their

business. In [33], FinTech is expected to help banks

improve credit information availability and

accuracy, increase the number of information

access channels and sources, and bridge the

information gap between banks and small

businesses. The introduction of FinTech has

resulted in more information exchange among

credit market participants. Sharing data with other

banks' extensive databases could reduce the cost of

locating potential borrowers and credit risk. The

introduction of FinTech has resulted in more

information interchange in the credit market.

Sharing data with other banks' large databases

might lower the cost of finding potential borrowers

while lowering credit risk. As a result, the

following four hypotheses are proposed in this

study:

Hypothesis 5 (H5). Financial technology

(FinTech) has a direct and positive effect on the

flow of bank credit to Ap-SMEs

Hypothesis 6 (H6). Financial technology positively

moderates transaction costs to increase Ap-SMEs'

bank credit flow.

Hypothesis 7 (H7). Financial technology positively

moderates information asymmetry to increase bank

credit flow to Ap-SMEs.

Hypothesis 8 (H8). Financial technology positively

moderates credit risk to increase bank credit flow to

Ap-SMEs.

Fig. 1: The proposed research model

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2022.19.172

Justus Mwemezi, Abdelhak Senadjki, Lau Lin Sea

E-ISSN: 2224-2899

1917

Volume 19, 2022

3 Research Methodology

3.1 Sampling Method

This study used a quantitative technique to

ascertain the relationships between the researched

constructs to provide precise and reliable results. A

questionnaire was distributed to lending officers at

commercial banks using both the drop-off and

pick-up techniques and the online questionnaire.

The loan experts contacted included branch

managers, credit officers, credit analysts, and

relationship managers. Since it was difficult to

gather data from the entire population due to the

abundance and widespread dispersion of bank

branches across the nation, sampling was done to

determine the sample size. 397 bank branches were

used as the sample size for this study by the

researchers. Since the study population met the

requirements of the Yamane formula, the sample

size was established using that formula, [34]. The

questionnaire was therefore given out to 484

respondents to address the problem of a low

response rate.

A multi-stage cluster sampling technique was also

used to choose the research sample. By segmenting

Tanzania into zones, regions, and eventually cities,

the multi-stage cluster sampling technique, which

has greater statistical efficiency and is simpler to

carry out, ensured that the intended respondent was

reached. The cities were also picked because they

are commercial hubs and are concentrated with

many banks and agro-processing SMEs, [35]. The

participant was selected using the purposive

sampling strategy based on their seniority and

credit-related experience because just one

respondent from each bank branch was necessary

to complete the survey, [36]. Finally, only 401 of

the 484 issued questionnaires were returned. Two

(2) questionnaires were rejected because at least

10% of their variables had missing data, [37], [38].

399 acceptable sets are retained, equivalent to

82.4% of the response rate

3.2 Instrument Development

The items in the questionnaire were mostly

modified from previous empirical research, which

was then validated through a detailed analysis.

Sections A and B of the self-administered

questionnaire were created. Section A was used for

profiling, and Section B measured different

constructs. The questions to measure bank credit

flow were taken from [39] and [40], transaction

costs from [19] and [41] information asymmetry

from [20] institutional structure from [42] and

financial technology items from [43] and [44].

Section B items were scored on a five-point Likert

scale ranging from 1 (strongly disagree) to 5

(strongly agree) (strongly agree).

3.3 Demographic Characteristics of

Respondents

The respondents' profiles are summarized in Table

1. Approximately 79.5% of lending officers have

four years of experience in the banking industry.

This represents the lending experience and skill of

the respondents. According to statistics, 72.2% of

banks have set up agro-processing SME units. This

could indicate that Ap-SMEs' funding is a priority

in Tanzanian banks. Most of the banks, about

64.4%, have been in business in Tanzania for over

15 years. This likely demonstrates their familiarity

with the Tanzanian SMEs financing sector.

Furthermore, the experience of these banks may

explain why most of them, 61.7%, have more than

20 locations spread around the country.

Table 1. Demographic statistics

Demographic

Category

Frequency

Percentage (%)

Position of respondent

Branch Manager

15.8

Credit Manager

15.8

Credit Officer/Analyst

174

43.6

Relationship Manager

24.8

Experience of respondent

0 year – 3 years

20.6

4 years – 6 years

132

33.1

7 years – 10 years

128

32.1

11 years and above

14.3

Bank's age in Tanzania

0 – 5 years

6 -10 years

12.8

11 – 15 years

18.8

Above 15 years

257

64.4

Bank's branch number

0 – 10 branches

14.5

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2022.19.172

Justus Mwemezi, Abdelhak Senadjki, Lau Lin Sea

E-ISSN: 2224-2899

1918

Volume 19, 2022

11 – 20 branches

23.8

21 – 30 branches

9.3

31 – 40 branches

8.8

41 – 50 branches

9.0

Above 51 branches

138

34.6

Banks with agro-processing

SMEs unit

Yes

287

72.2

111

27.8

4 Analysis of Data

4.1 Measurement Model

Smart PLS software Version 3 was used to

investigate the research hypotheses and model.

Convergent validity, discriminant validity, and

reliability of each construct were confirmed in the

measurement model, [45]. Furthermore, due to low

loadings, five (5) of the 28 items investigated (CF4,

IA4, FinTech 5, ILS4, and ILS5) were excluded

from the measurement model to improve model

fitness. Table 2 shows the 23 item loadings that

were kept because they exceeded the 0.7 threshold

value, [46]. However in [47] one component,

CRM4, was kept with a loading of 0.683.

Table 2. Reliability and Validity of Measurement Model

Construct

Items

Loadings

AVE

VIF

Credit Flow

CF1

0.753

0.819

0.531

1.329

CF2

0.726

1.314

CF3

0.726

1.384

CF5

0.710

1.346

Credit risk

management

CRM1

0.721

0.813

0.521

1.292

CRM2

0.739

1.511

CRM3

0.760

1.539

CRM4

0.683

1.185

CT4

0.698

1.292

Financial Technology

FinTech1

0.722

0.865

0.617

1.470

FinTech2

0.826

1.721

FinTech3

0.821

1.757

FinTech4

0.768

1.539

Information

Asymmetry

IA1

0.938

0.884

0.719

2.727

IA2

0.738

1.450

IA3

0.857

2.286

Institutional Lending

Structure

ILS1

0.796

0.835

0.560

1.487

ILS2

0.707

1.569

ILS3

0.755

1.605

ILS6

0.727

1.289

Transaction Cost

TC1

0.877

0.913

0.724

2.315

TC2

0.939

2.860

TC3

0.793

2.020

TC4

0.786

2.100

The AVEs for the constructs ranged from 0.521

(credit risk management) to 0.724 (transaction

cost). Furthermore, the composite reliability for

each construct was higher than the 0.700 threshold

value that was advised. As a result, it is concluded

that the measurement model was convergently

valid. The ross-loadings, Fornell and Larker

criterion, and Heterotrait-Monotrait (HTMT)

criterion were utilized to examine discriminant

validity. The factor loading for each item was at

least 0.100 times the cross-loading value.

Furthermore, the square root of each construct's

AVE had the highest correlation value, [48]. The

HTMT for each build was less than the applied

threshold value of HTMT 0.85, as indicated in

Table 3, [49]. As a result, discriminant validity was

established.

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2022.19.172

Justus Mwemezi, Abdelhak Senadjki, Lau Lin Sea

E-ISSN: 2224-2899

1919

Volume 19, 2022

Table 3. The Heterotrait-Monotrait Ratio (HTMT) Criterion Results

Construct

CRM

FinTech

ILS

CRM

0.274

FinTech

0.566

0.086

0.259

0.264

0.174

ILS

0.429

0.057

0.519

0.100

0.164

0.298

0.079

0.253

0.067

4.2 Structural Model

First, without the moderating effects, the baseline

model was evaluated. The internal variance

inflation factor (VIF) values range from 1.185 to

2.860, which is less than the cutoff value of 5. (See

Table 2). This shows no concerns with lateral

collinearity in the study, [45]. Paper in [46] states

that four criteria exist to evaluate the structure

model. According to [50] the R-square would have

a value of 0.75 for large, 0.50 for medium, and 0.25

for small. The results of this study show that TC,

IA, CRM, ILS, and FinTech can explain 27.3% of

the variance in CF. Predictive relevance (Q2) is the

second requirement indicating whether the

variables can predict the dependent variable. If the

value of Q2 is greater than zero, the model is

considered predictive, [48]. As shown in Table 5,

the values of Q2 for CF in this study (Q2 = 0.13)

are significantly higher than zero. In each of the

study's paths, the value of f2 was more than 0.02,

[51] and [50], report that the impact magnitude is

acceptable if f2 is greater than 0,02. The path

coefficient is the final criterion, which is evaluated

in the following section.

4.1.1 Direct Effects

A 5000-sample complete bootstrapping with bias-

Corrected and accelerated (BCa) at a significance

threshold of 0.05 was used to get t-statistics for all

path coefficients. A one-tailed test was chosen

because each hypothesis is directed, [52]. Table 4

shows the effect size for each predictor construct

on endogenous components.

Table 4. Structural Model Assessment (direct and effects results and decision)



Path

β-Path

coefficient

Standard

Deviation

t-Statistics

p-Value

Decision

0.005

TC -> CF

-0.063

0.042

1.496

0.067

Not

accepted

0.012

IA -> CF

-0.097

0.043

2.262*

0.012

Accepted

0.043

CRM ->

-0.184

0.047

3.938**

0.000

Accepted

0.033

ILS -> CF

0.169

0.049

3.438**

0.000

Accepted

0.145

FinTech -

> CF

0.357

0.049

7.350**

0.000

Accepted

Note: ** p< 0.01, *p < 0.05

According to the analysis, except for H1, whose t-

value was less than 1.645, all direct hypotheses

(H2, H3, H4, H5) were accepted. The predictors of

IA (β = -0.097, t = 2.262, p 0.012) and CRM (β = -

0.184, t = 3.938, p=0.000) were related negatively

to CF. ILS (β = 0.169, t = 3.438, p=0.000) and

FinTech (β = 0.357, t = 7.350, p=0.00) were both

positively associated with CF. TC, on the other

hand, was not a significant predictor of CF (β = -

0.063, t = 1.496, p = 0.067).

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2022.19.172

Justus Mwemezi, Abdelhak Senadjki, Lau Lin Sea

E-ISSN: 2224-2899

1920

Volume 19, 2022

Table 5. Construct cross-validated redundancy

Construct

Sum Square of

Observations (SSO)

Sum Square of

Errors (SSE)

Q2 =

(1 - SSE/SSO)

1,596.000

1,386.921

0.131

CRM

1,596.000

FinTech

1,596.000

1,197.000

ILS

1,596.000

4.1.2 Moderating Analysis

The baseline model was extended to incorporate

the moderator to evaluate a moderating effect in

this study. This study employs a two-stage

approach to moderation analysis. This strategy is

advised if the study aims to see if the moderator

significantly impacts the relationship, [53], [54].

Furthermore, the two-stage strategy has greater

statistical power than the product-indicator or

orthogonalizing approaches. Furthermore, the two-

stage approach has more statistical power than the

product-indicator or orthogonalization approaches,

[55]. By reanalyzing, the measurement model's

reliability and validity are confirmed. Table 6

depicts the structural model's evaluation with

FinTech as the moderator. The interaction path’s

path coefficient (TC*FinTech) is 0.008 (t =0.154).

According to [56] the minimum path coefficient

value in the postulated path relationship between

two variables should be approximately 0.10 to be

statistically significant. As a result, this interaction

path is not significant; hence, hypothesis H6 is not

supported. Table 6 shows that hypotheses H7 and

H8 are significant with a threshold of t- statistics p

≤0.1, as advocated by [46].

Fig. 2: The structural model

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2022.19.172

Justus Mwemezi, Abdelhak Senadjki, Lau Lin Sea

E-ISSN: 2224-2899

1921

Volume 19, 2022

Table 6. Structural model assessment with the inclusion of FinTech as the moderator

Hypothesis

Path

Coefficient(β)

Standard

Deviation

(STDEV)

t Statistics

(|β

/STDEV|)

Value

Decision

FinTech-TC ->

-0.008

0.050

0.154

0.439

Not

accepted

FinTech-IA ->

0.076

0.049

1.554*

0.060

Accepted

FinTech-CRM ->

-0.084

0.058

1.455*

0.073

Accepted

Note: *** p< 0.01, **p < 0.05, * p< 0.1 (based on one tailed test)

Furthermore, in moderation analysis, the  change

becomes a critical concern. The baseline model has

an R2 of 0.273, whereas the interaction effect

model has an  of 0.282. The inclusion of the

financial technology interaction factor changed 

by 3.2%, as seen by the  change of 0.009

(additional variance). The following formula

calculates the interaction effect magnitude, [45]

 =





 = 

 = 0.05

With the  of 0.05, FinTech has a medium

influence size of moderation in this study, [57].

According to the author, more realistic thresholds

for moderation's small, medium, and large impact

sizes are 0.005, 0.01, and 0.025, respectively.

Alternatively, the three lines in Figure 3 depict the

link between transaction costs (TC) and credit flow

(CF). For higher levels of FinTech (for every

standard deviation unit increase of FinTech), the

negative relationship between TC and CF rises by

the magnitude of the interaction term from -0.063

to -0.143 (-0.063 +(-0.008) = -0.143) because the

green line slope is steep. On the other hand, for

lower levels of FinTech (for every standard

deviation unit fall in FinTech), the link between TC

and CF reduces by the magnitude of the interaction

term from -0.063 to -0.055 (-0.063 - (-0.008) = -

0.055), because the slope of the red line is not

steeper. In Table 6, the path coefficient of the

interaction term between TC and CF is -0.008 less

0.10 to be statistically significant, as suggested by

[56]. Additionally, the interaction term's t-statistics

value of 0.154 at a p-value of 0.439 is statistically

insignificant and below the advised threshold at a

confidence interval of 0.1, [58]. This means that

FinTech does not moderate the relationship

between TC and CF.

Fig. 3: Moderating effect of bank FinTech on the impact of transaction costs

Based on Figure 4, for higher levels of FinTech (for

every standard deviation unit rise of FinTech – green line), the negative relationship between

information asymmetry (IA) and bank credit flow

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2022.19.172

Justus Mwemezi, Abdelhak Senadjki, Lau Lin Sea

E-ISSN: 2224-2899

1922

Volume 19, 2022

(CF) diminishes by the size of the interaction term

from -0.097 to -0.021 (-0.097 +0.076) = -0.021)

because the green line slope is not as steep. On the

contrary, for lower levels of FinTech (for every

standard deviation unit decrease of FinTech -red

line), the negative relationship between IA and CF

rises by the size of the interaction term from -0.097

to -0.173 (-0. 097 – 0.076) = -0.173), since the red

line slope is steeper. In Table 6, the path coefficient

of the interaction term between (IA) and (CF) is

0.076, approximately 0.10, to be statistically

significant, as suggested by, [56]. Additionally, the

interaction term's t-statistics value of 1.554 at a p-

value of 0.060 and confidence interval of 0.1 is

statistically significant, [58]. Conclusively,

FinTech reduces the negative relationship between

IA and CF.

Fig. 4: Moderating effect of FinTech on the impact of information asymmetry

The three lines in Fig. 5 depict the link between

credit risk management (CRM) and bank credit

flow (CF) to AP-SMEs. For higher levels of

FinTech (for every standard deviation unit increase

of FinTech), the negative relationship between

CRM and CF diminishes by the magnitude of the

interaction term from -0.184 to -0.168 (-0.184 +(-

0.084) = -0.168) because the green line slope is not

steep. On the other hand, for lower levels of

FinTech (for every standard deviation unit fall in

FinTech), the link between CRM and CF rises by

the magnitude of the interaction term from -0.184

to -0.21 (-0.184−(-0.084) = -0.21) because the

slope of the red line is steeper. In Table 6, the path

coefficient of the interaction term between CRM

and CF is -0.082, approximately 0.10, to be

statistically significant. Additionally, the

interaction term's t-statistics value of 1.455 at a p-

value of 0.073, with a confidence interval of 0.1, is

statistically significant. This means that FinTech

moderates the negative relationship between CRM

and CF.

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2022.19.172

Justus Mwemezi, Abdelhak Senadjki, Lau Lin Sea

E-ISSN: 2224-2899

1923

Volume 19, 2022

Fig. 5: Moderating effect of FinTech on the impact of credit risk management.

5 Discussion of Research Results

The impact of FinTech on banks' credit supply to

Ap-SMEs is discussed in this paper, focusing on

bank FinTech as a moderator. Our empirical

research reveals that FinTech can promote the

overall credit supply to Ap-SMEs from banks,

based on a quantitative survey of loan officers from

399 bank branches in Tanzania. The study also

considers transaction costs, information

asymmetry, credit risk management, and

institutional lending structures to reflect the theory

of financial intermediation.

This study found that, contrary to earlier research,

TC has no significant impact on CF to Ap-SMEs,

[21], [19], [18]. The plausible reasons for the

study's findings could be: First, commercial banks

in Tanzania pass on the loan's lending expenses to

the borrower. The cost is paid upfront by deducting

from the extended amount. This means that even if

the transaction costs of processing and servicing a

loan are considerable, bankers will continue to

issue credits because the expenses have been

passed on to the borrowers, [59], [18]. Second, as

[8] postulated, FinTech development in the credit

market has lowered banks' transaction costs.

Generally, TC in the Tanzanian commercial

lending business would not impede offering loans

to Ap-SMEs

According to this study, the banks' ILS show a high

positive correlation with the flow of loans to Ap-

SMEs. This finding is consistent with the findings

of previous studies, which have emphasized the

importance of not overlooking the positive effects

of lending policies, rules, procedures, and other

regulations on credit supply, [60], [28]. The

findings show that commercial banks' tailored

regulations, procedures, and lending policies

increase credit flow to Ap-SMEs. On the other

hand, commercial banks' lending policies and other

structures could simplify the credit decisions of

lending officers when extending loans to Ap-

SMEs. Therefore, it is appreciated that the initiative

to improve credit availability to any sector cannot

be successful without considering institutional

lending mechanisms.

In addition to the analysis, information asymmetry

(IA) negatively impacts loan flow to Ap-SMEs. If

commercial banks do not have adequate

information to aid them in making credit decisions,

they will be hesitant to lend to SMEs. This research

backs up prior research that found a negative

relationship between IA and CF, [61], [62]. It could

be confirmed that banks' lending appetite for Ap-

SMEs is limited because of their lack of

information transparency, limiting the flow of

loans. Consistent with our findings, this study also

found that banks' credit risk management has a

negative impact on credit flow to Ap-SMEs, [63],

[64]. This supports the reality that commercial

banks' techniques for lowering credit losses, such

as covenants, stringent collateral requirements,

credit rationing, loan securitization and high

lending rates in Tanzania, almost result in SMEs'

loan requests being denied. Banks are concerned

about the long-term survival of ap-SMEs, and as a

result, they have little faith in the industry and are

hesitant to lend to them.

The findings also successfully justified the study's

gap by demonstrating a significant direct

relationship between FinTech and CF. Notably, the

study showed FinTech to moderate the negative

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2022.19.172

Justus Mwemezi, Abdelhak Senadjki, Lau Lin Sea

E-ISSN: 2224-2899

1924

Volume 19, 2022

relationships of IA and CRM to CF. As a result,

except for H6, two hypotheses (H7 and H8) were

accepted. Furthermore, the direct hypothesis (H5)

linking FinTech, and CF was confirmed. The

outcomes of this study back up the idea that

financial technology has a direct and significant

impact on credit flow to agro-related SMEs. The

findings are related to the financial intermediation

theory, which says that developments in ICT have

simplified the intermediation process to the point

where the traditional reasons for banks' existence

appear to have faded, [7]. Similarly, this research

aligns with the previous literature, suggesting that

FinTech is a powerful tool for SMEs' credit access,

[30], [65], [9].

In terms of FinTech as a moderator, the outcomes

of this study show that FinTech does not reduce the

Tanzanian commercial banks' transaction costs.

Tanzanian commercial banks have primarily stuck

to traditional lending methods and other

psychometric means of processing and assessing

borrowers. Banks have not invested much in

financial technology aspects like blockchain

technology, machine learning and artificial

intelligence. As a result, banks have failed to

capitalize on advances in FinTech by digitizing

lending transactions, which involve less

documentation, fewer employees, and few physical

branches. However, the current study supports the

hypothesis that FinTech minimizes the negative

link between IA and CRM and credit flow. FinTech

has changed the supply of various banking services

in bank operations, processing, and delivery

outlets. According to previous studies, FinTech will

likely assist banks in improving credit information

availability and accuracy, expanding the number of

information access channels and sources, [33],

[10]. In other words, FinTech closes the

information gap between banks and small

businesses.

Furthermore, according to the same studies, sharing

data with other banks' large databases could reduce

the cost of identifying potential borrowers and

credit risk. The findings of this study show that

using bank FinTech increases banks' ability to

innovate and creates a supportive environment for

increasing loans to AP-SMEs. Fintech helps

expand the availability of borrowers' information

and lessens banks' risks when lending to the agro-

processing sector.

6 Conclusion and Policy

Implications

This research aimed to investigate the moderating

effect of FinTech in Tanzania's commercial bank

lending business and its impact on the supply side

of credit delivery to Ap-SMEs. The study

attempted to close the bank credit gap in Tanzania's

agro-processing sector and offer ways to boost

credit availability for the same sector and other

SMEs. The financial intermediation theory

relationships, alongside bank FinTech, were

investigated to determine their impact on credit

delivery to Ap-SMEs.

The findings have some critical theoretical

implications from the supply side. FinTech

execution has proven to be a helpful instrument for

adjusting the unpredictability of bank credit

availability in the agro-processing sector. The

moderation role of FinTech has been assessed in

this article, with a particular focus on the

connection between FinTech and financial

intermediation relationships. Several concerns were

investigated, including bank transaction costs,

information asymmetry in the credit market, credit

risk management features, and bank lending

structures. Overall findings show that FinTech

significantly reduces the negative effects of IA and

CRM on bank flow to Ap-SMEs. The results reflect

that the higher the FinTech, the weaker the

negative relationship between information

asymmetry and bank credit risk management to the

bank credit flow. Furthermore, the present study

coincides with the available literature that

institutional lending structures act as a catalyst for

more loans to SMEs, [42], [26], [66].

The findings of this study add to the body of

knowledge and offer policy implications for

Tanzanian policymakers and commercial banks.

First, FinTech companies and banks should

develop software compatible with and readily

accepted by Ap-SMEs for loan appraisals. Banks'

easy acceptance and deployment of FinTech could

boost lending to Ap-SMEs and lower the burden of

lending expenses imposed on them. Commercial

banks could connect loans to the agro-processing

industry through technological advancements such

as the internet of things, mobile services, cloud

computing and big data analytics. Around the

globe, it is well known that financial technology

pulls out borrowers' information at 360 degrees

around them, [14], [4], [9]. Commercial banks

should strategize and utilize the potential of

FinTech when processing and disbursing loans to

Ap-SMEs. Generally, online applications and the

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2022.19.172

Justus Mwemezi, Abdelhak Senadjki, Lau Lin Sea

E-ISSN: 2224-2899

1925

Volume 19, 2022

automation of due diligence, loan servicing, and

regulatory compliance may help the traditional

lending process.

Secondly, The Tanzanian government must

strengthen the information environment, the legal

and judicial environment, and the tax and

regulatory regime to achieve an industrial economy

in this sector. Policymakers should reduce the

effect of information asymmetry and credit risk

management by digitalizing financial practices to

expedite the loan application services, availability,

and approval processes. We recommend that future

research be duplicated further by integrating more

credit dimensions and other lending financial

institutions in the investigation to explore the

impact of studied variables on loan supply in other

countries. It will also be interesting to investigate

the role of FinTech in microfinance institutions and

other providers of funds to small businesses and

compare their lending practices and business model

to that of large commercial banks.

References:

[1] World Bank, Tanzania. (2020). Bank credit to

the private sector.

https://www.theglobaleconomy.com/Tanzania

/Bank_credit_to_the_private_sector/

[2] Boushnak, E., Rageb, M. A., Ragab, A. A., &

Sakr, A. M. (2018). Factors Influencing Credit

Decision for Lending SMEs: A Case Study on

National Bank of Egypt. Open Access Library

Journal, 5(11), 1–17.

[3] Jude, F. A. (2021). Financing of Small and

Medium-Sized Enterprises: A Supply-Side

Approach Based on the Lending Decisions of

Commercial Banks

[4] Mushtaq, R., Gull, A. A., & Usman, M.

(2022). ICT adoption, innovation, and SMEs’

access to finance. Telecommunications Policy,

46(3), 102275.

[5] Mbowe, W. E., Shirima, F. R., & Kimolo, D.

(2020). Role of Financial Innovation in

Enhancing MSMEs Access to Credit: An

Empirical Investigation on Tanzania. Applied

Economics and Finance, 7(3), 126–144.

[6] Nkwabi, J., Mboya, L., Nkwabi, J., &

Nkwabi, J. (2019). A Review Of The

Challenges Affecting The Agroprocessing

Sector In Tanzania. Asian Journal Of

Sustainable Business Research, 1(2), 68–77.

[7] Scholtens, B., & Van Wensveen, D. (2003).

The theory of financial intermediation: an

essay on what it does (does not) explain (Issue

2003/1). SUERF Studies.

[8] Murinde, V., Rizopoulos, E., & Zachariadis,

M. (2022). The impact of the FinTech

revolution on the future of banking:

Opportunities and risks. International Review

of Financial Analysis, 81, 102103.

[9] Sheng, T. (2020). The effect of fintech on

banks' credit provision to SMEs: Evidence

from China. Finance Research Letters, March,

101558.

https://doi.org/10.1016/j.frl.2020.101558

[10] Zhang, L., Hsu, S., Xu, Z., & Cheng, E.

(2020). Responding to the financial crisis:

Bank credit expansion with Chinese

characteristics. China Economic Review,

61(August 2018), 101233.

https://doi.org/10.1016/j.chieco.2018.09.014

[11] Fu, X., Woo, W. T., & Hou, J. (2016).

Technological innovation policy in China:

the lessons, and the necessary changes ahead.

Economic Change and Restructuring, 49(2–

3), 139–157.

[12] Gai, K., Qiu, M., & Sun, X. (2018). A survey

on FinTech. Journal of Network and

Computer Applications, 103, 262–273.

[13] The United Republic of Tanzania. (2003).

Small and Medium Enterprise Development

Policy.

[14] Cheng, M., & Qu, Y. (2020). Does bank

FinTech reduce credit risk? Evidence from

China. Pacific-Basin Finance Journal, 63,

101398.

[15] Allen, F., & Santomero, A. M. (1997). The

theory of financial intermediation. Journal of

Banking & Finance, 21(11–12), 1461–1485.

[16] Diamond, D. W. (1984). Financial

intermediation and delegated monitoring.

The Review of Economic Studies, 51(3),

393–414.

[17] Gurley, J. G., & Shaw, E. S. (1960). Money

in a Theory of Finance.

[18] Nguvava, H., & Ngaruko, D. (2016).

Transaction cost determinants of credit

governance structures of commercial banks

in Tanzania. African Journal of Economic

Review, 4(2), 222–247.

[19] Ekpu, V. U. (2015). The microstructure of

bank lending to SMEs: evidence from a

survey of loan officers in Nigerian banks.

[20] Mutezo, Ashley Teedzwi. (2015). Small and

medium enterprise financing and credit

rationing: the role of banks in South Africa.

[21] Berger, A. N., & Udell, G. F. (1995).

Relationship lending and lines of credit in

small firm finance. Journal of Business, 351–

381.

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2022.19.172

Justus Mwemezi, Abdelhak Senadjki, Lau Lin Sea

E-ISSN: 2224-2899

1926

Volume 19, 2022

[22] Bonini, S., Dell’Acqua, A., Fungo, M., &

Kysucky, V. (2016). Credit market

concentration, relationship lending and the

cost of debt. International Review of

Financial Analysis, 45, 172–179.

[23] Thampy, A. (2010). Financing SME firms in

India: an interview with Ranjana Kumar,

former CMD, Indian bank; vigilance

commissioner, Central vigilance commission.

IIMB Management Review, 22(3), 93–101.

[24] Duarte, F., Matias Gama, A. P., & Esperança,

J. P. (2016). The role of collateral in the

credit acquisition process: evidence from

SME lending. Journal of Business Finance &

Accounting, 43(5–6), 693–728.

[25] Jimenez, G., Salas, V., & Saurina, J. (2006).

Determinants of collateral. Journal of

Financial Economics, 81(2), 255–281.

[26] Kysucky, V. (2015). Access to Finance in a

Cros-Country Context (Issue EPS-2015-350-

F&A).

[27] Totolo, E. (2015). Essays on the demand and

supply of small business finance. University

of Trento.

[28] Moro, A., & Fink, M. (2013). Loan

managers’ trust and credit access for SMEs.

Journal of Banking & Finance, 37(3), 927–

936.

[29] Jagtiani, J., & Lemieux, C. (2017). Fintech

lending: Financial inclusion, risk pricing, and

alternative information.

[30] Jakšič, M., & Marinč, M. (2019).

Relationship banking and information

technology: The role of artificial intelligence

and FinTech. Risk Management, 21(1), 1–18.

[31] Purcell, F., & Toland, J. (2003). EFinance

for Development: Global Trends, National

Experience and SMEs. The Electronic

Journal of Information Systems in

Developing Countries, 11(1), 1–4.

[32] Cheng, T. C. E., Lam, D. Y. C., & Yeung, A.

C. L. (2006). Adoption of internet banking:

an empirical study in Hong Kong. Decision

Support Systems, 42(3), 1558–1572.

[33] Sanchez, J. M. (2018). The information

technology revolution and the unsecured

credit market. Economic Inquiry, 56(2), 914–

930.

[34] Yamane, T. (1967). Statistics: an

introductory analysis, 2nd edn, Harper and

Row, New York.

[35] United Republic of Tanzania. (2013). Census

of Industrial Production Summary Report

Tanzania Mainland.

[36] Leedy, P.D and Omrod, J. E. (2010).

Practical Research: Planning and Design (9th

ed.). Boston, MA: Pearson Education.

[37] Asiamah, N., Opuni, F. F., Muhonja, F.,

Danquah, E., Agyemang, S. M., Agyemang,

I., Omisore, A., Mensah, H. K., Hatsu, S., &

Baffoe, R. S. (2021). The relationship

between job components, neighbourhood

walkability and African academics’ physical

activity: a post-COVID-19 context. Health

Promotion International.

[38] Roda, C., Nicolis, I., Momas, I., &

Guihenneuc, C. (2014). New insights into

handling missing values in environmental

epidemiological studies. PloS One, 9(9),

e104254.

[39] Gill, S., Khurshid, M. K., Mahmood, S., &

Ali, A. (2018). Factors effecting investment

decision making behavior: The mediating

role of information searches. European

Online Journal of Natural and Social

Sciences, 7(4), pp-758.

[40] Khan, S. U., Khan, I. U., Khan, I., Din, S. U.,

& Khan, A. U. (2020). Evaluating ṣukūk

investment intentions in Pakistan from a

social cognitive perspective. ISRA

International Journal of Islamic Finance.

[41] Dahlstrom, R., & Nygaard, A. (2005).

Measurement of transaction costs and

falsification criteria: Toward future

directions in empirical research on

transaction costs theory. New Ideas in

Contracting and Organizational Economics

Research, 87–102.

[42] Kakuru, J. (2008). The supply-demand

factors interface and credit flow to small and

micro enterprises (SMEs) in Uganda.

[43] Wu, L., & Chen, J.-L. (2014). A stage-based

diffusion of IT innovation and the BSC

performance impact: A moderator of

technology–organization–environment.

Technological Forecasting and Social

Change, 88, 76–90.

[44] Wu, L., & Chuang, C.-H. (2010). Examining

the diffusion of electronic supply chain

management with external antecedents and

firm performance: A multi-stage analysis.

Decision Support Systems, 50(1), 103–115.

[45] Hair Jr, Joseph F, Hult, G. T. M., Ringle, C.

M., & Sarstedt, M. (2021). A primer on

partial least squares structural equation

modeling (PLS-SEM). Sage publications.

[46] Hair Jr, Joe F, Matthews, L. M., Matthews,

R. L., & Sarstedt, M. (2017). PLS-SEM or

CB-SEM: updated guidelines on which

method to use. International Journal of

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2022.19.172

Justus Mwemezi, Abdelhak Senadjki, Lau Lin Sea

E-ISSN: 2224-2899

1927

Volume 19, 2022

Multivariate Data Analysis, 1(2), 107–123.

[47] Gefen, D., & Straub, D. (2005). A practical

guide to factorial validity using PLS-Graph:

Tutorial and annotated example.

Communications of the Association for

Information Systems, 16(1), 5.

[48] Fornell, C., & Larcker, D. F. (1981).

Evaluating structural equation models with

unobservable variables and measurement

error. Journal of Marketing Research, 18(1),

39–50.

[49] Kline, R. B. (2011). Principles and practice

of structural equation modeling (3. Baskı).

New York, NY: Guilford.

[50] Cohen, J. (1988). Statistical power analysis

for the behavioral sciences. Lawrence

Erlbaum Associates. Hillsdale, NJ, 20–26.

[51] Swadi, A. F., & Al-Dalaien, A. A. A.-H.

(2022). The Effect of Smart University

Characteristic on Entrepreneurial Orientation

of Students: The Mediating Role of

Knowledge Sharing. WSEAS Transactions

on Business and Economics, 19, 1170–1179.

[52] Ramayah, T., Cheah, J., Chuah, F., Ting, H.,

& Memon, M. A. (2018). Partial least

squares structural equation modeling (PLS-

SEM) using smartPLS 3.0. Kuala Lumpur:

Pearson.

[53] Hair, Joseph F, Hult, G. T. M., Ringle, C. M.,

& Sarstedt, M. (2016). A Primer on Partial

Least Squares Structural Equation Modeling

(PLS-SEM). Sage.

[54] Sim, J. J., Loh, S. H., Wong, K. L., &

Choong, C. K. (2021). Do We Need Trust

Transfer Mechanisms? An M-Commerce

Adoption Perspective. Journal of Theoretical

and Applied Electronic Commerce Research,

16(6), 2241–2262.

[55] Hair, Joseph F, Sarstedt, M., & Ringle, C. M.

(2019). Rethinking some of the rethinking of

partial least squares. European Journal of

Marketing, 53(4), 566–584.

https://doi.org/10.1108/EJM-10-2018-0665

[56] Lohmöller, J.-B. (2013). Latent variable path

modeling with partial least squares. Springer

Science & Business Media.

[57] Kenny, D. A. (2018). Moderation. Retrieved

from.

http://davidakenny.net/%0Acm/moderation.h

[58] Carlo, J. L., Gaskin, J., Lyytinen, K., & Rose,

G. M. (2014). Early vs. late adoption of

radical information technology innovations

across software development organizations:

an extension of the disruptive information

technology innovation model. Information

Systems Journal, 24(6), 537–569.

[59] Berger, A. N., & Udell, G. F. (2006). A more

complete conceptual framework for SME

finance. Journal of Banking & Finance,

30(11), 2945–2966.

[60] Cantú, C., Claessens, S., & Gambacorta, L.

(2020). How do bank-specific characteristics

affect lending? New evidence based on credit

registry data from Latin America. Journal of

Banking & Finance, 105818.

[61] Distinguin, I., Rugemintwari, C., & Tacneng,

R. (2016). Can informal firms hurt registered

SMEs’ access to credit? World Development,

84, 18–40.

[62] Song, H., Yang, X., & Yu, K. (2020). How

do supply chain network and SMEs’

operational capabilities enhance working

capital financing? An integrative signaling

view. International Journal of Production

Economics, 220, 107447.

[63] Aysan, A. F., & Disli, M. (2019). Small

business lending and credit risk: Granger

causality evidence. Economic Modelling,

83(February), 245–255.

https://doi.org/10.1016/j.econmod.2019.02.0

[64] Beyhaghi, M., Firoozi, F., Jalilvand, A., &

Samarbakhsh, L. (2020). Components of

credit rationing. Journal of Financial

Stability, 50, 100762.

https://doi.org/10.1016/j.jfs.2020.100762

[65] Sedunov, J. (2017). Does bank technology

affect small business lending decisions?

Journal of Financial Research, 40(1), 5–32.

[66] Trönnberg, C., & Hemlin, S. (2012).

Banker’s lending decision making: a

psychological approach. Managerial Finance.

Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

Justus Mwemezi carried out the conception and

data gathering. Abdelhak Senadjki was responsible

for the data analysis and Lau Lin Sea organized the

study's methodology and literature review.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.e

n_US

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2022.19.172

Justus Mwemezi, Abdelhak Senadjki, Lau Lin Sea

E-ISSN: 2224-2899

1928

Volume 19, 2022