Effectiveness of Credit Terms on Management of Non-Performing Loans in Kenyan Commercial Banks

Effectiveness of Credit Terms on Management of Non-Performing Loans in Kenyan Commercial Banks

Eunice Macrean Atieno, Caroline Ayuma, Christine Ngari

Business Management Department, School of Business, Economics and Management Science, University of Eldoret, Kenya

DOI: https://doi.org/10.51244/IJRSI.2024.1104035

Received: 25 March 2024; Revised: 06 April 2024 ; Accepted: 10 April 2024; Published: 11 May 2024

ABSTRACT

This study sought to establish the effectiveness of Credit Terms on Management of Non-Performing Loans in Kenyan Commercial Banks. The specific objective was to establish how credit term was effective in managing non-performing loans by reviewing credit period, credit limit, interest rates and default term within the corporate, business and personal segments of commercial banks in Kenya. The study was guided by both the Theory of Information Asymmetry and Credit Scorecard theories. An explanatory research design was adopted employing both simple random and stratified sampling techniques. The sample comprised 222 credit officers employed in the 41 commercial banks in Kenya. Structured questionnaires were applied to the credit officers in the collection of primary data. Cronbach’s alpha and factor analysis were applied in testing both the reliability and validity of the research instruments. A multiple regression model using SPSS (Version 23) was used in the analysis of the obtained data and to test the hypotheses. The study identified that credit terms (ꞵ= .570) had a positive and significant effect on management of nonperforming loans. In regard to this new knowledge, the study recommends that credit terms be adopted by the management of commercial banks and policymakers. This will help the banks increase their profitability, enhance their growth and cope with the intense competition within the banking industry.

Keywords: Credit Terms, Credit period, Credit Limit, Interest rates, Default terms, Non–Performing Loans

INTRODUCTION

The economic environment in which commercial banks operate in is rapidly changing and the success of these banks largely depend on the effectiveness of their credit terms (Bii, 2016). Recently the credit sections of commercial banks have experienced an upsurge in Nonperforming loans (Ndero, Wepukhulu, & Bogonko, 2019). To mitigate the increasing challenge of default rates and NPLs, commercial banks have developed stringent credit terms to guide the credit period, credit limit, interest rates offered and default terms before approving loans to borrowers. Nonperforming loans are loans that have been in default for a period of ninety days (Atieno, Ayuma, & Ngari, 2022). According to Lake (2021), credit terms are contractual agreements in which an item of value is lent to a borrower by a lender for payment at a specified later date. These terms include loan repayment periods known as credit periods, interest rates including other fees that are associated with the loan being requested.

The origin of lending can be traced back to 3000 years ago during the historical period of the Industrial Revolution (Amery, 2018). During this time, both production and banking activities were hastened and accompanied by the need for large capital outlay for various commercial activities. These included the payday loans by the then farmers. This paper examines credit terms using four paradigms thus, credit period, credit limit, interest rates, and default terms.

Credit Period engages two types of credit periods; thus, short-term credit which includes commercial paper, lines of credit and overdrafts that finance working capital for a limited period mainly less than a year. On the other hand, long-term credit is suitable for financing fixed assets such as plants and machinery, and equipment and is used in high-return projects requiring commitment of capital for years. Hertzberg, Liberman, and Paravisini (2018) suggest that Credit terms can be used to screen borrowers based on unobserved creditworthiness in US consumer markets.

Credit Limits are the maximum amounts that a borrower can receive from a financial institution. This limit is set by lenders based on the credit information volunteered by a borrower including the borrower’s income and, employment status and credit history. Lenders use credit scores to evaluate the creditworthiness of borrowers such that higher limits are offered to borrowers viewed to be of lower risk and low limits are given to high-risk borrowers. According to Pham (2021) when credit limits are not guaranteed then credit risk is likely to increase. In this regard, securing credit limits requires strict banking governance, compliance that ensures the safety of bank assets.

Credit term measurement is also done using interest rates which is the price paid by a borrower to a lender from the borrowed assets (Mwangi, 2014). Interest rates reflect the market information regarding the expected changes in the purchasing power of money or inflation. The higher the interest rates, the harder it is for borrowers to meet their obligations of debt repayments (Mwangi, 2014). Commercial banks use Default Rate (DR) to change the terms of the borrowed loans from the normal terms to default terms, thus, these are new terms granted to borrowers who have missed payment on their loans. The default ratio is calculated by dividing nonperforming loans by the total loan amount (Poudel, 2012).

LITERATURE REVIEW

Non-Performing Loans

Financial institutions play a key role in any economy majorly by ensuring capital allocation from surplus agents to deficit agents necessary for both macroeconomic stability and sound financial growth (Radivojevic & Jovovic, 2017). In the past decade, the quality of credit remained relatively stable until the financial crisis hit the global economy in 2007 -2008 (Beck, Jakubik, & Piloiu, 2013). Many countries experienced high levels of Non-Performing Loans (NPLs) thereby compelling many financial institutions to review their credit resolutions (Pia, Damilano, De Vincentiis, & Isaia, 2016). For instance, in the European Union, NPLs doubled between 2009 to 2016 with some worst-affected countries recording an upsurge of 45% in NPLs (Aiyar et al., 2015). Exchange rate depreciations were attributed to an upsurge in nonperforming loans especially in countries lending in foreign currencies (Beck et al., 2013).

NPLs are borrowed money whose scheduled payments have not been repaid for over 90 days. It is where the borrower is in default and has not paid the monthly principal and interest accrued within a specified period (Khan, Siddique, & Sarwar, 2020). According to Kenya Financial Sector Stability KFSS (2023) report indicated that the total net assets of banks grew by 9.4 % while gross loans and advances rose by 11.5% in 2022 compared to 2021. The total net assets increased from 6.0 billion in December 2021 to 6.6 billion in December 2022. NPLs increased by 22.2% between 2013 and 2023. This was more than double the 9.7% growth rate in gross loans during the 10Yeae period under review. The upsurge in NPLs’ growth rate in the first 6 months of 2023 can be attributed to rising interest rates in response to monetary policy tightening to stem inflationary pressures. According to BSR (2022), CBK will closely monitor the four economic sectors to ensure that commercial banks make adequate provisions for the loans to mitigate the risk of default. The concentration of non-performing loans was mainly in trade, Manufacturing, Real estate, personal and household sectors.

A study by Ombaba (2023) revealed that NPLs affect operational efficiency that further affects liquidity, profitability and competitive functioning of banks. However, commercial banks have key performing indicators (KPI) that they use to gauge the extent of NPLs in their institutions. The study explored Loans to Deposit Ratio (LDR) which is a ratio used in gauging the liquidity of banks by comparing a bank’s total loans to the total deposits it holds. Minton, Taillard, and Williamson (2014) indicated that the optimal ratio of LDR should be between 80% to 90%. Another measure is the total percentage of doubtful debts which are loans which have not been recovered for 90 days and are set aside from the company’s profits while bad debts which are loans outstanding for a period of 180 days and are often written off. This study uses an explanatory approach to determine the effectiveness of credit terms on performance of NPLs.

Credit Terms

Lake (2021) refers to credit terms as a contractual agreement whereby an item of value is lent to a borrower by a lender for payment at a specified later date. According to Škarica (2014), a credit term is the duration taken for a loan owed to be completely paid off upon regular repayments by the borrower. Thus, the credit period a borrower takes to clear the outstanding debt. Credit terms are subdivided into short- or long-term facilities. The agreement made when signing the loan facility is sometimes called the credit terms and conditions. Anagnostopoulou and Drakos (2016) in their study on bank loan terms and conditions established that macroeconomic factors explained the terms and conditions of loans offered to corporate borrowers. However, other factors affecting credit terms are the credibility of borrowers, interest rates, credit period, default terms and the credit limit which is referred to be the highest loan value a lender can advance to a borrower. The study used the credit period, credit limit, interest rates and default terms to explain credit terms.

Credit period

Credit period is the duration of time a borrower is allowed to make payment for a loan drawn. Failure to meet the repayment obligation in good time renders the loan non-performing. The credit terms should indicate the preferred days of credit such as 30 days credit. According to CBK report (CBK/PG/04), the performance criteria of a loan is based on repayment capability of the borrowers and are classified as either normal, watch, substandard, doubtful or loss. The normal loans are those performing according to the contractual terms of the loan and are up to date on repayments. The watch loans are those which are past due by between 30 to 90 days. Substandard are past due by 90 days but are below 180 days and doubtful are loans past due for more than 180 days but less than 360 days. However, the loss criteria indicate those loans whose borrowers have breached the terms and are past due for 360 days or more (Bank Supervision Annual Report 2022). Credit period help lenders

Credit limit

The concept of credit limit originated from the banking industry following the growth of credit card purchasing (Bandara et al., 2019). Credit limit indicates the highest amount the lender can extend credit to the borrower. In commercial banks, credit limits are used on credit cards, overdrafts and other loans. An optimal credit limit should meet the customers demand for services offered to meet their needs encouraging them to stay connected with minimal default rates (Bandara et al., 2019). Credit limits in most consumer credit situations is set by the lender since the lender utilizes all available information to determine the borrower’s future earnings. In most cases, banks use histories of past credit as input in projections for making credit limit decisions. Apart from debt payment history, credit limit utilization is one of the important determinants of a borrower’s credit score alongside the borrowers levels of indebtedness and length of credit history (Dempsey & Ionescu, 2021). The information is majorly collected by Credit Reporting Agencies (CRA) and detailed in reports that can be used by both lenders and borrowers about the creditworthiness of consumers (Dempsey & Ionescu, 2021)

Interest rates

Haruna and Chiebonam (2022) define interest rate as the amount charged to borrowers over the duration of their credit utilization. Thus, the percentage charged on the capital loaned to a borrower by a lender.  A higher interest rate may cause the cost of borrowing to increase resulting in difficulty for borrowers to service their debts resulting in NPLs. Consequently, when the interest rates are lower, borrowers find it more affordable to make timely payments reducing the incidence of NPLs. A study by (Siddiqui, Malik, & Shah, 2012) also found that there is a positive correlation between default rates and real interest rates.

Default terms

 Default terms are agreements where the lender and borrower include sections that constitutes the rights of a creditor when in case of loan default. The agreement may specify the consequences the borrower may face in case of failure to pay their debt. The stability of the banking industry is fundamental in the sustainance of a country’s economic growth and also helps in the customer’s confidence level in the banking system. However, due to defaults, Bad debts are are experienced from the occurrence of NPLs (Amuakwa–Mensah & Boakye–Adjei, 2015). The prevalence of NPLs reduces the bank’s ability from settlling liabilities at the appropriate times. It further affects the level of private investment that can be made by banks and reduces the bank ability to grant credit facilities to prospective customers (Amuakwa–Mensah & Boakye–Adjei, 2015).  In regard to the above discussion, the below hypothesis was developed:

H_1: Credit Terms significantly and positively influences management of Non -Performing Loan

METHODOLOGY

Research Design

An Explanatory research design was employed in this study in determining the effectiveness of variables describing credit terms.

Study Area and Target Population

The sampling frame included all 41 commercial banks registered in Kenya. The research study was undertaken between 2^nd December 2021 to 15^th February 2022. The targeted population were the credit officers from corporate, business and personal credit sections of commercial banks. The below table 3.1 summarizes the survey done by the researcher.

Table 3.1: Target population

Category	No of Respondents	% of Respondents
Corporate credit officers	65	13%
Business credit officers	230	46%
Personal credit officers	205	41%
Total	500	100%

Source: Research survey (2022)

Sampling Technique and Sampling Size

The study used stratified random sampling technique to collect data from the credit officers of the 41 commercial banks in Nairobi County. The sample size was calculated using Yamane (1967) and the questionnaires were distributed according to the number of respondents as shown in table 3.2 below.

n = N / [ 1 + N (e²)]

Where:

N = Population size

n = Sample size

e = Level of precision (0.05).

1 = Constant

 n = 500/ [1 + 500 (0.05) ²] = 222

The calculated sample size used was 222 respondents.

Table 3.2: Sample Size Determination

Category	No. of respondents	Sample size	% share of respondents
Corporate credit officers	29	222	13% (0.13)
Business credit officers	102	222	46% (0.46)
Personal credit officers	91	222	41% (0.41)
Total	222	222	100% (1)

Source: Research survey (2022)

Types of Data, Sources and Collection Instruments

The study used primary data that was collected by the use of closed ended questionnaires, self-administered to the credit officers. The questionnaires addressed the objectives of the studies by (Owino, 2013), (Bonin & Valério, 2016). The first section measured the demographic variables of respondents including respondents’ designation and tenure. The second section utilized a 5- point Likert scale indicating (1) strongly disagree to (5) strongly agree which represented the measurement of the independent variable (Credit Terms) and dependent variable (Non -performing Loans).

DATA ANALYSIS

The study used data obtained from questionnaires that were self-administered to a sample of 222 credit officers in 41 commercial banks within Nairobi County. The questionnaires returned by respondents were 214 indicating a response rate of 96%. 2 questionnaires indicating 1% were returned but were defective while the unreturned questionnaires were 8 representing 4% of the total questionnaires issued and had to be excluded from the final tally. Thae sample size of 212 respondents showed an adequate representation of the study population having met the recommended adequacy rate of 50% (Simiyu, Komen, & Bonuke, 2019). Table 4.1 below summarizes the information above.

Table 4.1: Response Rate

Category	No. Of Questionnaires	Percentage
Effective Questionnaires	212	95%
Unreturned Questionnaires	8	4%
Returned but Defective	2	1%
Total	222	100%

Source: Research Data, (2022)

Demographic Characteristics of Respondents

The valid questionnaires which were used in the study were 212 representing 95% (n=212) adequate for the analysis. The respondents were credit officers from 41 commercial banks located in Nairobi County in Kenya indicating that the respondents held competent roles commensurate with their tasks. The Business Credit Officers were the majority at 45.85 followed by personal Credit officers at 41 while Corporate Credit officers represented 13.2% of the total questionnaire. The majority of respondents (40.1%) for less than 5 years, (38.2%) of respondents had worked for 6-10 years and (21.7%) respondents had worked for over 10 years indicating that were adequately experienced and have the skills required to carry out such tasks. Table 4.2 below shows the demographic characteristics of respondents.

Table 4.2: Demographic Characteristics of Respondents

Demographic factor		Frequency	Percentage
Designation	Corporate credit officers	28	13.2
	Business credit officers	97	45.8
	Personal credit officers	87	41.0
	Total	212	100
Tenure	Below 5 Years	85	40.1
	6-10 Years	81	38.2
	Above 10 Years	46	21.7
	Total	212	100

Source: Research Data (2022)

Descriptive Statistics for Non – Performing Loans

The Dependent variable was NPLs which was measured using Loans to Deposit Ratio (LDR), Provision for Doubtful Debts and Provisions Bad Debts. Both Provisions for Doubtful debts and Provisions for Bad Debts showed a high prevalence of NPLs with high mean scores of 4.25 each and standard deviations of .938 and .972 respectively. On the other hand, the study showed an effective use of LDR in evaluating the levels of NPLs with a mean score of 3.44 and a standard deviation of 1.289. Likewise, the respondents moderately agreed that a high ratio of LDR increases NPLs since it had a mean score of 3.41 and a standard deviation of 1. 337. The below Table 4.3 summaries the statistics obtained from the sampled variables.

Table 4.3 Mean and Standard Deviation for NPLs

Measuring item	Mean	Std. Deviation
A high rate of provisions increases NPLs in our bank	3.97	1.092
Provisions for doubtful debts has no effect on NPLs	4.03	1.057
Provisions for doubtful debts decrease NPLs	4.25	0.938
There is an effective use of LDR to evaluate	3.44	1.289
A high ratio of LDR increases NPLs.	3.41	1.337
A lower percentage of LDR reduces NPLs	3.45	1.282
LDR has no effect of LDR on NPLs	3.51	1.330
Bad debt is a positive indicator of NPLs	4.15	1.054
A high rate of bad debts indicates high levels of NPLs	4.25	0.972
Bad debts have no effect on NPLs	4.09	1.089
The bank still records high levels of NPLs despite procedures	4.08	1.101

Source: Research Data (2022)

Descriptive Statistics for Credit Terms

Credit terms was measured using six questions captured using a five-point Likert scale. The results revealed that the availability of credit policy in commercial banks recorded the highest mean of 4.38 and a standard deviation of 0. 826.The second measure was to establish whether longer repayment periods led to loan defaults. This measure indicated a mean score of 3.97 and a standard deviation of 1.047. The third measuring item was to find out if interest fluctuations led to default in loan repayments. The mean score was 3.98 and standard deviation of 1.037. Other objectives were to establish whether poorly negotiated credit limits led to loan default, the results indicated a mean score of 3.99 and a standard deviation of 1.049. The measure of whether effective credit terms reduce NPLs had a mean of 4.08 and a standard deviation of 1.000. The last item was whether borrowers were knowledgeable of the bank’s action in case of default indicated a mean score of 3.95 and a standard deviation of 1.036. Based on the average mean of 4.06, it can be concluded that respondents were of the opinion that credit term is a significant guideline for lending and should be used by commercial banks. Table 4.4 below summarizes the above results.

Table 4.4: Mean and Standard Deviation of Credit Terms

Measuring Items	Mean	Std. Deviation
The bank has a credit policy in place	4.38	0.826
Longer repayment periods lead to loan defaults	3.97	1.047
Interest rates fluctuations lead to default in loan repayments	3.98	1.037
Poorly negotiated credit limits lead to loan default	3.99	1.049
Effective credit terms reduce NPLs	4.08	1.000
Borrowers are knowledgeable of the bank’s action in case of default	3.95	1.036

Source: Research data (2022) n=212*Scale: 1= strongly disagree; 5=strongly agree.

Reliability Test for Variables

NPLs had 11 items which measured three constructs Loans to Deposit Ratio (LDR), Provisions for doubtful debts and provisions for bad debts on a 5point Likert scale. The average Cronbach Alpha value of 0.890 obtained was within the acceptable range recommended for the study (DiGabriele, 2011). The results also showed that all the items used for the analysis were retained. Credit Terms had 6 items which were measured on a 5-point Likert scale to obtain the opinions of the targeted respondents. The Cronbach Alpha measure for credit terms variable was 0.887 indicating that the internal consistency was also reliable and acceptable. All the items which were used for analysis were retained as illustrated in Table 4.5 below.

Table 4.5: Reliability Statistics

Variables	Number of Items	Cronbach Alpha value
Non-Performing Loans	11	0.890
Credit Terms	6	0.887

Source: Research Data (2022)

Factor Analysis

Factor analysis is a statistical method that reduces numerous variables into smaller number of factors. Each variable is subjected to factor analysis with the use of Extraction Principal Component (EPC) method before performing data analysis and hypotheses testing. According to Gabriel and Bonuke (2017), factor analysis should be conducted to investigate the validity of each construct by way of a purification process. Items with factor loadings of <0.5 were omitted from the analysis to increase construct validity which measures the degree to which a scale measures what it intends to measure.

The factor loading for credit terms were 6 items and indicated an Eigen value 3.937 and a variance of 23.19. NPLs had 11 items which were clustered into three components thus Factor 1 was loaded as Loans to deposit ratio with 4 items, an Eigen value of 5.255 and a variance of 47.773, Factor 2 was loaded as Provisions for bad debts having 4 items with an Eigen value of 1.625 and a variance of 14.770 while factor 3 was provisions for doubtful debts with 3 items, an Eigen value equals 1.333, and variance equals 12.117.

The results revealed that both NPLs and credit Terms had KMO values of 0.859 and 0.732 respectively which was above the threshold of 0.5. The Bartlett test of sphericity indicated a Chi-Square of 1380.893 for NPLs, 1622.420 for Credit Terms and a p=.000 for both variables. These results confirmed that factor analysis gave a suitable outcome of validity as indicated in Table 4.6 below.

Table 4.6 Factor Analysis

Variables	Scale Items	Factor Loadings	Eigen Values	% of Variance	KMO	Chi Square	p-Value
NPLs	There is an effective use of LDR to evaluate levels of NPLs	.839	2.738	74.659	.859	1380.893	.000
	A High ratio of LDR increases NPLs in our bank	.831
	A lower percentage of LDR reduces NPLs in our bank	.856
	LDR has no effect on NPLs	.875
	Bad debts are positive indicator of NPLs	.706
	A high rate of bad debts indicates low levels of NPLs	.869
	Bad debts have no effect on NPLs	.854
	The bank still records high levels of NPLs despite writing off bad debts	.831
	A high rate of provisions increases NPLs	.722
	Provision for doubtful debts has no effect on NPLs	.812
	Prov. for doubtful debts decreases NPLs	.829
Credit Terms	The bank has a credit policy that guides the terms of lending	.749	3.937	56.621	.732	1622.420	.000
	Longer repayment periods lead to default	.756
	Interest fluctuation to default in loan repayment	.830
	Poorly negotiated credit limits lead to loan defaults	.896
	Effective credit terms reduce NPLs	.795
	Borrowers are aware of bank actions in case of default	.767

Source: Research Data (2022)

Correlation Analysis

According to Gogtay and Thatte (2017), correlation analysis is the relationship that exists between two quantitative variables. da Cunha de Sa-Caputo et al. (2021) also suggests that a linear correlation coefficient that is > 0 indicates a positive relationship between variables whereas p<0 indicates a negative relationship.   Consequently, a value of zero indicates no relationship between two variables X and Y.

Table 4.7: Results for Pearson’s Coefficient

Variable (N=212)	1	2
Non-Performing Loans	1
Credit terms	0.795**	1

**Correlation is significant at 0.01 level (2- tailed)

Source: Research Data (2022)

Hypothesis Testing

A study by (Frost, 2020) suggests that regression analysis determines the way changes in the predictor variable relate to those of the dependent variable whereas coefficient analysis indicates whether the relationships are statistically significant. The p – Value determines whether the coefficients are significantly distinct away from zero. According to (Hinderer III, Flight, Dubey, MacLeod, & Moseley, 2019), a p- value is the probability index describing the likelihood that data would have occurred by chance. In this study, the p- value was used to establish the association between the dependent and independent variables. The analysis revealed that credit terms had a significant effect on Non-Performing Loans with a score (ꞵ₁= .570, p = .000). Since the p-value is less than 0.05, the null hypothesis is rejected indicating that credit terms positively and significantly influence Nonperforming Loans in Kenyan Commercial Banks.

Consequently, the R squared shows goodness of fit with R² = .745 indicating that 74.5% of the variance is explained by the independent variable. 74.5% suggest a strong relationship between credit terms and Non-Performing Loans. The F- Value of 202.596 indicates that the model is significant which further confirms that there is a significant relationship between the predictor variable; credit terms and the dependent variable (Non-Performing Loans) as shown in Table 4.8 below.

Table 4.8 regression Analysis

Variable	ꞵ- Coefficient	p- Value
Credit Terms	0.570	0.000
R2	0.745
F-Statistic	202.596	0.000

Source: Research Data (2022)

Effect of credit terms on non-performing loans

The hypothesis suggested that Credit Terms significantly and positively influences management of Non-Performing Loans in Kenyan Commercial Banks. The study findings revealed that credit terms had a significance of ꞵ¹= .570, p= .000. H₁ is therefore supported by the study findings since p- value is less than 0.05 as shown on Table 4.9 below.

Table 4.9 Regression Results

Hypothesis	Null Hypothesis	Beta	p-Value	Results
H01	Credit Terms has no significant effect on NPLs in Kenyan Commercial Banks	.570	0.000	Rejected

Source: Research data (2022)

DISCUSSION AND CONCLUSION

The study objective was to determine the effectiveness of credit terms on management of Non-Performing Loans in Kenyan Commercial Banks. The findings revealed that Credit Terms has a positive and significant effect on NPLs with a ꞵ= .570, p=0.000. The study reveals that high interest rates can increase the cost of borrowing making it expensive to repay back and may lead to defaults especially for low-income borrowers. Credit limits are also a key factor in negotiating credit terms. If the banks set too high credit limits more than the relative income of the borrower then, they will be at a risk of default which will then increase non-performing loans. The length of the credit period can also affect the borrowers payment ability in that longer periods call for lower monthly repayments. However shorter periods entail high monthly repayments which may strain the borrowers ability to repay the loans hence leading to the risk of default. On the other hand, clear default terms can help lenders in the bank establish when a borrower is considered to be a defaulter enabling them to control possible defaults. Laxed default terms may delay recognition of possible defaults leading to Non-performing Loans.

The findings can be compared to other studies carried out by (Asfaw, Bogale, & Teame, 2016) and (Ajambo Akaliza, Ignatieva, Martin, & Swatton, 2016) which also indicated that credit term has a positive relationship with management of NPLs in commercial Banks. This study brings new knowledge to the body of research as it reveals that credit term is a key component in management of Non-Performing Loans and the management of commercial banks should prioritize and adopt it as a key policy. Knowledge of credit term is beneficial to borrowers as they will be able to plan their finances and repay their loans as scheduled over the specified period. The final effect will lead to a boom in profitability by commercial banks and a further boom in the economy orchestrated by reduced NPLs. In the long run, the effects will assist the Kenyan government achieve its Agenda 2030 and the set sustainable development goals.

SUGGESTIONS FOR FURTHER STUDY

The current study used a quantitative approach to determine the effectiveness of credit terms on management of Non-performing Loans. The study also employed questionnaires for data collection. Future studies are recommended to use a mixed approach with open ended questionnaires and themes to analyze both quantitative and qualitative data. The approach may reveal other findings influencing the management Non-Performing Loans. Future studies also ought to use a larger population and cover a wider geographical scope in order to present different results. The study focused on credit term which was explained by credit period, interest rates, credit limits and default terms. Future studies need to focus on emerging issues like green finance and modern technology to determine their influence on non-performing loans. New studies also ought to use the Hayes Process Macro Model to determine the moderation and mediation effects of credit terms on management of NPLs.

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