Understanding the Factors Affecting the Increase in Short-Term Lending Rate from Retail Customers Through Questionnaire and Model Running.

- Business loans according to standard products for individual customers (business loans for fortune).

Short-term loan process for SMEs customers

- Short-term loans for the purpose of supplementing business capital

-Car loan to serve the Company's travel needs.

4.2. To study the factors affecting the increase in short-term lending rate from retail customers through questionnaire and model running .

4.2.1. Sample description

The total number of questionnaires distributed was 180, the number of questionnaires collected was 155. After checking and analyzing, 2 questionnaires were eliminated. Therefore, 153 questionnaires were used in the study (response rate was 85%), ensuring the sample size condition of n=5 xm (sample size is greater than 100 with m=20)

4.2.2. Analysis of the reliability of the scale

The results of Cronbach's Alpha test show:

All scales "All Scales "X1= credit policy and process", Scale "X2= interest rate/loan price of the Bank", Scale "X3= Service quality/staff capacity", Scale "X4= Image, reputation of the bank", Scale "X5= communication channel", Scale "Choice decision" all have Cronbach's Alpha coefficient >0.6. The total correlation coefficient of the observed variables is greater than 0.3. Therefore, all observed variables are accepted and used in the next factor analysis (Appendix 1)

Table 4.1. Cronbach's Alpha test results

STT

Factor			change initial close	Variable remaining observations	Cronbach's Alpha	Eliminated variable
	Independent variable
1	Procedure, Credit	main	book	3	3	0.742	0
2	Interest rate/loan price of the Bank			3	3	0.829	0
3	Service quality/capacity staff			3	3	0.822	0
4	Image/ bank	name	language	4	4	0.798	0

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Media channels	3	3	0.816	0
	Dependent variable
	Decision to choose	4	4	0.849	0

Source: self-survey data, 2019

4.2.3. EFA factor analysis

4.2.3.1. Factor analysis for independent variables

Conducted factor analysis with 5 variables after using Cronbach's Alpha coefficient method to eliminate bad variables belonging to 5 components. The results of EFS exploratory factor analysis showed that 16 observed variables were grouped into 5 different components. At Eigenvalue coefficient = 1.158, the extracted variance is 71.493%. KMO coefficient is 0.704 (greater than 0.5) with a significance level of 0 (sig = .000), so the observed variables are correlated with each other in the overall scope. Therefore, the extracted scales are acceptable. (Appendix 2)

Table 4.2. Rotated Component Matrix

Symbol

Meaning of symbols	Component
		1	2	3	4	5
CSTD1	Diverse products, suitable for many customer groups					0.88
CSTD2	Conditions on income and collateral when lending are appropriate, not too cautious so that customers can access loans. big and fast					0.72
CSTD3	Customer information security high level					0.81
GVV1	Low interest rates and fees (if any)		0.88
GVV2	Preferential interest rate program for each period and each diverse and competitive loan product		0.87
GVV3	Simple procedures, fast disbursement time fast		0.83
CLDV1	Respect for customers when coming to do business			0.86

CLDV2

Bank Staff's ability to grasp information and solve problems quickly			0.89
CLDV3	Loan products satisfy customers; information and regulations for each product package are fully understood and grasped by customers.			0.83
HADT1	Reputable bank in the market	0.83
HADT2	Branches and transaction offices are widespread and convenient transaction	0.67
HADT3	Wide customer contact area, cool, clean	0.82
HADT4	Brand, recognizable image	0.75
KTT1	Many promotional policies and rewards for customers coming to loan transactions				0.84
KTT2	Diversity in marketing methods: via mail, email, phone, advertising, marketing staff, flyers,…				0.83
KTT3	Frequency of Bank image appearance in the media				0.77

Source: Statistics from 2019 survey data

Table 4.3. Component Score Coefficient Matrix

Symbol

Meaning of symbols	Factor
		1	2	3	4	5
CSTD1	Diverse products, suitable for many customer groups					0.44
CSTD2	Conditions on income and collateral when lending are appropriate, not too cautious so that customers can access large loans. and fast					0.36
CSTD3	Customer information security high level					0.40
GVV1	Low interest rates and fees (if any)		0.38
GVV2	Preferential interest rate program for each period and each diverse and competitive loan product		0.38
GVV3	Simple procedures, fast disbursement time fast		0.36
CLDV1	Respect for customers when coming to do business			0.38

CLDV2

Bank Staff's ability to grasp information and solve problems quickly			0.39
CLDV3	Loan products satisfy customers; information and regulations for each product package are fully understood and grasped by customers.			0.37
HADT1	Reputable bank in the market	0.41
HADT2	Branches and transaction offices are widespread and convenient transaction	0.28
HADT3	Wide customer contact area, cool, clean	0.38
HADT4	Brand, recognizable image	0.28
KTT1	Many promotional policies and rewards for customers coming to loan transactions				0.41
KTT2	Diversity in marketing methods: mail, email, phone, advertising, sales staff market, flyer,…				0.46
KTT3	Frequency of Bank image appearance in the media				0.40

Source: Statistics from 2019 survey data

4.2.3.2. Factor analysis for dependent variables

Similar to above, we conduct factor analysis with 4 observed variables of the scale "Short-term loan decision of retail customers" using the Principal Components method. The result of factor analysis shows that the KMO index is 0.800 (greater than 0.5) with a significance level of 0 (sig = .000), indicating that factor analysis is appropriate. (Appendix 1) Table 4.4. Dependent variable factor matrix table after rotation (Comp onent Matrix)

Symbol

Meaning of symbols	Factor
QDCL1	Low interest rates/loan fees	0.85
QDCL2	Professional and enthusiastic bank staff	0.86
QDCL3	Easy and suitable loan conditions	0.77
QDCL4	VCB Bank is a reputable bank.	0.84

Source: Statistics from 2019 survey data

4.2.3.3. Influence of factors on short-term loan decisions of retail customers at VCB Can Tho Branch

Through the results of correlation regression analysis, the Cobb-Douglas loan function shows that the coefficient R2 is 0.606. That means that the factors: credit process and policy; interest rate/loan price of the Bank; Service quality/staff capacity; Bank image and reputation; communication channels explain 60.6% of the change in borrowing decisions in the lending activities of retail customers at VCB CN CT, the remaining 39.4% of borrowing decisions in the lending activities of retail customers are due to other factors that have not been included in the research model.

The significance level Sig.F is 0.000 which is very small and less than 5%, thus showing that the regression model is significant or the short-term borrowing decision variable (Y) is affected by the independent factors given in the research model.

Through table 4.6, the regression equation on factors affecting the borrowing decision in short-term borrowing activities of retail customers at VCB CN CT is established as follows:

Y= -1.497 + 0.5X1 + 0.32X2 + 0.1X3 + 0.12X4 + 0.18 X5

Table 4.5. Table describing the variables and expectations of the model variables

Independent variable

Symbol	Expectations vary
Credit policies and procedures	X1	+
Bank interest rate/loan price	X2	+
Service quality/staff competence	X3	+
Bank image and reputation	X4	+
Media channels	X5	+

Source: Statistics from 2019 survey data

Table 4.6: Results of regression model analysis

Independent variable

Symbol	Standardized Beta Coefficient	t value	Significance level
Credit policies and procedures	X1	0.50	7,932	0.000***
Bank interest rate/loan price	X2	0.32	5,940	0.000 ***
Service quality/staff competence	X3	0.10	1,961	0.052 **
Bank image and reputation	X4	0.12	2,329	0.021***
Media channels	X5	0.18	2,923	0.004***
	Constant		(1,497)	0.137
Significance level	0.000
F value	45.16
R2 coefficient	0.606

Source: Statistics from 2019 survey data Note : *** : 1% significance level; **: 5% significance level

The variables of the equation are explained as follows :

- Variable X1 (Credit process and policy), coefficient β1=0.5 shows that, when other factors remain unchanged, if the credit process and policy is reduced or in other words, made simpler by 1%, the number of customers choosing to borrow short-term capital at VCB CNCT increases by 0.5%. This result is consistent with the model's expectations, so when VCB CNCT simplifies the process and credit policies, it attracts more customers, especially during the business period that requires faster and simpler capital supply.

- Variable X2 (Interest rate/loan price of the Bank): with a significance level of 1% through regression analysis results, it shows that if the Interest rate/loan price of the Bank decreases, it will increase the choice of short-term loans at VCB CNCT of customers when other factors are fixed. Specifically, if the Interest rate/loan price of the Bank decreases by 1%, customers choosing short-term loans at VCB CNCT will increase by 0.32% (β 2 = 0.32). This factor is consistent with the model's expectations because the analysis results show that reducing interest rates and fees, if any, will increase competitiveness.

and customers choosing VCB to cooperate in capital investment will increase. This means that if customers use the loan capital for the right purpose, the efficiency will be higher.

- Variable X3 (Service quality/staff capacity): β3= 0.1 shows that when other factors are fixed, if service quality and staff capacity are invested and developed by 1%, it will increase the short-term borrowing decision of retail customers by 0.1%. In fact, the survey shows that banks are currently investing in the bank's image from services and people, so the competition is fiercer. VCB CN CT also has a team of experienced staff and good service quality (as shown by the results of the customer satisfaction survey from banks - VCB is in the top 5), however, this requires a long-term process, so the increase in short-term borrowing decision of retail customers through this point is not much compared to other analysis factors.

- Variable X4 (Bank image and reputation): with coefficient β4 = 0.12, when other factors are fixed, when VCB CNCT image and reputation are increased by 1%, the choice of short-term loans of retail customers increases by 0.12%. This result is consistent with the model's expectations, which shows the effectiveness of VCB's constant strengthening and promotion of brand recognition activities over the years, changing its appearance to be suitable and close to customers.

- Variable X5 (Communication channel): β5 = 0.18 shows that when VCB CNCT increases communication activities by 1%, the decision to choose short-term loans of retail customers increases by 0.18%. In fact, when VCB in general or VCB CN CT in particular establishes a widespread marketing plan, it creates more intimacy and closeness with customers; reducing the mentality that VCB is a big bank, difficult for customers to approach and work with. This propaganda helps customers grasp the products and services and know that VCB CNCT has strengthened its retail channel.

Chapter Summary

In this chapter, I conduct research from questionnaires to interested customer groups, then analyze the indicators through qualitative and quantitative research methods such as running regression models to assess the level of influence of factors.

CHAPTER 5: PROPOSED SOLUTIONS TO INCREASE SHORT-TERM LOANS

Currently, the "hot" industry for investment and requiring fast and large capital is real estate investment, following which the target customers are individuals or business owners and the appropriate loan period is medium and long term (the reason is from proving the source of debt repayment such as salary, business profits, property rental, etc.) Thus, increasing short-term debt will bring benefits and risks of bad debt as follows:

Benefit:

+ In favorable business conditions, short-term loans will be more beneficial for borrowers, specifically: interest rates for short-term loans are lower than medium and long-term loans; fast payment will limit early repayment fees, and the use of real estate as collateral will be faster in the current "surfing" activities of real estate traders.

Risk:

+ If business is difficult, using short-term capital for investment can easily lead to insolvency if liquidity is slow.

+ Misuse of capital can easily occur.

+ Banking activities will lead to bad debt if a series of medium and long-term loans are now short-term loans due to customers' inability to pay or the bank itself has to restructure the loan or make compulsory loans so that the loan term is suitable for customers' repayment sources.

Thus, after evaluating the factors that determine the increase in short-term lending rates or the short-term borrowing decisions of retail customers, I evaluate the influence of the following factors:

Credit processes and policies have the strongest impact. In fact, how customers can borrow capital quickly to seize business opportunities is more important. The bank offers low prices and costs (what we often think) but has too many conditions. Banks increase their competitiveness from psychological factors such as bank image, services, service staff and marketing strategies, so these factors have an average impact on increasing the short-term loan decision of retail customers.

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