Credit Service Quality Component Scale

by two factors: individual (household) characteristics and bank attributes such as credit interest rates and loan terms.

In addition, when customers come to commercial banks to seek funding, these customers also rely on assessments of the bank's credit interest rates, credit policies, processing time, network system, banking technology, procedural requirements, collateral, etc. Depending on these assessment criteria, customers will decide to access credit funding, thereby affecting the MRTD ability of commercial banks. The above criteria are rearranged into 3 main groups to serve the research design, including: credit prices, credit service quality, and credit transaction difficulties.

Ecotourism development is often carried out in rural areas because of favorable natural conditions and natural resources for investment and exploitation, but local people often have difficulty in finding capital sources. According to Anmar Siamwalla et al. (1990) in a study on credit in rural areas in Thailand, access to credit by farmers requires government intervention through its policies and guidelines. This study also concluded that the informal lending sector has higher interest rates, but households still accept loans because of difficulties in accessing commercial banks' capital. This affects the MRTD capacity of banks in rural areas.

Nguyen Dinh Cung (2012) stated that factors such as cumbersome procedures, no mortgage, having to pay additional fees, and no counterpart capital are barriers that make it difficult for customers to access bank loans.

From the above analysis, we can draw out the factors affecting the MRTD ability of commercial banks such as refusing to grant credit, continuing to grant credit, difficulties in credit transactions with banks, credit service quality and credit prices. This is the basis for building hypotheses and proposing research models. Therefore, we will build hypotheses from these factors as follows:

Credit denial and credit extension

When customers do not meet the loan conditions such as no collateral, unclear financial situation, etc., the bank refuses to lend and thereby reduces the bank's MRTD ability. The first hypothesis is stated as follows:

Hypothesis H 1 : As banks make more (fewer) credit rejection decisions, MRTD will decrease (increase).

Continue to provide credit and expand credit

For customers who have been granted credit by the bank and are currently being considered by the bank to see whether they should continue to borrow or not, it is based on factors such as having more collateral, good credit history, more flexible lending, etc. Thus, when the bank decides to continue to maintain and increase credit relations with old customers, it will contribute significantly to MRTD. The second hypothesis is stated as follows:

Hypothesis H 2 : When banks increase (decrease) the continuation of credit, MRTD will increase (decrease)

Difficulty in credit transactions and credit extension

When customers want to make credit transactions with banks but encounter certain difficulties and obstacles. If these difficulties cannot be resolved, customers may not receive any credit funding from the bank. When customers invest in developing ecotourism, they encounter some difficulties such as lack of collateral or no guarantee, difficult and complicated loan procedures, etc. Therefore, the third hypothesis is stated as follows:

Hypothesis H 3 : When there are more (less) difficulties in credit transactions with banks, MRTD will decrease (increase)

Credit service quality and credit expansion

The quality of credit services is clearly demonstrated through customers' feelings and feedback from customers recorded by the bank through transactions.

service, including factors related to service facilities, service attitude, ability to provide timely service, etc. Service quality determines the transaction is increased and repeated significantly, through word of mouth and customer retention. Therefore, credit service quality also has an impact on MRTD of commercial banks. The fourth hypothesis is proposed as follows:

Hypothesis H 4 : When credit service quality increases (decreases), the probability of MRTD will increase (decrease).

Credit prices and credit expansion

In credit transactions with banks, credit prices are viewed in terms of the cost of capital. Low costs will encourage customers to use funding from banks and vice versa. Therefore, credit prices have an inverse relationship with MRTD of commercial banks. The fifth hypothesis is stated as follows:

Hypothesis H 5 : When the price of bank credit is high (low), the MRTD will decrease (increase)

H 3 (-)

H 4 (+)

H 2 (+)

Continue credit

Credit expansion

Difficulty in credit transactions

Credit service quality

2.4.1.2. Research model

	H 1 (-)
			Credit denial

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H 5 (-)
		Credit service prices

Figure 2.1: Proposed research model

Source: Design from theories and research

The research model is shown in the figure above, which includes:

- Independent variables in the model: (1) Refusal to grant credit; (2) Continued granting credit; (3) Difficulty in credit transactions; (4) Credit service quality and (5) Credit price.

- The dependent variables in the model are: (1) Credit expansion.

2.4.2. Conduct research

2.4.2.1. Research design

The research was conducted in two phases: preliminary research and formal research. The specific implementation method is described as follows:

The purpose of the preliminary study is to study the factors affecting MRTD at the Branch in order to build a theory suitable for the research model. Therefore, discussions were conducted with 5 credit officers at the Branch; 3 owners, 3 managers of the ecotourism area. From the results of the discussion, a scale and a draft survey questionnaire were developed. Then, a survey was conducted on 150 subjects by convenient sampling to detect errors in the questionnaire and scale to adjust them into an official questionnaire and scale for the official research.

Formal research was conducted using quantitative methods, following the following 5 steps:

Step 1: Build the scale

Step 2: Select survey form

Step 3: Test the model using Cronbach Alpha Test for scales and Exploratory Factor Analysis (EFA)

Step 4: Test the hypothesis

Step 5: Impact Analysis

2.4.2.2. Building a scale

Credit Denial Scale

Factors affecting the decision to refuse credit by commercial banks are related to issues of collateral, financial statements, ability to draft loan plans, customers' own capital, prospects of production and business sectors, and customers' ability to repay debts. Qualitative research was conducted using expert discussion methods to provide a preliminary survey scale.

After conducting a preliminary survey of 150 subjects, then making appropriate adjustments to create the official survey questionnaire. The questions were selected based on their relationship to measuring the credit refusal factor. A 5-point Likert scale was used to arrange from small to large as follows: (1: Completely disagree; 2: Disagree; 3: Neutral; 4: Agree; 5: Completely agree).

The credit refusal component (TCCTD) is measured by 06 observed variables, from the observed variable with code TCCTD1 to TCCTD6 presented in the table below:

Table 2.17: Credit Refusal Component Scale

Symbol

Questions about observed variables
TCCTD	Credit denial
TCCTD1	Customers do not have collateral or guarantee
TCCTD2	Incomplete or missing customer financial statements transparent
TCCTD3	Clients are not capable of drafting business/project plans invest
TCCTD4	Low customer equity
TCCTD5	Insolvency
TCCTD6	The prospect of ecotourism development is not optimistic.

Credit continuation scale

Source: Appendix 1

Also applied in the same way as above, the credit continuation scale (TTCTD) is measured by 05 observed variables, from the observed variable with code TTCTD1 to TTCTD5, in which there are 04 observed variables evaluated according to customer-oriented criteria, 01 observed variable evaluated towards commercial banks. Specifically, the observed variables are given in the following table:

Table 2.18: Scale of continued credit components

Symbol

Questions about observed variables
TTCTD	Continue credit
TTCTD1	Customers add more collateral
TTCTD2	Customers with good credit and repayment history
TTCTD3	Customers with good financial capacity
TTCTD4	Have a good new business plan/investment project
TTCTD5	Banks lend more flexibly

Scale of difficulty in credit transactions with banks

Source: Appendix 1

To measure difficulties in credit transactions (KKCTD), 05 observed variables are used, from the observed variable with code KKCTD1 to KKCTD5. This scale is built based on the following contents: personal relationships, difficult procedures, collateral issues, financial capacity, and bad debt situation of the bank. The observed variables and corresponding codes are presented in the table below:

Table 2.19: Scale of difficulty components in credit transactions

Symbol

Questions about observed variables
KKCTD	Difficulty in credit transactions
KKCTD1	Customers have no personal relationship with the bank.
KKCTD2	Difficult/complicated loan procedures
KKCTD3	Insufficient collateral
KKCTD4	Must demonstrate sufficient financial capacity to carry out the work. debt payment
KKCTD5	The bank's bad debt situation is too high

Credit service quality scale

Source: Appendix 1

The scale for the credit service quality (CLTD) component is measured by 08 observed variables, from the observed variable with code CLTD1 to CLTD8, including the following observed variables:

Table 2.20: Credit service quality component scale

Symbol

Questions about observed variables
CLTD	Credit service quality
CLTD1	Fast loan review and decision time
CLTD2	Loan change notices are sent to customers promptly. time
CLTD3	Diverse credit products
CLTD4	The bank's equipment and technology are very modern.
CLTD5	Spacious bank offices and transaction offices
CLTD6	Wide and widespread banking network system
CLTD7	Good staff service attitude
CLTD8	Disbursement of loan capital on time as committed

Credit price scale

Source: Appendix 1

The credit price component (GCTD) is assessed based on customers' perception of the cost of using credit capital. This component is measured by 03 observed variables, from the observed variable with code GCTD1 to GCTD3, including 01 observed variable measuring the initial cost level, 01 observed variable measuring the monthly cost level, 01 observed variable measuring the cost level for the entire transaction.

Table 2.21: Credit price component scale

Symbol

Questions about observed variables
GCTD	Credit prices
GCTD1	The initially agreed interest rate/credit transaction fee is quite high.
GCTD2	The monthly interest/credit transaction fees are quite large.
GCTD3	The total payment per credit transaction is quite high.

Credit expansion scale

Source: Appendix 1

The study uses a scale to measure credit expansion (MRTD) including 03 observed variables, from observed variables with codes MRTD1 to MRTD3, presented in the table below:

Table 2.22: Credit expansion scale

Symbol

Statements
MRTD	Credit expansion
MRTD1	You will be willing to increase credit transactions with partners in terms of Number of uses for product or service
MRTD2	Are you willing to increase credit transactions with your partners in terms of digital? quantity of products and services
MRTD3	Are you willing to increase credit transactions with partners in terms of price? value of transaction

Source: Appendix 1

2.4.2.3. Research sample information

Directly distribute 385 questionnaires to credit officers at the Branch and Transaction Offices of Agribank Ben Tre, owners/managers of the ecotourism area. After the above subjects finished evaluating, the questionnaires were immediately collected, invalid questionnaires were eliminated, leaving 301 valid questionnaires. Based on personal information, in terms of gender, 148 people were male, accounting for 49.2%, and 153 people were female, accounting for 50.8%; in terms of age, 69 people were under 25 years old, accounting for 22.9%, 120 people were aged 25-35, accounting for 39.9%, 93 people were aged 36-45, accounting for 30.9%, 19 people were over 45, accounting for 6.3%; 75 credit officers account for 24.9%, 226 owners/managers account for 75.1% (Appendix 2).

2.4.2.4 Average scores for the scales

From the 301 collected survey forms, the data was entered into SPSS software, then the average score was calculated for each observed variable. We obtained the detailed results in Appendix 3. From the results of Appendix 3, we filtered the data and calculated the average score for each independent variable (by the arithmetic mean method) to see the level of agreement or disagreement with each factor. The average score of the factors of credit refusal, continued credit granting, credit transaction difficulties, credit quality and credit price were 3.71; 3.68; 3.76; 3.8; 4.04 respectively. Based on the scale, we see that the survey results agree with the above factors, in which the credit price factor at the Branch

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