Statistics on Number of Business Customers by Business Type

Qos Assurance Methods for Multimedia Communications zt2i3t4l5ee zt2a3gs zt2a3ge zc2o3n4t5e6n7ts low. The EF PHB requires a sufficiently large number of output ports to provide low delay, low loss, and low jitter. EF PHBs can be implemented if the output port's bandwidth is sufficiently large, combined with small buffer sizes and other network resources dedicated to EF packets, to allow the router's service rate for EF packets on an output port to exceed the arrival rate λ of packets at that port. This means that packets with PHB EF are considered with a pre-allocated amount of output bandwidth and a priority that ensures minimum loss, minimum delay and minimum jitter before being put into operation. PHB EF is suitable for channel simulation, leased line simulation, and real-time services such as voice, video without compromising on high loss, delay and jitter values. Figure 2.10 Example of EF installation Figure 2.10 shows an example of an EF PHB implementation. This is a simple priority queue scheduling technique. At the edges of the DS domain, EF packet traffic is prioritized according to the values agreed upon by the SLA. The EF queue in the figure needs to output packets at a rate higher than the packet arrival rate λ. To provide an EF PHB over an end-to-end DS domain, bandwidth at the output ports of the core routers needs to be allocated in advance to ensure the requirement μ > λ. This can be done by a pre-configured provisioning process. In the figure, EF packets are placed in the priority queue (the upper queue). With such a length, the queue can operate with μ > λ. Since EF was primarily used for real-time services such as voice and video, and since real-time services use UDP instead of TCP, RED is generally not suitable for EF queues because applications using UDP will not respond to random packet drop and RED will strip unnecessary packets. 2.2.4.2 Assured Forwarding (AF) PHB PHB AF is defined by RFC 2597. The purpose of PHB AF is to deliver packets reliably and therefore delay and jitter are considered less important than packet loss. PHB AF is suitable for non-real-time services such as applications using TCP. PHB AF first defines four classes: AF1, AF2, AF3, AF4. For each of these AF classes, packets are then classified into three subclasses with three distinct priority levels. Table 2.8 shows the four AF classes and 12 AF subclasses and the DSCP values for the 12 AF subclasses defined by RFC 2597. RFC 2597 also allows for more than three separate priority levels to be added for internal use. However, these separate priority levels will only have internal significance. PHB Class PHB Subclass Package type DSCP AF4 AF41 Short 100010 AF42 Medium 100100 AF43 High 100110 AF3 AF31 Short 011010 AF32 Medium 011100 AF33 High 011110 AF2 AF21 Short 010010 AF22 Medium 010100 AF23 High 010110 AF1 AF11 Short 001010 AF12 Medium 001100 AF13 High 001110 Table 2.8 AF DSCPs The AF PHB ensures that packets are forwarded with a high probability of delivery to the destination within the bounds of the rate agreed upon in an SLA. If AF traffic at an ingress port exceeds the pre-priority rate, which is considered non-compliant or “out of profile”, the excess packets will not be delivered to the destination with the same probability as the packets belonging to the defined traffic or “in profile” packets. When there is network congestion, the out of profile packets are dropped before the in profile packets are dropped. When service levels are defined using AF classes, different quantity and quality between AF classes can be realized by allocating different amounts of bandwidth and buffer space to the four AF classes. Unlike EF, most AF traffic is non-real-time traffic using TCP, and the RED queue management strategy is an AQM (Adaptive Queue Management) strategy suitable for use in AF PHBs. The four AF PHB layers can be implemented as four separate queues. The output port bandwidth is divided into four AF queues. For each AF queue, packets are marked with three “colors” corresponding to three separate priority levels. In addition to the 32 DSCP 1 groups defined in Table 2.8, 21 DSCPs have been standardized as follows: one for PHB EF, 12 for PHB AF, and 8 for CSCP. There are 11 DSCP 1 groups still available for other standards. 2.2.5.Example of Differentiated Services We will look at an example of the Differentiated Service model and mechanism of operation. The architecture of Differentiated Service consists of two basic sets of functions: Edge functions: include packet classification and traffic conditioning. At the inbound edge of the network, incoming packets are marked. In particular, the DS field in the packet header is set to a certain value. For example, in Figure 2.12, packets sent from H1 to H3 are marked at R1, while packets from H2 to H4 are marked at R2. The labels on the received packets identify the service class to which they belong. Different traffic classes receive different services in the core network. The RFC definition uses the term behavior aggregate rather than the term traffic class. After being marked, a packet can be forwarded immediately into the network, delayed for a period of time before being forwarded, or dropped. We will see that there are many factors that affect how a packet is marked, and whether it is forwarded immediately, delayed, or dropped. Figure 2.12 DiffServ Example Core functionality: When a DS-marked packet arrives at a Diffservcapable router, the packet is forwarded to the next router based on Per-hop behavior is associated with packet classes. Per-hop behavior affects router buffers and the bandwidth shared between competing classes. An important principle of the Differentiated Service architecture is that a router's per-hop behavior is based only on the packet's marking or the class to which it belongs. Therefore, if packets sent from H1 to H3 as shown in the figure receive the same marking as packets from H2 to H4, then the network routers treat the packets exactly the same, regardless of whether the packet originated from H1 or H2. For example, R3 does not distinguish between packets from h1 and H2 when forwarding packets to R4. Therefore, the Differentiated Service architecture avoids the need to maintain router state about separate source-destination pairs, which is important for network scalability. Chapter Conclusion Chapter 2 has presented and clarified two main models of deploying and installing quality of service in IP networks. While the traditional best-effort model has many disadvantages, later models such as IntServ and DiffServ have partly solved the problems that best-effort could not solve. IntServ follows the direction of ensuring quality of service for each separate flow, it is built similar to the circuit switching model with the use of the RSVP resource reservation protocol. IntSer is suitable for services that require fixed bandwidth that is not shared such as VoIP services, multicast TV services. However, IntSer has disadvantages such as using a lot of network resources, low scalability and lack of flexibility. DiffServ was born with the idea of solving the disadvantages of the IntServ model. DiffServ follows the direction of ensuring quality based on the principle of hop-by-hop behavior based on the priority of marked packets. The policy for different types of traffic is decided by the administrator and can be changed according to reality, so it is very flexible. DiffServ makes better use of network resources, avoiding idle bandwidth and processing capacity on routers. In addition, the DifServ model can be deployed on many independent domains, so the ability to expand the network becomes easy. Chapter 3: METHODS TO ENSURE QoS FOR MULTIMEDIA COMMUNICATIONS In packet-switched networks, different packet flows often have to share the transmission medium all the way to the destination station. To ensure the fair and efficient allocation of bandwidth to flows, appropriate serving mechanisms are required at network nodes, especially at gateways or routers, where many different data flows often pass through. The scheduler is responsible for serving packets of the selected flow and deciding which packet will be served next. Here, a flow is understood as a set of packets belonging to the same priority class, or originating from the same source, or having the same source and destination addresses, etc. In normal state when there is no congestion, packets will be sent as soon as they are delivered. In case of congestion, if QoS assurance methods are not applied, prolonged congestion can cause packet drops, affecting service quality. In some cases, congestion is prolonged and widespread in the network, which can easily lead to the network being "frozen", or many packets being dropped, seriously affecting service quality. Therefore, in this chapter, in sections 3.2 and 3.3, we introduce some typical network traffic load monitoring techniques to predict and prevent congestion before it occurs through the measure of dropping (removing) packets early when there are signs of impending congestion. 3.1. DropTail method DropTail is a simple, traditional queue management method based on FIFO mechanism. All incoming packets are placed in the queue, when the queue is full, the later packets are dropped. Due to its simplicity and ease of implementation, DropTail has been used for many years on Internet router systems. However, this algorithm has the following disadvantages: − Cannot avoid the phenomenon of “Lock out”: Occurs when 1 or several traffic streams monopolize the queue, making packets of other connections unable to pass through the router. This phenomenon greatly affects reliable transmission protocols such as TCP. According to the anti-congestion algorithm, when locked out, the TCP connection stream will reduce the window size and reduce the packet transmission speed exponentially. − Can cause Global Synchronization: This is the result of a severe “Lock out” phenomenon. Some neighboring routers have their queues monopolized by a number of connections, causing a series of other TCP connections to be unable to pass through and simultaneously reducing the transmission speed. After those monopolized connections are temporarily suspended, Once the queue is cleared, it takes a considerable amount of time for TCP connections to return to their original speed. − Full Queue phenomenon: Data transmitted on the Internet often has an explosion, packets arriving at the router are often in clusters rather than in turn. Therefore, the operating mechanism of DropTail makes the queue easily full for a long period of time, leading to the average delay time of large packets. To avoid this phenomenon, with DropTail, the only way is to increase the router's buffer, this method is very expensive and ineffective. − No QoS guarantee: With the DropTail mechanism, there is no way to prioritize important packets to be transmitted through the router earlier when all are in the queue. Meanwhile, with multimedia communication, ensuring connection and stable speed is extremely important and the DropTail algorithm cannot satisfy. The problem of choosing the buffer size of the routers in the network is to “absorb” short bursts of traffic without causing too much queuing delay. This is necessary in bursty data transmission. The queue size determines the size of the packet bursts (traffic spikes) that we want to be able to transmit without being dropped at the routers. In IP-based application networks, packet dropping is an important mechanism for indirectly reporting congestion to end stations. A solution that prevents router queues from filling up while reducing the packet drop rate is called dynamic queue management. 3.2. Random elimination method – RED 3.2.1 Overview RED (Random Early Detection of congestion; Random Early Drop) is one of the first AQM algorithms proposed in 1993 by Sally Floyd and Van Jacobson, two scientists at the Lawrence Berkeley Laboratory of the University of California, USA. Due to its outstanding advantages compared to previous queue management algorithms, RED has been widely installed and deployed on the Internet. The most fundamental point of their work is that the most effective place to detect congestion and react to it is at the gateway or router. Source entities (senders) can also do this by estimating end-to-end delay, throughput variability, or the rate of packet retransmissions due to drop. However, the sender and receiver view of a particular connection cannot tell which gateways on the network are congested, and cannot distinguish between propagation delay and queuing delay. Only the gateway has a true view of the state of the queue, the link share of the connections passing through it at any given time, and the quality of service requirements of the traffic flows. The RED gateway monitors the average queue length, which detects early signs of impending congestion (average queue length exceeding a predetermined threshold) and reacts appropriately in one of two ways: − Drop incoming packets with a certain probability, to indirectly inform the source of congestion, the source needs to reduce the transmission rate to keep the queue from filling up, maintaining the ability to absorb incoming traffic spikes. − Mark “congestion” with a certain probability in the ECN field in the header of TCP packets to notify the source (the receiving entity will copy this bit into the acknowledgement packet). Figure 3. 1 RED algorithm The main goal of RED is to avoid congestion by keeping the average queue size within a sufficiently small and stable region, which also means keeping the queuing delay sufficiently small and stable. Achieving this goal also helps: avoid global synchronization, not resist bursty traffic flows (i.e. flows with low average throughput but high volatility), and maintain an upper bound on the average queue size even in the absence of cooperation from transport layer protocols. To achieve the above goals, RED gateways must do the following: − The first is to detect congestion early and react appropriately to keep the average queue size small enough to keep the network operating in the low latency, high throughput region, while still allowing the queue size to fluctuate within a certain range to absorb short-term fluctuations. As discussed above, the gateway is the most appropriate place to detect congestion and is also the most appropriate place to decide which specific connection to report congestion to. − The second thing is to notify the source of congestion. This is done by marking and notifying the source to reduce traffic. Normally the RED gateway will randomly drop packets. However, if congestion If congestion is detected before the queue is full, it should be combined with packet marking to signal congestion. The RED gateway has two options: drop or mark; where marking is done by marking the ECN field of the packet with a certain probability, to signal the source to reduce the traffic entering the network. − An important goal that RED gateways need to achieve is to avoid global synchronization and not to resist traffic flows that have a sudden characteristic. Global synchronization occurs when all connections simultaneously reduce their transmission window size, leading to a severe drop in throughput at the same time. On the other hand, Drop Tail or Random Drop strategies are very sensitive to sudden flows; that is, the gateway queue will often overflow when packets from these flows arrive. To avoid these two phenomena, gateways can use special algorithms to detect congestion and decide which connections will be notified of congestion at the gateway. The RED gateway randomly selects incoming packets to mark; with this method, the probability of marking a packet from a particular connection is proportional to the connection's shared bandwidth at the gateway. − Another goal is to control the average queue size even without cooperation from the source entities. This can be done by dropping packets when the average size exceeds an upper threshold (instead of marking it). This approach is necessary in cases where most connections have transmission times that are less than the round-trip time, or where the source entities are not able to reduce traffic in response to marking or dropping packets (such as UDP flows). 3.2.2 Algorithm This section describes the algorithm for RED gateways. RED gateways calculate the average queue size using a low-pass filter. This average queue size is compared with two thresholds: minth and maxth. When the average queue size is less than the lower threshold, no incoming packets are marked or dropped; when the average queue size is greater than the upper threshold, all incoming packets are dropped. When the average queue size is between minth and maxth, each incoming packet is marked or dropped with a probability pa, where pa is a function of the average queue size avg; the probability of marking or dropping a packet for a particular connection is proportional to the bandwidth share of that connection at the gateway. The general algorithm for a RED gateway is described as follows: [5] For each packet arrival Caculate the average queue size avg If minth ≤ avg < maxth div.maincontent .s1 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 15pt; } div.maincontent .s2 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 15pt; } div.maincontent .p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; } div.maincontent p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; } div.maincontent .s3 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s4 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s5 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s6 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s7 { color: black; font-family:Wingdings; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s8 { color: black; font-family:Arial, sans-serif; font-style: italic; font-weight: bold; text-decoration: none; font-size: 15pt; } div.maincontent .s9 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s10 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 9pt; vertical-align: 6pt; } div.maincontent .s11 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 13pt; } div.maincontent .s12 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10pt; } div.maincontent .s13 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-d
Theoretical Basis of Lending to Corporate Customers at Commercial Banks

Corporate Clients

Business type

Table 4.18: Statistics of number of business customers by business type

Business type

Quantity	Percentage
State-owned enterprise	4	3.74
Private Enterprise	18	16.82
Company Limited	49	45.79
Joint Stock Company	22	20.56
Other	14	13.08
Total	107	100

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(Source: SPSS processing)

In 107 validly answered business customers collected, the type of business belonging to the Limited Liability Company group accounts for the highest percentage of 45.79% with 49 businesses. Second place is Joint Stock Companies with 22 businesses, accounting for 20.56%. The rest are Private enterprises, State-owned enterprises and some other types of businesses with a total of 36 businesses, accounting for 16.82%, 3.74% and 13.08% respectively.

Business size

Table 4.19: Statistics of number of corporate customers by business size

Business size

Quantity	Percentage
Medium Enterprise	28	26.17
Small Business	68	63.55
Micro Enterprise	11	10.28
Total	107	100

(Source: SPSS processing)

Among 107 validly responding enterprises, the number of small enterprises accounted for the highest proportion with 68 enterprises accounting for 63.55%, followed by medium-sized enterprises with 28 enterprises accounting for 26.17% and the number of micro-enterprises was 11 accounting for 10.28%.

Business activities

Table 4.20: Statistics on the number of corporate customers by business sector

Business activities

Quantity	Percentage
Industrial	27	25.23
Build	19	17.76
Trade – Services	41	38.32
Agriculture, forestry and fisheries	14	13.08
Other	6	5.61
Total	107	100

Vietinbank Branch 12's regular customers mainly focus on 3 business areas: trade and services with 41 enterprises accounting for 38.32%, industry with 27 enterprises accounting for 25.23%, construction with 19 enterprises accounting for 17.76%. The rest are in agriculture, forestry and fishery and some other fields of activity.

4.5.2 Scale analysis results

a) Cronbach's Alpha test

Table 4.21: Results of Cronbach's Alpha coefficient analysis

Corrected Item- Total Correlation	Cronbach's Alpha if Item Deleted
DTC	Cronbach's Alpha = 0.746
DTC1	0.505	0.703
DTC2	0.579	0.674
DTC3	0.585	0.673
DTC4	0.320	0.766
DTC5	0.569	0.678
SDU	Cronbach's Alpha = 0.776
SDU1	0.460	0.763
SDU2	0.555	0.733
SDU3	0.628	0.710
SDU4	0.578	0.725
SDU5	0.536	0.739
SDB	Cronbach's Alpha = 0.699
SDB1	0.464	0.647
SDB2	0.526	0.609
SDB3	0.479	0.639
SDB4	0.466	0.646
SCT	Cronbach's Alpha = 0.760
SCT1	0.518	0.726
SCT2	0.605	0.680
SCT3	0.618	0.671
SCT4	0.498	0.737
THH	Cronbach's Alpha = 0.745
THH1	0.520	0.699
THH2	0.524	0.696
THH3	0.560	0.676
THH4	0.556	0.677
TTC	Cronbach's Alpha = 0.770
TTC1	0.480	0.482
TTC2	0.606	0.289

Based on the results of two Cronbach's Alpha tests, comments on the Cronbach Alpha coefficient and the variable-total correlation coefficient are as follows:

The Reliability component has 5 observed variables, all of which have a variable-total correlation coefficient greater than 0.3, so all are accepted. In addition, the Cronbach Alpha reliability coefficient is 0.746 (greater than 0.6), so the component scale is accepted for inclusion in the next factor analysis.

The Responsiveness component has 5 observed variables, all of which have a variable-total correlation coefficient greater than 0.3, so all are accepted. In addition, the Cronbach Alpha reliability coefficient is 0.776 (greater than 0.6), so the component scale is accepted for inclusion in the next factor analysis.

The Assurance component has 4 observed variables, all of which have a variable-total correlation coefficient greater than 0.3, so all are accepted. In addition, the Cronbach Alpha reliability coefficient is 0.699 (greater than 0.6), so the component scale is accepted for inclusion in the next factor analysis.

The Empathy component has 4 observed variables, all of which have a variable-total correlation coefficient greater than 0.3, so all are accepted. In addition, the Cronbach Alpha reliability coefficient is 0.760 (greater than 0.6), so the component scale is accepted for inclusion in the next factor analysis.

The Tangibles component has 4 observed variables, all of which have a variable-total correlation coefficient greater than 0.3, so all are accepted. In addition, the Cronbach Alpha reliability coefficient is 0.745 (greater than 0.6), so the component scale is accepted for inclusion in the next factor analysis.

The Accessibility component has 3 observed variables, of which the observed variable TTC3 has a variable-total correlation coefficient of 0.272 < 0.3 so it was eliminated. In terms of economic significance, the observed variable TTC3 (Simple, fast and convenient loan procedures) is not highly appreciated by customers because the bank's loan procedures are still cumbersome, causing customers to wait for a long time, so this observed variable cannot explain the concept and was eliminated. The remaining 2 variables, TTC1 and TTC2, meet the requirement of a variable-total correlation coefficient greater than 4 so they are accepted. The Cronbach Alpha reliability coefficient after eliminating the variable TTC3 is 0.770 (greater than 0.6), so the component scale is accepted for inclusion in the next factor analysis.

b) Exploratory factor analysis (EFA)

The set of observed variables that have been tested for reliability (24 independent variables on factors affecting short-term credit quality) is included in factor analysis. KMO and Bartlett's tests in factor analysis show that the KMO coefficient is 0.755 (greater than 0.5), proving that factor analysis is suitable for the research data. The Bartlett's test value is 1589.064 with a significance level of Sig. = 0.000 < 0.05, so the observed variables are correlated with each other in the whole, which proves that the data used for factor analysis is completely suitable.

At Eigenvalues > 1 and with Principal Components extraction and Varimax rotation, factor analysis extracted 6 factors from 24 variables.

The observed variance extracted is 60.489% (greater than 50%) which meets the requirement, then it can be said that these 6 factors explain 60.489% of the variation in the data.

Based on the analysis of the Rotated Component Matrix table, no variable has a factor loading coefficient < 0.5. However, there are 2 variables SDU2 and DTC1 with factor loading coefficients that do not achieve high discrimination between factors, specifically less than 0.3, so they will be eliminated. The remaining set of observed variables is included in the second factor analysis.

EFA analysis 2

The observed variables that successfully underwent the first factor analysis (22 variables) were entered into the second factor analysis, resulting in a slight decrease in KMO to 0.724, Bartlett's test was statistically significant (Sig. = 0.000 < 0.05) and the total variance extracted was 61.758% (greater than 50%) meeting the requirements. In the second factor analysis, one more observed variable was eliminated, SDU3, because its factor loading coefficient did not achieve high discrimination between factors. Specifically, the observed variable SDU3 measures both factors and the difference between the two weights is less than 0.3, so it will be eliminated.

EFA analysis 3

After eliminating unsatisfactory variables in the section of factors affecting customers' general perception of short-term lending service quality measured by 22 observed variables. The results of the third factor analysis showed that the KMO coefficient achieved decreased slightly to 0.715 but still met the requirements (greater than 0.5), with Eigenvalues = 1.194 > 1, there were 6 factors extracted from the model. The Cumulative variance value of 62.420% showed that the extracted variance met the requirements (greater than 50%) and showed that the ability to use these 6 factors to explain 21 observed variables was 62.420%.

Through 3 factor analysis for independent variables, the factor correlation coefficient obtained from Varimax coordinate rotation method. There are 6 factors representing short-term credit quality with 21 characteristic variables rearranged as follows:

- The first factor has 3 observed variables including 1 variable belonging to the Responsiveness factor (SDU) and 2 variables belonging to the Accessibility factor (TTC). This shows that, in reality, customers identify these observed variables as belonging to the same group of factors. This factor includes factors related to accessibility such as the location of the bank, the ability to access information or the working hours of Vietinbank branch 12. Name this new factor Accessibility.

- The second factor has 4 observed variables belonging to the Tangibles group. This shows that, in reality, customers identify these observed variables with a group of factors similar to the theoretical model including issues of scale of operation, facilities, service equipment, staff uniforms and other services supporting credit activities at Vietinbank branch 12. Name this new factor Tangibles.

- The third factor has 4 observed variables belonging to the group of Empathy (SCT). This shows that, in reality, customers identify these observed variables with the same group of factors as the theoretical model including issues of concern and companionship of

Vietinbank branch 12 staff with customers. Name this new factor Empathy.

- The fourth factor has 4 observed variables belonging to the Trust group (DTC). This shows that, in reality, customers identify these observed variables with a similar group of factors as the theoretical model including issues of policies, regulations, credit procedures and the ability to create trust for customers. Name this new factor Trust.

- The fifth factor has 4 observed variables belonging to the Assurance group (SDB). This shows that, in reality, customers identify these observed variables with a similar group of factors as the theoretical model, including issues of customer service capacity of employees and the ability to ensure safety in transactions and information security. Name this new factor Assurance.

- The sixth factor has 2 observed variables belonging to the Responsiveness group (SDU). This shows that, in reality, customers identify these observed variables with the same group of factors as the theoretical model including issues of service staff's desire and readiness to provide service to customers. Name this new factor Responsiveness.

Table 4.22: Results of exploratory factor analysis

Component
	1	2	3	4	5	6
TTC1	0.887
SDU1	0.854
TTC2	0.817
THH3		0.748
THH4		0.746
THH1		0.732
THH2		0.716
SCT2			0.811
SCT3			0.796
SCT1			0.745
SCT4			0.683
DTC3				0.767
DTC5				0.741
DTC2				0.686
DTC4				0.598
SDB4					0.743
SDB3					0.713
SDB1					0.698
SDB2					0.694
SDU5						0.880
SDU4						0.845

(Source: SPSS processing)

c) Test the scale of the dependent variable with the General Perception scale

Table 4.23: Results of Cronbach's Alpha coefficient analysis of dependent variable

Corrected Item- Total Correlation	Cronbach's Alpha if Item Deleted
CNC	Cronbach's Alpha = 0.746
CNC1	0.546	0.693
CNC2	0.606	0.622
CNC3	0.565	0.670

(Source: SPSS processing)

Similar to the scale of independent variables on short-term credit quality of Vietinbank branch 12, the scale of general customer perception is also preliminarily evaluated by calculating Cronbach Alpha reliability coefficient and EFA exploratory factor analysis with the same testing standards as presented in the above section.

The results of testing the Cronbach Alpha coefficient of customers' general perception of the quality of short-term lending services at Vietinbank branch 12 are presented in the table of contents, showing that the variable-total correlation coefficient of the observed variables is greater than 0.3 and the Cronbach Alpha coefficient is 0.746, satisfying the condition. Therefore, the observed variables of customers' general perception are all used for the next EFA analysis.

Table 4.24: Results of exploratory factor analysis of dependent variable

KMO

0.685
Sig.	0.000
Cumulative %	66,359

(Source: SPSS processing)

The subsequent exploratory factor analysis showed that the KMO coefficient met the requirements (equal to 0.685), exploratory factor analysis is suitable for real data. Bartlett's test value has a significance level (Sig. =0.000 < 0.05), so the observed variables have a linear correlation with the representative variable. At the Eigenvalues > 1 level and with the Principal Components extraction method and Varimax rotation, the factor analysis extracted a single factor with a fairly high factor loading coefficient from 1 observed variable and with the extracted variance of 66.359% (greater than 50%) meeting the requirements.

d) Correction model

After analyzing the collected data through the steps of Cronbach Alpha reliability analysis and EFA exploratory factor analysis, the research model was adjusted to include 6 independent variables ( Accessibility, Tangibles, Empathy, Reliability, Assurance, Responsiveness ) to measure the dependent variable, which is the General Perception of Customers. All 6 variables affect and increase or decrease the customer's perception of the scales. The research model was adjusted from a scale of 6 factors with 25 questions to a scale of 6 factors with 21 questions:

Accessibility

Response

Tangible means

SERVICE QUALITY

Guarantee

Sympathy

Reliability

Figure 4.7: Diagram of the revised research model

Hypotheses

- H1: The Accessibility component has a positive impact on customers' general perception of short-term loan service quality.

- H2: Tangibles component has a positive impact on customers' overall perception of short-term loan service quality.

- H3: The Empathy component has a positive impact on customers' general perception of short-term loan service quality.

- H4: The Reliability component has a positive impact on customers' overall perception of short-term loan service quality.

- H5: The Assurance component has a positive impact on customers' overall perception of short-term loan service quality.

- H6: The Responsiveness component has a positive impact on customers' overall perception of short-term loan service quality.

e) Multiple regression analysis

After testing the scale by EFA, Cronbach Alpha, we have identified 6 factors affecting the General Perception of the quality of short-term lending services of customers. The values of the factors used for regression are the average values of the observed variables that have been tested. The overall regression method of variables (enter method) is used to determine whether this correlation is linear or not and the importance of each factor to service quality.

To test the six hypotheses H1, H2, H3, H4, H5, H6, a multiple linear regression model is developed as follows:

CNC = 𝛃 𝟎 + 𝛃 𝟏 TTC + 𝛃 𝟐 SCT + 𝛃 𝟑 THH + 𝛃 𝟒 SDB + 𝛃 𝟓 DTC + 𝛃 𝟔 SDU

In which: Regression coefficients: β 0 , β 1 , β 2 , β 3 , β 4 , β 5 , β 6

Dependent variable: CNC – General Perception component Independent variable includes: TTC – Accessibility component

SCT – the Empathy component

THH – Tangible Media component SDB – Assurance component

DTC – Reliability component SDU – Responsiveness component

Table 4.25: Regression analysis results

Coefficient		Significance level	VIF
Not standardized	Standardization	Significance level	VIF
TTC	0.318	0.322	0.000	1,206
THH	0.077	0.074	0.158	1,133
SCT	0.077	0.074	0.137	1,021
DTC	0.402	0.348	0.000	1,217
SDB	0.444	0.382	0.000	1,115
SDU	0.096	0.115	0.030	1,165

(Source: SPSS processing)

Model fit testing

To test the suitability between the components TTC, SCT, THH, SDB, DTC, SDU with CNC, the study uses linear regression with the one-step input method. Thus, the components TTC, SCT, THH, SDB, DTC, SDU are independent variables and CNC is dependent variable - Dependents will be put into the regression step by step. The results obtained show that the significance level Sig. is very small 0.000 < 0.05 and the adjusted R 2 coefficient is 0.525, proving the suitability of the model. Thus, 52.5% of the variation in general perception of the quality of short-term lending services at Vietinbank branch 12 of customers is explained by the linear relationship of the independent variables of the model.

Next, the F-test is a hypothesis test about the goodness of fit of the overall linear regression model. The idea behind this test about the linear relationship between the dependent variable and the independent variable is that it examines whether the dependent variable is linearly related to the entire set of independent variables.

Hypothesis H 0 : β 1 = β 2 = β 3 = β 4 = β 5 = β 6 = 0 <=> R 2 = 0

If the null hypothesis H0 is rejected we can conclude that the combination of variables present in the model can explain the change in the dependent variable, this means that the built model fits the data set.

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