Factors Affecting Customers' Use of Online Banking: History
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The online banking is a banking service that allows users to be “at home” and use the service at any time through an internet connection. In online banking services, the restrictions of time and geography have been removed, and customers can access their bank accounts and make transactions at almost anytime and anywhere via computers and an internet gateway.

  • intention
  • online banking services
  • customers

1. Introduction

From the early 1990s to the present, digital technology has continuously developed, reflecting the enormous revolution of new technologies and their application to corporations, firms, customers, as well as governments. In particular, the Internet is the most rapidly developing form of media in history, with the number of users increasing significantly year by year. The Internet has changed the business method in many fields. In the banking sectors, the Internet has created big changes in this business sector [1]. The banking sector is one of the sectors most affected by technology [2][3] due to its ability to process and provide service information to all users [4]. In addition, increasing competition in the banking sector forces suppliers to develop and use alternative distribution channels [5]. Therefore, the application of information technology and the Internet to create new products is a revolution in the approach of banks to provide convenient, reliable, and fast services to customers [4].
Many individual customers are reluctant to adopt and use online banking services because of many reasons such as society, culture, and economy [6][7][8]. This is because of two reasons: First, the perception of financial service customers is still limited, sometimes creating “security holes”, especially individual customers. People are still not aware of the confidentiality of personal information such as full name, identity card number, passport, address, date of birth, and account number. It greatly increases the risk of safety loss to customers themselves as well as commercial banks. Second, individual customers often have fewer online banking transactions than corporation customers, especially customers in small cities and rural and mountainous areas.

There are many studies aimed at determining the factors impacting the intention and decision to choose online banking. However, these studies mainly use the theory of reasoned action (TRA) [9][10], theory of planned behavior (TPB), and technology acceptance model (TAM) [11]. For example, the study of Naruetharadhol et al. [12] developed a model based on TAM to examine the factors affecting the intention to use mobile payments with 688 mobile payment service users in Thailand. Ananda et al. [13] extended TAM to examine the factors influencing the intention to use digital banking with 200 individual customers of seven local banks and two Islamic banks across Oman. Mortimer et al. [14] developed a model based on TAM to empirically examine the motivations affecting the intention to use mobile banking of 348 consumers in Thailand and Australia. These studies have shown the factors affecting the consumer’s acceptance of using banking services. However, according to Venkatesh et al. [15], the studies based on the above theories are not really comprehensive. On the basis of synthesizing the above theories in the most comprehensive way, Venkatesh et al. [16] proposed the Unified Theory of Acceptance and Use of Technology (UTAUT). Due to its high generalizability, UTAUT is used by many researchers to assess the adoption and use of technology [7][8][17][18]. However, UTAUT has still not covered all the factors affecting the adoption and use of technology [15]. Therefore, UTAUT2, as an extension of UTAUT, can assess the factors affecting the adoption and use of online banking services to overcome the limitations of previous studies.

Besides, the previous studies used structural equation modeling (SEM) to estimate the parameters and draw conclusions about the research hypothesis. However, SEM only evaluates the linear relationship between variables in the model but can not evaluate the non-linear relationship. To solve this issue, the artificial neural network model (ANN) can be used to evaluate the non-linear relationship between variables in the model.

2. Factors Affecting Customers' Use of Online Banking

The demographic structure of the samples is shown in Table 1.

Table 1. Demographic Structure of Participants.
  Frequency Percent Valid Percent Cumulative Percent
Age 18–35 132 29.8 29.8 29.8
35–45 239 54.0 54.0 83.7
45 or more 72 16.3 16.3 100.0
Total 443 100.0 100.0  
Gender Female 244 55.1 55.1 55.1
Male 199 44.9 44.9 100.0
Total 443 100.0 100.0  
Degree Bachelors 211 47.6 47.6 47.6
Doctorate 79 17.8 17.8 65.5
Masters 153 34.5 34.5 100.0
Total 443 100.0 100.0  

Table 2 shows the results of the reliability analysis of the scales corresponding to 8 factors in the model: effort expectancy, performance expectancy, brand image, perceived risk, cost value, social influence, behavioral intention to use online banking services, and decision to choose to use online banking services.

Table 2. Reliability Analysis.
Factors Items Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item-Total Correlation Cronbach’s Alpha if Item Deleted Cronbach’s Alpha
Effort Expectancy DSD1 10.13 3.396 0.601 0.751 0.797
DSD2 10.05 3.192 0.606 0.747
DSD3 10.07 3.280 0.571 0.764
DSD4 10.10 2.990 0.658 0.720
Performance Expectancy HI1 9.49 3.065 0.637 0.790 0.827
HI2 9.35 2.964 0.649 0.784
HI3 8.86 2.798 0.707 0.756
HI4 8.76 2.865 0.623 0.797
Perceived Risk RR1 8.33 3.037 0.637 0.812 0.838
RR2 7.85 3.221 0.651 0.803
RR3 8.28 3.088 0.710 0.777
RR4 8.42 3.153 0.686 0.788
Brand Image HA1 10.19 3.351 0.638 0.801 0.833
HA2 10.21 3.484 0.660 0.790
HA3 10.23 3.397 0.669 0.786
HA4 10.25 3.554 0.687 0.779
Cost Value CP1 10.15 4.320 0.631 0.872 0.885
CP2 10.16 4.218 0.710 0.867
CP3 10.14 3.988 0.805 0.830
CP4 10.16 3.830 0.860 0.808
Social Influence XH1 10.02 3.323 0.641 0.760 0.812
XH2 9.96 3.324 0.620 0.769
XH3 9.98 3.327 0.600 0.779
XH4 10.13 3.098 0.662 0.749
Behavioral Intention YD1 10.22 4.171 0.739 0.798 0.855
YD2 10.17 4.429 0.664 0.829
YD3 10.24 4.264 0.751 0.794
YD4 10.16 4.385 0.640 0.840
Decision to choose LC1 6.81 2.184 0.687 0.813 0.847
LC2 6.74 1.887 0.770 0.732
LC3 6.79 2.086 0.691 0.810

The result of the estimations of SEM is presented in Figure 1.


Figure 2. The Structural Equation Model. Effort expectancy (DSD), performance expectancy (HI), perceived risk (RR), brand image (HA), cost value (CP), social influence (XH), behavioral intention (YD), decision to choose (LC).

Table 3 presents the result of the intercorrelation matrix, the values of average variance extracted (AVE), and the composite reliability (CR) of each scale corresponding to each factor in the model. The result shows that AVEs are all greater than 0.5. Therefore, all of the factors in the model converge [19].

Table 3. Intercorrelation matrix, AVE, and CR.
  CR AVE Effort Expectancy Cost Value Performance Expectancy Perceived Risk Brand Image Social Influence Behavioral Intention
Effort Expectancy 0.798 0.598 0.706            
Cost Value 0.892 0.678 0.372 0.824          
Performance Expectancy 0.829 0.549 0.209 0.458 0.741        
Perceived Risk 0.840 0.568 −0.314 −0.447 −0.301 0.754      
Brand Image 0.835 0.558 0.303 0.457 0.322 −0.370 0.747    
Social Influence 0.813 0.521 0.153 0.262 0.256 −0.168 0.294 0.722  
Behavioral Intention 0.858 0.603 0.495 0.636 0.575 −0.537 0.564 0.417 0.777

Table 4 shows that the Chi-square/df value of 2.124 is lower than the threshold of 3, recommended by Carmines and McIver [20]. The values of RFI, AGFI, GFI, and NFI are 0.870, 0.865, 0.890, and 0.887, respectively. For the CFI, TLI, and IFI, the obtained values are all greater than 0.90. The RMSEA is also in the desired range between 0.05 and 0.08 [21]. Thus, the SEM is consistent with the data.

Table 4. The SEM Model’s Goodness of Fit Criteria.
Criteria Value Criteria Value
Chi-square 860.212 NFI 0.887
p-value 0.000 CFI 0.936
Chi-square/df 2.124 TLI 0.927
RFI 0.870 IFI 0.937
AGFI 0.865 RMSEA 0.057
GFI 0.890    

The estimation result of SEM shows that the factors affecting the behavioral intention to use online banking services are cost value, performance expectancy, perceived risk, brand image, social influence, effort expectancy. Therefore, these six factors will be brought to the input layer of the MLP model. The output layer is the behavioral intention to use online banking services factor. To the hidden layer, in the case of six input factors, the number of neurons in the hidden layer is log2(6)=2.58. Thus, the number of neurons in the hidden layer is 3. The Sigmoid function is used as the activation function of the neurons in the hidden and the output layers. It uses 90% of the sample data to train the model, and the remaining 10% is used to test the accuracy of the model. An MLP model is shown in Figure 2.


Figure 2. MLP model. Effort expectancy (DSD), performance expectancy (HI), perceived risk (RR), brand image (HA), cost value (CP), social influence (XH), behavioral intention (YD).

Image, and social influence all positively impact the intention to use online banking services. Thus, increasing the factors of performance expectancy, cost value, effort expectancy, brand image, and social influence can increase the customer’s intention to use online banking services. While the perceived risk has a negative impact on the intention to use online banking services. That means when customers feel that online banking services are risky, their intention to use online banking services will decrease

At the same time, the intention to use online banking services also has a positive impact on the decision to choose to use online banking services. This result indicates that when customers form an intention to use, they will quickly make a decision to choose online banking services. Thees findings are supported by Venkatesh et al. [15], Polatoglu and Ekin [22], Suganthi [23], Hernandez and Mazzon [24], Poon [25]; Fishbein and Ajzen [10], Taylor and Tood [26], Davis [11], Taylor and Todd [26], Kijsanayotin et al. [27], Tarhini et al. [17], Gupta and Arora [28], Alalwan et al. [29], Yaseen and Qirem [30], Rambocas et al. [31], Linh et al. [32]. Moreover, these results also shed light on the validity of the UTAUT2 model when conducting research on the adoption and use of technology in the Vietnamese market.

There are different impact levels of factors on intention to use online banking services between the MLP and SEM models. Specifically, the SEM model shows that the order of factors affecting the intention to use online banking services from strong to weak is effort expectancy, performance expectancy, perceived risk, brand image, social influence, and cost value, respectively. Meanwhile, the MLP model shows that the order of factors affecting the intention to use online banking services from strong to weak is performance expectancy, cost value, effort expectancy, brand image, perceived risk, and social influence, respectively. From the economic perspective, the results obtained from the MLP model show a better fit than the SEM model.

This entry is adapted from the peer-reviewed paper 10.3390/su14106021


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