Factors Affecting Customers' Use of Online Banking

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		ANH Hoang LE	--	2801	2022-08-20 04:00:35	\|
2	Reference format revised.	Lindsay Dong	-1319 word(s)	1482	2022-08-22 06:18:46	\| \|
3	Adjust figure legend	Lindsay Dong	Meta information modification	1482	2022-08-23 09:41:45	\|

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

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.

/media/item_content/202208/6303003cbec7esustainability-14-06021-g002.png

Figure 1. 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 $l o g_{2} (6) = 2.58$ . 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.

/media/item_content/202208/630300a2e56e5sustainability-14-06021-g003.png

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.

References

Jayawardhena, C.; Foley, P. Changes in the Banking Sector—The Case of Internet Banking Inthe UK. Internet Res. 2000, 10, 19–31.
Prendergast, G.P.; Marr, N.E. The Future of Self-Service Technologies in Retail Banking. Serv. Ind. J. 1994, 14, 94–114.
Mols, N.P. The Internet and Services Marketing—The Case of Danishretail Banking. Internet Res. 2000, 10, 7–18.
Tan, M.; Teo, T.S.H. Factors Influencing the Adoption of Internet Banking. J. Assoc. Inf. Syst. 2000, 1.
Daniel, E. Provision of Electronic Banking in the UK and the Republic of Ireland. Int. J. Bank Mark. 1999, 17, 72–83.
Al-Qeisi, K.; Dennis, C.; Alamanos, E.; Jayawardhena, C. Website Design Quality and Usage Behavior: Unified Theory of Acceptance and Use of Technology. J. Bus. Res. 2014, 67, 2282–2290.
AbuShanab, E.; Pearson, J.M. Internet Banking in Jordan: The Unified Theory of Acceptance and Use of Technology (UTAUT) Perspective. J. Syst. Inf. Technol. 2007, 9, 78–97.
Dajani, D.; Yaseen, S.G. The Applicability of Technology Acceptance Models in the Arab Business Setting. J. Bus. Retail. Manag. Res. 2016, 10. Available online: https://jbrmr.com/cdn/article_file/i-24_c-228.pdf (accessed on 21 March 2022).
Ajzen, I. The Theory of Planned Behavior. Organ. Behav. Hum. Decis. Processes 1991, 50, 179–211.
Fishbein, M.; Ajzen, I. Belief, Attitude, Intention and Behaviour: An Introduction to Theory and Research; Elsevier Science Publishing Co., Inc.: New York, NY, USA, 1975; Volume 27.
Davis, F.D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q. 1989, 13, 319–340.
Naruetharadhol, P.; Ketkaew, C.; Hongkanchanapong, N.; Thaniswannasri, P.; Uengkusolmongkol, T.; Prasomthong, S.; Gebsombut, N. Factors Affecting Sustainable Intention to Use Mobile Banking Services. SAGE Open 2021, 11, 21582440211029924.
Ananda, S.; Devesh, S.; Al Lawati, A.M. What Factors Drive the Adoption of Digital Banking? An Empirical Study from the Perspective of Omani Retail Banking. J. Financ. Serv. Mark. 2020, 25, 14–24.
Mortimer, G.; Neale, L.; Hasan, S.F.E.; Dunphy, B. Investigating the Factors Influencing the Adoption of M-Banking: A Cross Cultural Study. Int. J. Bank Mark. 2015, 33, 545–570.
Venkatesh, V.; Thong, J.Y.L.; Xu, X. Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Q. 2012, 36, 157–178.
Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User Acceptance of Information Technology: Toward a Unified View. MIS Q. 2003, 27, 425–478.
Yaseen, S.G.; El Qirem, I.A. Intention to Use E-Banking Services in the Jordanian Commercial Banks. Int. J. Bank Mark. 2018, 36, 557–571.
Compeau, D.R.; Higgins, C.A. Computer Self-Efficacy: Development of a Measure and Initial Test. MIS Q. 1995, 19, 189–211.
Fornell, C.; Larcker, D.F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res. 1981, 18, 39–50.
Carmines, E.; McIver, J. An Introduction to the Analysis of Models with Unobserved Variables. Political Methodol. 1983, 9, 51–102.
Hair, J.F.J.; Black, W.; Babin, B.; Anderson, R.; Tatham, R.L. Mutivariate Data Analysis; Pearson Prentice Hall: Upper Saddle River, NJ, USA, 2006; Volume 31.
Seth, N.; Deshmukh, S.G.; Vrat, P. Service Quality Models: A Review. Int. J. Qual. Reliab. Manag. 2005, 22, 913–949.
Laukkanen, T.; Sinkkonen, S.; Laukkanen, P. Communication Strategies to Overcome Functional and Psychological Resistance to Internet Banking. Int. J. Inf. Manag. 2009, 29, 111–118.
Kijsanayotin, B.; Pannarunothai, S.; Speedie, S.M. Factors Influencing Health Information Technology Adoption in Thailand’s Community Health Centers: Applying the UTAUT Model. Int. J. Med. Inform. 2009, 78, 404–416.
Kerem, K. Adoption of Electronic Banking: Underlying Consumer Behaviour and Critical Success Factors. Available online: https://www.semanticscholar.org/paper/Adoption-of-electronic-banking-%3A-underlying-and-Kerem/419a9497919289acea27baeb0c9e9909b340f278 (accessed on 21 March 2022).
Pikkarainen, T.; Pikkarainen, K.; Karjaluoto, H.; Pahnila, S. Consumer Acceptance of Online Banking: An Extension of the Technology Acceptance Model. Internet Res. 2004, 14, 224–235.
Chan, S.; Lu, M. Understanding Internet Banking Adoption and Use Behavior: A Hong Kong Perspective. J. Glob. Inf. Manag. (JGIM) 2004, 12, 21–43.
Farzin, M.; Sadeghi, M.; Yahyayi Kharkeshi, F.; Ruholahpur, H.; Fattahi, M. Extending UTAUT2 in M-Banking Adoption and Actual Use Behavior: Does WOM Communication Matter? Asian J. Econ. Bank. 2021, 5, 136–157.
Davis, F.D. User Acceptance of Information Technology: System Characteristics, User Perceptions and Behavioral Impacts. Int. J. Man-Mach. Stud. 1993, 38, 475–487.
Wallin Andreassen, T.; Lindestad, B. Customer Loyalty and Complex Services: The Impact of Corporate Image on Quality, Customer Satisfaction and Loyalty for Customers with Varying Degrees of Service Expertise. Int. J. Serv. Ind. Manag. 1998, 9, 7–23.
Thompson, C.J.; Locander, W.B.; Pollio, H.R. Putting Consumer Experience Back into Consumer Research: The Philosophy and Method of Existential-Phenomenology. J. Consum. Res. 1989, 16, 133–146.
Moore, G.C.; Benbasat, I. Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation. Inf. Syst. Res. 1991, 2, 192–222.

© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.

Upload a video for this entry

Information

Subjects: Business, Finance

Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

Anh Hoang Le

An Ha Thi Pham

Dong Xuan Pham

Eleftherios Thalassinos

View Times: 2.3K

Update Date: 23 Aug 2022

Table of Contents

Video Upload Options

Confirm

1. Introduction

2. Factors Affecting Customers' Use of Online Banking

References