Journal of Internet Banking and Commerce ISSN: 1204-5357
ISSN: 1204-5357
+44-175-271-2024Mahmood A Awan*
Associate Professor, SolBridge International School of International Business, Daejeon, South Korea, Tel: +821072630980; Email: mawan14143@solbridge.ac.kr
Habib Ullah Khan
Assistant Professor, College of Business and Economics (CBE), Qatar University, Doha, Qatar
Ho Han Chiang
Assistant Professor, SolBridge International School of International Business, Daejeon, South Korea
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Level of service quality (SQ) provided by an organization creates and develops company competitive advantage for it in the market place. The increasing technological service facilitators (e-services) create a new platform and bolster the banking industry in order to provide a high quality of service. Online banking provides a new way to connect with customers. The current research accrued data pertaining to customers’ views about the quality of online banking in two emerging economies: China and Kingdom of Saudi Arabia (KSA). Two major measuring scales - E-S-QUAL (a three item scale) and E-RecS-QUAL (a seventeen item scale) are used for this study. Data was collected through online survey in both countries covering a sample of 222 customers. The results showed similarities between the two countries. Loyalty intention is affected by perceived value and privacy while system is also affected by perceived value. While fulfillment impacts on loyalty intention in China, it does not influence any variables in KSA. Compensation impacts perceived value in China but shows no significant impact in KSA. In both countries, loyalty intention is more valuable than perceived value. Thus the study revealed an in-depth understanding of a consumer’s perceptions about the online services of various banking institutions in KSA and China.
Online Banking Service, E-S-Qual, E-Recs-Qual, China, Saudi Arabia, Satisfaction, Loyalty Intention
With the advancements in internet technology, banks have reengineered their operations globally by offering Internet-based banking services. Further, due to the emergence of low cost technologies, smart card technology, software cryptography and others, new players have entered into this industry as well [1,3]. This has created intense competition in the banking industry and hence customer service has become a big concern for them to attain sustainability and competitive advantage in the ever growing market.
Studies have shown that personal interaction between bank employees and customers is considered vital to determine the quality of customer service [4-6]. In an online banking scenario, the degree of service quality is a major concern. Thus if banks wish to develop strong relationships with their customers, they must not only provide easy access to the new banking but it also must be coupled with top class online service. This online banking service, also referred to as E-service, has evolved as a mode to offer better services [7]. Rowley [8] has listed some vital themes of the quality of E-services. These include the dimensions and measurement of E-service, elements of the web experience, relationship between the web experience, trust, customer satisfaction intention to purchase and loyalty.
Parasuraman et al. [9] have constructed a comprehensive scale with many components to measure the service quality of websites, which is called E-S-Qual scale. This model also has a subscale called E-RecS-Qual which deals with the service problems raised online in the process of inquiries and their three attributes; responsiveness, compensation, and contact.
Several studies have been done to gauge the consumer perception on internet banking and E-service quality. For example, Loonam et al. [10] revealed that the best way to increase the comfort of online banking was to improve the user friendliness and create a niche among members of the market. Similarly, Baskar et al. [11] have studied the intricacies of net banking and its feedback from the customers in an Indian context. Their findings revealed that the customer satisfaction mainly depends on the quality of service, efficacy of the information system, and the product designed. The effects of online service quality on the satisfaction levels of the customers and hence on the customer orientation of banking is studied by Herington et al. [12] which revealed that an organized and structurally designed website can build strong relationship with customers.
Most of previous research adopt the data from the U.S.A as the context of research, however, there might be differences across countries and cultures in consumers’ perceptions of service quality and in fact all the studies to date on the E-service quality of banks are lacking in the area of demonstrating the impact which various cultures may have on service quality in an online context. That is why this current research attempts to study the differences as well as the variances in customer’s perceptions of online banking service quality in China and Kingdom of Saudi Arabia (KSA). The author decided to choose Middle East and East Asia because of the big cultural differences between these regions in the Asian context, which obviously affect online behavior of customers and in turn influence customer perception as well as satisfaction in e-commerce. China and Kingdom of Saudi Arabia (KSA) are selected as regional leaders for the East Asia and Middle East respectively in terms of economy: China is the world’s second largest economy and also the leading in the East Asia with GDP of $10,666 in 2015 [13] whereas KSA is the biggest economy in the Middle East area with $646 billion of 2015 GDP [13]. Also each of them is the representative for the regional culture: China Confucianism along with its Buddhism had a huge effect on other neighbors such as Japan and South Korea while KSA represents for the Islamic economies in the Middle East, which heavily relies on Maqasid al-Shari‘ah objectives to adjust business, particularly in service sector. The proven models and techniques of E-S-Qual and E-RecS-Qual are used to access the quality of online and web-based services offered by the banking institutions across China and KSA.
The paper will proceed as follows. The literature review pertaining to the quality of service and the construct of e-service quality as well as the impact of culture on online behavior are provided in the following section, while the research methodology and the analysis carried out and explained in the subsequent section. The next section deals with the discussion about the findings of the study. The conclusions, limitations, and further scope for other research are briefed in the final section.
Service Quality and e-Service
The perceptual gap between the customer’s expectations and the service performance provided by the company can be understood as the Service Quality [14-16]. For any company, to develop competitive edge and to assess its service offering in meeting the customer expectation, the interactive information service (E-service) would be of great use [7,8,17,18]. Similarly, concepts such as the dimensions and measurements of E-service, the web experience and trust, customer satisfaction, purchase inclination and reliability are part of the E-service literature [8,19]. Therefore, over the decades, there has been a shift in the markets from the conventional service quality which has given prominence to the role of human beings in service delivery to the role of technological service facilitators [20-22]. Hongxiu et al. [12] conducted an empirical study on the online travel service to measure the effectiveness of e-service. The results revealed that a customer aspires the companies to maintain trust in relations and user friendliness with respect to the operations. It is also observed from the study that the perception of the customers about the system reliability, availability and responsiveness have a bearing on their opinion about the quality of service [24,25], but this influence is not as strong as that of trust and ease of use.
SERVQUAL and E-S-Qual Models
The demand to capture the customer’s pulse coupled with the increasing competition created a necessity for the scales to measure the level of quality of service. As a result, the SERVQUAL model and E-SERVQUAL models are developed to assess the perception of the customer about the service quality [20]. The latter one is developed by overcoming some of the drawbacks of the former one. The work of Loonam et al. [10] found a method to quantify the quality of the online service by considering its usefulness and level of customization.
Kim et al. [25] suggested through their work that rather by assessing the quality of entire service arena, assessing the quality of website alone is more important. However the work is very much censured. Based on web characteristics a four point scale [ease of use, aesthetic design, processing speed and security] SITEQUAL is developed by Yoo et al. [26]. The buying process of the consumer is given less importance in the studies thereby limiting the dimensions of service quality [27-29].
In light of these different ventures in addressing the issues in upgrading the drop back of online banking service delivery, Parasuraman et al. [9] suggested a measure like the multiple-item scale (E-S-Qual.) that helps in quantifying the quality of service rendered by the websites. This model has a sub-scale called E-RecS-Qual which deals with the service problems raised in the process of inquiries and has three attributes: responsiveness, compensation, and contact. This model is empirically tested using structural equation modeling and is validated for the goodness of fit [30,31].
As per Yen et al. [32], having the knowledge about the organization and its service efficacy has had much impact on the customer attraction. Many studies affirmed the relation between customer satisfaction and the service quality of the banks, but the information revolution has made it more significant [33-35]. Karjaluoto et al. [36] added by inferring that the customer satisfaction or dissatisfaction with the existing banking services and the knowledge of the customer in computer usage dictates their attitude towards electronic banking.
E-service in Relation to Loyalty Intention
Customer loyalty is the most precious intangible asset of a modern company. High loyalty is not only the driving factor to compete but also the fundamental guarantee of enterprise’s stability. In the context of service business, Provided online services of banks can have huge effect on customer loyalty in both direct and indirect way [16,37].
Study of Yen et al. [32] on the relationship between e-service and loyalty intention resulted in the findings showed that e-service quality dimensions consisting of efficiency, privacy protection, contact, fulfillment, and responsiveness have statistically significant impacts on buyer's disconfirmation, which is paired with their satisfaction. In other words, their satisfaction is positively associated with loyalty intentions. The relationship between e-commerce and loyalty intention is illustrated in the Figure 1 below.
Figure 1: The proposal model of relationship between E-Service and Loyalty Intention.
The Quality of E-service in Banks
Various factors were identified by authors for gauging the quality of e-service; however, the technological aspects of website delivery are recognized as the most important for delivering e-service [38,39]. In addition, the constraints such as security related, market related, procedure related issues and other issues are found to have an influence on eservice quality [40,41]. Various dimensions identified in net banking phenomenon by Ibrahim et al. [42] are accessibility based, reliability based, and customer centric attitude based.
In a study about the consumer perception on internet banking and E-Service quality in Ireland, Loonam et al. [10] revealed that the best way to increase the comfort of net banking is to improve the user friendliness and create a niche among the other members of the market. Therefore, it can be understood that the interaction of online marketing and customer oriented marketing strategies influence the level of penetration and the pace of e-banking services [43].
As per the work of Jun et al. [44], various dimensions linked with e-banking are identified and are regrouped under three categories: customer service quality, banking service product quality, and online systems quality. Numerous studies have concluded that the most frequently mentioned dimensions like trust, accountability and exactness are the main sources of satisfaction or dissatisfaction [40,45-48]. Baskar et al. [11] have studied the intricacies of net banking and its feedback from the customers in the Indian context. Their findings reveal that customer satisfaction is mainly dependent on the quality of service, information system, and product designed.
Al-Hawari et al. [49] have identified that customer retention is the main objective of providing quality online service. The impact of online service quality on the satisfactory levels of the customer and hence on the customer loyalty of banking is discussed by Herington et al. [12]. It has been concluded that focusing on the excellence of service through the internet, and attending the personal needs of the customers in online situations through organized website, can build a strong relationship with customers.
The work of Kim et al. [50] added another view of the subject. It was concluded that if banks wish to develop strong relationships with customers, they must provide easy access to the net banking coupled with reliability in the website [51,52]. Herington et al. [12] have provided an insight about the cause and effect of Online Service quality in customer group and their perceived value. However, all the studies mentioned are lacking the impact of various cultures on service quality in the context of online banking. Current research attempts to study the differences in the views of customers about the quality of net banking in China and KSA.
The Impact of Cultural Differences on Customer’s Online Behavior
Culture is deemed as a phenomenon consisting of collective values and people tend to learn patterns of thinking, perception, and potential acting from living within a social environment [53,54]. In other words, individual consumer tastes and perceptions are partly determined by the collective values of their local community-culture. The rapid increase in e-commerce use has change consumer behavior a lot and makes online behavior of consumer become more and more different in nature from traditional one.
Uncertainty avoidance and Masculinity versus Femininity dimensions of Hofstede’s [55] cultural framework can be applied to the case of online banking service. Uncertainty avoidance is a fear or perception of risk in investment, stock and bond or even threat from privacy, security of online banking services such as credit card or transactions and hence banks in culturally different countries manage security in different ways and in fact fear of risk is regarded as a vital bottle-neck in banking industry. In most of Asian countries, especially in China, regardless of some recent changes the role of women society is generally limited based on the patriarchal ideology, however the situation is even worse and more serious in the Islamic countries where women are still seriously repressed and oppressed in society with few choices or opportunities in their life, which implies that banking service in those countries still does not pay attention to women customer equally in comparison to men clients.
Religion is the most universal and influential social factor of culture significantly influencing attitude, value, psychology and behavior at both the individual and societal levels through four dimensions—beliefs, rituals, values, and community. Religious affiliation and level of religiosity may also be drivers of previously established differences in consumer behavior [56-58].
Status of Online Banking in China
Online banking in China is attributed to items like the development of Information Technology, availability of internet at all levels, more so than the users of computers and online banking consumers [59]. AC Nielsen Consult [60] found that the drivers of growth in online banking in China were a combination of convenience provided to those with internet access, the availability of secure high standard online banking, functionality, cost savings, and the necessity of banking services. The most emphasized features attributing to the development of online banking services in China include a mix of accessibility, comfort in usage, highly encrypted and secure pathway of access, variant functionalities based on process, financial impact on economy as savings and last but not the least is the dire need for banking services [61-63].
Based on findings from the CFCA report [64] (China Financial Certification Authority), in urban areas, the percentage of bank customers that are using net banking is around 20%; and in the case of corporate account holders that perform online operations, it has reached over 40%.
A study on customer attitude on online banking conducted by Laforet et al. [65] show that the most significant bottle necks in banking are perception of risks, skills in information technology and the orthodox way of banking which involves cash transactions. On account of this gap in the thought process between the orthodox way of working and the modern way, many of the financial institutions, primarily banking establishments, are unable to captivate new customers despite their marketing efforts [66]. It has become a major task for the banking institutions to convince customers with respect to credibility, in reference to online banking advantages. The findings of the study conducted by Laforet et al. [65] reiterate the generalized perceptions prevailing in China on e-banking [67].
Online Banking Environment in KSA
According to the Saudi Arabia Banking Sector Report [68], the banking services in KSA are more effective on account of the latest infrastructure provision. Financial institutions, especially banks in KSA, are catering to an average population of over 18 million nationals with 7 million expatriates and are keen on investing in alternate options to capture the share in the market. With online banking services readily available, there is tough competition among the banks in lowering the transaction costs and improving their operational and service efficiency [69,70].
With heavy competition among the banks, it is becoming more challenging to provide improvised services with customer’s focus on online transactions and customer satisfaction [71-73]. The Quality of online banking service in KSA is measured from the customer’s point of view by Sohail et al. [1]. The study revealed three major factors – efficiency, fulfillment and receptiveness that are influencing the quality of the online banking service.
The present study aimed on the perceived ‘quality of service’ of online banking. It encompasses the consumers of China and KSA markets comparing both cultures on the perceptions towards online banking. Having understood the views of researchers in this area, list of variables are identified for this study and the study tool is prepared by keeping them in view. The variables identified follow the renowned E-S-Qual model and E-RecS-Qual models, whose path diagrams are given in Figure 2. With the outcome thus obtained by synchronizing the study variables and the variables suggested in the path diagrams, the current study can enable the financial and banking sectors of China and KSA to implement the similarities in accordance to the customer’s perceived value (quality).
Figure 2: Path diagram for E-S-Qual. and E-RecS-Qual.
To investigate the quality of service perception of online banking users in China and KSA, a survey in both the countries was conducted. The objective was to find out the perception of the service excellence among online banking users in China and KSA. The structure adopted for the survey is taken from Parasuraman et al. [9]. The survey adopted two major measuring scales, E-S-QUAL, a 31 item scale for measuring the quality of service rendered and E-RecS-QUAL, a 17 parameter measuring scale for ‘e-recovery’. These are Likert scales which are modified as per the usage in the survey. The survey questions are designed with responses ranging from ‘‘strongly agree’’ (5) to ‘‘strongly disagree’’ (1). Other quality perception scales such as consumer’s shopping experience, value assessment and frequency, likelihood of online shopping (developed by Parasuraman et al. [9]) were used in the study. Demographic details were also captured from the respondents in the final section of the questionnaire.
Data Collection and Sample Characteristics
The data was collected over an online survey questionnaire covering a sample of 115 Chinese and 107 KSA customers who have experience in online banking services. The questionnaire has 5 sections. The first section covers the demographic details, gender, education, and annual household income (Table 1). The next section provides attributes on ‘e-service’ based on ‘e-SQ’ dimensions [seven dimensions] with service provider. The final section deals with the overall expected value of e-service excellence. The questionnaires were translated into Chinese and Arabic to ensure comparability and equivalence in the meaning of questionnaires as recommended by Hult et al. [74].
Table 1: Profiles of Two Samples, in %.
| China | KSA | |
|---|---|---|
| Gender | ||
| 1 Male | 45.22 | 92.59 |
| 2 Female | 54.78 | 7.40 |
| Education | ||
| 1 Less than High school | 0 | 0 |
| 2 High school/Trade/Technical school | 0.87 | 9.259 |
| 3 Some college | 56.52 | 1.852 |
| 4 College graduate | 36.52 | 62.03 |
| 5 Graduate school | 6.09 | 26.85 |
| Age | ||
| 1 15 or younger | 0 | 0 |
| 2 16-24 | 88.69 | 7.40 |
| 3 25-34 | 8.69 | 44.44 |
| 4 35-44 | 1.73 | 34.25 |
| 5 45-54 | 0.87 | 11.11 |
| 6 55 or over | 0 | 2.77 |
| Annual Household Income | ||
| 1 Under $10,000 | 27.82 | 22.22 |
| 2 $10,000-29,999 | 32.17 | 15.74 |
| 3 $30,000-49,999 | 19.13 | 5.55 |
| 4 $50,000-69,999 | 13.04 | 9.25 |
| 5 $70,000-99,999 | 2.60 | 13.88 |
| 6 $100,000 or over | 5.21 | 33.33 |
| Computer Usage | ||
| 1 1-5 years | 30.43 | 12.96 |
| 2 6-10 years | 53.91 | 18.51 |
| 3 11 years or over | 15.65 | 68.51 |
| Internet Usage | ||
| 1 Less than 6 months | 0 | 0 |
| 2 0.5-1 year | 21.73 | 6.48 |
| 3 1-2 years | 40.00 | 5.55 |
| 4 3-5 years | 34.78 | 36.11 |
| 5 More than five years | 3.47 | 51.85 |
| Internet Usage Frequency | ||
| 1 1-5 times a week | 6.95 | 2.77 |
| 2 1-4 times a day | 52.17 | 22.22 |
| 3 5-8 times a day | 26.08 | 23.14 |
| 4 Nine times a day | 14.78 | 51.85 |
| Web Page Browsing | ||
| 1 Less than one hour | 1.73 | 4.63 |
| 2 1-5 hours | 11.30 | 24.07 |
| 3 6-10 hours | 25.21 | 16.66 |
| 4 11-20 hours | 20.00 | 14.81 |
| 5 21-40 hours | 25.21 | 19.44 |
| 6 Over 40 hours | 16.52 | 20.37 |
Based on the observations recorded, the prominent variation is observed in the parameter – gender, with proportion of male to female in China is 42.2% to 54.8%. As KSA is considered to be a very conservative society the researchers found it as a challenging task to get the responses from females and so a big chunk (92.6%) of the respondents is males, this is best explained by restrains of women in Islamic society mentioned in literature review above. Observations of Chinese respondents revealed a greater count of young college students 93.04% (some college 56.52% and college graduate 36.52%) as against graduate students 88.88% (college graduate 62.03% and graduate school 26.85%) of KSA. From the collected data of computer usage, Internet usage, Internet usage frequency and Web page browsing, it is obvious that these respondents are suitable for this study, because on one hand, respondents have at least 6 years of experience using a computer and are familiar with the Internet technology (i.e. at least one year Internet experience and surfing the Internet at least one hour every day); on the other hand, generally students with a higher educational level will usually have higher knowledge and salaries. Thus they have more opportunities to use online banking services.
Data Analysis
Before starting the econometric analysis of the two models, this paper reports the results of Kaiser-Meyer-Olkin Measure of Sampling (KMO) and Bartlett’s Test of Sphericity. The results show that the KMOKSA= 0.919 and KMOChina = 0.905, which meet the requirements of KMO test [75,76]. In addition, it is found that Bartlett’s test for the two countries is significant, p < 0.01. Therefore, the data is suitable for doing fact analysis. Table 2 shows the mean value of each item and t-test values between China and KSA, exhibiting statistical differences between these two countries. Also, the findings related to all variables and their mean values are similar but significant differences between countries were found.
Table 2: Mean t-test differences between China and KSA.
| China | KSA | t-test | |
|---|---|---|---|
| Efficiency | 3.77 (0.67) | 3.37 (1.05) | 3.54*** |
| Fulfillment | 3.74 (0.65) | 3.41 (0.96) | 1.74* |
| System | 3.74 (0.61) | 3.41 (0.96) | 2.99*** |
| Privacy | 3.77 (0.67) | 3.49 (0.92) | 2.60*** |
| Responsiveness | 3.71 (0.61) | 2.82 (0.56) | 7.84*** |
| Compensation | 3.72 (0.75) | 2.73 (1.09) | 7.80*** |
| Contact | 3.82 (0.73) | 3.45 (1.39) | 2.77*** |
| Perceived Value | 3.24 (0.62) | 3.31 (0.72) | 9.06*** |
| Loyalty Intention | 3.86 (0.58) | 3.65 (1.13) | 2.03** |
Note: (1) The value in the parenthesis is standard deviation.
2) *p < 0.1, **p < 0.05, ***p < 0.01
Due to the limitation of the sample size of this research (nChina =115 and nKSA =107), this research adopts partial least squares (PLS) to analyze the model. This is because variance-based techniques for PLS work are best suited for the small sample size. PLS is popularly used in structural equation modeling because of the minimal requirements it makes on measurement scales and sample size [77]. In addition, PLS can be employed for theory confirmation and used to provide exploratory propositions for later examination.
Tables 3 and 4 show that factor loadings and Cronbach’s Alpha values of E-S-Qual and E-RecS-Qual in the two countries are adequate. The factor loading of the items measure the corresponding constructs that exceeds 0.7, which implies that the convergent validity is accepted [78]. The reliability of each construct is satisfactory with Cronbach’s Alpha values of at least 0.7 [79,80]. The values of composite reliability (CR) for all items are larger than the recommended threshold value of 0.7, which determines the internal consistency [47]. Therefore, the measurement models are acceptable.
Table 3: Reflective constructs and items of China and KSA for E-S-Qual.
| Items | China | KSA | ||||||
|---|---|---|---|---|---|---|---|---|
| Loading | CR* | Alpha | AVE | Loading | CR | Alpha | AVE | |
| Efficiency | 0.899 | 0.871 | 0.749 | 0.948 | 0.936 | 0.852 | ||
| Eff1 | 0.709 | 0.851 | ||||||
| Eff2 | 0.768 | 0.914 | ||||||
| Eff3 | 0.778 | 0.856 | ||||||
| Eff4 | 0.730 | 0.833 | ||||||
| Eff5 | 0.734 | 0.879 | ||||||
| Eff6 | 0.797 | 0.817 | ||||||
| Eff7 | 0.726 | 0.810 | ||||||
| System Availability | 0.867 | 0.808 | 0.753 | 0.919 | 0.890 | 0.834 | ||
| Sys1 | 0.748 | 0.819 | ||||||
| Sys2 | 0.764 | 0.871 | ||||||
| Sys3 | 0.835 | 0.872 | ||||||
| Sys4 | 0.702 | 0.780 | ||||||
| Sys5 | 0.711 | 0.828 | ||||||
| Fulfillment | 0.875 | 0.829 | 0.734 | 0.961 | 0.951 | 0.897 | ||
| Ful1 | 0.722 | 0.895 | ||||||
| Ful2 | 0.755 | 0.912 | ||||||
| Ful3 | 0.770 | 0.865 | ||||||
| Ful4 | 0.714 | 0.950 | ||||||
| Ful5 | 0.724 | 0.912 | ||||||
| Ful6 | 0.752 | 0.848 | ||||||
| Privacy | 0.870 | 0.806 | 0.792 | 0.919 | 0.882 | 0.860 | ||
| Pri1 | 0.792 | 0.882 | ||||||
| Pri2 | 0.782 | 0.890 | ||||||
| Pri3 | 0.817 | 0.877 | ||||||
| Pri4 | 0.772 | 0.788 | ||||||
| Perceived Value | 0.915 | 0.877 | 0.854 | 0.938 | 0.911 | 0.889 | ||
| PV1 | 0.873 | 0.893 | ||||||
| PV2 | 0.889 | 0.935 | ||||||
| PV3 | 0.824 | 0.913 | ||||||
| PV4 | 0.831 | 0.814 | ||||||
| Loyalty Intention | 0.837 | 0.740 | 0.751 | 0.958 | 0.942 | 0.923 | ||
| LI1 | 0.753 | 0.934 | ||||||
| LI2 | 0.721 | 0.951 | ||||||
| LI3 | 0.784 | 0.912 | ||||||
| LI4 | 0.742 | 0.897 | ||||||
Table 4: Reflective constructs and items of China and KSA for E-RecS-Qual.
| Items | China | KSA | ||||||
|---|---|---|---|---|---|---|---|---|
| Loading | CR* | Alpha | AVE | Loading | CR | Alpha | AVE | |
| Responsiveness | 0.848 | 0.761 | 0.763 | 0.942 | 0.918 | 0.896 | ||
| Res1 | 0.807 | 0.906 | ||||||
| Res2 | 0.759 | 0.902 | ||||||
| Res3 | 0.773 | 0.870 | ||||||
| Res4 | 0.713 | 0.907 | ||||||
| Compensation | 0.834 | 0.730 | 0.793 | 0.880 | 0.816 | 0.843 | ||
| Com1 | 0.876 | 0.754 | ||||||
| Com2 | 0.755 | 0.878 | ||||||
| Com3 | 0.741 | 0.892 | ||||||
| Contact | 0.823 | 0.759 | 0.836 | 0.920 | 0.828 | 0.923 | ||
| Con1 | 0.786 | 0.916 | ||||||
| Con2 | 0.885 | 0.931 | ||||||
| Perceived Value | 0.915 | 0.877 | 0.854 | 0.938 | 0.911 | 0.889 | ||
| PV1 | 0.870 | 0.894 | ||||||
| PV2 | 0.887 | 0.936 | ||||||
| PV3 | 0.827 | 0.912 | ||||||
| PV4 | 0.835 | 0.813 | ||||||
| Loyalty Intention | 0.837 | 0.740 | 0.751 | 0.958 | 0.942 | 0.923 | ||
| LI1 | 0.764 | 0.933 | ||||||
| LI2 | 0.725 | 0.950 | ||||||
| LI3 | 0.776 | 0.915 | ||||||
| LI4 | 0.738 | 0.897 | ||||||
| Note: *CR: Composite Reliability | ||||||||
In order to have a persuasive result, Chin [77] points out that the discriminant validity is necessary to be measured. The square roots of average variance were extracted (AVE) and compared with the correlations between constructs. A model has convergent validity when AVE values exceed the limit 0.6 [82]. Tables 5 and 6 present the square roots of AVE (diagonal values) with the correlations among the reflective constructs for both the models in the two countries. All AVE values of the latent variables are larger than 0.6 and larger than the square of the correlations among the latent variables, which shows convergent and discriminant validity are adequate [83].
Table 5: Correlations and square roots of the average variance extracted (AVE) of E-S-Qual for China and KSA.
| Eff | Ful | LI | PV | Pri | Sys | |
|---|---|---|---|---|---|---|
| China | ||||||
| Efficiency (Eff) | 0.749 | |||||
| Fulfilment (Ful) | 0.728 | 0.734 | ||||
| Loyalty (LI) | 0.495 | 0.639 | 0.750 | |||
| Perceived Value (PV) | 0.537 | 0.589 | 0.613 | 0.854 | ||
| Privacy (Pri) | 0.668 | 0.686 | 0.527 | 0.389 | 0.792 | |
| System (Sys) | 0.736 | 0.588 | 0.575 | 0.597 | 0.650 | 0.753 |
| KSA | ||||||
| Efficiency (Eff) | 0.852 | |||||
| Fulfilment (Ful) | 0.849 | 0.897 | ||||
| Loyalty (LI) | 0.713 | 0.754 | 0.923 | |||
| Perceived Value (PV) | 0.697 | 0.661 | 0.808 | 0.889 | ||
| Privacy (Pri) | 0.770 | 0.829 | 0.717 | 0.619 | 0.860 | |
| System (Sys) | 0.814 | 0.887 | 0.764 | 0.691 | 0.776 | 0.834 |
Note: Diagonal elements are the square roots of the AVE per construct and off-diagonal elements are the correlations between the constructs.
Table 6: Correlations and square roots of the average variance extracted (AVE) of E-RecS-Qual for China and KSA.
| China | Com | Con | LI | PV | Res |
|---|---|---|---|---|---|
| Compensation (Com) | 0.793 | ||||
| Contact (Con) | 0.565 | 0.836 | |||
| Loyalty (LI) | 0.446 | 0.463 | 0.750 | ||
| Perceived Value (PV) | 0.579 | 0.452 | 0.612 | 0.854 | |
| Responsiveness (Res) | 0.719 | 0.660 | 0.524 | 0.498 | 0.763 |
| KSA | |||||
| Compensation (Com) | 0.843 | ||||
| Contact (Con) | 0.790 | 0.923 | |||
| Loyalty (LI) | 0.457 | 0.605 | 0.923 | ||
| Perceived Value (PV) | 0.509 | 0.583 | 0.808 | 0.889 | |
| Responsiveness (Res) | 0.809 | 0.661 | 0.526 | 0.548 | 0.896 |
Note: Diagonal elements are the square roots of the AVE per construct and o off-diagonal elements are the correlations between the constructs.
E-S-Qual values of the two countries show similar results (Table 7). It can be understood that loyalty intention is affected by perceived value and privacy; besides, system is affected by perceived value. Efficiency plays an insignificant role in both countries. In terms of the different results, few differences existed. In the case of China, fulfillment impacts loyalty intention. On the other hand, fulfillment does not influence any variables in the case of KSA [84]. The result of the two countries showed that R2 values of loyalty intention are higher than the values of perceived value, which demonstrates that loyalty intention has better explained the total variability of the response data. Figure 3 shows the results for path coefficients of E-S-Qual for China and KSA.
Table 7: Path Coefficients (t) of the E-S-Qual for China and KSA.
| China | KSA | ||||
|---|---|---|---|---|---|
| Coefficient | t-value | Coefficient | t-value | ||
| Efficiency → Loyalty Intention | -0.153 | 0.979 | -0.071 | 0.677 | |
| Efficiency → Perceived Value | 0.165 | 1.126 | 0.387 | 0.259 | |
| Fulfillment → Loyalty Intention | 0.400 | 2.106** | 0.176 | 1.121 | |
| Fulfillment → Perceived Value | 0.289 | 1.465 | -0.072 | 0.379 | |
| Perceived Value → Loyalty Intention | 0.387 | 3.635*** | 0.511 | 6.379*** | |
| Privacy → Loyalty Intention | 0.225 | 2.198** | 0.169 | 1.651* | |
| Privacy → Perceived Value | -0.118 | 0.960 | 0.099 | 0.718 | |
| System → Loyalty Intention | -0.031 | 0.196 | 0.180 | 1.504 | |
| System → Perceived Value | 0.306 | 1.916* | 0.369 | 2.117** | |
| R2(Loyalty Intention) | 0.521 | 0.755 | |||
| R2(Perceived Value) | 0.403 | 0.534 | |||
| Note: Diagonal elements are the square roots of the AVE per construct and o off-diagonal elements are the correlations between the constructs. | |||||
Note: Only significant results are presented in the graph.
Figure 3: Results for path coefficients of E-S-Qual for China and KSA. Note: Only significant results are presented in the graph.
From the results of E-RecS-Qual (Table 8) it can be observed that loyalty intention is influenced by perceived value and responsiveness in both the countries. While compensation impacts perceived value in the case of China this relationship is found to insignificant in the case of KSA. Also, among the customers of China, contact does not have any impact on other variables. Similar results as E-S-Qual, the R2 values of loyalty intention are higher and demonstrates that loyalty has better explained the total variability of the response data. The significant path results are shown in Figure 4.
Table 8: Path Coefficients (t) of the E-RecS-Qual for China and KSA.
| China | KSA | |||
|---|---|---|---|---|
| Coefficient | t-value | Coefficient | t-value | |
| Compensation → Loyalty Intention | -0.090 | 0.755 | -0.273 | 2.246** |
| Compensation → Perceived Value | 0.427 | 4.195*** | -0.150 | 0.821 |
| Contact → Loyalty Intention | 0.119 | 1.042 | 0.318 | 2.570** |
| Contact → Perceived Value | 0.145 | 1.125 | 0.460 | 4.124*** |
| Perceived Value → Loyalty Intention | 0.475 | 3.827*** | 0.668 | 11.557*** |
| Responsiveness → Loyalty Intention | 0.272 | 2.007** | 0.171 | 1.655* |
| Responsiveness → Perceived Value | 0.098 | 0.762 | 0.365 | 2.354** |
| R2(Loyalty Intention) | 0.449 | 0.698 | ||
| R2(Perceived Value) | 0.362 | 0.392 | ||
| Note: *p < 0.1, **p < 0.05, ***p < 0. 01 | ||||
Note: Only significant results are presented in the graph.
Figure 4: Results for path coefficients of E-RecS-Qual for China and KSA. Note: Only significant results are presented in the graph.
The study revealed an in-depth understanding of a consumer’s perception of online services pertaining to various banking institutions in China and KSA. E-S-QUAL and E-RecS-QUAL models are adopted for data collection (made through an online survey) analysis. Loyalty and quality of electronic services are the main focus of this study.
With the dimensions of E-S-Qual and E-RecS-Qual, the analysis of partial least squares revealed that Chinese and Saudi Arabian consumers have similar and dissimilar behaviors towards online banking. In the E-S-Qual model, the attitudes of perceived value towards loyalty intention, privacy towards loyalty intention, and system towards perceived value are similar in both countries. Apart from the similarities, only Chinese consumers display that the attitude of fulfilment towards perceived value is vital. In terms of E-RecS-Qual, consumers in both countries show that they do consider the importance of perceived value and responsiveness towards loyalty intention. In the case of other relationships such as compensation towards loyalty intention, contact towards loyalty intention and perceived value, and responsiveness towards perceived value, Saudi Arabian consumers showed significant consideration. Nevertheless, Chinese consumers exhibit more concern for compensation towards perceived value against KSA consumers.
Within the highly competitive market the multinational companies are striving to establish their niche. Hence, the research conducted in such an environment depicted that perceived value and responsiveness have a prominent effect on the loyalty of consumers. Also, it is proved that consumers always preferred uninterrupted and efficient responsiveness systems. Over the years, throughout the developed world, a nocturnal lifestyle has become a routine and hence, uninterrupted services are made available to capture the interest of online consumers. By and large, it is quite evident that the lifestyle and cultural framework of China and the KSA affect the opinion and experiences of online services used by the respective respondents.
Although our study has some important implications for academic researchers and practitioners, some limitations of the current research should be also noticed. Firstly, this study focused on investigating online customers of a certain age group and the findings may not be applicable to the other age groups for perceptions of online service quality. Thus it would be interesting to include other age groups in future research. Secondly, the focus of the study was to determine the perceptions on online banking regardless of the financial institution they have been dealing with. Hence the future research may consider investigating some specific financial institutions since their perceptions to online service quality may have been affected due to these differences. Thirdly, the study was limited to two leading economies in their specific regions i.e. the Far East and the Middle East. Although these two countries have influence on the relatively small regional countries, the results of the current study may not be specifically applicable to those countries. Thus a separate study addressing the issues of smaller countries in those regions could help us better understand the customer’s perceptions of online service quality.
The present study shows that managers can take into consideration the following insights to improve a positive attitude and loyalty intention of consumers toward online banking services.
Firstly, the quality of the website can increase the quality of the online banking services, but managers should be aware of the influence of culture on the online web services since it has a significantly direct impact on the attitude of consumers toward online services. For this reason, online marketing managers should not only focus on website quality, but also keep in the mind that in which culture they are providing or planning to provide online banking services. Customers of different cultures sometimes respond inversely towards the online banking services.
As evident in the case of China, fulfilment significantly impacts loyalty intention while in the case of KSA fulfilment does not influence any variables, moreover, compensation impacts perceived value in the case of China, but it is insignificant in the case of KSA. In this regard, managers should need to identify the social groups and individuals belonging to their brand and plan strategies accordingly in order to provide better online banking services. Managers should aim to evaluate and control the online quality services as well as the demand of the culture in order to positively influence consumers’ attitude and loyalty intention.
Secondly, the current study proved that perceived value and privacy have a significant impact on loyalty intention in both countries. Consumers of both countries evaluate online banking services based not only on the company websites, but also were concerned about the privacy issues on the website. Managers of both countries responsible for providing online banking services, while considering the quality of website also need to focus on privacy concerns of the customers in order to improve loyalty intentions of the customers.
Thirdly, the study concludes that consumers preferred uninterrupted and efficient responsiveness systems since consumers have multiple channels to interact online with the company and use its online services. The prominence of this is emphasized in our results showing that continuous and efficient responsiveness system will lead to higher loyalty intentions. It is important for managers to play a striking role in providing continuous and efficient responsiveness system to their consumers.
Finally, the very high rate of respondents who are college students and graduates indicates that this group would be a potential market segment in the online banking industry that online marketing managers can target and put more effort into improving online banking services toward this promising segment.
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