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Abstract

The primary goal of this research was to investigate the evolution of customer perceived value in the e-banking service industry through understanding the impacts of service quality dimensions and perceived risk. This study used the convenience sampling technique to survey 700 bank customers in Cambodia. Furthermore, the study used the structural equation model to analyse the data. The results reveal that staff behaviour, physical evidence and IT transaction showed significant effects on perceived risk. Finally, only physical evidence and IT transaction were found to have significant effects on customer perceived value.

Executive Summary

To survive in the current market competition, banks have tried to promote customer perceived value by offering superior electronic banking (e-banking) services to their customers since perceived value is found to have significant associations with customer satisfaction, trust, loyalty and repurchase behaviour. Subsequently, making customers see high service value in e-banking services can help banks achieve customer retention, profits and business sustainability in the long term. To achieve this goal, banks have to find possible ways to further develop their e-banking service value for their customers. In response to this issue, this research aims to understand the development of perceived e-banking service value through examining how service quality dimensions (staff behaviour, physical evidence and IT transaction) are associated with perceived risk and perceived value. Seven hundred bank customers from different banks in the Kingdom of Cambodia volunteered to participate in this research via self-administered questionnaires. These data are analysed using a structural equation model (SEM), and the results highlight that staff behaviour, physical evidence and IT transactions have significant associations with perceived risk. This suggests that when customers receive better service quality dimensions: staff behaviour (e.g., customer care and willingness to serve), physical evidence (e.g., facilities and pieces of technology) and IT transactions (e.g., online banking, mobile banking and banking cards), they do not show any serious concern with e-banking risks. Meanwhile, e-banking service value is mainly derived from the contributions of physical evidence and IT transactions. This indicates that the value of e-banking services relies significantly upon the improvement of physical evidence and IT transactions marking these two aspects of service quality as the technological factors influencing perceived e-banking service value among customers. Therefore, banks should pay closer attention to modernizing their physical evidence and upgrading their IT transactions so that customers can see more e-banking service value.

Keywords

E-banking service Cambodia Service Quality Staff Behaviour Value

Nowadays, the technological innovation of the electronic channel plays a huge role in reducing the industrial, geographical and regulatory barriers of many businesses (Firdous & Farooqi, 2017). Firdous and Farooqi (2017) identify the internet as a new way for communication between businesses and customers. In the banking industry, Asad et al. (2016) explain that while market behaviour adjusts and follows the trends of rapid technological advancement, e-banking services have emerged to serve current market needs.

Meanwhile, Wong (2022) estimates that around 90% of the Cambodian people actively use the internet. As many commercial activities, such as shopping, banking and payments, are conducted online (Firdous & Farooqi, 2017), many e-banking services have been developed to support the transactions in the Cambodian markets (Wong, 2022; Yang et al., 2021). However, the banks need to ensure that customers can see the obvious benefits of using the e-banking service and, thus, will continue using it. E-banking has some distinctive features that set it apart from other industries or service channels. E-banking offers several benefits to customers, such as saving time and money, providing convenience and accessibility, enhancing reliability and performance and reducing environmental impact. However, e-banking also faces some challenges, such as customer lack of knowledge and skill, security and privacy risks and trust and loyalty issues. These features affect how customers perceive value in e-banking and how they behave online. Some studies have investigated customer perception and experience in e-banking, such as Alalwan et al. (2016), who examined the factors influencing customer adoption of online banking, and Singh and Srivastava (2020), who reviewed the literature on customer experience in digital banking. Therefore, customer perceived value in the e-banking service has to be promoted, because customer perceived value is the key factor to business success (Ettinger & Miles, 1998).

Customer perceived value is a key concept in marketing that refers to the difference between the benefits and costs that customers perceive from a product or service. Customer perceived value can vary depending on the context and the customer segment and, therefore, it is important to measure it accurately. Several studies have proposed different methods and scales to measure customer perceived value, especially in the banking sector. For example, Khan et al. (2015) developed a customer perceived value scale for the banking industry based on four dimensions: functional value, emotional value, social value and monetary value. Ranjan and Read (2016) explored how value co-creation, or the joint creation of value by customers and service providers, can enhance customer perceived value in digital banking. They proposed a framework of value co-creation practices and outcomes in the context of digital banking. Hasan et al. (2014) argue that perceived value has a positive psychological impact on customer behaviour. Likewise, Shuhaiber et al. (2025) explain that a service which is highly valued by many customers can help the firms gain high customer satisfaction with a high chance of repeat customer purchases (Tam, 2000). Therefore, perceived value has to be investigated properly in order to promote the value of the e-banking service to customers.

Because of its significant impact on businesses, previous studies conducted by Li et al. (2020) in the garment industry and by Piri and Lotfizadeh (2016) in the education industry suggest reducing perceived risk in order to obtain high perceived value. The service that demonstrates a low risk to customers can significantly raise customer confidence in the service with the firms due to its low negative impact on the customers. In contrast, previous studies conducted by Jahmani et al. (2020) in the tourism industry and by Konuk (2019) in the restaurant industry suggest enhancing service quality for customers. High service quality demonstrates accuracy and high service performance to customers. Therefore, it may help save time and reduce customer frustration and stress.

OBJECTIVES

Despite the impacts of service quality and perceived risk, which may individually influence customer perceived value in different industries, their integrated impacts on the evolution of customer perceived value in the e-banking service have only been studied slightly. In addition, the complex structural model which consists of service quality dimensions along with perceived risk and perceived value remains narrow in the current literature. Therefore, the primary goal of this study was to investigate customer perceived value in the e-banking service industry by integrating the service quality dimensions, perceived risk and perceived value into a complex structural model so that their relationships could be examined.

LITERATURE REVIEW

Customer Perceived Value

Perceived value is defined as the overall assessment which results from a comparison between benefits and sacrifices (Shuhaiber et al., 2025). Auka (2012) reveals that the concept of value indicates an individual’s psychology which compares with what he or she gives to and what he or she gets from the firms. In other words, customer perceived value reflects exceeding benefits over the costs. According to the multi-dimensional approach of Bajs (2015), perceived value can be assessed based on social, emotional and functional values. First, social value shows the service quality that is evaluated by the group of people (Sun et al., 2017). Second, emotional value shows the level of customer pleasure which is derived from the affection of the service or product (Srivastava & Dey, 2018). Finally, functional value shows the degree of performance which serves the individual’s needs (Eskafi et al., 2013). Overall, enhancing the service or product value for customers can increase customer trust and create a better chance of repeat purchases with the firms. Therefore, the significance of perceived value has been acknowledged by many businesses, including online businesses (Sharma & Klein, 2020).

As perceived value is important to many businesses, perceived value has been investigated in different industries. For example, Jeng and Lo (2019) used 2 × 2 analysis of ANOVA to investigate refund depth, refund condition and believability with perceived value in the airline service industry. Karjaluoto et al. (2019) used the SEM to investigate personal innovativeness, self-congruence and perceived risk with perceived value in the banking service industry. Choi and Lee (2019) used SEM to investigate trust in domain-specific information and brand loyalty with perceived value in the cosmetic industry. Hussein et al. (2018) used partial least squares to investigate the physical environment and social interactions with perceived value in the hotel service industry. Pham et al. (2018) used SEM to investigate the dimensions of convenience (assess, search, evaluation, transaction and possession conveniences) with perceived value in the online shopping service industry. Finally, Konuk (2019) used partial least squares multigroup analysis to investigate utilitarian benefits and hedonic benefits with perceived value in the organic food industry.

Based on the research gap analysis, although several factors have been investigated with perceived value, service quality and perceived risk have not been extensively investigated with perceived value, particularly in the e-banking service industry. Moreover, a complex structural model, which includes the dimensions of service quality, needs to be examined with perceived risk and perceived value, which also remain narrow in the current literature.

Service Quality and Perceived Risk

Jahmani et al. (2020) define service quality as the overall evaluation of service utility after customers have experience with the firms. According to the SERVQUAL dimensions, tangibility, empathy, assurance, responsiveness and reliability are the main dimensions of service quality (Lenka et al., 2009). In service marketing, Avkiran (1994), however, argues that service quality can be evaluated based on staff behaviour, communication, credibility and access. Unlike these authors, Kim and Jindabot (2021) have suggested evaluating service quality based on three main dimensions, namely staff behaviour, physical evidence and IT transaction process. In comparison, although staff behaviour, physical evidence and IT transaction process are likely to provide fewer details compared to the service quality dimensions of Avkiran (1994) and Lenka et al. (2009), the three main dimensions are seen to offer an overall justification of service quality, particularly in the e-banking service industry. Therefore, staff behaviour, physical evidence and IT transaction process are the main dimensions of service quality in this study.

In conceptual comparison, the impact of quality can positively enhance satisfaction and trust (Diputra & Yasa, 2021) whereas risk causes consequences and negatively affects customer trust (Rafqi Ilhamalimy & Ali, 2021). The concepts between quality and risk indicate opposite directions. The study of behavioural intention in the banking service reveals that if the quality does not meet customer expectations, the service generates uncertainty to customers (Namahoot & Laohavichien, 2018). In contrast, Beneke et al. (2013) argued that when the service or product is of a high quality and achieves the customer’s needs, the perception of risk drops significantly.

According to the above theoretical arguments, service quality seems to negatively influence perceived risk. For instance, in environmentally friendly electronic products, Marakanon and Panjakajornsak (2017) argue that customers perceive less risk when they receive high service quality. In the smartphone industry, Samadou and Kim (2018) identify that service quality negatively influences customer perceived risk. In the organic food industry, Pandey et al. (2020) reveal that risk can be lowered only if the product contains a high quality. Therefore, service quality (staff behaviour, physical evidence and IT transaction process) may negatively influence perceived risk. The hypotheses are proposed below:

H₁: Staff behaviour has a negative effect on perceived risk.

H₂: Physical evidence has a negative effect on perceived risk.

H₃: IT transaction process has a negative effect on perceived risk.

Service Quality and Perceived Value

Based on the service marketing literature, Özkan et al. (2019) indicate that service quality is service performance which can meet customer expectations. In contrast, Aufegger et al. (2021) and Konuk (2019) consider the concept of value as the perceived benefits which are derived from the overall evaluation of service utility. In other words, the concept of value occurs when sacrifices are lower than received benefits. Based on the current conceptual explanations, both concepts show positive directions. According to the structural relationship concept, Ge et al. (2021) explained that services attract a lot of positive opinions from customers when the service serves customer utility well. In the research on consumer behaviour, Suttikun and Meeprom (2021) reveal that services become significant to customers unless there is an existence of high perceived quality.

Based on the above theoretical explanations, service quality seems to positively influence perceived value. For example, in the education service industry, Tukiran et al. (2021) consider service quality as a positive factor in relation to perceived value. In the tourism industry, Jahmani et al. (2020) have found that customers perceive high service value when they receive high service quality. In the restaurant industry, Konuk (2019) reveals that providing strong service quality can result in high customer-perceived value. Therefore, service quality (staff behaviour, physical evidence and IT transaction process) may positively influence perceived value. The hypotheses are proposed below:

H₄: Staff behaviour has a positive effect on perceived value.

H₅: Physical evidence has a positive effect on perceived value.

H₆: IT transaction process has a positive effect on perceived value.

Perceived Risk and Perceived Value

Perceived risk refers to the degree of uncertainty which customers experience using the service with the firms (Nawi et al., 2024). Similarly, Snoj et al. (2004) define perceived risk as the perceived uncertainty which causes consequences to customers. In consumer psychology, Li et al. (2020) categorized risk into two main types: objective risk and subjective risk. Objective risk shows the personal risk attitude, whereas subjective risk reveals individual judgement on the possibility and severity of the adverse result (Li et al., 2020).

Regardless of conceptual comparisons between the concept of risk and value, perceived risk demonstrates the possible losses resulting from poor service performance (Xie et al., 2022), whereas perceived value concentrates on the benefits received from the investment with the firms (Nawi et al., 2024). Based on the current arguments, the two concepts reflect different outcomes. According to the green willingness and behaviours, once customers consider high risk, they expect fewer benefits (Li et al., 2020). Based on the fintech adoption model, high risk creates an unfavourable desire towards the service (Xie et al., 2022). Therefore, the service can be perceived as less important to customers if the perceived risk regarding the service remains.

Based on the above theoretical arguments, perceived risk seems to negatively influence perceived value. For example, Li et al. (2020) identify perceived risk as a negative factor in perceived value in the garment industry. In the education industry, Piri and Lotfizadeh (2016) have found that perceived value increases after the degree of perceived risk is reduced. In the mobile payment service industry, Yang et al. (2015) explain that when customers consider less risk with the firms, the customers significantly consider the service for their utilities. Therefore, the hypothesis is proposed below:

H₇: Perceived risk has a negative effect on perceived value.

MODEL CONSTRUCT

In the contemporary literature, the current study has attempted to fill in the research gap by developing a research model in order to examine the factors which influence customer perceived value in the e-banking service industry.

Thus, this study’s objective was to investigate the systematic impacts of service quality dimensions (staff behaviour, physical evidence and IT transaction) and perceived risk on customer perceived value in the e-banking service industry. Based on the above theoretical discussions, the hypotheses were proposed as below (Figure 1).

Figure 1:

Structural Model of Perceived Value.

RESEARCH METHODOLOGY

Sample and Data Collection

The study involved a broad pool of 700 individuals, all of whom came from a variety of different walks of life. Targeting respondents who had previous experience with e-banking services was accomplished through the use of a pragmatic convenience sampling strategy. The gathering of data from people encountered in a variety of public areas was eased by this method, which ensured a sample that was both varied and representative of the population.

Before beginning the survey, the researchers made it a point to go out of their way to solicit and receive informed consent from each potential participant. This allowed them to uphold the highest possible ethical standards. After obtaining the participants’ agreement, we provided them with detailed guidelines and instructions, which enabled them to accurately and thoroughly complete the survey instruments within a given timeframe of approximately 10–15 minutes.

Measurement Construct

There were four major variable constructs in this study: First, the construct of service quality which consisted of three main dimensions (staff behaviour = 3 components, physical evidence = 3 components and IT transaction = 2 items) was originally adopted from Kim and Jindabot (2021). Next, the construct of perceived risk which consisted of three components was originally adopted from Udo et al. (2010). Finally, the construct of perceived value which consisted of three components was originally adopted from Mosavi et al. (2018).

To obtain the information from customers, the constructs of this study were rated using the 5-point Likert scale (1 = strongly agree, to 5 = strongly disagree). The current rating scale had the mid-point (3) as a neutral scale which clearly separated the boundaries between positive and negative scales in the survey (Garland, 1991). In addition, Babakus and Mangold (1992) suggested that adopting this rating technique could minimize the level of frustration and stress to customers. As a result, acceptable answers could be obtained from customers within a reasonable amount of time.

SEM Measurement

The SEM was used to analyse the data in this study. However, there were four steps (e.g., data validity check and model fit and model measurement) which were performed in this study.

First, data validity was performed using a data-clearing process. In this process, a Mahalanobis technique (using possibility scores < 0.001 indicating outliers) was employed to identify and eliminate all outliers in a statistical software SPSS so that the number of biased data could be minimized. Furthermore, past researchers also applied this technique to purify their data validity but also received reliable results when running their preferred analysis techniques. In this study, 129 outliers were identified and eliminated while the remaining 546 datasets were considered as valid data and kept for data analysis.

Second, model fit was conducted to ensure acceptable regression results using confirmatory factor analysis. Therefore, the main indicators (CMIN²/df, GFI, CFI, NFI, AGFI, RMSEA and P-CLOSE) for model fit needed to pass the thresholds recommended by Kim and Jindabot (2021). Furthermore, the model was modified to obtain a better model fit in Table 1. Therefore, after modification, all of the indicators passed the thresholds, which revealed that the model construct was acceptable to perform the regressions.

Table 1:

Model Fit Index.

Indicators	Index		Thresholds	Results
Indicators	Before Modification	After Modification	Thresholds	Results
CMIN²/df	3.007	2.802	<3	Good
GFI	0.903	0.931	>0.9	Good
CFI	0.920	0.959	>0.9	Good
NFI	0.899	0.978	>0.9	Good
AGFI	0.840	0.892	>0.8	Good
RMSEA	0.809	0.071	<0.08	Good
P-CLOSE	0.078	0.103	>0.05	Good

Notes: CMIN²/df: Chi-square/degrees of freedom; GFI: Goodness-of-fit index; CFI: Comparative fit index; NFI: Normed fit index; AGFI: Adjusted goodness-of-fit index; RMSEA: Root mean square error of approximation; P-CLOSE: Probability of close.

Finally, the SEM measurement was conducted to ensure a reliable construct in each variable in this study so that the SEM results could also be reliable. First, a bootstrap with at least 500 samples was set in the statical software, following a suggestion by Stine (1989). Next, the loading factors which had scores above 0.5 were kept for examining relationships. Then, the Cronbach’s alpha scores of each variable that had scores above 0.7 showed reliable content in each variable construct of this study. After that, all composite reliability (CR) scores which were above 0.7 were acceptable (Kim & Jindabot, 2021). Finally, all of the average variance extracted (AVE) scores that were above 0.5 also indicated acceptability for the overall measurement in this model. Based on the results of Table 2, SEM measurement was acceptable to perform the SEM regressions.

Table 2:

SEM Measurement.

Variable	Components	Loading Factors	Cronbach’s Alpha	CR	AVE
Service quality	Staff behaviourSB1: Bank staff has willingness to help customers.SB2: Bank staff helps solve e-banking service problems.SB3: Bank staff serves customers with care.	0.940.960.86	0.731	0.853	0.699
	Physical evidencePE1: Bank has modern equipment.PE2: The facilities are new and contain high technology.PE3: Infrastructures are strong and good-looking.	0.860.890.60	0.821	0.803	0.679
	IT transaction processesIT1: IT of its service helps keep customer’s confidentiality.IT2: IT of its service shows the correct information.	0.640.82	0.880	0.799	0.732
Perceived Risk	PR1: Worrying about credit card information being stolen.PR2: Worrying about poor service quality on the internet.PR3: Worrying about unsafe online transactions.	0.950.980.69	0.908	0.769	0.795
Perceived Value	PV1: Attractive cost for using e-banking services.PV2: Paying fairly for similar e-banking services.PV3: This e-banking service is valuable to me.	0.970.950.83	0.892	0.819	0.704

Note: CR: Composite reliability; AVE: Average variance extracted.

RESULTS AND DISCUSSION

Discussion of the Effects on Perceived Risk

The results obtained from SEM analysis are summarized in Figure 2 and Table 3. Regardless of the effects on perceived risk, staff behaviour showed a negative effect on perceived risk (β = −0.49, p <.001), which accepted hypothesis 1. Customers really cared a lot about the way that bank staff treated them. The care and willingness to service customers raised the comfort of customers to use the bank service. For instance, when any error or system complexity occurred, customers almost always required staff assistance to solve the problems. If customers could obtain the best service assistance from bank staff to fix any e-banking system error, they would feel secure to use the service with the banks. In contrast, if this problem was ignored by bank staff, customers probably raised lots of suspicion regarding the safety of using the current e-banking service with the current banks.

Figure 2:

SEM Results.

Second, physical evidence showed a negative effect on perceived risk (β = −0.39, p < .001), which accepted hypothesis 2. Physical evidence could be related to infrastructures, facilities and other designs. Strong infrastructures ensured high protection to customer investments with the banks in terms of storing cash and wealth. In addition, the facilities and other designs provided strong support to the service offered to customers, particularly while using the e-banking service. The modern facilities were considered to offer fast service and accurate information to customers; thus, they possibly enhanced customer confidence to use the e-banking service. As a result, the risk of using the e-banking service could be reduced.

Finally, IT transaction showed a negative effect on perceived risk (β = −0.12, p < .001), which accepted hypothesis three. Having a high level of information technology support could ensure a strong information system to customers. Many online transactions, such as payments, transfers and other deposits, require strong technological assistance in order to smooth these transactions for customers. In addition, the core function of IT could play a huge role in combating the stealing of online information. Customers simply demanded IT assistance to protect their personal information, as well as their credit card information. Thus, more risk could be minimized if the e-banking service contained strong IT transaction assistance.

Discussion of the Effects on Perceived Value

Regardless of the effects on perceived value, the physical evidence of the bank showed a positive effect on perceived value (β = 0.55, p < .001), which accepted hypothesis 5. Having strong infrastructure, facilities and other designs enhanced not only the safety of customer investments but also the quality of service, particularly the quality of the e-banking service. Customers may have had less hesitation to use the service once they witnessed the modern facilities of the banks. Based on this situation, the benefits received from using the e-banking service were highly considered among customers. Therefore, the e-banking service possibly gained more positive views from customers.

Second, IT transaction showed a positive effect on perceived value (β = 0.51, p < .001), which accepted hypothesis 6. Any online service which contained strong information technological assistance demonstrated fewer system errors. A healthy IT system provided not only accurate information (e.g., financial statements, payment slips and in-outward transfer services) but also a high level of information protection. Customers probably considered IT as the key factor when they decided to use the e-banking service with the banks. Therefore, if IT could support the transactions well, the value of the e-banking service was highly perceived among customers.

Third, staff behaviour showed a positive effect on perceived value; however, its effect on perceived value was insignificant (β = 0.01, p >.05), which rejected hypothesis 4. Based on the current empirical results, despite staff behaviour being considered the significant factor for minimizing customer perceived risk, its direct effect on customer perceived value remained very low. Compared to the strength of IT transaction and physical evidence, the effect of staff behaviour was considered to be the lowest. In the current e-banking service, only IT transaction and physical evidence of the banks were seen to contribute high value to the e-banking service industry. One explanation is that staff behaviour is not a salient factor for customers who use e-banking, as they rely more on the technology and the physical evidence of the service. Staff behaviour may be more relevant for customers who use traditional banking channels, such as branches or ATMs, where they interact with bank employees face-to-face. Therefore, staff behaviour may not have a significant impact on customer perceived value in e-banking, as compared to other factors, such as IT transaction and physical evidence.

Finally, perceived risk showed a negative effect on perceived value, but its effect was insignificant on perceived value (β = −0.04, p > .05), which rejected hypothesis 7. One reason is that the perceived risk in this study was measured as a single construct, rather than a multi-dimensional construct. Previous studies have shown that perceived risk has different dimensions, such as financial, performance, security, privacy, time and social risk (Khedmatgozar, 2021). These dimensions may have different effects on perceived value in e-banking, depending on the context and the customer segment. For example, security and privacy risk may have a stronger negative effect on perceived value than time and social risk. Therefore, by aggregating all the dimensions of perceived risk into one construct, the effect of perceived risk on perceived value may be diluted or cancelled out by the opposing effects of the different dimensions. According to the empirical results of this study, the direct effects of physical evidence and IT transaction significantly changed the degree of customer perceived value in the e-banking service. In other words, IT transaction and physical evidence gained high attention from customers who used the e-banking service. They may have thought that the main benefits obtained from the e-banking service were substantial due to the high contribution of IT transaction and physical evidence, as these contributions were expected to provide high service quality, as well as high protection to the customers, especially regarding their credit card information and other financial information. As long as the e-banking service had strong physical evidence and IT transaction, the risk of the e-banking service was less likely to be the main concern for customers.

Overall, the results of hypotheses testing are reported in Table 3. As a result, five hypotheses were accepted while hypotheses 4 and 7 were rejected.

Table 3:

Hypothesis Summary.

No.	Hypothesized Relationships		Beta	p Value	Sig.	Result
No.	Independent variable	Dependent Variable	Beta	p Value	Sig.	Result
1	Staff’s behaviour	Perceived risk	−0.49	.000***	Sig.	Accepted
2	Physical evidence	Perceived risk	−0.39	.000***	Sig.	Accepted
3	IT transaction	Perceived risk	−0.12	.000***	Sig.	Accepted
4	Staff behaviour	Perceived value	0.01	.190	Insig.	Rejected
5	Physical evidence	Perceived value	0.55	.000***	Sig.	Accepted
6	IT transaction	Perceived value	0.51	.000***	Sig.	Accepted
7	Perceived risk	Perceived value	−0.04	.201	Insig.	Rejected

Note: ***Significant level p < .001.

IMPLICATIONS

Theoretical Implications

The findings and theoretical implications derived from this study provide valuable insights into the dynamics of perceived risk and perceived value in the context of e-banking services. First, the study underscores the pivotal role of customer–staff interactions, highlighting that positive interactions and effective customer service can significantly mitigate perceived risks. This finding emphasizes the importance of banks and financial institutions training their staff to ensure a high standard of service quality, ultimately fostering customer trust. Additionally, the positive impact of physical evidence on perceived value suggests that investments in infrastructure and tangible aspects of service delivery can enhance the overall quality and customer perception of value. This insight is essential for businesses aiming to create a competitive edge in the e-banking sector by focusing on the physical aspects of their service environments. Second, the study sheds light on the critical role of information technology in shaping customer perceptions and trust in e-banking systems. The positive effects of IT transaction support on both perceived value and the reduction of perceived risk emphasize the necessity for banks to maintain robust IT infrastructure and security measures. Furthermore, the study highlights the interplay between perceived risk and perceived value, suggesting that changes in one aspect can impact both simultaneously. This insight prompts further research into the complex relationships between these two dimensions and their influence on customer decision-making. In summary, the study’s theoretical implications provide a solid foundation for future research and strategic planning in the e-banking industry, emphasizing the significance of customer interactions, physical infrastructure and information technology in shaping customer perceptions and fostering trust.

Managerial Implications

The results that appear in this study reveal how the value of the e-banking service develops through the contributions of service quality dimensions and perceived risk. Furthermore, this study has highlighted the significant effects of physical evidence and IT transaction on perceived value in the e-banking service industry. Therefore, some suggestions regarding the managerial implications are proposed as follows:

First, the physical evidence, such as bank infrastructure, facilities and other equipment, has to be improved. Building a good infrastructure can increase wealth protections for customers who usually deposit cash and other properties with the banks. At the same time, the facilities and other equipment have to be updated or modernized. Modern facilities and other equipment can speed up the service offerings to customers, particularly when using e-banking services. Thus, if the physical evidence is well developed, customers may have high confidence in using the e-banking services with the banks. Finally, the IT transaction process needs to stay updated. In the e-banking service, information technology plays a huge role in organizing, controlling and preventing technical errors in the systems. This enhancement can be simply to provide the correct financial information while offering high protection against customer credit card information from being stolen. In addition, it also minimizes the unexpected risks that can occur in the middle of transactions, such as checking financial statements and payments and transferring other electronic cash inwards and outwards across the country. As a result, it may highly capture customer trust and reliance on e-banking services.

CONCLUSION

This study investigated customer perceived value in the e-banking service industry in Cambodia. The original and innovative business perspectives in the e-banking service industry, which provide a notion of future customer perceived value, have been derived from the current empirical results of this study. Based on the current results, staff behaviour, physical evidence and IT transaction significantly influence customer perceived risk. Finally, physical evidence and IT transaction, with the exception of staff behaviour and perceived risk, are found to have insignificant effects on customer perceived value. In comparison, the strength of physical evidence on perceived value is higher than the strength of IT transaction. However, the strengths between these two variables differ slightly. Thus, the evolution of customer perceived value in the e-banking service industry depends largely on both physical evidence and IT transaction.

Despite the investigation meeting the objective of this study, some limitations are found. For instance, the evolution of perceived value may not have been fully explained due to the results of this study being dependent mainly on the impacts of perceived risk and the dimensions of service quality. In fact, the evolution of perceived value could be based on other contributing factors, such as price and convenience. Therefore, future research should include an examination of these factors with perceived value. Finally, the results of this study focused mainly on the e-banking service industry. Thus, applying these results to different industries, such as restaurant, tourism and other online shopping services, might not be appropriate. Therefore, any future study must adopt these variables in order to continue investigating these industries for the purpose of reaching a new and acceptable conclusion for its related industries.

Footnotes

DECLARATION OF CONFLICTING INTERESTS

The authors declared no potential conflicts of interest with respect to the research,authorship and/or publication of this article.

FUNDING

The authors received no financial support for the research,authorship and/or publication of this article.

ORCID IDs

Long Kim

Kanyanit Wichianrat

Long Kim is an Assistant Professor in Business Administration. He is currently working as a lecturer at the School of Accountancy and Finance,Walailak University,Thailand. He holds a Master of Business Administration (International Program) from Prince of Songkla University,Thailand. Meanwhile,he is currently studying for his Doctoral Degree in Management at Walailak University. As a result,his research interests have covered several areas,such as marketing and branding,marketing strategy in digital era,general consumer behaviours and human resource management. So far,he has published several research articles in Scopus and Web of Science Databases.

e-mail: longkim500@yahoo.com / songkimryu@gmail.com

Teerasak Jindabot is currently an Assistant Professor in Business Administration,majoring in Marketing at Prince of Songkla University,Thailand. He is also serving as the Director of the Global Affairs and Corporate Communication Center. He holds a PhD in marketing from Thammasat University. His research areas are focused on psychological marketing,consumer psychology and behaviour and digital marketing. He also published several research articles and reviews in national and international journals.

e-mail: teerasak.j@psu.ac.th

Sook Fern Yeo is an Assistant Professor at the Faculty of Business,Multimedia University,Melaka,where she currently holds the Director of Centre of Excellence (Business Innovation and Communication) and the Deputy Dean for Research and Industrial Collaborations role. She also serves as a Visiting Professor with the Department of Business Administration at Daffodil International University. She teaches marketing for bachelor’s degree programmes,specifically fundamentals of marketing,service marketing,brand management,strategic marketing,understanding management and strategy by management. She supervises BBA,MBA,MPhil and PhD students,mainly in marketing,human resource and international business. She has published internationally refereed conference papers,journal articles and book chapters.

e-mail: yeo.sook.fern@mmu.edu.my

Kanyanit Wichianrat is a lecturer in the Modern Trade and Logistics Program at Thaksin University’s Faculty of Economics and Business Administration in Thailand. She had two years of industry experience before embarking on an academic career. She has a PhD in management from the University of Exeter in the United Kingdom,with a focus on tour operator service quality. Her research interests include logistics management,quality control and improvement,procurement and international transportation.

e-mail: kanyanit.wichianrat@gmail.com

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