The chances of losing any customers due to the pursuit of popular Internet Banking results can be regarded as the perceived risk PR (Featherman and Pavlou, 2003). Indeed, aspects related to perceived risk (Curran and Meuter, 2005; Kesharwani and Bisht, 2012; Namahoot and Laohavichien, 2018) have been widely regarded as crucial negative determinants of customers' intentions and the adoption of IB (Laukkanen et al., 2008; Baabdullah et al., 2016; Chauhan et al., 2019). Some interest in these factors may be due to the high volatility, instability, uncertainty, and ambiguity noted in the Internet Banking environment, and the lack of human communication (Al-Gahtani, 2011; Kesharwani and Bisht, 2012; Martins et al., 2014). Internet banking's information quality should contain useful and updated information for the user. Weak information quality can frustrate the users' experience as the need to spend much effort in the search for information would affect the level of users' satisfaction. Thus, the hypothesis can be drawn:

Price is thought to be “the consumer's intellectual trade between the perceived benefits of the app and the financial costs of using it” (Venkatesh et al., 2012). Instead of an employee context where the concept of cost may be represented by time and effort, which is thought to be less than expected, financial costs represent an important role in shaping the customer's willingness to embrace and embrace new technologies (Mallat et al., 2008; Venkatesh et al., 2012). In addition, “based on marketing literature, fair value is often determined by how the customer compares it by understanding how much it should pay instead of services and quality achieved” (Dodds et al., 1991). Therefore, “if the services, which the customer can obtain by obtaining a high-tech product, are higher than financial costs, the price will accurately predict the customer's intention to use the technology” (Venkatesh et al., 2012). In theory, “there are various studies in the relevant areas that address the impact of the limited price and the price point on the client's intention to use power channels” (Gerrard et al., 2006; Mohammed and Ward, 2006; Ho and Ko, 2008). Price value is positive when the perceived benefits of utilizing any system exceed its expenses. According to Chiang and Jang (2007), who demonstrated that user intentions are highly influenced by perceived price, the cost element plays an important role in user decision-making. Chan et al. (2008) found that the acceptance of SMS (short message service), in Malaysia is attributed to its cheap cost relative to other internet channels. Several of IS studies have indicated a positive influence of Price value on user behavioral intentions (Arenas-Gaitán et al., 2015; Khan et al., 2018). The current study proposes that Price value will positively impact behavioral intentions to use internet banking in Malaysia. Therefore, the following hypothesis suggests:

The degree of usage of any internet banking application to an extent where the consumers believe that their usage of online banking helps them in performing any banking task is known as Performance expectancy (Venkatesh et al., 2003). Morosan and DeFranco (2016) defined the term as effect on behavior of the customer who adopts E-banking. A few other scholars explain performance expectancy as the intentional behavior to adopt Internet banking (Sok Foon and Chan Yin Fah, 2011; Martins et al., 2014; Montazemi and Qahri-Saremi, 2015; Oliveira et al., 2016). According to Sharma et al. (2020), PE was positively associated with the behavioral intentions of bank customers. Taking the perspective of the consumers, they confirmed that the intentions of bank customers toward internet banking are dependent on performance expectancy. This research used PE as the antecedent of customers' behavioral intention to use mobile banking. Customers are more likely to adopt online banking if their performance increases as a result of utilizing the online system. The rise in performance is linked to how long-term customers feel about doing their transactions and managing their finances online. Therefore, the following hypothesis suggests:

The extent to which the user is comfortable to use and does not need much effort while using internet banking is defined as Effort expectancy. The ease in the use increases the chances of the adoption of the internet banking (Venkatesh et al., 2003). People, when believe that Internet Banking is effortless, can more easily adopt it (Chaouali et al., 2016). Earlier research has shown that effort expectancy has a substantial effect on behavioral intention to use online banking (Riffai et al., 2012; Martins et al., 2014; Rahi et al., 2018b). These findings indicate that the use of technology will improve performance, manage difficulties, and reduce the work required by customers who conduct online banking transactions. However, some research showed that EE has no impacts on intention (Baptista and Oliveira, 2016; Khan et al., 2018). However, this research hypothesizes that EE will positively affect the behavioral intentions of bank customers in Malaysia. We, therefore, hypothesized that:

FC is another platform that drives Internet Banking users. FC is defined as “the degree to which a person believes that system utilization is supported by an organization and technological infrastructure” (Venkatesh et al., 2003). To access Internet Banking, you need to have internet, powerful internet resources (tablets, laptops, and PCs), the right software, and programming skills. To make full use of Internet Banking services, one must have some kind of skill, technology, and resources (Zhou et al., 2010; Tarhini et al., 2016). Therefore, in view of the support services and services available to customers, they will be strongly encouraged to use Internet Banking. Previous researchers found that FC has a positive relationship with usage behavior in multiple contexts. For example, McKenna et al. (2013) found the relationship in adoption of information services; Martins et al. (2014) found it in the adoption of internet banking. In the context of Malaysia, it is posited that FC will affect the behavioral intentions to use internet banking in Malaysia (Morosan and DeFranco, 2016; Tarhini et al., 2016). Therefore, it is hypothesized that:

The D&M model has been used in several ISSM-related studies (Lee and Chung, 2009). The qualities of the information are measured by the output of the information systems and sources. The services used in the post-implementation phase such as the MB data format are affected by the need of the services required. Irrelevant, old fashioned, unreliable services can cost the credibility of the services but cause a distress among the users (Cheung et al., 2008; Zhou, 2011, 2013). Studies show that information Quality is a basic predictor of user satisfaction. Whether it is information quality or service quality, both are important (Ofori et al., 2017). It also ensures the continuity of the user to use the service. According to Sharma and Sharma (2019), information quality can be seen as the main factor that leads to satisfaction, as it affects behavioral beliefs that can lead to the intention to use internet banking (Ho et al., 2019). Jung et al. (2009) also said that the quality of the content can affect how the user feels about the site. Jung et al. (2009) highlighted that content quality may impact the user experience. Zhou (2013) said that if the information was not of high quality, users would have to put in a lot of work to figure it out, which would make it harder for them to do their jobs. Gao and Waechter (2017) and Mezhuyev et al. (2018) found that online banking customers may be less satisfied if the information they get is not of high quality. We derived the following hypothesis:

System quality as the Technical side in the IS model. DeLone and McLean (2003) described the nature of the framework as good elements of the data structure itself, data creation. Web banking structures have a variety of contrasting effects, including “small screens, a strong keypad, limited data transfer, and various barriers (Zwass, 2003; Chae and Kim, 2004; Zhou, 2011). The low quality of the framework will improve customer experience because online banking builds its complexity and is not able to fill customers' needs (Petter and McLean, 2009). The quality of the online banking system has a significant impact on customer satisfaction (Cheong and Park, 2005; Koo et al., 2013). Bhattacherjee (2001) found that if the user experiences better system performance, it would lead to user satisfaction and continued usage, and vice versa. Based on the principles mentioned above, a better system quality may boost user satisfaction while compensating for online banking's limited physical dimensions. We can derive the following hypothesis:

The title in ISSM model Service Quality, Management quality, is seen as the type of management or support clients receive from an IS organization and employees who support the overall IT or clear IS (DeLone and McLean, 2003). In D&M, “management quality is a standard service provided by a co-op specialist”, and quotes (Petter et al., 2008), “the quality of emotionally supportive network clients entering the IS category and IT support staff” (e.g., responsiveness, reliability, speculation, special capacity, and empathy of employees). According to Ofori et al. (2017), providing greater support and ensuring customer satisfaction are widely regarded as the basic values that lead to sustainable use and service progress. In a financial business investigation, Marinkovic and Obradovic (2015) found that the quality of services significantly affected customer satisfaction and their impact on customer response (Ofori et al., 2017). The organization of internet banking professionals will provide comprehensive quality services linked to the smallest deviation in the financial business, which will significantly affect customer utilization and satisfaction. As a result, the service quality may be improved, which may lead to customer satisfaction in terms of their connection with service personnel, and will also favorably affect their desire to use if their use issues are promptly addressed and handled efficiently. Hence, it is believed that focusing on the service quality of the internet banking system may boost user satisfaction and experience. Therefore, also on the side of this work (Lin et al., 2015; Humbani and Wiese, 2019), we derive the following hypothesis:

Customer satisfaction is expected to be the major difference between service perceptions and UTAUT features (Tarhini et al., 2016). According to Li and Suomi (Li and Suomi, 2009), the level of customer satisfaction at Internet Banking has a strong relationship with how the Internet Banking service meets banking standards. Starting with the best product and delivering the product differently will create recurring customers, increase traffic to the related company site, and encourage new users who may be starting to use the product (Li and Suomi, 2009). Similarly, Hoehle et al. (2012) noted that practical assistance, expected assurance, and continuous purpose have a significant impact on customer satisfaction. Therefore, a better quality service can increase the level of customer satisfaction. Furthermore, Banerjee and Sah (2012) said clients who are generally happy with the digital financial help have all the earmarks of being faithful to the assistance and utilize the assistance again later on. The more clients are happy with their Internet Banking, the more extended the relationship they have with these administrations, the more dependable they are in these administrations, and, subsequently, the more they can collaborate as they utilize these administrations (Al-alak, 2014; Levy, 2014; Al-Sharafi et al., 2021). On which Tam and Oliveira (2016) and Albanna et al. (2022) further built the positive connection between user satisfaction and intention to use internet banking services, and they advanced the knowledge base by stressing upon user satisfaction as the outcome of the overall quality provided by the service provider in the context of internet banking. Therefore, we derive the following hypotheses:

Petter et al. (2013) defined “The intention of Use, or the users' belief about their potential to use IS”. Moreover, Petter and McLean (2009) have described the “intention of Using the Expected Use of IS or Outputs”. Many studies have indicated that Intention to Use as is the user's attitude toward IS (Petter et al., 2013). Aladwani (2002) said that customers in some countries do not think Internet banking is very important. They think ATMs and internet banking are more important than other ways of banking. Customers' use of internet banking requires acceptance of the technology, which can be complicated because it involves the changing of behavioral patterns and developing a familiarity with both the technology and the financial services (Tsai et al., 2014). Behavioral intention is a measure of one's intention to perform a specific behavior (Montazemi and Qahri-Saremi, 2015). The Internet requires the adoption of technology, which can be difficult because it involves changing patterns of behavior and adapting to both technology and financial services (Tarhini et al., 2016; Yuan et al., 2019; Alkhowaiter, 2020). In the previous studies, it has been proved that intentional behavior is related to actual behavior (Stewart and Segars, 2002). In the discussion of internet banking, it is thought that a person with good intentions to acquire or use will use Internet Banking services more or less often (Lin et al., 2015). The following hypothesis is proposed.

This survey study aims at reporting findings on the strength of quality assurance in medical education. The study included one primary endogenous construct, namely, adoption of internet banking. The proposed model shown in Figure 1 consists of ten constructs, namely, Information Quality (IQ), Service Quality (SEQ), System Quality (SQ), Facilitating conditions (FC), Performance expectancy (PE), Price value (PV), and Perceived risk (PR), satisfaction (SAT), Behavioral intention to use internet banking (BI), and adoption of internet banking (AIB). Ten different paths were suggested for each of the six constructs; four different paths were proposed for Facilitating conditions, Performance expectancy, Price value, and Perceived risk hypothesized to significantly predict Behavioral intention to use internet banking. Moreover, three path lines were proposed for Information Quality, Service Quality, System Quality, hypothesized to significantly predict satisfaction; and, also, one line of satisfaction was developed, hypothesized to strongly predict behavioral intention to use online banking. Meanwhile, it was hypothesized that satisfaction and behavioral intention to use online banking were predicted of internet banking adoption (AIB) (Figure 1).

Nearly 380 questions were distributed, of which 362 were returned by the respondents, indicating a 95.2% return rate. These question papers were examined in person, and 18 questions were incomplete. Hence, they had to be excluded. The rest of 362 questionnaires were submitted to SPSS. From 362 of the questionnaires, 257 (71.0%) were answered by male; and 105 (11%) were answered by female respondents. From this study, 40 (30.4%) were between the ages of 18 and 22, 108 (29.8%) were between the ages of 23 and 28, 146 (40.3%) were between 29 years old and 34 years, 56 (15.5%) were between 35 and 40 years, and 12 (3.3%) were over 40 years. Table 1 has depicted the majority of the respondents were male (71.0%). The respondents are mostly Malay (177 respondents or 48.9 percent). In terms of revenue, majority of the respondents (92.1%) were from the income group of RM 2,000–2,999 per month, which suggests that the majority of respondents were probably middle-income government servants. The table also shows that almost half of the respondents used ATM (50.8%), and, out of this, 49.2% of them are using Maybank accounts.

All data obtained were assessed using a 5-point Likert scale, which includes features of the ISSM model, and UTAUT variables, demographics. The questionnaire was physically distributed, and all the respondents were required to answer it manually on papers, so an accurate collection of data could be obtained. Collected data were tested with IBM SPSS, as well as Structural Equation Modeling (Smart-PLS). IBM SPSS and Smart-PLS 3.3.3 are considered the main mathematical method used in our two-phase study; the first phase followed the construction of the suitability of the steps, the suitability of the flexible steps, the bias of the measurement suitability, and the second phase investigated the model of the structure. This approach is suggested by Alzahrani et al. (2012) and Hair et al. (2019).

Therefore, it can be argued that the above factors (Samar and Mazuri, 2019; Raza et al., 2020; Alarifi and Husain, 2021) increase customer service in banks with satisfaction and purpose of using Internet Banking and, respectively, to improve Internet Banking acceptance (Alalwan et al., 2018). The estimated risk factor was rated five (5) items, and each was accepted from Featherman and Pavlou (2003), Janahi (2013) and Sharma et al. (2020); and price value in five (5) items received from (Alalwan et al., 2018). Switched to auxiliary conditions and an expected operating time count to five items (5) factors considered from Rahi et al. (2019), Samar and Mazuri (2019), and Sharma et al. (2020). Service quality, and System Quality were also considered in more than five (5) items and followed from Tam and Oliveira (2016), Rahi et al. (2019), and Samar and Mazuri (2019). In addition, the behavioral intention of using Internet Banking equipment is measured by five options specifically (Rahi et al., 2019; Sharma et al., 2020); Satisfaction factor was assessed over five (5) items suggested by Tam and Oliveira (2016) and Sharma et al. (2020); Finally, Adoption of internet banking was measured through five (5) items suggested by Janahi (2013), Rahi et al. (2019), and Sudarsono et al. (2020); see Appendix A.

For the purpose of the study, we distributed 380 questionnaires, of which 362 were returned back by the respondents. After the manual analysis of the questionnaires, 18 of the 380 questionnaires were incomplete, i.e., the customers did not finish the survey; thus, they had to be excluded. Thus, the number of questionnaire that can be considered is 362. Such exclusions were recommended by Arteaga Sánchez et al. (2014), who related that outliers could lead to inaccurate statistical results and should be eliminated. The chosen research model comprised internet banking users as its sample, and their behavioral intention to use internet banking and customer satisfaction were also examined. Data were collected from randomly selected local and international customers of Malaysian Bank from various states: Johor Bharu, Perak, Malacca, Selangor, and Negeri Sembilan. Based on that, the sample size of this study (N = 362) is acceptable according to Hair et al. (2012); they stated the minimum sample size for quantitative research is (N = 354). For data analysis, procedures “(PLS-SEM)” were used. In this review, the Smart PLS 3.3.3 framework was used to test the scope and models of the design. Information accuracy and reliability are measured within your calculations in the measurement model: –≥0.500. The Fornell–Larcker criteria, crossloading, and heterotrait–monotrait ratio computation approaches were used to assess discriminant validity (HTMT). The Cronbach's alpha and composite steadfastness (CR) were two types of instability; the two attributes should be significantly higher than the 0.700. For the assessment model, we reported the significance of the relationship through path coefficient, t-value, and p-value.

Hair et al. (2019) advocated for four types of measurement model evaluations for PLSSEM: reflective indicator loadings, discriminant validity, convergent validity, and internal consistency reliability.

The seven dimensions of internet banking adoption, namely, perceived risk, price value, performance expectancy, facilitating conditions, service quality, information quality, and system quality were used as potential constructs that may influence customers' satisfactions and behavioral intentions to use internet banking in Malaysia.

This study used two models, namely, ISSM and UTAUT models to measure and evaluate the findings. The major impacts and implications of this study are mentioned below:

This study has a number of practical implications for decision-makers in banking. The proposed research model and understanding of the interrelationships among multiple decision variables provide substantial information for decision-makers from the banking and telecommunication industry. The current study offers two perspectives; the ISSM and UTUAT model promotes a wide range of priorities to understand customer behavior in using IB and, also, customers' satisfaction in order to increase their adoption of IB. In addition, test metrics and steps are important factors in research or practice. Therefore, the online bank acceptance model developed in this study can be used in the practical evaluations of the Internet Banking designed with the aim to increase IB customer acceptance. At present, although some hypotheses of this study are not supported by any research theories, they were still significant to be used to quantify the various aspects of IB. Another important theater contribution of our research—we successfully figured out some information that might be useful to be used in fulfilling customers' satisfactions and demands in using IB.

This study provides two lines of evidence: initial evidence of operational intent to be used in the context of motivation, expected operating time, price, and perceived risk. “The second strongest evidence of customer satisfaction is a method of Information Quality, Service Quality, System Quality that can improve customer acceptance in the context of an Internet Banking.” This is a major theoretical contribution to previous ISSM and UTAUT studies that have not seen the effect of customer satisfaction on the ethical purpose to be applied (Alalwan et al., 2018; Rahi et al., 2019, 2021; Geebren et al., 2021; Saadilah et al., 2021). The main conclusions that have been drawn from the study are: