1. Introduction
Distribution of behavioral intention holds significant importance in marketing as it signifies consumers' readiness and willingness to buy a specific product or a service (Carricano et al., 2020). The banking industry has been undergoing a paradigm shift driven by rapid technological advancements, giving rise to innovative banking services such as mobile banking apps, online payment systems, and AI-powered customer supports. Meanwhile, distribution of behavioral intention is considered vital as it sheds light on customer behaviors and decision-making processes concerning the adoption of banking services (Biswas et al., 2020). This banking service is the service innovation that has reshaped customer behaviors and expectations, requiring the banks to not only provide efficiently financial services but also create exceptional customer experiences (Indriasari et al., 2019). Understanding the impact of banking service innovation on distribution of behavioral intention of customer has become crucial for the banks to stay competitive in the digital era. Additionally, corporate image and customer satisfaction are pivotal factors influencing distribution of customers' intention to utilize mobile banking services and ultimately impacting their purchasing behaviors.
Corporate image is also important in mobile banking as it can influence customers’ perceptions of the bank and their trust in the service (Purwanto et al., 2020). A positive corporate image can increase customers' confidence in the security and reliability of the mobile banking service, which, in turn, influences their intention to use the service and make the purchases through it (Darmawan, 2018). A negative corporate image, on the other hand, can lead to distrustfulness and reluctance to use the service, negatively impacting the bank's customer base and revenues (Darmawan, 2018). Additionally, customer satisfaction is a critical factor influencing customers' intention to use mobile banking and make purchases through the service (Cholisati et al., 2019). Customers are satisfied with the mobile banking service will likely continue using it and recommending it to others. Positive word-of-mouth and repeat purchases can increase the banks' revenue and market share (Nurittamont, 2020). Conversely, if customers are dissatisfied with the service, they may switch to a competitor or discontinue using it altogether, negatively impacting the bank's revenue and market share (Purwanto et al., 2020). In conclusion, service innovation, corporate image, and customer satisfaction are critical factors influencing distribution of behavioral intention of customers to use mobile banking and make purchases through the services. Banks that invest in service innovation, maintain a positive corporate image, and prioritize customer satisfaction can increase their customer base and revenues in the mobile banking market.
Therefore, this research aims to explore service innovation and corporate image as the factors influencing of distribution of behavioral intention. Moreover, this study examines the mediating effect of customer satisfaction in the relationship between service innovation and distribution of behavioral intention, as well as the relationship between corporate image and distribution of behavioral intention. The study aims to gain a deeper understanding of the factors affecting distribution of behavioral intention consumers in the context of mobile banking services, which may vary across different countries (Sharma & Sharma, 2019). The study focuses on the customers of commercial banks in Thailand. Studying the relationships among these factors can help researchers identify potential areas for improvement in a business's customer service and marketing strategies. By analyzing the factors influencing customers' distribution of behavioral intention, researchers can identify critical areas for improvement and develop targeted interventions to enhance customer satisfaction, loyalty, and purchase behaviors.
2. Literature Review and Hypothesis
2.1. Service Innovation
Salunke et al. (2019) defined service innovation as combining resources and organizational capabilities to create a valuable customer service. (Chu et al.,2018). stated that service innovation was the development of new forms or improvements to services that responded to new customer needs. Zheng et al. (2018) identified service innovation as a new service experience or solution for a service.
In summary, service innovation refers to creating new customer service ideas by organizations, resulting in new service experiences or solutions that help solve existing customer problems. The level of service innovation development within an organization depends on its ability to improve organizational structure, technology, personnel, and customer service experiences. This research defines service innovation as a component consisting of four dimensions: new delivery processes, interaction with customers, new technologies, and new service concepts (Gebauer et al., 2021).
2.2. Corporate Image
The corporate image in service market research is important because it is an overall assessment of the organization in the service market (Hussain et al., 2020). According to the study by Hussain et al. (2020), the corporate image accumulates customer experiences with the organization's products and services for a period of time. Riyadi (2021) stated that corporate image is a combination of physical and behavioral characteristics of an organization, such as its business name, architecture, diversity of products or services, and the quality perception communicated by the organization that resided in the consumers' minds. In the context of a banking study, the corporate image can impact customer satisfaction and behavioral intention if the financial services provided by the bank are of high quality and perception to be different from competitors (Hu et al., 2019). Nowadays, the banking industry's business objectives emphasize competition to provide fast, cost-effective, and easy-to-use financial services, leading to rapid growth in mobile banking transactions (Indriasari et al., 2019). In this study, the components of the corporate image have been investigated and identified into four elements as follows: organization, employees, products and services, and communication.
2.3. Customer Satisfaction
Customer satisfaction is one of the main strategic objectives set by all organizations (Zouari et al., 2021). Extensive empirical research has consistently demonstrated a noteworthy positive relationship between customer satisfaction and various favorable outcomes, such as increasing intention to repurchase and the positive word of mouth (Kusuma et al., 2021). Consequently, organizations prioritizing customer satisfaction are more likely to cultivate a positive reputation, foster customer loyalty toward their brands (Ali, 2022), and ultimately enjoy higher profitability while reducing costs is associated with repeat service usage (Eklof et al., 2020). To increase customer satisfaction, organizations should focus on providing quality services that meet customer needs in all aspects of their marketing mix. The quality of service the organization provides can be seen as a determinant of customer satisfaction (Shikhara et al., 2020). This study defines customer satisfaction as the level of perception about the results of the organization's operations compared to the customer's expectations. In this study, the components of customer satisfaction have been investigated and identified into four elements as follows: utility, overall satisfaction, value perception, and safety (Kaur et al., 2021; Gomachab & Maseke, 2018).
2.4. Distribution of Behavioral Intention
Distribution of behavioral intention refers to the tendency of an individual to exhibit any behavior. Researchers use distribution of behavioral intention to measure levels of user acceptance of technology. Yusof, Munir et al. (2018) studied factors that influenced distribution of behavioral intention based on the theory of planned behaviors. According to such a theory, human actions result from their reasoning combined with other information to decide whether to act. Therefore, predicting human behaviors is influenced by three components that affect the behavior exhibited in each individual: behavioral intention, attitudes, and social norms.
2.5. Satisfaction
Satisfaction is the expression of the positive emotions and feelings of customers. The comparison of the expectations of customers who buy products/services and the results obtained from actual purchases has demonstrated that if the results are higher than the expectations, the customers will be satisfied (Kotler & Keller, 2012).
In the banking industry, financial services that are impersonal, complex, and different levels of customer demands are offered. Herjanto and Amin (2020) stated that the foundation of building customer relationships was to make a repurchase. However, encouraging repurchasing poses challenges for banks. This study divides the components of distribution of behavioral intention into two parts: the intention to repurchase and the word of mouth.
3. Research Hypotheses and Research Model
3.1. Research Hypotheses
3.1.1. The Relationship between Service Innovation and Distribution of Behavioral Intention
Customers evaluate the benefits and the cost of using the service innovation before making a decision (Zietsman et al., 2018), by choosing easy-to-use, uncomplicated options that customers understand. Each customer's experience with technology of service innovation affects their decision to use it differently (Albayati et al., 2020). Distribution of behavioral intention of customers is evaluated based on the possibility that customers will accept the technology of service innovation and start using it in the future the adoption of banking services (Biswas et al., 2020). When the customer implements service innovation successfully, it means that customers in the future or present have the intention to start using technology. In other words, if the customer accepts the technology of service innovation, distribution of behavioral intention of customer to use technology is high. Therefore, this study posits the following hypothesis:
H1: Service innovation has a positive impact on distribution of behavioral intention.
3.1.2. The Relationship between Corporate Image and Distribution of Behavioral Intention
The researchers attributed this finding to the increasing competition among banks, which led to similar financial services not generating enough profits. Therefore, the banks needed to adjust and develop their personal financial services, focusing on improving the quality of services to increase customer awareness and differentiate themselves from their competitors. This approach would increase customer satisfaction, which could lead to increasing market share and profits, allowing businesses to generate long-term profitability. Purwanto et al. (2020) suggested that attitude could predict behavioral intention and behaviors. Customers were proud to purchase or use products or services from companies with good images as it elevates their social status.
Shareef et al. (2018) found a correlation between customers' perception of an organization's image and their use of mobile banking services. Behavioral intention included two components: 1) the intention to reuse the service and 2) the intention to recommend the service to others, which are both elements of customers' goodwill. The study by Özkan et al. (2019) suggested that the image and reputation of a bank could serve as an indicator of the bank's performance if customers had perceived the value of the service and had been satisfied with it. Similarly, the study by Igbudu et al. (2018) suggested that the image of a bank had a positive impact on customer loyalty and sustainable business practices, creating a good organizational image and increasing customer loyalty. Therefore, this study posits the following hypothesis:
H2: Corporate image has a positive impact on distribution of behavioral intention.
3.1.3. The Mediating Role of Customer Satisfaction on the Relationship between Service Innovation and Distribution of Behavioral Intention
Customer satisfaction is a personal feeling that arises after receiving a service and the experience which can create either a sense of satisfaction or dissatisfaction (Azhar et al., 2018). Customers are satisfied when their experience exceeds expectations and dissatisfied when it falls short. Customer satisfaction from the experience of purchasing or receiving a service is a factor in creating intention to purchase and in recommending the product or the service to others. When customers perceive a good service delivery process, it results in satisfaction towards the service (Azhar et al., 2018). In the context of banking studies, research has found that customer satisfaction has an influence on behavioral intention, and there is a positive relationship between customer satisfaction and behavior (Rather, & Hollebeek, 2021). However, customers who are dissatisfied with the service will reduce their intention to recommend it to others (Rather, & Hollebeek, 2021) and may switch to using services provided by others. Therefore, this study posits the following hypothesis:
H3: Customer satisfaction is a mediating variable in the positive relationship between service innovation and distribution of behavioral intention.
3.1.4. The Mediating Role of Customer Satisfaction on the Relationship between Corporate Image and Distribution of Behavioral Intention
An organization with a good corporate image is likely to have higher levels of customer satisfaction (Darmawan, 2018). According to Martínez et al. (2011) businesses with a good corporate image could increase customer satisfaction and purchase intention. Islam et al. (2021) stated that physical factors such as tools, personnel, and communication created impressions and satisfaction for customers because they could perceive them. Customer satisfaction leads to positive attitudes, and customers are satisfied when they receive services from the bank (Khatoon et al., 2020). On the other hand, dissatisfied customers are likely to reduce their purchases (Liu et al., 2019). Satisfied customers tend to recommend and share products and services with others when they receive services from the bank (Khatoon et al., 2020). In summary, customer satisfaction creates a positive image, and satisfied customers are more likely to purchase more. Therefore, this study posits the following hypothesis:
H4: Customer satisfaction mediatesthe positive relationship between corporate image and distribution of behavioral intention.
3.2. Research Model
This research sets the research model as shown in Figure 1.
Figure 1: The research model of the impact of service innovation, corporate image, and customer satisfaction on distribution of behavioral intention
4. Methodology
4.1. Sample
This study utilized the customers of 12 commercial banks in Thailand who utilized mobile banking services or applications for their financial transactions as samples. A confidence level of 95% and a maximum error rate of 0.05 were employed to determine the appropriate sample size (Singh & Masuku, 2014). The samples were selected through using purposive sampling (Cochran, 1977), which involved deliberately selecting participants who closely resembled the target population to ensure that the obtained results were a reliable reference.
4.2. Research Instruments
After an intensive review of the literature, the concept and the questionnaire of the previous research were adopted to develop and customize the questions for this research. The questionnaire was designed as a checklist of answers and rating scales (7-point Likert scale). The questionnaire comprised five sections: demographic information, service innovation, corporate image, customer satisfaction, and distribution of behavioral intention. The researcher sought the input from five experts to ensure the validity of the questionnaire's content. Their expertise helped in assessing the appropriateness of the wording and making necessary adjustments to enhance accuracy and relevance. The questionnaire was the tryout by administering it to a group of 30 individuals who closely resembled the target sample. This tryout aimed to determine the questionnaire's reliability value (α = 0.928), measured through using Cronbach's alpha coefficient.
4.3. Data Collection
Samples were chosen by trained employees who traveled to different branches of each of the 12 banks in Thailand. In order to cover the behaviors of commercial bank customers in Thailand, the regions to conduct surveys were divided into 5 areas: Bangkok, central Thailand, northern Thailand, northeastern Thailand, and southern Thailand. Five shopping malls were chosen as the survey collection points because they have commercial banks inside, and a large number of people used banking services in shopping malls, making it convenient to access the sample group. This was also suitable for the research's time and cost. Finally, 444 data were collected for data analysis.
Table 1: The Goodness of Fit Statistics for the Measurement Model (Hair et al., 2018).
5. Results
In this research, the data were collected from 444 sets of questionnaires. The demographic information of the respondents was divided into six categories: gender, age, career, income range, experience with m-banking, and m-banking application.
Table 2: Mean (\(\begin{aligned}\bar x\end{aligned}\)) and standard deviation (S.D) of four variables: service innovation, corporate image, customer satisfaction, and distribution of behavioral intention
Based on the study results shown in Table 1, it indicated that the overall level of service innovation was at a high level (\(\begin{aligned}\bar x=5.74\end{aligned}\)). The new technology was found to have the highest mean value (\(\begin{aligned}\bar x=6.18\end{aligned}\)), followed by the new service concept (\(\begin{aligned}\bar x =5.95\end{aligned}\)), the new delivery process (\(\begin{aligned}\bar x=5.55\end{aligned}\)), and interaction with customers (\(\begin{aligned}\bar x=5.30\end{aligned}\)). The overall corporate image was at a high level (\(\begin{aligned}\bar x=5.79\end{aligned}\)). Products and services had the highest mean value (\(\begin{aligned}\bar x=5.99\end{aligned}\)), followed by organization (\(\begin{aligned}\bar x= 5.87\end{aligned}\)), communication (\(\begin{aligned}\bar x = 5.72\end{aligned}\)), and employees (\(\begin{aligned}\bar x=5.59\end{aligned}\)). The overall customer satisfaction was at a high level (\(\begin{aligned}\bar x = 5.52\end{aligned}\)). The utility demonstrated the highest mean value (\(\begin{aligned}\bar x = 6.22\end{aligned}\)), followed by overall satisfaction (\(\begin{aligned}\bar x = 5.9\end{aligned}\)), value perception (\(\begin{aligned}\bar x=5.51\end{aligned}\)), and safety (\(\begin{aligned}\bar x = 5.33\end{aligned}\)). The overall distribution of behavioral intention was also at a high level (\(\begin{aligned}\bar x = 5.80\end{aligned}\)). Word of mouth was observed to have the highest mean value (\(\begin{aligned}\bar x =5.90\end{aligned}\)), followed by intention to repurchase (\(\begin{aligned}\bar x = 5.71\end{aligned}\)).
The results of Table 3 demonstrated that Cronbach’s Alpha was stable and adequate for research (α of service innovation = 0.892, α of corporate image = 0.984, α of customer satisfaction = 0.893, and α of distribution of behavioral intention = 0.893). The four variables were reliable.
Table 3: Scale Reliability Statistics.
Table 4 depicted the correlation and reliability matrix. All correlations were found to have a statistically significant positive correlation at the 0.001 level. The correlation levels were between 0.649 and 0.756, indicating that service innovation, corporate image, customer satisfaction, and distribution of behavioral intention were positively correlated. This suggested that there was a mutual relationship between these factors.
Table 4: The Correlation and Reliability Matrix
**p <.001, N = 444
5.1. Measurable Model
Confirmatory factor analysis (CFA) shown in figure 2 was to evaluate the extent to which a set of observed variables measured the same construct.
Figure 2: The First Order Confirmatory Factor Analysis (CFA)
The results indicated that the model with the lowest value had a χ2 value of 14.574, a degree of freedom of 26, and a p-value of 0.965, implying statistical significance. This finding suggested that the hypothetical models aligned well with the empirical data. Additional evaluation indices were also taken into account, yielding the following results: a comparative fit index (CFI) of 1.000, a goodness-of-fit index (GFI) of 0.995, and a normed fit index (NFI) of 0.997. Therefore, the fitness of the model aligned with the empirical data. Moreover, the root means square error of approximation (RMSEA) index of 0.000 indicated a good fit. A normal χ2 /df value of 0.560, which was below 3, was considered eligible. It could be concluded that the theoretical model was fitted with the empirical data.
Table 5 presented the reliability and validity of the measurement instrument. The average variance extracted (AVE) represented the extent to which different indicators or measures intended to measure the same construct demonstrating convergence or agreement. High levels of shared variability among these indicators indicated that they effectively measured the same underlying construct or factor. The AVE values for service innovation, corporate image, customer satisfaction, and distribution of behavioral intention were 0.76, 0.93, 0.83, and 0.58, respectively. Since these values exceeded the threshold of 0.5, the measurement model exhibits good convergent validity (Fornell & Larcker, 1981). Additionally, and the composite reliability (C.R.) values for all factors were above 0.7 (Fornell & Larcker, 1981).
Table 5: Confirmatory Factor Analysis of Measurement model
According to Table 6, discriminant validity was assessed by comparing the AVE values with the squared correlation values between four variables. An AVE was value greater than the squared correlation value between the components raised to the power of two indicates good discriminant validity (Fornell & Larcker, 1981).
Table 6: Discriminant Validity
5.2. Structural EquationModel
To test the study hypotheses, a structural equation model (SEM) was analyzed through using the maximum likelihood method. The results of the SEM analysis were shown in Table 7 Overall, the SEM results revealed that the model was fitted well with the data. For the relationship between service innovation and distribution of behavioral intention (c1), the results were as follows: χ2 = 17.495, df = 7 (n = 444), p < 0.014; relative χ2 = 2.499; GFI = 0.987; NFI = 0.988; TLI = 0.985; CFI = 0.993; RMR = 0.034; and RMSEA = 0.058. Regarding the relationship between corporate image and distribution of behavioral intention (c2), the findings were as follows: χ2 = 5.040, df = 3 (n = 444), p < 0.196; relative χ2 = 1.680; GFI = 0.996; NFI = 0.998; TLI = 0.995; CFI = 0.999; RMR = 0.011; and RMSEA = 0.039.
Table 7: The Results of the Structural Equation Model Analysis
N = 444, C.R. = Composite reliability
The results in Table 7 demonstrated direct relationships. The SEM outputs supported the direct positive and significant impacts of service innovation on distribution of behavioral intention (β = 0.823, p < 0.001) (path c1) and the corporate image on distribution of behavioral intention (β = 0.884, p < 0.001) (path c2); accordingly, hypotheses H1 and H2 were supported.
To test for mediation, the direct effects of service innovation and corporate image on distribution of behavioral intention were examined. Table 8 showed the indirect effect which was the effect of service innovation and corporate image on distribution of behavioral intention through customer satisfaction. The total effect was the direct effect plus the indirect effect. Table 8 showed the result of total effect of the study.
Table 8: The Results of Mediation Analysis
N = 444, p < 0.001
The Figure 3 and Figure 4 showed the relationship between service innovation, corporate image, on distribution of behavioral intention after take customer satisfaction as a mediator.
According to Figure 3, after taking customer satisfaction as a mediating variable between service innovation and distribution of behavioral intention, the regression coefficient for the service innovation on the distribution of behavioral intention was 0.859 (c’1), which was statistically significant at the 0.001 level which slightly increased from 0.823 (c1) of the direct effect between service innovation and distribution of behavioral intention before taking customer satisfaction as a mediator. As a result, the service innovation had no mediator effect on distribution of behavioral intention through customer satisfaction. Hypothesis 3 was not supported.
Figure 3: The influence of service innovation on distribution of behavioral intention affecting customer satisfaction as a mediating variable
According to Figure 4, after taking customer satisfaction as a mediating variable between corporate image and distribution of behavioral intention, the regression coefficient for the service innovation on distribution of behavioral intention was 0.590 (c'2), which was statistically significant at the 0.001 level which slightly decreased from 0.884 (c2) of the direct effect between service innovation and distribution of behavioral intention before take customer satisfaction as a mediator. As a result, the corporate image had a partial mediator effect on distribution of behavioral intention through customer satisfaction. Hypothesis 4 was supported.
Figure 4: The influence of corporate image on distribution of behavioral intention affecting customer satisfaction as a mediating variable
6. Conclusion and Discussion
The findings of this study emphasize the significance of considering the unique value generated by service innovation when predicting customers' distribution of behavioral intention. Service innovations that effectively address customer concerns promote transparency and ensure reliability to play a pivotal role in fostering trust. Trust significantly influences customers' distribution of behavioral intention. Customers who perceive an innovative service as trustworthy are likelier to exhibit positive distribution of behavioral intention, including continued usage and positive word of mouth (Carricano et al., 2020). Furthermore, the study reveals that corporate image notably impacts distribution of behavioral intention. The corporate image influences customers' perceptions of the quality of its products or services. A positive corporate image is associated with higher perceived quality, positively influencing customers' distribution of behavioral intention. According to Özkan et al. (2020) customers are more likely to choose and continue using products or services from organizations with a positive image.
These findings find that customer satisfaction partially mediates the relationship between corporate image and distribution of behavioral intention. Customer satisfaction helps maintain cognitive consistency between the positive corporate image and customers' actual experiences. When customers have a positive image of an organization but encounter dissatisfaction, it creates cognitive dissonance. Conversely, when their experiences align with the positive image, it leads to cognitive consistency and reinforces their distribution of behavioral intention to engage with the organization (Ali et al., 2018) Customer satisfaction can be influenced by multiple factors related to corporate image: first, a positive corporate image can shape customers' expectations for the service, affecting their satisfaction. Second, a positive corporate image can signal to customers that the service provider is committed to quality and customer satisfaction, thereby enhancing their satisfaction. Third, customer satisfaction encompasses the overall service experience, encompassing factors beyond corporate image, such as price, convenience, and customer service.
This study finds that customer satisfaction does not mediate the relationship between service innovation and distribution of behavioral intention. Chen et al. (2019) posited that, depending on the specific context and variables being studied, other variables such as perceived usefulness, ease of use, trust, and enjoyment of the innovative service may play a more significant role in mediating this relationship.
Recommendations
Policy recommendations
In mobile banking, service innovation can include developing new or improved features like biometric authentication, personalized financial advice, and real-time transaction monitoring. Given the positive effect of service innovation on distribution of behavioral intention, it is recommended to prioritize implementing such innovative features to encourage customers’ intention to use mobile banking services.
Similarly, considering the positive effect of corporate image on distribution of behavioral intention, policy makers can take specific steps to foster customer trust and confidence. Encouraging transparency and accountability within financial institutions can play a significant role in building customer trust. Financial institutions can promote openness and ensure responsible practices to increase customers' behavioral intention to utilize mobile banking services. Furthermore, the relationship between corporate image and distribution of behavioral intention is partially mediated by customer satisfaction. Policymakers can advocate for financial institutions to enhance the customer experience by focusing on various aspects. This includes improving the usability of mobile banking interfaces, offering prompt and effective customer support, and providing personalized services. Financial institutions can bolster customer satisfaction by investment in these areas, subsequently influencing customers' distribution of behavioral intention to engage with mobile banking services.
Recommendations for future researcher
To further advance the understanding of the mediating effect of customer satisfaction on the relationship among service innovation, corporate image, and distribution of behavioral intention in the context of mobile banking, future researchers can explore the following areas:
1. The moderating effect of customer segments and cultural contexts, explore whether the mediating effect of customer satisfaction varies across different customer segments or in different cultural contexts. Cultural factors, for example, may influence the degree to which customers value customer satisfaction as a driver of distribution of behavioral intention. Examining these variations can provide valuable insights into how different factors influence the relationships between the variables.
2. Consider conducting longitudinal studies to examine the temporal relationships among service innovation, corporate image, customer satisfaction, and distribution of behavioral intention over time. By observing these relationships over an extended period, researchers can identify the causal links between these constructs and gain insights into how they evolve and change over time.
3. Employ advanced mediation analysis techniques such as moderated mediation or serial mediation. These techniques can provide a more comprehensive understanding of the complex relationships among service innovation, corporate image, customer satisfaction, and distribution of behavioral intention in mobile banking. Researchers can reveal additional nuances in these relationships by considering potential moderators or serial mediators.
By addressing these research areas, future studies can contribute to a deeper understanding of customer satisfaction's mediating role and the dynamics among service innovation, corporate image, customer satisfaction, and distribution of behavioral intention in mobile banking.
References
- Albayati, H., Kim, S. K., & Rho, J. J. (2020). Accepting financial transactions using blockchain technology and cryptocurrency: A customer perspective approach. Technology in Society, 62, 1-14.
- Ali, M. A. B. (2022). The Effect of Firm's Brand Reputation on Customer Loyalty and Customer Word of Mouth: The Mediating Role of Customer Satisfaction and Customer Trust. International Business Research, 15(7), 1-30.
- Ali, F., Kim, W. G., Li, J., & Jeon, H. M. (2018). Make it delightful: Customers' experience, satisfaction and loyalty in Malaysian theme parks. Journal of destination marketing & management, 7, 1-11.
- Azhar, M. E., Prayogi, M. A., & Sari, M. (2018). The role of marketing mix and service quality on tourist satisfaction and loyalty at Samosir. Revista de turism-studii si cercetari in turism, 26.
- Biswas, S., Carson, B., Chung, V., Singh, S., & Thomas, R. (2020). AI-bank of the future: Can banks meet the AI challenge. New York: McKinsey & Company.
- Carricano, M., Korchia, M., & Kerbache, L. (2020). The impact of innovation attributes on customers' trust and behavioral intentions. Journal of Business Research, 108, 214-224.
- Chen, L., Gillenson, M. L., & Sherrell, D. L. (2019). Enticing online consumers: An extended technology acceptance perspective. Information & Management, 56(2), 191-203.
- Cholisati, N., Maesaroh, E. S., Rohman, F., & Puspaningrum, A. (2019). The role of customer satisfaction on the effect of service quality and corporate image on customer loyalty. MECJ (Management and Economics Journal), 3(3), 255-264.
- Chu, Z., Feng, B., & Lai, F. (2018). Logistics service innovation by third party logistics providers in China: Aligning guanxi and organizational structure. Transportation Research Part E: Logistics and Transportation Review, 118, 291-307.
- Cochran, W. G. (1977). Sampling techniques (3rd ed.). New York: John Wiley & Sons.
- Darmawan, D. (2018). The effect of service quality, customer satisfaction and corporate image on customer loyalty in the banking sector in Indonesia. IOSR Journal of Business and Management (IOSR-JBM), 19(11). 46-51.
- Eklof, J., Podkorytova, O., & Malova, A. (2020). Linking customer satisfaction with financial performance: an empirical study of Scandinavian banks. Total Quality Management & Business Excellence, 31(15-16), 1684-1702.
- Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
- Gebauer, H., Paiola, M., Saccani, N., & Rapaccini, M. (2021). Digital servitization: Crossing the perspectives of digitization and servitization. Industrial Marketing Management, 93, 382-388.
- Gomachab, R., & Maseke, B. F. (2018). The impact of mobile banking on customer satisfaction: Commercial banks of Namibia (Keetmanshoop). Journal of Internet Banking and Commerce, 23(2), 1-18.
- Herjanto, H., & Amin, M. (2020). Repurchase intention: the effect of similarity and client knowledge. International Journal of Bank Marketing, 38(6), 1351-1371.
- Hu, Z., Ding, S., Li, S., Chen, L., & Yang, S. (2019). Adoption intention of fintech services for bank users: An empirical examination with an extended technology acceptance model. Symmetry, 11(3), 340.
- Hussain, S., Melewar, T. C., Priporas, C. V., & Foroudi, P. (2020). Examining the effects of advertising credibility on brand credibility, corporate credibility and corporate image: a qualitative approach. Qualitative Market Research: An International Journal, 23(4), 549-573.
- Igbudu, N., Garanti, Z., & Popoola, T. (2018). Enhancing bank loyalty through sustainable banking practices: The mediating effect of corporate image. Sustainability, 10(11), 4050.
- Indriasari, E., Gaol, F. L., & Matsuo, T. (2019). Digital banking transformation: Application of artificial intelligence and big data analytics for leveraging customer experience in the Indonesia banking sector. In 2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI) (pp. 863-868). IEEE.
- Islam, T., Islam, R., Pitafi, A. H., Xiaobei, L., Rehmani, M., Irfan, M., & Mubarak, M. S. (2021). The impact of corporate social responsibility on customer loyalty: The mediating role of corporate reputation, customer satisfaction, and trust. Sustainable Production and Consumption, 25, 123-135.
- Kaur, B., Kiran, S., Grima, S., & Rupeika-Apoga, R. (2021). Digital banking in Northern India: The risks on customer satisfaction. Risks, 9(11), 209.
- Khatoon, S., Zhengliang, X., & Hussain, H. (2020). The Mediating Effect of customer satisfaction on the relationship between Electronic banking service quality and customer Purchase intention: Evidence from the Qatar banking sector. Sage Open, 10(2), 2158244020935887.
- Kusuma, I. G. A. E. T., Yasmari, N. N. W., Agung, A. A. P., & Landra, N. (2021). When satisfaction is not enough to build a word of mouth and repurchase intention. APMBA (Asia Pacific Management and Business Application), 10(1), 1-20.
- Liu, H., Lobschat, L., Verhoef, P. C., & Zhao, H. (2019). App adoption: The effect on purchasing of customers who have used a mobile website previously. Journal of Interactive Marketing, 47(1), 16-34.
- Nurittamont, W. (2020). Enhancing Electronic Word of Mouth through Customer Satisfaction for Young Customers' Mobile Phone Banking Applications. International Journal of Innovation, Creativity and Change, 13(12), 661-673.
- Ozkan, P., Suer, S., Keser, I. K., & Kocakoc, I. D. (2020). The effect of service quality and customer satisfaction on customer loyalty: The mediation of perceived value of services, corporate image, and corporate reputation. International Journal of Bank Marketing, 38(2), 384-405.
- Purwanto, E., Deviny, J., & Mutahar, A. M. (2020). The mediating role of trust in the relationship between corporate image, security, word of mouth and loyalty in M-banking using among the millennial generation in Indonesia. Management & Marketing, 15(2), 255-274.
- Riyadi, S. (2021). The effects of image, brand and quality on customer loyalty of Sharia banking. The Journal of Asian Finance, Economics and Business, 8(3), 1315-1325.
- Salunke, S., Weerawardena, J., & McColl-Kennedy, J. R. (2019). The central role of knowledge integration capability in service innovation-based competitive strategy. Industrial Marketing Management, 76, 144-156.
- Shareef, M. A., Baabdullah, A., Dutta, S., Kumar, V., & Dwivedi, Y. K. (2018). Consumer adoption of mobile banking services: An empirical examination of factors according to adoption stages. Journal of Retailing and Consumer Services, 43, 54-67.
- Sharma, S. K., & Sharma, M. (2019). Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation. International Journal of Information Management, 44, 65-75.
- Shokouhyar, S., Shokoohyar, S., & Safari, S. (2020). Research on the influence of after-sales service quality factors on customer satisfaction. Journal of Retailing and Consumer Services, 56, 102139.
- Singh, A. S., & Masuku, M. B. (2014). Sampling techniques & determination of sample size in applied statistics research: An overview. International Journal of Economics, Commerce and Management, 2(11), 1-22.
- Yusof, H., Munir, M. F. M. B., Zolkaply, Z., Jing, C. L., Hao, C. Y., Ying, D. S., ... & Leong, T. K. (2018). Behavioral intention to adopt blockchain technology: Viewpoint of the banking institutions in Malaysia. International Journal of Advanced Scientific Research and Management, 3(10), 274-279.
- Zheng, P., Lin, T. J., Chen, C. H., & Xu, X. (2018). A systematic design approach for service innovation of smart product-service systems. Journal of cleaner production, 201, 657-667.
- Zietsman, M. L., Mostert, P., & Svensson, G. (2018). Perceived price and service quality as mediators between price fairness and perceived value in business banking relationships: A micro-enterprise perspective. International Journal of Bank Marketing, 37(1), 2-19.
- Zouari, G., & Abdelhedi, M. (2021). Customer satisfaction in the digital era: evidence from Islamic banking. Journal of Innovation and Entrepreneurship, 10(1), 1-18.