Next Article in Journal
Linking Biodiversity and Human Wellbeing in Systematic Conservation Assessments of Working Landscapes
Previous Article in Journal
Training Leaders to Facilitate an Energy Transition: Retrospective Evaluation of Course Design
Previous Article in Special Issue
The Impact of Marketing Communication and Islamic Financial Literacy on Islamic Financial Inclusion and MSMEs Performance: Evidence from Halal Tourism in Indonesia
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 15
Issue 13
10.3390/su15139908
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Investigating the Role of Perceived Risk, Perceived Security and Perceived Trust on Smart m-Banking Application Using SEM

by
Mohammed Amin Almaiah
1,2,*,
Shaha Al-Otaibi
3,*,
Rima Shishakly
4,
Lamia Hassan
5,
Abdalwali Lutfi
5,
Mahmoad Alrawad
5,6,
Mohammad Qatawneh
7 and
Orieb Abu Alghanam
7
1
Faculty of Information Technology, Applied Science Private University, Amman 11931, Jordan
2
Department of Computer Networks, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia
3
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
4
Management Department, College of Business Administration, Ajman University, Ajman 346, United Arab Emirates
5
College of Business, King Faisal University, Al-Ahsa 31982, Saudi Arabia
6
College of Business Administration and Economics, Al-Hussein Bin Talal University, Ma’an 71111, Jordan
7
King Abdullah the II IT School, Department of Computer Science, The University of Jordan, Amman 11942, Jordan
*
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(13), 9908; https://doi.org/10.3390/su15139908
Submission received: 31 March 2023 / Revised: 31 May 2023 / Accepted: 2 June 2023 / Published: 21 June 2023
(This article belongs to the Special Issue Marketing and Sustainable Development: A Predictive Empirical Insight)
Browse Figures
Versions Notes

Abstract

:
Effective security support remains a challenge, even for mobile banking applications; this is leading to the loss of many customers due to limited protection of customer data and privacy. Cyber threats include everything from identity theft to malware threats and email and online fraud. Thus, businesses and individuals should use risk assessment methods and countermeasures to protect their m-banking apps. With this in mind, a new model using the Technology Acceptance Model (TAM) has been proposed. The model has been broken down into six main countermeasure categories, namely: perceived risk, perceived security, perceived trust, ease of use, usefulness and service quality. To test this model, structural equation modelling (SEM) was used. Our findings reveal that perceived security, perceived trust and service quality play key roles in improving the adoption of mobile banking apps. In addition, the findings indicate that perceived risk had a negative impact on both clients’ trust and their attitudes toward the use of mobile banking services. The proposed model could increase the adoption of m-banking apps by enhancing their defenses against security risk issues. The model enhances the risk reduction (63.0%), data protection (75.0%), trust (32.1%), quality of service (74.0%), ease of use (44.0%) and usefulness (45.3%) ratios.

1. Introduction

With the great advancements in information and communication technologies (ICT) that have occurred in recent years, high mobility services are now a reality [1]. Furthermore, with significant investments in mobile application services from industrial sectors, academic research has investigated the usage of mobile applications in terms of their capabilities, services and benefits to users [2]. From the technological side, mobile banking applications are characterized by features such as service automation, remote access and control and the provision of services anywhere and anytime [3,4,5]. In addition, mobile banking services provide several benefits for both users and banks, such as access to banking services anywhere and anytime, time efficiency and greater security for bank customers [6].
Mobile banking applications have become more commonly accepted and used since the COVID-19 pandemic in Saudi Arabia [7]. During the COVID-19 pandemic, mobile banking applications allowed customers to avoid visiting a bank branch. In addition, these applications offered ease of-use, convenience, privacy, security and a high level of interactivity [8,9,10,11,12]. Furthermore, bank clients can use the provided mobile banking services with no fees or cost [13]. According to a report by the BBC [14], bank clients in the UK can save up to £7bn per the year through utilizing these applications. Based on these benefits, it is clear that using mobile banking services will improve users’ quality of life significantly [15,16,17,18].
Despite the benefits of the mobile banking services provided by the Saudi Investment Bank, a limited numbers of bank clients use these services [19]. In fact, the adoption rate of mobile banking among Saudi users is barely satisfactory, with 55% of bank clients utilizing mobile services [20]. This percentage is much lower than was expected. According to a statistical analysis, only 41% of clients use mobile banking applications to access their accounts, and only 19% use mobile applications to perform their financial transactions [21]. Furthermore, 33% of clients use mobile applications to transfer funds, while 66% use ATM services for such operations [22]. There are several factors that might contribute to enhancing the usage of mobile banking, including the following. (1) Convenience: Mobile banking is a convenient and flexible way for users to access their financial accounts and perform transactions. (2) Security and privacy: Ensuring that mobile banking services are secure and protect user privacy could increase usage by building trust and reducing concerns about financial information being compromised. (3) Availability of services: Offering a wide range of services and features may increase usage by providing customers with more options and flexibility. (4) User-friendliness: Making mobile banking apps and websites easy to use and navigate could increase usage by reducing barriers to their adoption and making them more appealing to users. (5) Customer support: Providing timely and helpful customer support could increase usage by helping users resolve any issues or questions they may have about a service. Overall, by addressing these issues, service providers could increase the usage of mobile banking and improve the overall user experience.
Regarding investigations of mobile banking service adoption, several studies have already been conducted [23,24,25,26,27,28] with the aim of understanding the main aspects influencing the usage mobile banking applications. It has been widely observed that the usage of mobile banking applications before COVID-19 was very different to that after the pandemic. However, studies on mobile banking application usage after the COVID-19 pandemic are still very limited [29,30,31]. Based on this, the purpose of this study is to explore the key drivers that influence users regarding the comprehensive adoption (or not) of mobile banking services. The study focuses on Saudi Arabia, which a country in the Middle East countries and part of the Arab world. Despite the high level of technical support and infrastructure technologies that are available to Saudi people, the mobile banking penetration rate in Saudi Arabia has remained low [32].
This research applies a conceptual model using the technological acceptance model (TAM) with two external factors, i.e., perceived trust and perceived risk, to investigate the key drivers that influence the use of mobile banking services in Saudi Arabia. This research aims to help the banking sector to better understand its clients in order to increase the usage of mobile banking services. Our research outcomes are expected to fill the research gap related to the investigation of mobile banking acceptance through examining perceived trust, perceived risk and service quality on actual use of mobile banking. Finally, our findings offer important recommendations and insights for both the banking sector and academic research about the critical factors that have encouraged users to adopt mobile banking services since the COVID-19 pandemic. This study mainly aims to answer the following question:
Which measures could contribute to the adoption of smart mobile-banking apps?

2. Literature Review

2.1. Mobile Banking

Mobile banking is a type of banking service that allows customers to access their bank accounts and perform financial transactions using a mobile device, such as a smartphone or tablet [30]. Mobile banking services can be accessed through a dedicated mobile banking app or through a mobile web browser and can include a wide range of features and functions, such as account management, money transfers, bill payments and more [31]. Mobile banking has become increasingly popular in recent years due to the convenience and flexibility it offers to users [31]. With mobile banking, customers can access their accounts and perform financial transactions at any time and from any location, as long as they have an internet connection. This can be especially useful for people who are on the go or who do not have easy access to traditional banking services. Overall, mobile banking is a convenient and accessible way for customers to manage their financial affairs, and it has become an essential service for many people around the world.
However, mobile banking is still in its infancy. It is a form of application that allows customers to access financial services and conduct transactions using their mobile devices, such as smartphones or tablets. It has the potential to greatly enhance the convenience and accessibility of financial services for customers, particularly in regions where traditional banking infrastructure may be limited. In order for mobile banking to be successful, it is important for multiple stakeholders to work together to establish the necessary agreements and infrastructure [32].
For mobile banking to be successful in Saudi Arabia, it will be important for these stakeholders to come together and establish a common interest in the development and growth of the mobile banking ecosystem [33]. This may involve establishing agreements around security, interoperability and other key issues. In addition to providing a convenient and accessible means for customers to interact with banks and other financial institutions, mobile banking applications can also offer a range of services, such as financial transactions, applications and mobile payments. These services can help to further enhance the convenience and functionality of mobile banking for customers and drive its adoption and usage in Saudi Arabia and other regions [33].
Mobile banking applications have become increasingly popular in recent years, particularly after the COVID-19 pandemic, as they provide a convenient means for individuals to access financial services and conduct transactions using their mobile devices [33]. One important feature of mobile banking applications is their ability to make mobile payments using technologies like near-field communication (NFC) or contactless payment. This allows customers to make payments simply by tapping their mobile device against a payment terminal, without the need to enter a PIN or sign a receipt. Mobile payments have become particularly popular due to their convenience and speed, as well as their potential to reduce the risk of fraud and identity theft. Mobile banking applications that support mobile payments can also offer other benefits, such as the ability to track and manage finances, access to discounts and rewards and the ability to make secure, seamless transactions with other individuals or businesses [33]. There are a variety of mobile banking applications on the market that use different approaches and technologies to support mobile payments and other financial services. It is important for customers to carefully research and compare these options in order to find the solution that best meets their needs and preferences.
Therefore, in order to better understand the usage of mobile banking applications in Saudi Arabia, it may be helpful to consider a range of factors that could influence adoption, including technological, economic, social and psychological factors. By examining these factors and their interrelationships, it may be possible to develop a more comprehensive model of mobile banking adoption and usage in Saudi Arabia. This could help to inform the development of strategies to increase adoption and usage, as well as to identify potential barriers or challenges that may need to be addressed in order to drive the wider adoption and usage of mobile banking applications in the region. The conclusions of such a study can have a significant impact on the acceptance and adoption of mobile banking applications in Saudi Arabia, as they can help to identify the key factors that influence user behavior and guide the development of strategies to increase adoption and usage.

2.2. Related Works

Despite the widespread adoption of mobile applications, some services are still underutilized. Many aspects can influence the usage of mobile applications, and various models have been developed to analyze user intention, actual behavior and acceptance of new technologies. One such model is the Unified Theory of Acceptance and Use of Technology (UTAUT) model, which was developed to explain how individuals adopt and use technology. The model identifies several key factors that influence adoption, including performance expectation, effort expectation, social influence and facilitating conditions. Another model that has been widely used to study the adoption of mobile technologies is the Technology Acceptance Model (TAM), which proposes that the perceived usefulness and perceived ease of use of a technology are key determinants of its acceptance and usage. Other models that have been used to study the adoption of mobile technologies include the Theory of Reasoned Action (TRA), the Diffusion of Innovations (DOI) model and the DeLone and McLean Information Systems Success (DL&ML) model.
In the literature, previous studies [33,34,35,36,37,38] have looked at the factors that influence user acceptance of mobile banking. For example, the authors of [39] employed the TAM model and found that perceived trust was a primary factor motivating users to adopt mobile banking services. This suggests that building trust with users may be an important factor in increasing the adoption and usage of mobile banking applications. Several researchers have mentioned that it is important for mobile banking services to consider security and trust factors when seeking to increase adoption and usage. Several studies have found that perceived trust, perceived ease of use and perceived usefulness are key factors that influence the adoption of mobile banking services [40,41,42]. A study conducted by Ntsiful et al. [43] investigated the adoption of m-banking in Ghana using Partial Least Square Structural Equation Modeling (PLS-SEM). They found that performance expectations and hedonic motivation were the main factors influencing m-banking application adoption. Nair et al. [44] found that personal innovativeness, perceived ease of use, perceived usefulness, trust and subjective norms have significantly influenced intention to adopt mobile banking. In the same way, Kumar et al. [45] indicated that self-efficacy and personal innovativeness have a statistically substantial impact on m-banking application adoption. Inaddition, Alnemer [46] confirmed that the adoption of digital banking in Saudi Arabia was significantly influenced by perceived usefulness, perceived ease of use and trust. Trust in banking has become a significant factor influencing the adoption of digital banking, which banks should consider by strengthening the security and privacy of their customers. According to a study by Jouda [47], consumer intention to use mobile banking services is negatively impacted by perceived risk. In Palestine, attitudes, facilitating conditions, perceived ease of use, website usability and perceived trust were found to be the key determinants of consumers’ intentions to use mobile banking services.
According to the literature, there are several problems that can impact the adoption and usage of mobile banking. Some of the key issues include:
  • Security and privacy concerns: Mobile banking involves the transfer of sensitive financial information over the internet, which can raise concerns about the security and privacy of these transactions [48].
  • Technological barriers: Some users may be hesitant to adopt mobile banking due to a lack of familiarity with the technology or due to issues with device compatibility or internet connectivity [49].
  • Limited availability of services: Mobile banking services may not be available in all areas or may not offer all of the features and functions that users require [50].
  • Cost: Mobile banking services may require users to pay fees for certain transactions, which can be a barrier to adoption for some users [51].
  • Trust: Users may be hesitant to adopt mobile banking if they do not feel that their financial information is secure or if they do not trust the service provider [52].
Overall, the above issues can impact the adoption and usage of mobile banking, and it is important for service providers to address these concerns in order to increase adoption and usage.
Despite several studies on m-banking adoption, previous mobile banking adoption models have some weaknesses, like a lack of factors related to security, risk, privacy and trust. To compensate for these limitations, we have added additional factors, such as perceived security, perceived risk and perceived trust, to the TAM model in order to study the usage of mobile banking. By adding these factors to the model, it may be possible to more fully understand the adoption and usage of these technologies and to identify the key drivers of user adoption. We have added perceived risk and perceived trust factors to the TAM model in order to assess the usage behavior of mobile banking applications. This may help to provide a more comprehensive understanding of the factors that influence the adoption and usage of mobile banking applications and to identify strategies for increasing adoption and usage.

3. The Proposed Research Model and Hypotheses Development

Several researchers have employed TAM to evaluate the adoption of mobile banking applications [42,43,44,45]. TAM is a widely used framework for understanding the adoption and usage of technology, including mobile banking applications. The model proposes that the perceived usefulness and perceived ease of use of a technology are key determinants of its acceptance and usage. It has been indicated that TAM has a strong predictive power compared to other models [42,43,44,45]. Overall, TAM can be a useful tool for understanding the adoption and usage of mobile banking applications and for identifying the key factors that influence user behavior. By taking these factors into account, it may be possible to develop strategies to increase the adoption and usage of mobile banking applications and to improve the overall user experience [46,47,48,49,50,51,52]. The proposed model is shown in Figure 1.

3.1. Perceived Risk

Perceived risk (PR) is the degree to which a potential user views a mobile banking application as being dangerous [48]. PR is one of the primary inhibitors to the use or acceptance of new technology. When deciding whether to adopt a given technology or not, users compare the level of perceived danger with the convenience that that technology delivers. PR can significantly affect users regarding their decision to adopt or not. Previous studies on mobile banking have noted that the effect of perceived risk is negative [49,50,51,52]. Prior studies [53,54,55] claimed that there should be a connection between perceived risk and perceived trust, as user trust can be increased if the perceived risk levels are as low as possible. Accordingly, in our proposed model, perceived risk has an influence on both perceived trust and attitude toward the use of mobile banking services. Based on that, we propose the following hypotheses:
H1.
Perceived risk has a negative effect on perceived trust.
H2.
Perceived risk has a negative effect on attitudes regarding the use of mobile banking services.

3.2. Perceived Trust

User trust is a crucial factor for the success of any new technology [56]. For mobile banking, trust is a primary determinant of usage and adoption. In fact, clients must trust the services provided by their bank via a mobile banking application based on the absence of risk regarding their transactions as well as the benefits that are delivered to them. Trust has an influence on client loyalty [57], and untrusted mobile banking applications will lead to lower consumer loyalty and trust. Based on this, in our study, a trust factor has been added to the TAM model to measure the influence of perceived trust on attitude toward the use of mobile banking services. Thus:
H3.
Perceived trust has a positive or negative effect on attitude toward the use of mobile banking services.

3.3. Perceived Security

Perceived security (PS) is defined as the degree to which a user feels that a mobile banking application is secure against any risks [58]. The application of security techniques to guarantee the security of user services, transactions, privacy and data is considered the top priority for the success of mobile banking services among clients. Accordingly, the absence of security techniques will reduce trust and discourage users from adopting mobile banking applications. For the above reasons, the banking sector always seeks to provide a large budget for the development of security mechanisms for their clients, such as multi-factor authentication, transaction encryption and others. Therefore, in this study, we assumed that by providing a high level of security, the trust of users would increase, and thus, users’ attitudes toward mobile banking services would become more positive. Based on this, we propose that:
H4.
Perceived security has a positive effect on perceived trust.
H5.
Perceived security has a positive effect on attitude toward the use of mobile banking services.

3.4. Perceived Usefulness

Within the context of this study, perceived usefulness can be defined as the level to which utilizing a mobile banking application will provide benefits for clients in performing financial transactions [59]. Prior studies [60,61,62,63] have mentioned that perceived usefulness is one of the strongest predictors in the TAM model and has a positive effect on intention to use technology. Studies in e-banking [64,65] have confirmed that perceived usefulness has positive relationship with user intention to adopt e-banking systems. Accordingly, in our study, perceived usefulness will play an important role in encouraging bank clients to adopt and use mobile banking applications if they expect to receive benefits from using this new technology. Thus,
H6.
Perceived usefulness has a positive effect on attitude toward using mobile banking services.

3.5. Perceived Ease of Use

Perceived ease of use is the second predictor of the TAM model. Within the context of this study, perceived ease of use can be defined as the level of ease related to the use of a mobile banking application. Based on this, when bank clients perceive that a mobile banking application is user-friendly, it will increase the chances of adopting that application. In addition, when clients find that interactions with a mobile banking application are simple, understandable and clear, this will improve their intention to use that application. Prior studies [66,67,68] have mentioned that perceived ease of use is the strongest predictor in the TAM model and has positive effect on intention to use technology. Studies on e-banking [69,70] have confirmed that perceived ease of use has positive relationship with intention to use e-banking systems. Accordingly, in our study, perceived ease of use will play an important role in encouraging bank clients to adopt and use mobile banking applications. Thus,
H7.
Perceived ease of use has a positive effect on attitude toward the use of mobile banking services.

3.6. Social Influence

Within the context of this study, social influence can be defined as the level of effect that other people, such as family and friends, has on intention to adopt a new technology like mobile banking [71]. Social influence is one of the most important factors in the UTAUT model, and it has been added to the TAM model in our study. Some of studies on e-banking [72,73] have found that there is no significant relationship between social influence and intention to use. Thus, this work investigates the impact of social influence on intention to use within the context of mobile banking. Based on this, we propose that:
H8.
Social influence has a positive effect on attitude toward the use of mobile banking services.

3.7. Service Quality

Achieving excellent quality of mobile banking services is the first step toward the success of a mobile banking application. According to previous studies [74,75,76,77], the higher the service quality, the higher the perceived value, and thus, the better the user attitude toward the technology. If the service quality factor is used to measure the quality of an application or product services and related benefits, the value of a mobile banking application is seen to be higher. Based on this, when a bank client perceives that a mobile banking application has high quality services, this will increase the chances of adopting that application. Prior studies [78,79,80] have mentioned that service quality is one of the strongest predictors in the Delone and Mclean model and has a positive effect on actual use. Studies on e-banking [81,82,83,84] have confirmed that service quality has a positive relationship with intention to use e-banking systems. Accordingly, in our study, service quality will play an important role in encouraging bank clients to adopt and use mobile banking applications if they expect high quality services when using such applications. Thus:
H9.
Service quality has a positive effect on attitude toward the use of mobile banking services.

3.8. Attitude toward Use

According to the TAM model, attitude toward use is defined as the subjective probability that a user will adopt a mobile banking application. Attitude toward use is one of the most important predictors in the TAM model and in other technology acceptance models, like UTAUT, TRA and others. Studies on e-banking [85,86,87,88] have confirmed that attitude toward use has a positive relationship with intention to use e-banking systems. Accordingly, in our study, attitude toward use will play an important role in predicting the adoption by clients of mobile banking applications. Based on this, we propose that:
H10.
Attitude toward use has a positive effect on the use of mobile banking services.

4. Methodology

In our study, a quantitative research approach was used. It was important to carefully plan and execute this methodology in order to ensure the validity and reliability of the results. This involved preparing the measurements of the model, devising a data collection method and examining the characteristics of the data and the methods used to collect and analyze that data. Figure 2 presents the research methodology applied in our study.
In order to ensure the validity and reliability of the results, pilot testing for all items was applied, as shown in Table 1. Based on the findings, all items have a high level of internal consistency and reliability, which is important for ensuring the validity and reliability of the results.

Measurements within the Model

The measurement items in the proposed model were chosen based on previous research that has used similar conceptual frameworks to study the adoption of the aforementioned technologies. This helped us to ensure that the items were relevant and appropriate within the context of our study and that they could measure the variables of interest in a valid and reliable manner. The items related to TAM regarding perceived usefulness and ease of use were adopted from Gupta and Dhingra [89], who have been active in mobile banking application studies. Items intended to examine attitudes and intention to use were also adopted from Gupta and Dhingra [89]. The items related to perceived security, perceived trust and perceived risk were adopted from Lew et al. [90]. Finally, service quality and social influence were assessed using three items as proposed by Migliore et al. [91].

5. Results and Analysis

5.1. Reliability Analysis

To assess the reliability of measurement items in our proposed model, coefficient alphas, composite reliabilities and average variances (AVE) were used, as these could help us to assess the quality of the measurement of each factor in the model. Firstly, we assessed the internal consistency using Cronbach’s alpha. Cronbach’s alpha is a measure of the internal consistency or reliability of each item. It is calculated by dividing the sum of the variance of the items in the proposed model by the total variance and is typically expressed as a coefficient between 0 and 1. A coefficient of 0.7 or higher is generally considered to indicate a good level of internal consistency or reliability. As shown in Table 2, the Cronbach’s alpha values were higher than 0.7, which indicated a good level of internal consistency and reliability for all variables.

5.2. Construct Validity

Construct validity is the second step to ensure that a measurement tool has good construct validity [92]. It can be used to ensure the accuracy and meaningfulness of the results of a study. Table 3 shows the components that must be included in the measurement structure, as well as their corresponding loadings. These components and loadings can help to demonstrate the construct validity of the measurement tool, as they show how well the tool is able to capture the concept or construct of interest. By carefully examining these components and loadings, it is possible to ensure that the measurement tool is measuring the variables of interest in a valid and reliable manner [93]. According to these findings, all variables were shown to be valid and reliable.

5.3. Validity of Convergent

In the third step of data analysis, discriminant validity was assessed to examine the correlations between the elements within the constructs being measured. According to Hair [93], the correlations between these elements should be less than the average variance extracted (AVE) square root shared by items that represent a single concept. This can help to demonstrate that the measurement tool is able to accurately distinguish between different concepts or constructs and is not measuring unrelated variables. In our study, the values were greater than 0.50 and significance was at p = 0.001, which supports the discriminant validity for all items, as shown in Table 4.

5.4. Analysis of the Structural Model

To test the proposed model, structural equation modelling (SEM) was used to analyze the proposed hypotheses in the model. Table 5 presents the results of the path coefficient and the t-values.

6. Discussion

This work aimed to develop a conceptual model that integrated the TAM model constructs with five external factors, namely, perceived risk, perceived trust, perceived security, service quality and social influence, to investigate the key drivers that influence user intention to comprehensively adopt mobile banking services in Saudi Arabia. This research aims to help the banking sector to better understand its clients, with the aim of increasing the usage of the mobile banking services provided to customers. Our research outcomes are expected to fill the research gap related to the limited investigation of mobile banking acceptance through examining perceived trust, perceived risk and service quality on actual use of mobile banking technology. Finally, our findings offer important recommendations and insights for both the banking sector and academic research about the critical factors that have encouraged users to adopt mobile banking services since the COVID-19 pandemic.
The findings of the present study reveal that perceived risk has a negative impact on both trust and attitude toward the use of mobile banking services. This result can be attributed to some applications being risky and susceptible to attack or financial fraud by hackers. This result will negatively affect user trust regarding mobile banking and is in accordance with previous studies [94]. In addition, this study proved that there is a link between perceived risk and user trust. When bank clients are deciding whether to use technology or not, they compare the level of perceived danger with the convenience benefits they would receive. Prior studies [92,93,94] have claimed that there is a connection between perceived risk and perceived trust, as user trust can increase if the perceived risk levels are as low as possible.
In addition, our results found that perceived trust has a positive impact on clients’ attitudes toward the use mobile banking services. This result can be attributed to the fact that client trust of the services provided by banks via mobile banking applications can increase due to the low risk regarding transactions, as well as the benefits that such services offer. In addition, this study proved that there is a link between perceived trust and client loyalty, particularly when mobile banking services are highly trusted. Our findings are in accordance with those of previous studies in the context of e-banking [92,93,94], which found that user trust is a crucial factor for the success of an e-banking system.
The findings of the present study reveal that perceived security has a positive impact on both trust and attitude toward the use mobile banking services. This can be attributed to the implementation of security defense techniques to guarantee the security of services and transactions. Privacy and data protection are considered top priorities among mobile banking services clients. Furthermore, this study proved that there is a link between perceived security and user trust. Therefore, the banking sector must seek to provide a large budget to invest in the development of security mechanisms for their clients, such as multi-factor authentication, transaction encryption and others. Our findings are in accordance with those of previous studies [92,93,94], which found that by providing high security procedures, the trust of users will increase, and thus, users’ attitudes toward e-banking services will improve.
This study also found that perceived ease of use and perceived usefulness have a positive impact on attitude toward mobile banking services. This means that emphasizing the simplicity of mobile banking applications is an important aspect for the success of mobile banking technology. In addition, this study proved that perceived ease of use without a strong sense of usefulness may be insufficient to encourage clients to use mobile banking services in Saudi Arabia. This result can be attributed to the fact that when a bank client finds his/her interactions with a mobile banking application to be simple, understandable and clear, his/her attitude toward that application will improve. Our findings are in accordance with those of prior studies [66,67,68], which noted that perceived ease of use is the strongest predictor in the TAM model and has positive effect on attitude toward such technology. Studies on e-banking [92,93,94] have confirmed that perceived ease of use has a positive relationship with intention to use e-banking system.
Based on the findings of this study, service quality has a substantial favorable and direct influence on attitude toward the use of mobile banking services. This means that focusing on the development of mobile banking applications with high quality of service is a critical aspect to the success of mobile banking technologies. In addition, this study proved that mobile banking applications without high quality features and services may be insufficient to encourage clients to use such services in Saudi Arabia. Based on this, when bank clients perceive that a mobile banking application offers high quality services, they will be more likely to adopt that application. Our findings are in accordance with those of prior studies [92,93,94] that mentioned that service quality is one of the strongest predictors in the Delone and Mclean model and has a positive effect on the actual use of such technology. Studies on e-banking [92,93,94] have confirmed that service quality has a positive relationship with intention to use e-banking systems. Accordingly, in our study, service quality was found to play an important role in encouraging bank clients to adopt and use mobile banking applications. Furthermore, the findings revealed that social influence has a positive impact on clients’ attitudes toward mobile banking services. In this regard, our findings are not in accordance with those of previous studies [92,93,94], which found that there was no significant relationship between social influence and intention to use. Finally, our findings indicated that attitude toward use has a positive impact on intention to use mobile banking services; this is in accordance with studies on e-banking [92,93,94] that observed that attitude toward use has positive relationship with users’ intention to use e-banking systems. Accordingly, in our study, attitude toward use played an important role in predicting the likelihood that bank clients would adopt and use mobile banking applications.

Research and Practical Contributions and Implications

This research has both theoretical and practical implications. First, by providing a conceptual model that considers the significant drivers of mobile banking adoption among clients in Saudi universities, this research makes an important contribution to the existing body of knowledge on mobile banking adoption. This contribution is important because it helps to identify the key factors that influence the adoption of mobile banking among university clients in Saudi Arabia. By understanding these factors, it may be possible to develop strategies to increase the adoption and usage of mobile banking technologies in this context and to improve the overall user experience. Second, future research could explore the specific mechanisms through which different factors influence mobile banking adoption or could examine the impact of these factors in other contexts or countries. Our research can provide a starting point for this work by identifying the key factors that are relevant to mobile banking adoption in Saudi Arabia and by offering insights into how these factors may interact and influence user behavior. Third, the findings of our study are important to policymakers and practitioners who are seeking to understand the factors that influence mobile banking adoption and usage in Saudi Arabia. These findings can inform the development of strategies and initiatives to promote the adoption and usage of mobile banking in this context, and to improve the overall user experience. Finally, our findings highlight the importance of perceived risk and perceived trust in mobile banking adoption and suggest that banks need to focus on improving the security and privacy of their mobile banking applications in order to increase adoption and usage. By understanding the specific concerns and priorities of their customers, banks can develop targeted strategies to address these issues and improve the overall user experience.

7. Conclusions

Since the COVID-19 pandemic, mobile banking applications have been become a fundamental tool for customers to perform their transactions. Banks have also noted the shift of their clients in terms of the use of mobile banking services via mobile devices in recent years. Therefore, this research aimed to investigate the perceptions of users toward mobile banking services in Saudi Arabia by using the technological acceptance model (TAM) with added external factors, i.e., perceived security, perceived trust, perceived risk and service quality. The model proposed in this study aimed to measure the acceptance levels of users toward the mobile banking services offered by The Saudi Investment Bank. Structural equation modelling (SEM) was used to analyze the proposed hypotheses. The findings revealed that perceived security, perceived trust and service quality play key roles in improving the adoption of mobile banking services in Saudi Arabia. In addition, the findings indicated that perceived risk has negative impact on both client trust and their attitudes toward the use of mobile banking services. Furthermore, the findings revealed that social influence has a positive impact on clients’ attitudes toward mobile banking services. Accordingly, the present research findings offer a better understanding of factors that could increase the adoption of mobile banking technology by customers in order to help both the banking sector and academia in applying new strategies to increase the level of utilization of mobile banking services in Saudi Arabia.
Although this research provides useful recommendations, some limitations should be mentioned. First, the model proposed in this study could be improved by adding other factors related to system quality, content quality, ICT and technological factors with the aiming of offering more robust research solutions in order to address mobile banking adoption issues. Second, the factors that influence mobile banking adoption in Saudi Arabia may be different from those in other countries, due to cultural differences, economic conditions, technological infrastructure and other factors. Therefore, it will be interesting to see if the findings of the present study are replicated in other countries and to examine the extent to which they are applicable or generalizable to different contexts. Finally, this study was focused only on customer perception. Thus, future work could investigate the perceptions of bank managers and could take into account their opinions in terms of the adoption of mobile banking applications.

Author Contributions

Conceptualization, M.A.A., R.S. and S.A.-O.; methodology, M.A.A., M.Q. and O.A.A.; software, A.L. and M.A. validation M.A.A., A.L. and M.Q.; formal analysis, O.A.A. and M.A.; investigation, L.H. and M.A.A.; resources, S.A.-O. and R.S.; data curation, M.A.A. and L.H.; writing—original draft preparation, O.A.A.; writing—review and editing, M.Q., O.A.A., M.A.A. and S.A.-O.; visualization, M.A. and A.L.; supervision, M.A.A. and M.Q.; project administration, O.A.A. and L.H.; funding acquisition, M.A.A. and S.A.-O. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded from King Faisal University.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not Applicable.

Acknowledgments

This work was supported through the Annual Funding track by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia (Project No. Grant No. 3554) and Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R136), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Almaiah, M.; Al-Khasawneh, A.; Althunibat, A.; Khawatreh, S. Mobile government adoption model based on combining GAM and UTAUT to explain factors according to adoption of mobile government services. Int. J. Interact. Mob. Technol. 2020, 14, 199–225. [Google Scholar] [CrossRef] [Green Version]
  2. Ali, M.; Amir, H.; Ahmed, M. The role of university switching costs, perceived service quality, perceived university image and student satisfaction in shaping student loyalty. J. Mark. High. Educ. 2021, 31, 1–22. [Google Scholar] [CrossRef]
  3. Almaiah, M.A.; Al-Rahmi, A.; Alturise, F.; Hassan, L.; Lutfi, A.; Alrawad, M.; Alkhalaf, S.; Al-Rahmi, W.M.; Al-sharaieh, S.; Aldhyani, T.H. Investigating the Effect of Perceived Security, Perceived Trust, and Information Quality on Mobile Payment Usage through Near-Field Communication (NFC) in Saudi Arabia. Electronics 2022, 11, 3926. [Google Scholar] [CrossRef]
  4. Althunibat, A.; Binsawad, M.; Almaiah, M.A.; Almomani, O.; Alsaaidah, A.; Al-Rahmi, W.; Seliaman, M.E. Sustainable applications of smart-government services: A model to understand smart-government adoption. Sustainability 2021, 13, 3028. [Google Scholar] [CrossRef]
  5. Almaiah, M.A.; Nasereddin, Y. Factors influencing the adoption of e-government services among Jordanian citizens. Electron. Gov. Int. J. 2020, 16, 236–259. [Google Scholar] [CrossRef]
  6. Lutfi, A.; Alsyouf, A.; Almaiah, M.A.; Alrawad, M.; Abdo, A.A.; Al-Khasawneh, A.L.; Ibrahim, N.; Saad, M. Factors influencing the adoption of big data analytics in the digital transformation era: Case study of Jordanian SMEs. Sustainability 2022, 14, 1802. [Google Scholar] [CrossRef]
  7. Achsan, W.; Achsani, N.A.; Bandono, B. The Demographic and Behavior Determinant of Credit Card Default in Indonesia. Signifikan J. Ilmu Ekon. 2022, 11, 43–56. [Google Scholar] [CrossRef]
  8. Afandi, A.; Fadhillah, A.; Sari, D.P. Pengaruh Persepsi Kegunaan, Persepsi Kemudahan dan Persepsi Kepercayaan Terhadap Niat Menggunakan E-Wallet Dengan Sikap Sebagai Variabel Intervening. Innov. J. Soc. Sci. Res. 2021, 1, 568–577. [Google Scholar]
  9. Almaiah, M.A.; Al-Khasawneh, A.; Althunibat, A. Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic. Educ. Inf. Technol. 2020, 25, 5261–5280. [Google Scholar] [CrossRef]
  10. Ahmed, W.; Rasool, A.; Javed, A.R.; Kumar, N.; Gadekallu, T.R.; Jalil, Z.; Kryvinska, N. Security in next generation mobile payment systems: A comprehensive survey. IEEE Access 2021, 9, 115932–115950. [Google Scholar] [CrossRef]
  11. Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
  12. Almaiah, M.A.; Jalil, M.A.; Man, M. Extending the TAM to examine the effects of quality features on mobile learning acceptance. J. Comput. Education. 2016, 3, 453–485. [Google Scholar] [CrossRef]
  13. Alamoudi, H. Examining Retailing Sustainability in the QR Code-Enabled Mobile Payments Context During the COVID-19 Pandemic. Int. J. Cust. Relatsh. Mark. Manag. (IJCRMM) 2022, 13, 1–22. [Google Scholar] [CrossRef]
  14. Asravor, R.K.; Boakye, A.N.; Essuman, J. Adoption and intensity of use of mobile money among smallholder farmers in rural Ghana. Inf. Dev. 2021, 38, 204–217. [Google Scholar] [CrossRef]
  15. Almaiah, M.A.; Alismaiel, O.A. Examination of factors influencing the use of mobile learning system: An empirical study. Educ. Inf. Technol. 2019, 24, 885–909. [Google Scholar] [CrossRef]
  16. Baabdullah, A.M.; Alalwan, A.A.; Rana, N.P.; Kizgin, H.; Patil, P. Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model. Int. J. Inf. Manag. 2019, 44, 38–52. [Google Scholar] [CrossRef] [Green Version]
  17. Bagozzi, R.P.; Yi, Y. On the evaluation of structural equation models. J. Acad. Mark. Sci. 1988, 16, 74–94. [Google Scholar] [CrossRef]
  18. Balakrishnan, V.; Shuib, N.L.M. Drivers and inhibitors for digital payment adoption using the Cashless Society Readiness-Adoption model in Malaysia. Technol. Soc. 2021, 65, 101554. [Google Scholar] [CrossRef]
  19. Almaiah, M.A.; Hajjej, F.; Ali, A.; Pasha, M.F.; Almomani, O. A novel hybrid trustworthy decentralized authentication and data preservation model for digital healthcare IoT based CPS. Sensors 2022, 22, 1448. [Google Scholar] [CrossRef]
  20. Almaiah, M.A.; Man, M. Empirical investigation to explore factors that achieve high quality of mobile learning system based on students’ perspectives. Eng. Sci. Technol. Int. J. 2016, 19, 1314–1320. [Google Scholar] [CrossRef] [Green Version]
  21. Biswas, A.; Jaiswal, D.; Kant, R. Augmenting bank service quality dimensions: Moderation of perceived trust and perceived risk. Int. J. Product. Perform. Manag. 2021, 72, 469–490. [Google Scholar] [CrossRef]
  22. Bojjagani, S.; Sastry, V.N.; Chen, C.M.; Kumari, S.; Khan, M.K. Systematic survey of mobile payments, protocols, and security infrastructure. J. Ambient. Intell. Humaniz. Comput. 2021, 14, 609–654. [Google Scholar] [CrossRef]
  23. Almaiah, M.A.; Al Mulhem, A. Analysis of the essential factors affecting of intention to use of mobile learning applications: A comparison between universities adopters and non-adopters. Educ. Inf. Technol. 2019, 24, 1433–1468. [Google Scholar] [CrossRef]
  24. Caldeira, T.A.; Ferreira, J.B.; Freitas, A.; Falcão, R.P.D.Q. Adoption of Mobile Payments in Brazil: Technology Readiness, Trust and Perceived Quality. Braz. Bus. Rev. 2021, 18, 415–432. [Google Scholar]
  25. Cao, T. The study of factors on the small and medium enterprises’ adoption of mobile payment: Implications for the COVID-19 Era. Front. Public Health 2021, 9, 646592. [Google Scholar] [CrossRef] [PubMed]
  26. Almaiah, M.A.; Al-Khasawneh, A. Investigating the main determinants of mobile cloud computing adoption in university campus. Educ. Inf. Technol. 2020, 25, 3087–3107. [Google Scholar] [CrossRef]
  27. Coskun, E.; Ferman, M. Factors Encouraging and Hindering A Wider Acceptance And More Frequent Utilization Of Mobile Payment Systems: An Empirical Study Among Mobile Phone Subscribers In Turkey. J. Manag. Mark. Logist. 2021, 8, 164–183. [Google Scholar] [CrossRef]
  28. Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef] [Green Version]
  29. Almaiah, M.A.; Jalil, M.M. Investigating Students’ Perceptions on Mobile Learning Services. Int. J. Interact. Mob. Technol. 2014, 8, 31–36. [Google Scholar] [CrossRef] [Green Version]
  30. Dogra, P.; Kaushal, A. The impact of Digital Marketing and Promotional Strategies on attitude and purchase intention towards financial products and service: A Case of emerging economy. J. Mark. Commun. 2022, 29, 403–430. [Google Scholar] [CrossRef]
  31. Dospinescu, O.; Anastasiei, B.; Dospinescu, N. Key factors determining the expected benefit of customers when using bank cards: An analysis on millennials and generation Z in Romania. Symmetry 2019, 11, 1449. [Google Scholar] [CrossRef] [Green Version]
  32. Duggal, E.; Verma, H.V. Offline to online shopping: Shift in volition or escape from violation. J. Indian Bus. Res. 2022, 14, 339–357. [Google Scholar] [CrossRef]
  33. Lutfi, A.; Saad, M.; Almaiah, M.A.; Alsaad, A.; Al-Khasawneh, A.; Alrawad, M.; Alsyouf, A.; Al-Khasawneh, A.L. Actual use of mobile learning technologies during social distancing circumstances: Case study of King Faisal University students. Sustainability 2022, 14, 7323. [Google Scholar] [CrossRef]
  34. Gbongli, K.; Xu, Y.; Amedjonekou, K.M. Extended technology acceptance model to predict mobile-based money acceptance and sustainability: A multi-analytical structural equation modeling and neural network approach. Sustainability 2019, 11, 3639. [Google Scholar] [CrossRef] [Green Version]
  35. Geebren, A.; Jabbar, A.; Luo, M. Examining the role of consumer satisfaction within mobile eco-systems: Evidence from mobile banking services. Comput. Hum. Behav. 2021, 114, 106584. [Google Scholar] [CrossRef]
  36. Gerbing, D.W.; Anderson, J.C. Monte Carlo evaluations of goodness of fit indices for structural equation models. Sociol. Methods Res. 1992, 21, 132–160. [Google Scholar] [CrossRef]
  37. Almaiah, M.A.; Al Mulhem, A. Thematic analysis for classifying the main challenges and factors influencing the successful implementation of e-learning system using NVivo. Int. J. Adv. Trends Comput. Sci. Eng. 2020, 9, 142–152. [Google Scholar] [CrossRef]
  38. Gupta, S.; Dhingra, S. Modeling the key factors influencing the adoption of mobile financial services: An interpretive structural modeling approach. J. Financ. Serv. Mark. 2021, 27, 96–110. [Google Scholar] [CrossRef]
  39. Ntsiful, A.; Kwarteng, M.A.; Jibril, A.B.; Popesko, B.; Pilik, M. Factors Driving the Adoption of Mobile Banking App: An Empirical Assessment in the Less Digitalized Economy. In Reimagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation, Proceedings of the IFIP WG 8.6 International Conference on Transfer and Diffusion of IT (TDIT 2020), Tiruchirappalli, India, 18–19 December 2020; Proceedings, Part II; Springer International Publishing: Cham, Switzerland, 2020; pp. 114–125. [Google Scholar]
  40. Nair, A.B.; Prabhu, K.S.; Aditya, B.R.; Durgalashmi, C.V.; Prabhu, A.S. Study on the usage of mobile banking application during COVID-19 pandemic. Webology 2021, 18, 190–207. [Google Scholar] [CrossRef]
  41. Kumar, A.; Dhingra, S.; Batra, V.; Purohit, H. A framework of mobile banking adoption in India. J. Open Innov. Technol. Mark. Complex. 2020, 6, 40. [Google Scholar] [CrossRef]
  42. Alnemer, H.A. Determinants of digital banking adoption in the Kingdom of Saudi Arabia: A technology acceptance model approach. Digit. Business. 2022, 2, 100037. [Google Scholar] [CrossRef]
  43. Jouda, H. Expanding TAM and investigating the factors that effect consumer intention to adopt mobile banking in Palestine. Financ. Internet Q. 2020, 16, 29–50. [Google Scholar] [CrossRef]
  44. Fülöp, M.T.; Topor, D.I.; Ionescu, C.A.; Căpușneanu, S.; Breaz, T.O.; Stanescu, S.G. Fintech accounting and Industry 4.0: Future-proofing or threats to the accounting profession? J. Bus. Econ. Manag. 2022, 23, 997–1015. [Google Scholar] [CrossRef]
  45. Cordoş, G.S.; Fülöp, M.T. Understanding audit reporting changes: Introduction of Key Audit Matters. Account. Manag. Inf. Syst. Contab. Inform. Gestiune 2015, 14, 128–152. [Google Scholar]
  46. Hair, J.F., Jr.; Howard, M.C.; Nitzl, C. Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. J. Bus. Res. 2020, 109, 101–110. [Google Scholar] [CrossRef]
  47. Kratschmann, M.; Dütschke, E. Selling the sun: A critical review of the sustainability of solar energy marketing and advertising in Germany. Energy Res. Soc. Sci. 2021, 73, 101919. [Google Scholar] [CrossRef]
  48. Almaiah, M.A.; Ayouni, S.; Hajjej, F.; Lutfi, A.; Almomani, O.; Awad, A.B. Smart Mobile Learning Success Model for Higher Educational Institutions in the Context of the COVID-19 Pandemic. Electronics 2022, 11, 1278. [Google Scholar] [CrossRef]
  49. Lew, S.; Tan, G.W.H.; Loh, X.M.; Hew, J.J.; Ooi, K.B. The disruptive mobile wallet in the hospitality industry: An extended mobile technology acceptance model. Technol. Soc. 2020, 63, 101430. [Google Scholar] [CrossRef]
  50. Liébana-Cabanillas, F.; Molinillo, S.; Japutra, A. Exploring the determinants of intention to use P2P mobile payment in Spain. Inf. Syst. Manag. 2021, 38, 165–180. [Google Scholar] [CrossRef]
  51. Liébana-Cabanillas, F.; Singh, N.; Kalinic, Z.; Carvajal-Trujillo, E. Examining the determinants of continuance intention to use and the moderating effect of the gender and age of users of NFC mobile payments: A multi-analytical approach. Inf. Technol. Manag. 2021, 22, 133–161. [Google Scholar] [CrossRef]
  52. Luna, I.R.D.; Montoro-Ríos, F.; Liébana-Cabanillas, F.; Luna, J.G.D. NFC technology acceptance for mobile payments: A Brazilian Perspective. Rev. Bras. Gestão Negócio 2017, 19, 82–103. [Google Scholar]
  53. Malaquias, R.F.; Hwang, Y. Mobile banking use: A comparative study with Brazilian and US participants. Int. J. Inf. Manag. 2019, 44, 132–140. [Google Scholar] [CrossRef]
  54. Tatnall, A. Editorial for EAIT issue 2, 2019. Educ. Inf. Technol. 2019, 24, 953–962. [Google Scholar] [CrossRef] [Green Version]
  55. Migliore, G.; Wagner, R.; Cechella, F.S.; Liébana-Cabanillas, F. Antecedents to the Adoption of Mobile Payment in China and Italy: An Integration of UTAUT2 and Innovation Resistance Theory. Inf. Syst. Front. 2022, 24, 2099–2122. [Google Scholar] [CrossRef] [PubMed]
  56. Almaiah, M.A.; Hajjej, F.; Lutfi, A.; Al-Khasawneh, A.; Shehab, R.; Al-Otaibi, S.; Alrawad, M. Explaining the factors affecting students’ attitudes to using online learning (Madrasati Platform) during COVID-19. Electronics 2022, 11, 973. [Google Scholar] [CrossRef]
  57. Adil, M.; Khan, R.; Almaiah, M.A.; Binsawad, M.; Ali, J.; Al Saaidah, A.; Ta, Q.T. An efficient load balancing scheme of energy gauge nodes to maximize the lifespan of constraint oriented networks. IEEE Access 2020, 8, 148510–148527. [Google Scholar] [CrossRef]
  58. Mohd Thas Thaker, H.; Mohd Thas Thaker, M.A.; Khaliq, A.; Allah Pitchay, A.; Iqbal Hussain, H. Behavioural intention and adoption of internet banking among clients’ of Islamic banks in Malaysia: An analysis using UTAUT2. J. Islam. Mark. 2021, 13, 1171–1197. [Google Scholar] [CrossRef]
  59. Mombeuil, C.; Uhde, H. Relative convenience, relative advantage, perceived security, perceived privacy, and continuous use intention of China’s WeChat Pay: A mixed-method two-phase design study. J. Retail. Consum. Serv. 2021, 59, 102384. [Google Scholar] [CrossRef]
  60. Almaiah, M.A.; Alfaisal, R.; Salloum, S.A.; Hajjej, F.; Shishakly, R.; Lutfi, A.; Alrawad, M.; Al Mulhem, A.; Alkhdour, T.; Al-Maroof, R.S. Measuring institutions’ adoption of artificial intelligence applications in online learning environments: Integrating the innovation diffusion theory with technology adoption rate. Electronics 2022, 11, 3291. [Google Scholar] [CrossRef]
  61. Moore, G.C.; Benbasat, I. Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf. Syst. Res. 1991, 2, 192–222. [Google Scholar] [CrossRef] [Green Version]
  62. Almaiah, M.A.; Al-Rahmi, A.M.; Alturise, F.; Alrawad, M.; Alkhalaf, S.; Lutfi, A.; Al-Rahmi, W.M.; Awad, A.B. Factors influencing the adoption of internet banking: An integration of ISSM and UTAUT with price value and perceived risk. Front. Psychol. 2022, 13, 919198. [Google Scholar] [CrossRef]
  63. Alrawad, M.; Lutfi, A.; Alyatama, S.; Al Khattab, A.; Alsoboa, S.S.; Almaiah, M.A.; Ramadan, M.H.; Arafa, H.M.; Ahmed, N.A.; Alsyouf, A.; et al. Assessing customers perception of online shopping risks: A structural equation modeling–based multigroup analysis. J. Retail. Consum. Services. 2023, 71, 103188. [Google Scholar] [CrossRef]
  64. Patil, P.; Tamilmani, K.; Rana, N.P.; Raghavan, V. Understanding consumer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. Int. J. Inf. Manag. 2020, 54, 102144. [Google Scholar] [CrossRef]
  65. Pelaez, A.; Chen, C.W.; Chen, Y.X. Effects of perceived risk on intention to purchase: A meta-analysis. J. Comput. Inf. Syst. 2019, 59, 73–84. [Google Scholar] [CrossRef]
  66. Akour, I.; Alnazzawi, N.; Alshurideh, M.; Almaiah, M.A.; Al Kurdi, B.; Alfaisal, R.M.; Salloum, S. A Conceptual Model for Investigating the Effect of Privacy Concerns on E-Commerce Adoption: A Study on United Arab Emirates Consumers. Electronics 2022, 11, 3648. [Google Scholar] [CrossRef]
  67. Purwanto, A.; Asbari, M.; Santoso, T.I.; Paramarta, V.; Sunarsi, D. Social and Management Research Quantitative Analysis for Medium Sample: Comparing of Lisrel, Tetrad, GSCA, Amos, SmartPLS, WarpPLS, and SPSS. J. Ilm. Ilmu Adm. Publik 2020, 10, 518–532. [Google Scholar]
  68. Althunibat, A.; Abdallah, M.; Almaiah, M.A.; Alabwaini, N.; Alrawashdeh, T.A. An Acceptance Model of Using Mobile-Government Services (AMGS). Comput. Model. Eng. Sci. 2022, 131, 865–880. [Google Scholar] [CrossRef]
  69. Rabaa’i, A.A.; AlMaati, S. Exploring the determinants of users’ continuance intention to use mobile banking services in Kuwait: Extending the expectation-confirmation model. Asia Pac. J. Inf. Syst. 2021, 31, 141–184. [Google Scholar] [CrossRef]
  70. Ramos-de-Luna, I.; Montoro-Ríos, F.; Liébana-Cabanillas, F. Determinants of the intention to use NFC technology as a payment system: An acceptance model approach. Inf. Syst. e-Bus. Manag. 2016, 14, 293–314. [Google Scholar] [CrossRef]
  71. Rodrick, S.S.; Islam, H.; Sarker, S.A.; Tisha, F.F. Prospects and Challenges of using Credit Card Services: A Study on the users in Dhaka City. AIUB J. Bus. Econ. 2021, 18, 161–186. [Google Scholar]
  72. Sahi, A.M.; Khalid, H.; Abbas, A.F.; Khatib, S.F. The Evolving Research of Customer Adoption of Digital Payment: Learning from Content and Statistical Analysis of the Literature. J. Open Innov. Technol. Mark. Complex. 2021, 7, 230. [Google Scholar] [CrossRef]
  73. Saprikis, V.; Avlogiaris, G.; Katarachia, A. A Comparative Study of Users versus Non-Users’ Behavioral Intention towards M-Banking Apps’ Adoption. Information 2022, 13, 30. [Google Scholar] [CrossRef]
  74. Lutfi, A.; Al-Khasawneh, A.L.; Almaiah, M.A.; Alshira’h, A.F.; Alshirah, M.H.; Alsyouf, A.; Alrawad, M.; Al-Khasawneh, A.; Saad, M.; Ali, R.A. Antecedents of Big Data Analytic Adoption and Impacts on Performance: Contingent Effect. Sustainability 2022, 14, 15516. [Google Scholar] [CrossRef]
  75. Shahid, S.; Islam, J.U.; Malik, S.; Hasan, U. Examining consumer experience in using m-banking apps: A study of its antecedents and outcomes. J. Retail. Consum. Serv. 2022, 65, 102870. [Google Scholar] [CrossRef]
  76. Shin, S.; Lee, W.J. Factors affecting user acceptance for NFC mobile wallets in the US and Korea. Innov. Manag. Review. 2021, 18, 417–433. [Google Scholar] [CrossRef]
  77. Singh, N.; Sinha, N.; Liébana-Cabanillas, F.J. Determining factors in the adoption and recommendation of mobile wallet services in India: Analysis of the effect of innovativeness, stress to use and social influence. Int. J. Inf. Manag. 2020, 50, 191–205. [Google Scholar] [CrossRef]
  78. Sreelakshmi, C.C.; Prathap, S.K. Continuance adoption of mobile-based payments in COVID-19 context: An integrated framework of health belief model and expectation confirmation model. Int. J. Pervasive Comput. Commun. 2020, 16, 351–369. [Google Scholar]
  79. Sulaiman, S.N.A.; Almunawar, M.N. The adoption of biometric point-of-sale terminal for payments. J. Sci. Technol. Policy Manag. 2021, 13, 585–609. [Google Scholar] [CrossRef]
  80. Sun, S.; Law, R.; Schuckert, M.; Hyun, S.S. Impacts of mobile payment-related attributes on consumers’ repurchase intention. Int. J. Tour. Res. 2022, 24, 44–57. [Google Scholar] [CrossRef]
  81. Taylor, S.; Todd, P.A. Understanding information technology usage: A test of competing models. Inf. Syst. Res. 1995, 6, 144–176. [Google Scholar] [CrossRef]
  82. Teng, S.; Khong, K.W. Examining actual consumer usage of E-wallet: A case study of big data analytics. Comput. Hum. Behav. 2021, 121, 106778. [Google Scholar] [CrossRef]
  83. Lutfi, A.; Al-Khasawneh, A.L.; Almaiah, M.A.; Alsyouf, A.; Alrawad, M. Business sustainability of small and medium enterprises during the COVID-19 pandemic: The role of AIS implementation. Sustainability 2022, 14, 5362. [Google Scholar] [CrossRef]
  84. Althunibat, A.; Almaiah, M.A.; Altarawneh, F. Examining the factors influencing the mobile learning applications usage in higher education during the COVID-19 pandemic. Electronics 2021, 10, 2676. [Google Scholar] [CrossRef]
  85. Trinh, H.N.; Tran, H.H.; Vuong, D.H.Q. Determinants of consumers’ intention to use credit card: A perspective of multifaceted perceived risk. Asian J. Econ. Bank. 2020, 4, 105–120. [Google Scholar] [CrossRef]
  86. Almaiah, M.A.; Hajjej, F.; Lutfi, A.; Al-Khasawneh, A.; Alkhdour, T.; Almomani, O.; Shehab, R. A conceptual framework for determining quality requirements for mobile learning applications using Delphi Method. Electronics 2022, 11, 788. [Google Scholar] [CrossRef]
  87. Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User acceptance of information technology: Toward a unified view. MIS Q. 2003, 27, 425–478. [Google Scholar] [CrossRef] [Green Version]
  88. Vladimirovich Platov, A.; Kazbekovich Tarchokov, S.; Sobirovna Zikirova, S.; Igorevna Litvinova, O.; Eduardovich Udalov, D. NFC Technology Acceptance Factors in Tourism. In Proceedings of the IV International Scientific and Practical Conference, St. Petersburg, Russia, 18–19 March 2021; pp. 1–7. [Google Scholar]
  89. Almaiah, M.A.; Alfaisal, R.; Salloum, S.A.; Hajjej, F.; Thabit, S.; El-Qirem, F.A.; Lutfi, A.; Alrawad, M.; Al Mulhem, A.; Alkhdour, T.; et al. Examining the impact of artificial intelligence and social and computer anxiety in e-learning settings: Students’ perceptions at the university level. Electronics 2022, 11, 3662. [Google Scholar] [CrossRef]
  90. Wang, E.S.T. Influences of Innovation Attributes on Value Perceptions And Usage Intentions Of Mobile Payment. J. Electron. Commer. Res. 2022, 23, 45–58. [Google Scholar]
  91. Wei, M.F.; Luh, Y.H.; Huang, Y.H.; Chang, Y.C. Young generation’s mobile payment adoption behavior: Analysis based on an extended UTAUT model. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 618–637. [Google Scholar] [CrossRef]
  92. Widagdo, B.; Roz, K. Hedonic shopping motivation and impulse buying: The effect of website quality on customer satisfaction. J. Asian Financ. Econ. Bus. 2021, 8, 395–405. [Google Scholar]
  93. Widyanto, H.A.; Kusumawardani, K.A.; Yohanes, H. Safety first: Extending UTAUT to better predict mobile payment adoption by incorporating perceived security, perceived risk and trust. J. Sci. Technol. Policy Manag. 2021, 13, 952–973. [Google Scholar] [CrossRef]
  94. Chatterjee, S.; Rana, N.P.; Khorana, S.; Mikalef, P.; Sharma, A. Assessing organizational users’ intentions and behavior to AI integrated CRM systems: A Meta-UTAUT approach. Inf. Syst. Front. 2021, 23, 1–5. [Google Scholar] [CrossRef]
Sustainability 15 09908 g001 550
Figure 1. The Proposed Model.
Figure 1. The Proposed Model.
Sustainability 15 09908 g001
Sustainability 15 09908 g002 550
Figure 2. The steps of the research methodology.
Figure 2. The steps of the research methodology.
Sustainability 15 09908 g002
Table
Table 1. Pilot Test.
Table 1. Pilot Test.
No.FactorsCodePilot TestFinal Test
1Perceived SecurityPS0.7080.856
2Perceived TrustPT0.7770.820
3Perceived RiskPR0.8010.890
4Service QualitySQ0.7110.881
5Perceived UsefulnessPEU0.7820.889
6Perceived Ease ofPES0.7160.865
7Social InfluenceSI0.7860.864
8Attitude towards to UseATU0.7920.927
9Intention to UseINU0.8090.824
Table
Table 2. Factor Analysis and Factor Loadings.
Table 2. Factor Analysis and Factor Loadings.
FactorsItemsFactor LoadingsComposite ReliabilityCronbach’s AlphaAVER Square
ATUATU10.8620.8950.8240.7410.649
ATU20.919
ATU30.798
SISI10.8980.9120.8560.7750.000
SI20.880
SI30.862
INUINU10.8900.9220.8740.7980.487
INU 20.887
INU 30.903
PRPR10.9010.9260.8810.8080.000
PR20.898
PR30.896
PESPES10.8460.8760.8900.7020.000
PES20.868
PES30.799
PUPU10.8650.8850.8040.7200.000
PU20.905
PU30.770
PSPS10.8570.8620.8650.6750.731
PS20.792
PS30.816
SQSQ10.9390.9530.9270.8720.762
SQ20.938
SQ30.924
PTPT10.8710.9170.8640.7860.621
PT20.917
PT30.871
Table
Table 3. Loadings and cross-loadings of items.
Table 3. Loadings and cross-loadings of items.
FactorsItemsATUSIINUPRPESPUPSSQPT
ATUATU10.8620.5390.5990.6170.5220.6300.7160.6570.670
ATU 20.9190.5650.6140.6030.5380.5990.6940.6870.707
ATU 30.7980.5440.4770.5070.3720.3660.5490.5860.572
SISI10.5840.8980.5790.4960.4760.4650.5020.5040.556
SI20.6130.8800.5570.5160.4260.4290.5350.5700.539
SI30.4610.8620.5180.4390.3410.3280.3710.4620.425
INUINU10.5920.5370.8900.6570.5660.6240.5840.5280.576
INU 20.5410.5170.8870.5580.5090.5330.5280.5540.527
INU 30.6280.6250.9030.6800.5300.5480.6070.5990.605
PRPR10.6310.5320.6680.9010.5320.6250.7640.6500.609
PR20.5680.5030.6200.8980.5380.5770.7060.6670.588
PR30.6120.4520.6230.8960.5180.5730.6650.5870.582
PESPES10.4010.3620.5100.4360.8460.4670.4360.4080.414
PES 20.5110.4460.5390.5300.8680.6320.6170.5170.512
PES 30.4840.3810.4530.5030.7990.4340.4950.4230.484
PUPU10.5170.3190.5100.5440.5210.8650.6580.4780.528
PU20.5950.4850.6110.5800.5770.9050.6690.5050.506
PU30.4830.3970.4950.5590.4740.7700.5630.3900.405
PSPS10.8150.5740.6280.7040.5110.5550.8570.8240.808
PS20.5080.4170.4820.6090.4920.6680.7920.4640.449
PS30.4890.3000.4400.6320.5500.6500.8160.5290.494
SQSQ10.6950.5880.6050.6330.4840.4940.7030.9390.787
SQ 20.7290.5570.5800.7020.5660.5230.7680.9380.793
SQ 30.6750.4970.5760.6450.4670.5030.6790.9240.798
PTPT10.6430.5500.5450.5550.3740.4260.5940.7350.871
PT20.6950.4700.6110.5640.5270.5030.6430.7920.917
PT30.6790.5350.5430.6380.5960.5800.7340.7290.871
Table
Table 4. Discriminant validity.
Table 4. Discriminant validity.
No.Factors12345678910
1ATU1.000
2INU0.6591.000
3SQ0.7500.6281.000
4PES0.5610.5990.5421.000
5PR0.6720.7100.7070.5891.000
6PS0.7640.6430.7680.6260.7941.000
7PT0.7580.6390.8480.5660.6610.7421.000
8PU0.6280.6360.5430.6190.6590.7450.5691.000
9SI0.6360.6290.5860.4770.5530.5430.5830.4701.000
Table
Table 5. Validation of the research model.
Table 5. Validation of the research model.
No.Hypotheses LinksPath CoefficientMeanS.DS.Et-Values
1PR → PT−0.0630.0710.0910.091−0.694
2PR → ATU−0.1930.1710.1170.117−1.650
3PT → ATU0.3210.3300.1070.1072.999
4PS → PT0.0750.0800.1270.1270.592
5PS → ATU0.0770.0980.1170.1170.658
6PEU → ATU0.0440.0510.1010.1010.432
7PES → ATU0.4530.4430.1090.1094.162
8SI → ATU0.0720.0970.1600.1600.451
9SQ → ATU0.0740.0600.1130.1130.659
10ATU → INU0.1820.1760.1390.1391.309
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Almaiah, M.A.; Al-Otaibi, S.; Shishakly, R.; Hassan, L.; Lutfi, A.; Alrawad, M.; Qatawneh, M.; Alghanam, O.A. Investigating the Role of Perceived Risk, Perceived Security and Perceived Trust on Smart m-Banking Application Using SEM. Sustainability 2023, 15, 9908. https://doi.org/10.3390/su15139908

AMA Style

Almaiah MA, Al-Otaibi S, Shishakly R, Hassan L, Lutfi A, Alrawad M, Qatawneh M, Alghanam OA. Investigating the Role of Perceived Risk, Perceived Security and Perceived Trust on Smart m-Banking Application Using SEM. Sustainability. 2023; 15(13):9908. https://doi.org/10.3390/su15139908

Chicago/Turabian Style

Almaiah, Mohammed Amin, Shaha Al-Otaibi, Rima Shishakly, Lamia Hassan, Abdalwali Lutfi, Mahmoad Alrawad, Mohammad Qatawneh, and Orieb Abu Alghanam. 2023. "Investigating the Role of Perceived Risk, Perceived Security and Perceived Trust on Smart m-Banking Application Using SEM" Sustainability 15, no. 13: 9908. https://doi.org/10.3390/su15139908

APA Style

Almaiah, M. A., Al-Otaibi, S., Shishakly, R., Hassan, L., Lutfi, A., Alrawad, M., Qatawneh, M., & Alghanam, O. A. (2023). Investigating the Role of Perceived Risk, Perceived Security and Perceived Trust on Smart m-Banking Application Using SEM. Sustainability, 15(13), 9908. https://doi.org/10.3390/su15139908

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop