1. Introduction
The rapid advancement of technology is continuously pushing service providers and consumers toward digitization. On the other hand, for a considerable amount of time, governments of various nations have consistently regulated and formulated policies to bring individuals from various regions of the nation under the umbrella of financial inclusion. In this regard, the emergence of communication technology enables the government to achieve its goals more quickly and reach even the most distant citizens (Klapper and Miller, 2021). In addition, the recent pandemic has affected virtually everyone and everything on the planet. Rouf et al. (2022) and Almekhlafy (2020) report that the ongoing pandemic has accelerated the digitization of financial services.
Remittances, merchant payments, savings, insurance, etc. are being dramatically transferred from physical mode to electronic mode, with the Internet, communication technology and smartphones playing a crucial role in reaching the most remote individuals. The ongoing pandemic also provided more opportunities for digital platform services and brought individuals under financial inclusion in a short amount of time since the individuals had no option due to lockdown and social distance laws. Additionally, it is beneficial for the users due to low costs, less time required and data encryption (Klapper and Miller, 2021).
The current study uses the theory of planned behavior (Ajzen, 1991), the technology acceptance model, Bauer's risk-taking in consumer behavior and the gamification effect (Davis, 1989; Bauer, 1967; Zichermann and Cunningham, 2011) and intends to investigate individuals' intentions toward m-payment usage. The existing literature on intention to use m-payment applications reveals a number of variables that influence intentions, including perceived usefulness, perceived risk, situational influence, attitude, subjective norms and perceived behavioral control. Hossain (2019), and Yiu et al. (2007) found that perceived usefulness and risk are significant predictors of Internet banking services. Davis (1989) defined perceived efficacy as the degree to which an individual believes that employing a particular system will enhance performance. Users intend to use m-payment applications, making it pertinent to investigate how the perceived utility of a service affects their intention to use that service. Bauer (1967) brought the issue of perceived risk through two factors: uncertainty and potential negative outcomes associated with the purchase or consumption decision to illustrate how perceived risk can influence an individual's intention to use. This necessitates an understanding of how perceived risk influences individuals' intention to use, particularly in the case of m-payment usage, where cyber risk is always assumed to be high.
Further, the current examination also tries to determine how gamification moderates the impact of perceived utility, attitude, subjective norms, perceived behavioral control and situational influence on the intention to use m-payment applications. Gamification is the application of game elements in non-game contexts (Zichermann and Cunningham, 2011). It relates to the characters, visuals, scripts, points and rewards that comprise the essence of gamified systems' artistic design. The purpose of gamifying payment applications is to entice and motivate users to utilize the applications. Moreover, due to the pandemic, the world has experienced dramatic changes over the past three years. It, therefore, encourages post-pandemic research into how situational influence affects individuals' intent to use m-payment applications.
Among the countries of the Middle East, Bahrain is a significant market for e-wallets, with 51.7 million transactions valued at BD 2.4 billion in 2020. Second, the nation's financial system is steadily advancing toward digitization. Multiple fintech applications and payment systems are inundating the nation's financial system, and efforts are being made to create a cashless economy. This makes it a fascinating region to research. Research in the Gulf Cooperation Council (GCC) nations, including Bahrain, has emphasized the significance of comprehending user intentions and attitudes regarding mobile payment applications. According to a study by Alalwan et al. (2017), perceived usefulness, simplicity of use and security are the most significant factors influencing the adoption of mobile payment applications in the GCC countries. Alalwan et al. (2018) identified trust, perceived risk and social influence as significant factors that influence the adoption of mobile payment applications.
Gamification has been identified as a potential strategy to increase user engagement and motivation toward mobile payment applications in the GCC region. A study by Al-Adwan and Al-Majali (2018) in Jordan found that gamification positively influenced user attitudes toward mobile payment applications. Similarly, a study by Alshurideh et al. (2019) in the UAE found that gamification elements increased user motivation and adoption of mobile payment applications.
In Bahrain, there is limited research specifically examining the moderating role of gamification in affecting intentions to use mobile payment applications. Despite the potential benefits of gamification in increasing user engagement and adoption of mobile payment applications, there is still a research gap in understanding the moderating role of gamification in influencing intentions to use mobile payment applications in Bahrain. Additionally, there is a need to identify which gamification strategies are most effective in promoting user engagement and the adoption of mobile payment applications in Bahrain.
Based on the above discussion, the present study aims to offer an integrated framework based on the TPB, TAM and UTAUT2 to examine intentions to use m-payment applications in Bahrain. The integration of the Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) can provide a comprehensive framework for examining the intentions to use mobile payment applications in Bahrain. TPB provides a basis for understanding the factors that influence an individual's behavioral intentions, including attitudes, subjective norms and perceived behavioral control. TAM focuses on the factors that affect the acceptance and use of technology. UTAUT2 expands TAM and includes additional factors such as social influence (Venkatesh et al., 2012). By integrating these three models, researchers can gain a more comprehensive understanding of the determinants of mobile payment adoption and usage.
Thus, the current examination tries to answer the following research question.
The outcomes of the study can be helpful to service providers, policymakers and researchers in the further investigation of existing literature or service improvement. The results can be useful to other Middle Eastern nations that have a similar cultural and economic system.
2. Literature review
TPB, TAM and UTAUT2 are based on the theory of reasoned action (Ajzen and Fishbein, 1980; Hill et al., 1977); they also assume that the most important factor influencing behavior is intention (Paul et al., 2016). In the case of technological items, researchers have claimed that intentions can be a good predictor of actual behavior (Shin, 2009). TAM is a key concept in information system management that explains how people adopt and use new technology (Legris et al., 2003). According to TAM, a person's behavioral purpose determines his or her true conduct (Davis, 1989). The theories integrated into the current examination are widely used for evaluating the consumer acceptability of any internet-based technology (Shankar and Kumari, 2019; Chen and Tung, 2014). By adding other variables to TAM, several researchers have been able to construct novel models (Galib et al., 2018; Arora and Sahney, 2019). In recent times, the emerging trend of gamification and the impact of pandemics have had an impact on human behavior. In this respect, it becomes imperative to understand how these can further influence behavioral intentions, and the same is the core strength of the present study model. In addition, there is still not enough evidence in the existing literature when it comes to using an integrated model inclusive of gamification and situational influence (pandemic influence) to examine intentions to use m-payment applications in the Gulf region. During and post-pandemic, the usage of mobile banking has been raised due to social distance and digital adoption, specifically in the context of geolocation, where the majority are ex-pats and have a higher income level; hence, the proposed model (Figure 1) can bring useful knowledge for researchers and policymakers.
2.1 Perceived usefulness (PU)
Perceived Usefulness (PU) is a measure of how strongly consumers believe that implementing a specific piece of technology would improve their lives (Davis, 1989). According to empirical evidence, PU is an important predictor of intentions to use new technologies in the future (Arvidsson, 2014). With respect to mobile payment systems, PU refers to the ease and speed with which transactions may be completed, as well as the fact that they are complementary to cash transactions (Chen et al., 2018). To add to this, according to Taylor and Todd (1995), perceived usefulness (PU) was a significant antecedent in distinguishing the differences between users and non-users. Liébana-Cabanillas et al. (2014) conducted an empirical study in which they discovered that PU was a powerful predictor of the behavioral intention to utilize mobile payment services. In recent studies, Kavitha and Kannan (2020) confirm that perceived usefulness has a significant impact on consumers' attitudes toward mobile applications. Another study that corroborated the same impact discovered that perceived usefulness significantly influences customers' intentions to use mobile payments (Denaputri and Usman, 2019). A recent study mentioned that perceived usefulness is the most crucial indicator of future intention to utilize mobile payments (Franque et al., 2021). Thus, it would be interesting to examine how it works in the current model.
2.2 Attitude (AT)
Whether they are positive or negative, people's responses to particular conduct have an impact on their attitudes (Fishbein and Ajzen, 1975). Attitude is thought to be a complex mix of behavior, thinking and feeling (de Luna et al., 2018). When it comes to technology adoption, the TAM (Davis, 1989) and the TPB (Ajzen, 1991) both consider attitude to be a significant factor. Attitude has also been identified as a common element influencing the uptake of mobile payment services (de Luna et al., 2018; Liebana-Cabanillas et al., 2014). According to studies by Gupta and Arora (2017) and Wulandari (2017), attitude toward mobile payment services have a positive effect on their acceptance. Safari et al. (2022) proposed a similar association in the case of new payment systems, which is consistent with earlier research. There are significant positive relationships between tourists' attitudes regarding augmented reality technology and their intentions to use it (Zhuang et al., 2021). The relationship between attitude and intent to use is both positive and substantial (Hussein et al., 2017). Similar findings may have an impact on mobile payment apps as well, according to another study (Hossain et al., 2019) that found that people with a positive outlook on information systems might show more interest if they have a positive outlook on learning about mHealth apps. Hence, attitude forms a critical aspect of the current model under study to examine how attitude affects intention to use the m-payment applications in Bahrain.
2.3 Subjective norms (SN)
Subjective norms (SN) refer to the perceived social pressures that individuals feel from certain significant referents in their lives, such as families, friends and anyone else who is related to them, to engage in specific conduct (Ajzen, 1991). Studies have found that the SN is an important factor in the adoption of technology-based services, and it has been used in TAM-based models (Chong et al., 2012; Venkatesh and Davis, 2000). As Davis (1989) stated, the concept of social influence is linked to the concept of subjective norms. de Luna et al. (2018) and Zhou (2011), concurred with this conclusion.
Adoption studies of digital payments (Dahlberg et al., 2015) have found that subjective norms are one of the most frequently studied variables among participants in relation to intentions to use. Subjective norms have been found to be an important indicator of intentions toward social media for learning and investments (Al Kurdi et al., 2021; Shahzalal and Adnan, 2022; Akhtar et al., 2023). Therefore, the model under examination also examines the relationship between SN and intentions to use m-payment applications in the context of Bahrain.
2.4 Perceived behavioral control (PB)
Perceived behavioral control (PB) is a measure of how easy or difficult it is to carry out a behavior (Ajzen, 1991). According to past research, the notion that one has control over one's behavior is a crucial influence on the intention of customers to make online purchases (Ting et al., 2016; Khatimah and Halim, 2016). When it comes to deciding, PB has a major impact on the outcome (Albayrak et al., 2013; Moser, 2015; Paul et al., 2016). Using mobile payment services, users' expressions of displeasure are predicted by their perceptions of behavioral control, suggesting a positive influence on behavioral intention (Dorfleitner et al., 2017). Hence, the authors included PB as a variable under examination in the current model.
2.5 Perceived risk (PR)
In the context of an innovative product, perceived risk (PR) is described as the degree of ambiguity among customers about the potential negative repercussions of utilizing new technology, which may discourage adoption (Bauer, 1967). This suggests that growing unpredictability will raise the risk perception of m-payment users. Using the example of Schierz et al. (2010), who observed that customers are less likely to use mobile wallets if they perceive a higher risk of failure, PR is thought to be very important when people are not sure how to use new technology because they are afraid of what could happen when they use it. According to Esawe (2022), perceived risk has a significant impact on retailers' behavioral intentions to use mobile payments. Another study found a negative impact of PR on millennials' attitudes toward using m-payment (Zhu et al., 2022). The previous evidence of interaction between PR and intention to use mobile payments makes it imperative to include PR as an important predictor in the current model.
2.6 Situational influence (SI)
In the short term, conditions that have an impact on how customers act—whether they buy a product or something else or do nothing at all—are referred to as “situational factors” (Ross and Robertson, 2003). Previous research has suggested that situational factors play an important influence on choice when it comes to a wide range of issues (Wallenius et al., 2014). Entrepreneurship and innovation experts suggest that situational factors have a significant influence on the intentions of entrepreneurs (Liñán et al., 2011). Previous research examinations have revealed that SARS-nCOV and related virus-like influenza can also infect the paper surface (van Doremalen et al., 2020). This has also affected individual behavior, and it is also backed by research, like the outcomes of a recent investigation, which demonstrated that the COVID-19-induced scenario had a significant impact on the online book purchase behavior of customers in Vietnam (Nguyen et al., 2020). Situational influence is one of the factors that positively affect intentions to use mobile payment applications (Maleki and Hosseini, 2020). Thus, situational influence has gained prominence as a variable to investigate intentions to use during the pandemic; hence, it also forms a prominent part of the model under examination.
2.7 Gamification (GM)
Gamification is the application of game design concepts in non-gaming situations (Deterding et al., 2011). Motivation and engagement, the two most studied gamification concepts, both influence usage behaviors ( Koivisto and Hamari, 2019; Liu et al., 2017).
According to Landers et al. (2018), gamification research should be conducted to better understand “how to best affect human behavior, attitudes and other states with tailored treatments derived from games.” Furthermore, the existing literature in the field indicates that gamification has a positive influence on users' intentions by allowing them to participate in activities that they find pleasurable (Lee and Jin, 2019). The findings demonstrated that gamification boosts user engagement by meeting their demands for relatedness, competence and autonomy. User involvement, in turn, increases the intention to use (Bitrián et al., 2021). A systematic review of recent studies found positive outcomes from their analysis showing that gamification can influence intention to use (Fang, 2019). Thus, the current examination utilizes gamification as the moderating variable to test how the variables in the model interact with gamification to boost users' intentions to use m-payment applications in Bahrain. Thus, the hypotheses to be tested are framed as follows.
3. Research methodology
3.1 Research design
The current investigation is quantitative in nature. An explanatory-predictive technique is employed in this study to assess statistical inference, path coefficients, size of impact and the model's predictive skills, as well as any errors in prediction (Henseler, 2018). The study employs a self-report questionnaire, which is thought to be appropriate for assessing behavioral intentions (Chan, 2009).
3.2 Sampling procedure
The authors collected 50 questionnaires as a pilot test to ensure satisfactory Cronbach alpha, validity and reliability. In order to ensure that participants represented different age groups, gender, income and educational level, stratified random sampling was used. Google Forms were used to collect data from the participants through social media platforms from November 2021 to January 2022. The authors utilized G* Power 3.1.9.4 software to determine the minimal sample size required for the analysis. At a significance level of 5% (0.05), a sample size of 89 observations was recommended. Despite the restrictions imposed due to the emergence of newer variants of Covid-19, the authors were able to collect 250 duly filled questionnaires, excluding the samples collected for pilot tests. Even after removing the questionnaires with missing values (45) and the questionnaires collected for the pilot tests (50), the sample size remained at 205, which satisfied the minimum sample size criterion (please refer Table 1).
4. Analytical procedure
The authors applied the multivariate PLS-SEM in the current examination, which is considered appropriate for analyzing theories and latent variables (Hair et al., 2017). Moreover, the data collected was not normal (Henseler et al., 2009; Hair et al., 2019). The model was analyzed through SmartPLS 3.3.3 (Ringle et al., 2015; Sarstedt et al., 2020).
4.1 Measurement model
The examination utilizes self-appraised questions on a five-point Likert scale (1 = strongly disagree, 5 = strongly agree) (please refer Table 2). The questionnaire was classified into eight different sections; however, the respondents were not informed about the section to which various items belonged. A separate section of respondents' profiles was added during the first round of data collection. All the questions used in the survey were taken from published sources.
4.2 Common method bias
Common method bias is a major concern in all primary data research. During data collection, reverse scaling and mixing of items in the questionnaire were employed (Osakwe et al., 2016). The authors also conducted Harman's single-factor test, which revealed that one factor explained only 32% of the variation, which is within the threshold, thereby ruling out the possibility of CMB in the data under examination.
5. Results
The authors calculated Cronbach's alpha, Composite Reliability, Rho_A, AVE and HTMT to establish the reliability, convergent and discriminant validity of the variables (Henseler et al., 2014). The values for Cronbach's alpha and Rho_A were above 0.7 thereby establishing the reliability of the construct through both conservative and modest approaches (please refer Table 3) (Hair et al., 2017, 2019).
All the values for AVE reported were above 0.5 which further compliments the reliability and values of HTMT were also found within the threshold to establish the discriminant validity of the constructs (please refer Table 4) (Sarstedt et al., 2020).
6. Assessment of structural model
The assessment of structural model started with the authors calculating the inner VIF values which fell between 1.453 and 2.569 which is within the threshold to signal any collinearity (Ringle et al., 2015). The structural model is analyzed using a two-tailed percentile bootstrapping test with 10,000 sub-samples and a significance threshold of 5% to test the framed hypotheses (Streukens and Leroi-Werelds, 2016).
6.1 Direct relationship
The analysis of the structural model revealed that AT, PB, PR and GM has a positive influence on intentions to use mobile payment applications with a (β = 0.184, f2 = 0.038, p < 0.05), (β = 0.536, f2 = 0.308, p < 0.05), (β = −0.174, f2 = 0.077, p < 0.05) and (β = 0.362, f2 = 0.033, p < 0.05) respectively, thereby supporting the H2, H4, H5 and H7. The results are further supported by the t-values which are (t = 2.433), (t = 6.51), (t = 1.934) and (t = 3.05) respectively. The outcomes of the analysis also disclosed that PU, SN and SI do not have a positive influence on the intentions to use mobile payment applications with a (β = 0.073, f2 = 0.024, p > 0.05), (β = −0.079, f2 = 0.002, p > 0.05) and (β = −0.066, f2 = 0.008, p > 0.05) respectively. Further, the t-statistics also reveals the same with a (t = 0.931), (t = 0.941) and (t = 0.873) which is below the threshold (t > 1.96) (please see Table 5 and Figure 2). Hence, H1, H3 and H6 are rejected.
6.2 Moderating role of gamification
The analysis of the indirect relationship revealed that GM moderates the relationship between PR and IN with (β = 0.15, p < 0.05, t > 1.96), and between SI and IN with (β = −0.148, p < 0.05, t > 1.96), hence we have enough evidence to accept H5a, H6a however, the moderating influence of GM on other variables cannot be established during the analysis leading to rejecting of H1a, H2a, H3a and H4a (please see Table 5, Figures 2 and 3).
6.3 R2
In the next step, the authors analyzed the model's predictive capabilities. Based on the model's R2 values, the constructs connected to it explain 59.4% of the variation in the dependent variable, which is within the moderate range according to the researchers' criteria (Hair et al., 2019) (please refer Table 6).
6.4 Model fit
The current examination revealed SRMR = 0.082, which is within the threshold to depict a satisfactory model fit (Hu and Bentler, 1998; Clark and Bowles, 2018). Moreover, the values for Chi-square = 2926.126 and NFI = 0.637 also indicated that an acceptable model fit for the study was achieved (please refer Table 7).
7. Discussion and conclusion
The current examination happens to be the first inference in the context of Bahrain, wherein an extended TPB-based model incorporating variables from TAM and UTAUT2 is used along with gamification and situational influence to examine the intentions to use m-payment applications. The findings revealed that among the variants of the TPB, AT and PB significantly affect the intentions to use m-payment applications in Bahrain. This finding adds value to the model, as the previous literature mentions that in the case of technological and innovative products, AT is the key influencer of intentions to use (Chen and Tung, 2014; Moser, 2015). Similarly, with respect to PB, the researchers have argued that it is one of the key influencers when it comes to technological products and services and plays a vital role in the adoption of innovative financial products as well (Albayrak et al., 2013; Paul et al., 2016). Thus, the variables addressed in the model complement previous literature with their outcomes. However, among the variates of the TPB SN, fails to register a significant impact on intentions to use m-payment applications in Bahrain, which is interesting as the researchers have always argued that in the case of innovative financial products and services, trust is always the key concern and users tend to trust the opinion shared by their close contacts (Moser, 2015). The factor that makes this outcome stand out is the fact that another variable taken from TAM, perceived risk, reflects a significant impact on intentions to use m-payment applications among the population under examination. This may mean that even though the population under examination does not rely much on the opinion of peers, they may be open to professional financial advice as they feel pressure when they are choosing an innovative financial product. However, this can only be addressed in future endeavors.
The findings further revealed that PU fails to impact intentions to use m-payment applications among the users in Bahrain. This is also very vital, as it indicates that the users, even though they have a positive attitude toward the m-payment applications, do not feel that they make a significant value addition to their financial transactions. Thus, the population under examination feels that m-payment applications offer limited utility to them. This conclusion is also backed up by the outcome of SI on intentions to use m-payment applications, which further displayed that situational influence is not affecting intentions to use m-payment applications. Thus, it becomes quite evident that the population under examination does not feel that m-payment offers any additional utility, even during the pandemic.
Gamification (GM), on the other hand, displayed a significant impact on intentions to use m-payment applications, which supports the available literature that claims GM affects user engagement positively (Kuo and Chuang, 2016). GM also moderates the relationship between PR and IN, SI and IN, which explain that gamification strengthens the relationship between these variables, and this outcome is particularly important given that even though SI does not affect the intentions to use, SI, along with the GM, has a significant impact on intentions, thereby offering some crucial practical implications for the service providers. The moderating effect of gamification revealed that when gamification levels are higher, the positive association between consumer intentions to adopt and recommend will be stronger (Rahi and Abd Ghani, 2019). The findings demonstrate mixed results; some of the relationships tested above show a moderate effect, and some do not. Gamification has a considerable impact on the intention to continue using m-payments. The findings give evidence for further exploration, and theoretically, this study adds to the body of knowledge by demonstrating how gamification in payments has a major impact on users' intentions to continue using mobile payments. In order to assure their continued use, the m-payment providers are advised by this research to keep the gamification components.
8. Theoretical contribution
This model offers some important theoretical contributions: one in the form of PR being a significant variable affecting intentions but SN not registering a significant impact; another wherein SI fails to affect intentions as a variable but when moderated by GM it registers a significant impact on intentions. Moreover, the model also shows that PU fails to affect intentions significantly. This means that even though the population under study has trust issues, they do not rely on the information shared by their peers, so they may be inclined toward the opinion of the experts. This finding opens the door for future research on examining the intention to adopt innovative financial products or services through financial advice. Similarly, SI fails to affect intentions, thereby proving that the pandemic fails to drive the population under study toward using m-payment applications, but when the application offers gaming features, SI affects intentions significantly through GM; thus, experience along with the situation drives their intentions, and this becomes the major contribution of the model. Moreover, the fact that PU fails to drive intention toward m-payment applications would mean that users do not feel that m-payment applications offer any utility to them, which further substantiates the need for financial literacy and financial advice. Precisely, the major theoretical contribution is the moderating role of gamification, which was not covered comprehensively in the existing literature. Therefore, the present article enriches the current literature and opens avenues for further research.
9. Practical implications
This study also offers useful implications for service providers, who can use the outcomes of the study to their advantage. The findings revealed that GM affects intentions to use m-payment applications and that GM moderates the relationship between PR and IN, as well as SI and IN, which can be used by the service providers to provide an improved gaming experience and achieve better acceptance of their application. Further, the service providers may also use the gaming environment of the applications to create educational materials for the customers so that they can better understand the utility of the applications and participate in a better way. Since the outcomes also revealed that AT has a significant impact on intentions to use m-payment applications, service providers may design promotional campaigns utilizing a gaming environment so that a sustainable customer base for such applications can be created in Bahrain and the objectives of the digital economy can be achieved in a better way. In short, the application of gamification can be useful for telecom companies, banking firms and other online services to attract, serve and retain users and customers. Also, to reach and retain the remotest customers where there are no physical branches of services available, gamification can play an important role in customer relationship management and building customer loyalty as well.
10. Limitations
The current examination also suffers from certain limitations. It would be desirable to use the model among a considerably large population chosen from different countries in the MENA region in order to generalize the findings. Moreover, multigroup analysis can be performed in order to understand the differences among groups created using demographic indicators.
Since acceptance of this article, the following author have updated their affiliations: Khurram Ajaz Khan is at the Department of Finance, School of Business and Economics, Westminster International University in Tashkent, Uzbekistan.
Figure 1
Authors' proposed model
[Figure omitted. See PDF]
Figure 2
Author generated model through SmartPLS 3.3.3
[Figure omitted. See PDF]
Figure 3
Simple slope analysis author generated model through SmartPLS 3.3.3
[Figure omitted. See PDF]
Table 1
Descriptive statistics
| N | Minimum | Maximum | Mean | Std. Deviation | |
|---|---|---|---|---|---|
| Gender | 205 | 1 | 2 | 1.365853659 | 0.482847841 |
| Age group | 205 | 1 | 5 | 2.346341463 | 1.099040928 |
| Partnership status | 205 | 1 | 3 | 1.717073171 | 0.540471127 |
| Level of education | 205 | 1 | 5 | 3.180487805 | 0.919084026 |
| Level of income (Monthly; in Bahraini Dinar) | 205 | 1 | 5 | 3.063414634 | 1.244941942 |
| Employment status | 205 | 1 | 2 | 1.824390244 | 0.381419233 |
| Valid N (listwise) | 205 |
Source(s): Author-generated content through IBM-SPSS 20
Table 2
Measurement model
| Code | Items | Loadings | VIF | Sources |
|---|---|---|---|---|
| PU1 | Using mobile payment application makes the process of bill payment easier for me | 0.830 | 2.41 | Davis (1989) |
| PU2 | In general, mobile payment applications offers too much utility | 0.867 | 2.827 | |
| PU3 | Using m-payment application makes me manage my liabilities effectively | 0.913 | 3.45 | |
| PU4 | Using mobile payment application saves me time for other important activities | 0.872 | 2.874 | |
| PU5 | In today's digital world m-payment application is extremely vital for everyone | 0.687 | 1.743 | |
| ATT2 | It's great that such applications are available to use in Bahrain | 0.646 | 1.496 | Mostafa Mohamed (2006), Mostafa (2009), Paul et al. (2016), Taylor and Todd (1995) |
| ATT3 | I have a positive attitude toward using mobile payment applications | 0.766 | 2.188 | |
| ATT4 | The availability of mobile payments applications is useful for everyone | 0.795 | 2.407 | |
| ATT5 | By using mobile payments applications for transactions, I can save time and money | 0.799 | 2.175 | |
| ATT6 | I like to explore various mobile payment applications available to use | 0.743 | 2.102 | |
| ATT7 | I am able to get various offers on using mobile payment applications | 0.625 | 1.671 | |
| SN1 | I know that most people who use mobile payment applications are able to save money and time | 0.781 | 1.862 | Arvola et al. (2008), Chen and Peng (2012), Dean et al. (2012), Kim and Ko (2012), Paul et al. (2016) |
| SN2 | People who influence my behavior think that I should use mobile payment applications for transactions | 0.705 | 2.048 | |
| SN3 | People who are important to me think that I should use mobile payment applications for transactions | 0.859 | 3.372 | |
| SN4 | My friends'/colleagues' positive opinion about mobile payment applications also affect my intentions to use them | 0.793 | 2.253 | |
| SN5 | I know that most people close to me approve of using mobile payment applications | 0.734 | 1.839 | |
| SN6 | I always follow my family and friends in choosing mobile payment applications | 0.736 | 1.67 | |
| PB1 | I feel I have the capability to save time and money through mobile payment applications | 0.753 | 2.051 | Armitage and Conner (1999), Chen and Peng (2012), Dean et al. (2012), Kim and Ko (2012), Paul et al. (2016), Pavlou (2003), Taylor and Todd (1995) |
| PB2 | If it is totally up to me when it comes to using mobile payment applications for transactions | 0.756 | 2.362 | |
| PB3 | I have amenities, time and access to various mobile payment applications | 0.855 | 2.762 | |
| PB4 | I can afford to handle the security issues related with mobile payment applications | 0.837 | 2.658 | |
| PB5 | I can avail various offers while making payment through mobile payment applications and thus save money | 0.827 | 2.686 | |
| PB6 | Updates and reminders received on mobile payment applications are extremely helpful | 0.824 | 2.084 | |
| SI1 | Many of the bank branches were closed during Covid-19 | 0.830 | 2.249 | Nguyen et al. (2020) |
| SI2 | There were significant health risks associated with visiting bank branches and ATMs during Covid-19 | 0.843 | 2.536 | |
| SI3 | Mobile payment applications gave numerous offers during Covid-19 | 0.806 | 2.189 | |
| SI4 | Mobile payment applications resort to aggressive sales promotion strategy during Covid-19 | 0.819 | 2.572 | |
| SI5 | Making and receiving payments using mobile payment applications became a trend during Covid-19 | 0.746 | 1.983 | |
| PR1 | I am afraid that my personal information may be misused by the service providers | 0.809 | 2.763 | Hong and Cha (2013) |
| PR2 | While making a payment I am not sure whether it will succeed or not | 0.927 | 4.715 | |
| PR3 | I am worried that if the transaction fails my money would be blocked | 0.935 | 3.572 | |
| PR4 | I have heard that mobile payment applications are not safe | 0.882 | 4.238 | |
| PR5 | There may be a variation in amount deducted from the bank account and amount actually paid using the m-payment application | 0.823 | 3.194 | |
| GM1 | When I use my mobile payment application I feel like playing a game | 0.870 | 2.706 | De Canio et al. (2021), Hsu and Chen (2018) |
| GM2 | The concept of points earned on every transaction makes me feel like completing a stage in a game | 0.894 | 3.529 | |
| GM3 | The various steps involved in making payments on the application creates an environment like playing a game | 0.877 | 3.315 | |
| GM5 | The challenge involved in making payment each time is similar to gaming experience | 0.859 | 2.68 | |
| IN1 | I will consider using mobile payment applications if I have seen my close contacts using it | 0.810 | 2.368 | Liébana-Cabanillas et al. (2017), Teng et al. (2018) |
| IN2 | I will consider using mobile payment applications if I have easy access to Internet | 0.829 | 2.553 | |
| IN3 | I will prefer using mobile payment applications if the speed of Internet is good | 0.893 | 3.407 | |
| IN4 | I will prefer using mobile payment applications if it ensures sufficient safety | 0.849 | 3.114 | |
| IN5 | I will prefer to use a mobile payment application if it is easy and interesting to use | 0.843 | 3.331 | |
| IN6 | I will prefer to use mobile payment applications if there are sufficient offers to save money | 0.773 | 2.158 |
Source(s): Author calculated values through SmartPLS 3.3.3
Table 3
Construct reliability and validity
| Cronbach's Alpha | rho_A | Composite Reliability | Average Variance Extracted (AVE) | |
|---|---|---|---|---|
| ATT | 0.824 | 0.825 | 0.873 | 0.536 |
| GM | 0.899 | 0.91 | 0.929 | 0.766 |
| IN | 0.912 | 0.917 | 0.932 | 0.695 |
| PB | 0.895 | 0.908 | 0.919 | 0.656 |
| PR | 0.928 | 1.102 | 0.943 | 0.768 |
| PU | 0.894 | 0.926 | 0.921 | 0.702 |
| SI | 0.869 | 0.883 | 0.904 | 0.655 |
| SN | 0.862 | 0.868 | 0.897 | 0.592 |
Source(s): Author calculated values through SmartPLS 3.3.3
Table 4
Discriminant validity
| ATT | GM | IN | PB | PR | PU | SI | SN | |
|---|---|---|---|---|---|---|---|---|
| ATT | ||||||||
| GM | 0.415 | |||||||
| IN | 0.567 | 0.439 | ||||||
| PB | 0.605 | 0.54 | 0.737 | |||||
| PR | 0.26 | 0.687 | 0.129 | 0.309 | ||||
| PU | 0.257 | 0.318 | 0.276 | 0.293 | 0.54 | |||
| SI | 0.415 | 0.526 | 0.407 | 0.433 | 0.474 | 0.514 | ||
| SN | 0.761 | 0.509 | 0.563 | 0.794 | 0.367 | 0.31 | 0.4 |
Source(s): Author calculated values through SmartPLS 3.3.3
Table 5
Path coefficient
| Hypotheses | Original Sample (O) | Sample Mean (M) | Standard Deviation (STDEV) | T-statistics (|O/STDEV|) | p-values | Outcome | |
|---|---|---|---|---|---|---|---|
| H1 | PU → IN | 0.073 | 0.086 | 0.078 | 0.931 | 0.352 | Rejected |
| H1a | PU GM IN → IN | 0.088 | 0.088 | 0.071 | 1.229 | 0.219 | Rejected |
| H2 | AT → IN | 0.184 | 0.192 | 0.076 | 2.433 | 0.015 | Accepted |
| H2a | AT GM IN → IN | −0.092 | −0.068 | 0.11 | 0.833 | 0.405 | Rejected |
| H3 | SN → IN | −0.079 | −0.082 | 0.084 | 0.941 | 0.347 | Rejected |
| H3a | SN GM IN → IN | 0.118 | 0.113 | 0.135 | 0.871 | 0.384 | Rejected |
| H4 | PB → IN | 0.536 | 0.533 | 0.082 | 6.51 | 0 | Accepted |
| H4a | PB GM IN → IN | −0.082 | −0.096 | 0.113 | 0.729 | 0.466 | Rejected |
| H5 | PR → IN | −0.174 | −0.161 | 0.09 | 1.934 | 0.053 | Accepted |
| H5a | PR GM IN → IN | 0.15 | 0.15 | 0.069 | 2.178 | 0.029 | Accepted |
| H6 | SI → IN | −0.066 | −0.064 | 0.076 | 0.873 | 0.383 | Rejected |
| H6a | SI GM IN → IN | −0.148 | −0.152 | 0.065 | 2.268 | 0.023 | Accepted |
| H7 | GM → IN | 0.362 | 0.343 | 0.119 | 3.05 | 0.002 | Accepted |
Note(s): In Table 5, italicized values indicate statistical significance of the relationships examined. Specifically, path coefficients presented in italics are significant at a predetermined level (e.g. p < 0.05), implying that there is strong evidence to support the relationships or effects they represent and vice-versa
Source(s): Author calculated values through SmartPLS 3.3.3
Table 6
R2
| R Square | R Square Adjusted | |
|---|---|---|
| IN | 0.60 | 0.594 |
Source(s): Author calculated values through SmartPLS 3.3.3
Table 7
Model fit
| Saturated Model | Estimated Model | |
|---|---|---|
| SRMR | 0.082 | 0.082 |
| d_ULS | 6.365 | 6.365 |
| d_G | 2.895 | 2.895 |
| Chi-Square | 2926.126 | 2926.126 |
| NFI | 0.637 | 0.637 |
Source(s): Author calculated values through SmartPLS 3.3.3
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Abstract
Purpose
Using an integrated theoretical model, this study aims to examine the moderating role of gamification in influencing intentions to use mobile payment applications in Bahrain.
Design/methodology/approach
The current examination happens to be the first approximation in the context of Bahrain wherein an extended TPB-based model integrating variables from TAM and UTAUT2 is used along with gamification and situational influence to examine the intentions to use m-payment applications.
Findings
The findings revealed that among the variates of the TPB, AT and PB significantly affect the intentions (IN) to use m-payment applications in Bahrain, but SN fails to affect intentions, similarly SI also fails to affect intentions thereby proving that the pandemic fails to drive the intention of the population under study toward using m-payment applications. However, when the application offers gamification (GM) features, SI significantly affects intentions through GM, thus experience along with situation drives intentions and this becomes the major theoretical contribution of the study.
Practical implications
This examination offers useful practical implications in the form of the findings revealing that GM affects intentions to use m-payment applications and that GM moderates the relationship between perceived risk (PR) and IN, as well as SI and IN, which can be used by the service providers to improve the user experience and achieve better acceptance of their application.
Originality/value
The novelty of the study lies in testing the integrated theoretical model in the context of a GCC nation, Bahrain.
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Details
; Sarea, Adel 2 ; Khan, Imran 3
; Khan, Khurram Ajaz 4
; Singh, Madhvendra Pratap 5
1 Department of Accounting and Finance, Institute of Management Technology, Dubai, Dubai, United Arab Emirates
2 Ahlia University, Manama, Bahrain
3 Birla Institute of Technology and Sciences, Dubai, United Arab Emirates
4 Faculty of Management and Economics, Tomas Bata University in Zlin, Zlin, Czech Republic
5 Department of Business Administration, University of Kanpur, Kanpur, India