1. Introduction
The modernization of financial transaction systems has increased the velocity of monetary transactions, resulting in knock-on effects, and leading to the growth of economies. Emerging economies, especially, have experienced significant progress as digitalization has evolved the ways in which people conducted fintech transactions. For Bangladesh’s low and middle income, mobile fintech services (MFS) have proved to be the most convenient and cost-efficient [1]. Mobile fintech services are an e-money services where the funds of the person with a mobile account are recorded on an electronic general ledger. The transactions are made either from the mobile phone or via a digital process to ensure their authenticity [2]. The foundation of Mobile Fintech Services established in 2011 by Bangladesh Bank, the central bank of Bangladesh, vigorously grew the number of account holders and the volume of transactions [2]. In 2013, the number of registered accounts for mobile fintech services rose faster than in any other country [3]. The average daily transactions increased by 3.08% from December 2021 to January 2022 [4]. The current 13 MFS providers, which are licensed scheduled banks and financial institutions, provide a range category of services, such as sending money, paying bills, cashing out, making payments to merchants, salary disbursement, and many other services. With the growth of mobile phone subscribers to 178.61 million as of August 2021, there is prodigious opportunity for mobile fintech service providers [5]. To continue the growth rate of mobile fintech service adoption, it is very crucial for the MFS providers to understand what motivates the users in Bangladesh to adopt mobile fintech services.
In Bangladesh, several studies on specific mobile payment systems have already been conducted. However, in most cases, the unified theory of acceptance and use of technology (UTAUT) [6,7] model has been incorporated to assess the behavioral intention to use mobile banking [8]. In other research, the technology acceptance model (TAM) [9,10] has been used by combining risk and trust [11]. However, rare studies have been launched using an extended valence framework [12], suggesting a research gap. The extended valence framework incorporates three important variables—trust, perceived benefit, and perceived risk. These variables are found to have a strong impact on the behavioral intention to use [12,13]. Nevertheless, unlike other popular theories of technology adoption such as UTAUT, TAM does not consider variables such as trust and risk. Therefore, this study will suggest a new research framework to address the research gaps by utilizing EVF and UTAUT models and will propose a new research framework.
The objective of the study is to identify the drivers that influence the intention of Bangladeshi people to adopt mobile fintech services to perform transactions. Previous studies used different models to examine the underlying intention relating to the adoption of mobile payment services [14]. The significance of this paper is the new research framework combining (i) the unified theory of acceptance and use of technology (UTAUT) and (ii) an extended valence framework to explain the adoption intention towards mobile fintech services. In addition, the results of these findings will assist the MFS providers in understanding the consumers’ intention and responding accordingly. Thus, it will allow fintech companies to flourish in this field through obtaining an increased market share and to contribute to the boom of the fintech sector and the digitalization of services in the country.
The rest of the paper is organized into the following sections. Section 2 includes a literature review, research framework, and hypotheses development. Section 3 outlines the methodology followed by the findings, analysis, and discussion in Section 4. Section 5 includes the conclusions, theoretical and managerial contributions, limitations, and future research directions.
2. Literature Review
Financial technologies are some of the tremendous innovations in modern science, which attract customers through more user-friendly, transparent, efficient, and automated products and services [15]. According to Putritama [16], fintech products and services comprise asset management, financing, payments, and other fintech business models. The most popular form of fintech services that people are familiar with is mobile fintech services. Mobile fintech services are a technology that enables users to conduct financial transactions using smartphones or tablets [17,18]. Globally, the rate of fintech adoption has gone up to 64% around the world, and 96% of the consumers are informed about fintech services [19]. According to the EY Fintech adoption index, seventy-five (75) per cent of consumers use fintech services for money transfers and payments, which shows the extensive consumer adoption of fintech services [19]. Several studies have been conducted on fintech service adoption. Hasan et al. [20] evaluated the determinants of fintech adoption in the Netherlands. They concluded that trust, perceived ease of use, safety, and perceived usefulness are the dominant forces in mobile fintech adoption. In addition, Ali et al. [21] studied users’ Islamic fintech adoption in Pakistan and found that perceived risk and trust significantly impact adoption intention. Moreover, a study conducted in Indonesia found that user innovativeness directly and indirectly affects fintech adoption [22]. Vasenska et al. conducted a study in Bulgaria to determine the consumption of fintech services for financial transactions during COVID-19 [23].
The unified theory of acceptance and use of technology (UTAUT) was developed by Venkatesh et al. [6] to identify the technology adoption behavior of employees. Later, Venkatesh et al. [7] extended the model to describe the behavior of consumers toward technology adoption. The UTAUT model includes performance expectancy, social influence, effort expectancy, and facilitating conditions as the predicting variables for the behavioral intention to use and actual usage of technology. The UTAUT model has been utilized in different fields [24,25]. Hoque and Sorwar [26] developed a mobile health model. This concept was applied to home telehealth services [27]. The UTAUT model, on the other hand, is based solely on the context of organizations and does not take into account characteristics at the consumer level. Performance expectancy was found to drive the behavioral intentions or acceptability of technology in previous studies. The addition of appropriate constructs to this UTAUT model [28] can aid in the understanding of crucial phenomena linked to the use of technologies by customers in general. Alshare et al. analyzed the end-user intention to change expert systems by using a modified UTAUT model [29]. In addition, to measure the continuous adoption of an e-wallet application, a modified UTAUT2 model was utilized by incorporating trust [30]. Yohanes et al. also analyzed the enhanced user acceptance of fintech applications and revealed that effort expectancy, social influence, and facilitating conditions are strong predictors behind the fintech adoption [31]. Moreover, in a study based on a blockchain application in supply chain financing, a UTUAT model was used [11]. Even in a study on fintech adoption in small businesses, a UTUAT model was employed [32]. Furthermore, Ahmed et al. utilized an extended UTAUT model in a learning management system during COVID-19 [33]. The versatile use of UTAUT reflects the significance of the model. According to Dowdy [34], UTAUT is a casual, comprehensive model that describes and predicts technology adoption in various sectors and has been empirically validated for validity and reliability.
The extended valence framework (EVF) was developed by Kim et al. [12], which focused on technology adoption. The EVF incorporates three predicting variables: perceived trust, perceived benefit, and perceived risk. The study was conducted on electronic commerce adoption, mainly focusing on the trust-based decision-making model. The result revealed that all the factors significantly impact behavioral intention. Later, the model was utilized in different studies. Kim et al. conducted a longitudinal study using the EVF model to find successful e-commerce relationship [35]. The results showed that privacy concerns and risk are negatively related, whereas convenience and utilitarian value are positively related. In addition, in a study on the mobile application installation intention of consumers, the EVF was employed [5,36] utilized the extended valence framework to identify the factors behind mobile payment adoption. Moreover, Cui et al. integrated the valence framework and information success model to measure the seller’s acceptance of cross-border e-commerce [37]. Mou et al. utilized the valence framework to ascertain an international buyer’s repurchase decision in a Chinese cross-border e-commerce platform [38]. Furthermore, Chin et al. employed the EVF model to identify the intention of customers to adopt mobile payment systems [13].
Mobile fintech services in Bangladesh are proliferating. As an emerging country in Asia, there is much scope to increase the usage of mobile fintech services, which will help increase social and financial inclusion. However, to meet the user demand, the mobile fintech providers must understand the critical factors influencing customers’ intentions to adopt a technology. Based on the previous literature and industrial requirements, this study utilizes both the extended valence framework and the UTAUT model to identify the factors influencing Bangladeshi users’ intention to adopt mobile fintech services. In addition, scant studies have been conducted on mobile fintech services, but no prior studies have utilized the extended valence framework and UTAUT model together. This study proposes the below research framework in Figure 1.
2.1. Behavioral Intention
According to Fishbein and Ajzen [39], “A person’s subjective probability that he will undertake a behavior is termed as behavioral intention”. It can be defined as “the extent to which a person has made intentional arrangements to engage in or refrain from engaging in a specific future conduct” [40]. Customers’ adaption behavior to mobile wallet services has been studied using behavioral intention as a dependent variable in several models and theories [41,42]. This study will use the behavioral intention to use mobile fintech services as the outcome variable.
2.2. Perceived Trust
Trust is crucial in financial transactions, primarily when they are conducted using technology such as smartphones or tablets. Bisdikian et al. [43] defined trust as a situation in which one party (the trustor) is reliant on the actions of another (trustee). Perception is crucial in the client adoption of technology, such as mobile banking and mobile wallet services. Perceived trust positively impacts behavioral intention [44]. Nowadays, companies place a greater emphasis on developing good commercial connections to gain client trust. This impacts not only the behavioral intention but also the perceived risk and benefit. Customers will be less hesitant to adopt a technology if they believe in it. That is not to say that customers are not involved in dangerous technology adaptation behavior. Even in earlier studies, trust was found to have a positive relationship with perceived risk [45]. In addition, trust positively influences the perceived benefit [46]. This study suggests the following hypothesis based on the preceding discussion:
H1: Trust has a positive influence on behavioral intention.
H2: Trust has a positive influence on the perceived benefit.
H3: Trust has a positive influence on the perceived risk.
2.3. Perceived Risk
Risk is a barrier to technology adoption. Risk can be a crucial element in limiting users’ adoption of mobile fintech services. Based on a study on mobile wallet adoption behavior conducted by Al-Saedi and Al-Emran [47], the risk is negatively related to the intention to adopt. Studies have even used risk as a mediating variable between trust and behavioral intention [35,48]. Based on the understanding of the literature, the following hypothesis is proposed in this study:
H4: The perceived risk has a negative influence on behavioral intention.
2.4. Perceived Benefit
The perception of how a person will gain an advantage from using any product or service is referred to as the perceived benefit. According to Madan and Yadav [49], perception positively influences adoption intention. The study is based on a developing economy like Bangladesh, so the perceived benefit can be significant for the users to adopt mobile fintech services. In addition, a mediating role of the perceived benefit is found between trust and the behavioral intention to use [13]. The following hypothesis is proposed:
H5: The perceived benefit has a positive influence on behavioral intention.
2.5. Social Influence
Social influence can be defined as when “the important others (family, friends) think that a person should use a certain technology” [7]. A comparative study of users and non-users’ intentions to use m-banking found that social influence is positively related to intention to use [50]. A similar result was found in the case of banking technology service adoption [51,52].
Social influence has a positive influence on behavioral intention.
2.6. Facilitating Conditions
The facilitating conditions can be defined as the consumer’s perception of having resources and support available to perform an activity [7]. According to Utaminingsih and Alianto [53], the facilitating conditions positively impact the mobile wallet adoption intention. This is a vital variable influencing mobile technology adoption [54]. To perform mobile fintech transactions, a smartphone and Internet connection are considered as the facilitating conditions. A similar positive impact was found in earlier studies [55,56,57]. For this study, the following hypothesis is proposed:
H7: The facilitating conditions have a positive influence on behavioral intention.
2.7. Effort Expectancy
According to Venkatesh et al. [7], effort expectancy can be defined as the degree of ease the customer perceives during the use of the technology. A study on the intention to adopt direct mobile purchases using social media apps concluded that effort expectancy has a positive influence [58]. Different studies found similar results [59,60]. The following hypothesis is proposed:
H8: Effort expectancy has a positive influence on behavioral intention.
3. Methodology
This was a cross-sectional and quantitative study. The units of analysis were the users of mobile fintech services in Dhaka city. The data were collected using purposive sampling. In addition, this research employed a deductive approach. The research framework has been developed from the UTAUT model and the EVF. A questionnaire was created based on the framework to collect the respondents’ observations. The study was carried out using an explanatory research design. This research will uncover new information about the adoption intention of mobile fintech services in Bangladesh.
3.1. Data Collection
The study used primary data. Data were gathered via a survey questionnaire. The survey was administered using Google forms. The questionnaire included the measurement constructs of users’ perceptions of trust, risk, benefit, effort expectancy, facilitating conditions, social influence, and the intention to use mobile fintech services. The survey also gathered some basic demographic information. The measurement scales were redesigned from earlier studies. The measurement instruments of risk, trust, and benefit were adapted from earlier studies conducted on mobile technology adoption [13]. Moreover, the measurement items of behavioral intention, social influence, effort expectancy, and facilitating conditions were adapted from the technology adoption literature [61,62]. All of the attitude items were evaluated using a seven-point Likert scale ranging from “strongly disagree” to “strongly agree” [63]. To assess the constructs’ reliability and validity, pilot research was conducted with 50 multi-national personnel. The average variance extracted (AVE) was more significant than 0.50, and Cronbach’s alpha was greater than 0.70. As a result, no adjustments to the measurement items were required. The survey questionnaire was distributed to university students and MNC professionals. According to Barclay et al. [64], the sample should be, “Ten times the largest number of formative indicators used to measure one construct” or “ten times the largest number of inner model paths directed at a particular construct in the inner model”, whichever is higher. Finally, a sample of 218 responses was collected. The survey questionnaire is placed in the Appendix A of the paper.
3.2. Data Analysis
To perform the analysis, structural equation modelling was employed. The PLS-SEM approach is most suited to this scenario because the study is exploratory and predictive [65]. The study used SmartPLS 3.0 software to run the data analysis. According to Hair et al. [66], the SmartPLS software can provide excellent results for small data samples. The data analysis was divided into two sections, including a measurement assessment and structural assessment.
4. Findings and Analysis
Table 1 demonstrates the demographic characteristics of the respondents.
According to the demographic data, more than half of the respondents were male (59.17%) and majority of the respondents were aged between 18 and 34 (84.86%). More than half of the respondents (55.04%) were graduates, followed by 36.70 per cent being postgraduate respondents. Regarding ownership, most respondents (90.82 per cent) said they had a mobile fintech account. Regarding MFS usage, 48.15 per cent of respondents said they used mobile fintech services sometimes, while another 38.98 per cent said they use MFS frequently.
4.1. Measurement Model
The first step of a measurement model assessment is to create the research framework by creating a new project. Then, the data are extracted from the excel file (csv) file. After constructing the path model initially, the path analysis is conducted to check the reliability and validity of the model. According to Hair et al. [67], the recommended value for the indicator loadings should be more than 0.708. Another important aspect to check is the internal consistency reliability. Two indicators that can verify the internal consistency reliability are Cronbach’s alpha and the composite reliability. The recommend value for the constructs is 0.70 [67]. The next phase is to check the convergent validity. The average variance extracted is the indicator used to verify the validity. The construct score should be more than 0.50 to meet the requirements. The final phase is to check the discriminant validity. Three different techniques are used to verify the discriminant validity, including the Fornell–Larcker criterion, cross-loading, and heterotrait–monotrait (HTMT) ratio. The Fornell–Lacker criterion explains whether the square root of the AVE of a particular construct is greater than other constructs [68]. For cross–loadings, a construct’s own parent construct should have higher loadings than with other constructs. In addition, Henseler et al. suggested that the threshold value for conceptually related constructs in structural models is 0.90, while 0.85 is the suggested threshold for conceptually separate constructs. A higher value indicates a lack of discriminant validity [69].
As per Table 2, the loadings of the construct are greater than 0.708. In addition, Table 3 demonstrates that all constructs show values of more than 0.70 for Cronbach’s alpha and the composite reliability. Similar results were found in earlier technology adoption studies [61,62]. Moreover, the AVE values for all constructs are greater than 0.50, which indicates that the model meets the reliability and convergent validity requirements.
Table 4 indicates that all the diagonal values are higher for a particular construct than for other constructs, which meets the Fornell–Lacker criterion. In addition, in most cases the HTMT values of the conceptually related constructs are less than 0.90. However, FC-BI, FC-EE, PB-EE, PB-FC, and PT-FC show higher HTMT values in Table 5, which indicate that no discriminant validity exists between these conceptually related constructs.
4.2. Structural Model Assessment
By using the explanatory power and statistical significance of the path coefficients, the structural model used in the study was evaluated. Before evaluating the structural model, it was necessary to evaluate the multicollinearity among the constructs. According to Hair et al. [66], the VIF values should be less than 5 to show no multicollinearity. Table 6 illustrates that all VIF values meet the criteria.
To test the model’s explanatory capacity, the coefficient of determination (R2) for all endogenous constructs was calculated. The coefficient of determination (R2) results in Table 7 clearly suggest that the model has moderate explanatory power that can explain the 62.6% variance in intention to use mobile fintech services. However, the explanatory power of the perceived risk is only 7.9%, which means that trust of the people in Bangladesh in technology adoption intention can explain only around 7.9% of the variability in perceived risk.
Result of T-values from SmartPLS are indicated in Figure 2. Table 8 shows that six hypotheses are supported, while the other two are rejected. The hypotheses’ acceptance depends on their p-values. If the p-value is less than 0.5, then the hypothesis is accepted. From Table 8, it can be concluded that H1, H2, H3, H5, H6, and H7 are all accepted because the p-values are below 0.05. However, hypotheses H4 and H8 are rejected because the p-values are greater than 0.05.
4.3. Post Hoc Analysis
The post hoc analysis showed the specific indirect effects of the constructs. As per Table 9, there exists a mediating effect of the perceived benefit, as the p-value is less than 0.05. Therefore, the mediating effect of the perceived benefit exists between trust and the behavioral intention to use. However, the result shows that the perceived risk has no mediating effect.
4.4. Discussion
As an emerging market in Asia, Bangladesh ranked 78th among 83 countries in the Global Findex rankings. This means that Bangladesh is still behind in adopting new technologies, like other emerging countries. However, the number of users of mobile phones has increased significantly. As of August 2021, Bangladesh had 178.61 million mobile phone subscribers. In addition, the Internet user penetration rate is 31.5% in Bangladesh [70]. The increased users of mobile phones and the Internet have enabled fintech companies and banks to offer new platforms for customers to perform daily transactions through their mobile phones. Due to the benefits of the mobile fintech technologies, the number of users of mobile fintech apps is increasing. Mobile fintech services are now used for different purposes in Bangladesh, ranging from sending and receiving money to paying medical bill.
The primary objective of this study was to identify the drivers that influence the adoption intention of mobile fintech services in Bangladesh. We proposed a research framework by combining the EVF and UTAUT models.
The study’s outcomes indicate that consumer perceptions of trust positively and significantly affect their inclination to adopt MFS. These findings are similar to previous studies [13,36]. It is interesting to note that the perceived risk does not affect the behavioral intention. As a result, users are less hesitant to use mobile fintech service platforms. One of the key causes is a lack of understanding of the risks associated with using mobile fintech service applications. However, the findings align with those of other research conducted in emerging nations [71,72]. In addition, the perceived benefit positively influences the behavioral intention. Jun et al. found similar results [73]. This implies that users are more concerned with the perks of the mobile fintech service than the drawbacks. In addition, the facilitating conditions show a strong positive impact on behavioral intention, as the required technology, Internet connection, and call center support are essential for using mobile fintech platforms. This result is consistent with the earlier studies [74,75]. MFS providers should focus on expanding their additional offerings to improve their clients’ perceptions of value. Additionally, the perceived benefit is a mediator between perceived trust and behavioral intention.
The result for effort expectancy shows no impact, which is consistent with the result from another study [13]. This is a contrasting outcome, as effort expectancy was found to positively impact behavioral intention in most investigations [76]. The reason behind this is the availability of MFS agent shops, where a person can efficiently conduct transactions without interacting with fintech technology. On the other hand, social influence has a strong positive correlation with behavioral intention. Like most previous studies, the studies carried out by Al-Nawayseh [61] and Liu et al. [77] confirmed the effect of social influence on user behavior. People in emerging nations such as Bangladesh place high importance on their family, friends, coworkers, and peers; hence, their opinions are essential in order to purchase a product or service. The below Table 10 shows the summary of the results of the hypotheses analyzed in the study.
5. Conclusions
The growth of mobile fintech services in Bangladesh has been noteworthy recently, especially after the outbreak of the novel coronavirus. People around the country are getting used to new technologies such as fintech services. Almost every sector is now utilizing mobile fintech service applications to perform financial transactions, ranging from salary disbursement to utility payments. This study aimed to determine the factors influencing the consumers’ intention to adopt mobile fintech applications. To determine these factors, this study proposed a combined research framework by utilizing two models, EVF and UTAUT. According to the structural equation modelling analysis, consumers’ intentions to use MFS are influenced by several factors. The perceived benefits have a significant positive influence because consumers focus on the benefits, they can get from using a new technology. In addition, social influence showed a positive influence, as people are inspired by their family, peers, and friends’ opinions. Moreover, the facilitating conditions also demonstrated a significant positive impact, since the available resources such as smartphones and tablets and the MFS organizations’ contact center services are crucial to performing smooth financial transactions. Finally, perceived trust significantly positively impacts the intention towards MFS adoption. The trust towards MFS providers plays a significant role in online financial transactions using mobile technology. However, the perceived risk and effort expectancy did not influence the behavioral intention to use MFS. Furthermore, the post hoc analysis revealed that the perceived benefit mediates the relationship between the perceived trust and intention to utilize MFS. Mobile fintech services are expected to grow substantially in Bangladesh soon. Therefore, the MFS providers must work on the consumer expectations and preferences to boost the acceptance of mobile fintech services.
5.1. Theoretical Contribution
The major theoretical contribution of this study is the combined approach proposed to explore the drivers behind technology adoption intention. In the past, many authors had adopted, adapted, or modified the UTAUT model to facilitate their studies. However, few studies were found where the EVF framework had been combined with the UTAUT model. This study has contributed to the literature on the unified theory of acceptance and use of technology and extended the valence framework by integrating these two models. This approach can be used in future research to identify the adoption intention towards any technology.
5.2. Managerial Implications
Digital transformation is required in this era of the 4th industrial revolution. This is possible only when the people in a country are willing to adopt new technologies. The results of this study are important for both the mobile fintech service providers and designated government agencies. The mobile fintech providers should focus on the added benefits and required support services offered to customers. In addition, adequate promotion should be placed around product and service awareness. Most importantly, trust towards the service should be ensured to motivate people to adopt fintech services. Moreover, government agencies can foster the adoption of mobile fintech services among the customers by forcing people to make government0related payments through mobile fintech applications, which result in the greater adoption of mobile fintech services.
5.3. Limitations and Future Research
This study collected data from Dhaka city, especially from university students and MNC professionals currently using mobile fintech services. The major drawback of this study is the use of a small sample only from Dhaka city. This limits the generalization of the research findings across diverse demographics and geographic places in Bangladesh. In addition, the study only considered the variables from the UTAUT and EVF models. Future research studies could consider using one or two exogenous variables as moderating or mediating variables, which could display new dimensions of technology adoption intention.
Conceptualization, M.S.H., M.A.I. and H.N.; methodology, M.S.H. and I.M.; software, M.S.H. and F.T.Z.; validation, F.A.S., M.A.I. and H.N.; formal analysis, M.S.H. and I.M.; investigation, F.A.S. and F.T.Z.; resources, F.A.S.; data curation, H.N.; writing—original draft preparation, M.S.H., I.M. and F.T.Z.; writing—review and editing, M.A.I., F.A.S. and H.N.; visualization, F.T.Z.; supervision, M.A.I. and H.N.; project administration, M.S.H. and F.A.S. All authors have read and agreed to the published version of the manuscript.
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The authors declare no conflict of interest.
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Demographic profile.
| Demographic Variable | Frequency | Percentage |
|---|---|---|
| Gender | ||
| Male | 129 | 59.17 |
| Female | 89 | 40.83 |
| Total | 218 | 100.0 |
| Age | ||
| 18–24 | 85 | 38.99 |
| 25–34 | 100 | 45.87 |
| 35–44 | 29 | 13.31 |
| 45 and above | 4 | 1.83 |
| Total | 218 | 100.0 |
| Academic Qualification | ||
| Secondary | 0 | 0 |
| Higher Secondary | 18 | 8.26 |
| Graduate | 120 | 55.04 |
| Postgraduate | 80 | 36.70 |
| Total | 218 | 100.0 |
| Ownership of MFS Account | ||
| Yes | 198 | 90.82 |
| No | 20 | 9.18 |
| Total | 218 | 100.0 |
| Usage of MFS | ||
| Never | 8 | 3.70 |
| Sometimes | 105 | 48.15 |
| Frequently | 85 | 38.98 |
| Always | 20 | 9.18 |
| Total | 218 | 100.0 |
Outer loadings values of the measurement construct.
| BI | EE | FC | PB | PR | PT | SI | |
|---|---|---|---|---|---|---|---|
| BI1 | 0.851 | ||||||
| BI2 | 0.890 | ||||||
| BI3 | 0.865 | ||||||
| EE1 | 0.848 | ||||||
| EE2 | 0.848 | ||||||
| EE3 | 0.798 | ||||||
| FC1 | 0.759 | ||||||
| FC2 | 0.850 | ||||||
| FC3 | 0.790 | ||||||
| PB1 | 0.857 | ||||||
| PB2 | 0.813 | ||||||
| PB3 | 0.763 | ||||||
| PR2 | 0.791 | ||||||
| PR3 | 0.948 | ||||||
| PT1 | 0.880 | ||||||
| PT2 | 0.861 | ||||||
| PT3 | 0.905 | ||||||
| SI1 | 0.892 | ||||||
| SI2 | 0.906 | ||||||
| SI3 | 0.845 |
Construct reliability and validity values.
| Cronbach’s Alpha | Composite Reliability | Average Variance Extracted (AVE) | |
|---|---|---|---|
| BI | 0.838 | 0.903 | 0.755 |
| EE | 0.776 | 0.870 | 0.691 |
| FC | 0.721 | 0.842 | 0.641 |
| PB | 0.740 | 0.853 | 0.659 |
| PR | 0.714 | 0.864 | 0.762 |
| PT | 0.858 | 0.913 | 0.778 |
| SI | 0.856 | 0.913 | 0.777 |
Discriminant validity and average variance extracted (AVE) values.
| BI | EE | FC | PB | PR | PT | SI | |
|---|---|---|---|---|---|---|---|
| BI | 0.869 | ||||||
| EE | 0.634 | 0.831 | |||||
| FC | 0.738 | 0.760 | 0.800 | ||||
| PB | 0.713 | 0.744 | 0.782 | 0.812 | |||
| PR | 0.291 | 0.182 | 0.291 | 0.259 | 0.873 | ||
| PT | 0.660 | 0.543 | 0.745 | 0.610 | 0.282 | 0.882 | |
| SI | 0.565 | 0.443 | 0.500 | 0.541 | 0.242 | 0.485 | 0.881 |
Discriminant validity and heterotrait–monotrait (HTMT) values.
| BI | EE | FC | PB | PR | PT | SI | |
|---|---|---|---|---|---|---|---|
| BI | |||||||
| EE | 0.786 | ||||||
| FC | 0.937 | 1.018 | |||||
| PB | 0.905 | 0.979 | 1.069 | ||||
| PR | 0.348 | 0.231 | 0.379 | 0.322 | |||
| PT | 0.774 | 0.661 | 0.932 | 0.760 | 0.331 | ||
| SI | 0.664 | 0.541 | 0.622 | 0.681 | 0.278 | 0.562 |
Values of variance information (VIF).
| VIF | |
|---|---|
| BI1 | 1.872 |
| BI2 | 2.256 |
| BI3 | 1.910 |
| EE1 | 1.782 |
| EE2 | 1.758 |
| EE3 | 1.425 |
| FC1 | 1.427 |
| FC2 | 1.531 |
| FC3 | 1.346 |
| PB1 | 1.699 |
| PB2 | 1.524 |
| PB3 | 1.357 |
| PR2 | 1.444 |
| PR3 | 1.444 |
| PT1 | 2.007 |
| PT2 | 2.131 |
| PT3 | 2.458 |
| SI1 | 2.306 |
| SI2 | 2.717 |
| SI3 | 1.859 |
Coefficient of determination (R2) values.
| R Square | R Square Adjusted | |
|---|---|---|
| BI | 0.626 | 0.618 |
| PB | 0.372 | 0.369 |
| PR | 0.079 | 0.075 |
Path coefficient values.
| Original Sample (O) | Sample Mean (M) | Standard Deviation (STDEV) | T Statistics (|O/STDEV|) | p Values | |
|---|---|---|---|---|---|
| EE -> BI | 0.061 | 0.060 | 0.086 | 0.713 | 0.476 |
| FC -> BI | 0.385 | 0.387 | 0.076 | 5.027 | 0.000 |
| PB -> BI | 0.244 | 0.245 | 0.077 | 3.158 | 0.002 |
| PR -> BI | 0.056 | 0.055 | 0.042 | 1.326 | 0.185 |
| PT -> PB | 0.610 | 0.610 | 0.040 | 15.434 | 0.000 |
| PT -> PR | 0.282 | 0.286 | 0.066 | 4.239 | 0.000 |
| SI -> BI | 0.200 | 0.202 | 0.051 | 3.898 | 0.000 |
Indirect effects.
| Original Sample (O) | Sample Mean (M) | Standard Deviation (STDEV) | T Statistics (|O/STDEV|) | p Values | |
|---|---|---|---|---|---|
| PT -> PB -> BI | 0.149 | 0.149 | 0.049 | 3.054 | 0.002 |
| PT -> PR -> BI | 0.016 | 0.016 | 0.013 | 1.187 | 0.236 |
Summary of hypotheses results.
| Hypothesis | Results | |
|---|---|---|
| H1 | Trust has a positive influence on behavioral intention | Supported |
| H2 | Trust has a positive influence on perceived benefit | Supported |
| H3 | Trust has a positive influence on perceived risk | Supported |
| H4 | The perceived risk has a negative influence on behavioral intention | Not Supported |
| H5 | The perceived benefit has a positive influence on behavioral intention | Supported |
| H6 | Social influence has a positive influence on behavioral intention | Supported |
| H7 | The facilitating conditions have a positive influence on behavioral intention | Supported |
| H8 | Effort expectancy has a positive influence on behavioral intention | Not supported |
Appendix A
Survey questionnaire.
| Variables | Measurement Constructs |
|---|---|
| Behavioral Intention to adapt MFS (BI) | BI1: I intend to adopt mobile fintech service in the future. |
| Perceived Benefit (PB) | PB1: Using Mobile Fintech Services has many advantages |
| Perceived Trust (PT) | PT1: I trust MFS systems to be reliable. |
| Perceived Risk (PR) | PR1: Using Mobile Fintech Services is associated with a high level of risk. |
| Social Influence (SI) | SI1: People who are important to me think that I should use Mobile Fintech Services. |
| Effort Expectancy (EE) | EE1: It is easy for me to understand the operation of Mobile Fintech Services |
| Facilitating Conditions (FC) | FC1: I have necessary resources to use Mobile Fintech Services |
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Details
; Islam, Md Aminul 2
; Sobhani, Farid Ahammad 3 ; Hussen Nasir 2 ; Mahmud, Imroz 4
; Fatema Tuz Zahra 5 1 Faculty of Applied and Human Sciences, Universiti Malaysia Perlis (UniMAP), Kangar 01000, Perlis, Malaysia;
2 Faculty of Applied and Human Sciences, Universiti Malaysia Perlis (UniMAP), Kangar 01000, Perlis, Malaysia;
3 School of Business and Economics, United International University, Dhaka 1212, Bangladesh;
4 Department of Business Administration, University of Asia Pacific, Dhaka 1205, Bangladesh;
5 Department of MIS, University of Dhaka, Dhaka 1000, Bangladesh;