This study extends the technology acceptance model (TAM) to identify factors impacting the ongoing use of electronic payment (e-payment) systems in Bangladesh in order to create a cashless society.
Data were acquired from 442 Bangladeshi e-payment consumers. Structural equation modeling (SEM) and confirmatory factor analysis (CFA) were used to investigate the correlations between the suggested variables and user behavioral intentions.
Trust, security, individual mobility and conducive conditions all have a substantial impact on perceived utility and ease of use. Along with social impact, these impressions strongly predict consumers’ long-term desire to utilize e-payment systems, which then affects actual use. Furthermore, self-efficacy considerably modifies the link between continuous use intention and actual usage.
This study expands on existing e-payment work by incorporating more components into TAM, resulting in a more comprehensive understanding of behavioral drivers in developing nations. The findings provide practical insights for e-payment service providers looking to boost user confidence, bolster trust and security measures and increase adoption through increased user assistance and training.
1. Introduction
The digital revolution has significantly altered how people conduct their daily and economic operations, making technology adoption an urgent management and societal priority. The world is evolving toward a civilization in which people choose to use technology. The growing usage of wireless devices, communication networks and technical breakthroughs has resulted in the incorporation of gadgets like the Internet of Things (IoT) into our everyday life. The Internet of Things has emerged as a critical component of the Industry 4.0 digital age (Laghari et al., 2021). Such technology spread has transformed not only industries but also consumer behavior and business models, forcing firms to reconsider how they give value and convenience via digital platforms.
IoT networks have a wide range of applications, including monitoring houses and tracking patients' health on a regular basis. By leveraging data processing and analytics capabilities, IoT technology allows physical devices in the real world to make informed decisions using the internet’s power. Essentially, it functions as a system that connects intelligent devices known as “Things” that use the internet as a platform to acquire and exchange information (Sadhu et al., 2022). A larger digital ecosystem that includes sectors like e-commerce, financial technology (FinTech) and e-payment systems has emerged as a result of this technological integration (Sadhu et al., 2022). The development of information and communication technology (ICT), which provides strong instruments for improving corporate operations, is a result of the globalization of the Internet and technology. FinTech, which connects digital solutions with financial services, is one of the most revolutionary developments in ICT. As a subset of FinTech, e-payments entail sending money and information online and provide many benefits over conventional payment methods (Ahmad et al., 2021). It supports more general economic objectives including financial inclusion and cashless economies while improving transaction speed, transparency and inclusivity (Chen et al., 2022). For service providers and governments, knowing what motivates ongoing e-payment use is essential for maintaining digital transformation. Even with the rise in user adoption, long-term engagement and retention are still managerial obstacles that have a direct impact on customer trust and service profitability.
While Bangladesh's digital payment ecosystem is fast expanding, powered by a boom in fintech innovation and government-backed cashless efforts, one important overlook remains: what motivates users to stick with e-payment systems in the long run is unknown. Existing studies are narrowly focused on individual elements such as trust or security, ignoring the interaction of behavioral, technological and sociocultural influences. This prevents policymakers and providers from gaining strategic insights about long-term user engagement. Per the research voids stated earlier, we endeavored to reach the subsequent research questions (RQs):
How likely are Bangladeshi consumers to use e-payment systems in the future?
What variables influence Bangladeshi consumers’ willingness to use e-payment systems?
How does self-efficacy moderate the perceived usefulness and ease of use to continue the intention to use e-payment systems?
As a result, the purpose of this study is to identify and analyze the factors that contribute to ongoing e-payment usage and technical adoption, thereby assisting Bangladesh’s transition to a cashless society. The study modifies the technology acceptance model (TAM) by including crucial variables such as trust, security, individual mobility, facilitating conditions, perceived usefulness, perceived ease of use, social influence, continuous intention, actual use and self-efficacy.
We make the following contributions to the existing corpus of knowledge. First, we incorporate environmental and behavioral factors that are pertinent to emerging economies into the conventional TAM paradigm. Second, we present empirical data from Bangladesh, a context that is rapidly digitizing but has received little attention, highlighting the variables that affect users’ long-term intents to utilize electronic payments. Third, in order to improve user retention tactics and promote a sustainable cashless ecosystem, this study provides useful advice for financial institutions, FinTech companies and legislators.
With one of the fastest-growing digital economies in South Asia, Bangladesh is the focus of this study. The government strongly supports cashless efforts, but there are still infrastructure and digital literacy gaps. Researching this setting yields knowledge that can be used in other developing economies going through comparable changes. This paper’s remaining sections are organized as follows: The literature review and hypothesis development are presented in Section 2, the research methodology is explained in Section 3, the data analysis and results are reported in Section 4, and the implications, limits, and future research directions are covered in Section 5.
2. Literature review
2.1 Theoretical foundation of the study
2.1.1 Extended technology acceptance model (ETAM)
The extended technology acceptance model (ETAM) serves as the foundation for this study and expands upon Davis’s initial technology acceptance model (TAM) (1989). In this study, ETAM provides a complete structure for investigating users’ acceptance and continued use of e-payment systems in Bangladesh. It incorporates new variables – trust (T), security (S), individual mobility (IM), facilitating conditions (FC) and self-efficacy (SE) – that have been identified in recent digital payment literature as crucial to technology adoption and usage. By combining these variables, the ETAM framework captures both the technological and psychological elements that influence consumers’ behavioral intents to adopt e-payment. The technology acceptance model (TAM) explains technology acceptance and adoption. The original TAM identified perceived usefulness and ease of use as key factors. TAM 2 added perceived enjoyment and TAM 3 included trust as a key factor influencing user acceptance. These models were proposed by Davis in 1989, Venkatesh and Davis in 2000 and Venkatesh and Bala in 2008, respectively.
This theoretical lens contributes to the study design by explaining how users generate perceptions of e-payment systems and how those perceptions transfer into continuous intention (CI) and actual usage (AU). Furthermore, self-efficacy (SE) is incorporated as a moderating variable, indicating users’ confidence in their capacity to effectively use e-payment systems and thereby improving the model’s predictive validity. Overall, ETAM provides a solid theoretical foundation that connects individual beliefs, environmental conditions and behavioral goals to explain technology acceptance and utilization.
2.2 Definitions of e-payment
According to Nadler et al. (2019), e-payment emerged in the late 1950s in the US as a response to the need for non-cash payment methods and has evolved with technology to allow for selling products and services electronically without time or space limitations. An e-payment infrastructure includes various parties such as customers, merchants, banking institutions, payment service providers, security and authentication providers and internet service providers. The definition of e-payment has evolved over time. Shon and Swatman (1998) define it broadly as transferring money electronically, while Gans and Scheelings (2008) define it more narrowly as payments linked to deposit or credit accounts. In Bangladesh, popular e-payment systems include mobile payment, internet banking, electronic wallets and national payment switches.
2.3 Cashless society
Balakrishnan and Shuib (2021) define a cashless society as a fintech innovation aligned with the Fourth Industrial Revolution (IR 4.0), wherein transactions are carried out through digital cards or electronic devices, thereby creating a culture that relies on digital means for purchasing goods and services. The core idea of a cashless society, as stated by Rahadi et al. (2022), is centered on transactions made using e-payment instruments. However, this does not imply that cash transactions are nonexistent in the economic environment, but rather that they are kept to a minimum. Similarly, other definitions align with the previous ones, stating that a cashless society is one where cash-based transactions make up a minimal portion of the overall economic transactions.
2.4 Actual use (AU)
AU is the time an individual spends using a technology. Huang (2020) adds that an individual's intention to continue using an information system depends on their experience with it. Acceptance is measured in technology acceptance and use models by linking behavioral intention and use. Current research focuses on predicting AU through continuous intention (CI). This study examines the effect of CI on e-payment systems’ AU and business success and explores factors influencing customer perceptions of e-payment services (Davis et al., 1989). Figure 1 illustrates the proposed model.
The flowchart shows four text boxes on the left arranged in a vertical series from top to bottom and labeled as follows: “Trust,” “Security,” “Individual Mobility,” and “Facilitating Condition.” Rightward arrows extend from each of these four boxes to a text box at the center labeled “Perceived Usefulness,” via “H 1 a,” “H 2 a,” “H 3 a,” and “H 4 a.” More rightward arrows labeled “H 1 b,” “H 2 b,” “H 3 b,” and “H 4 b” extend from each of these four text boxes to a text box at the bottom center below “Perceived Usefulness,” labeled “Perceived Ease of Use.” A text box at the top center is labeled “Social Influence,” with a downward arrow labeled “H 5” pointing to “Perceived Usefulness.” Another arrow labeled “H 6” extends from “Social Influence” and points to a text box labeled “Continuous Intention” at the center right. Rightward arrows labeled “H 7” and “H 8” extend from “Perceived Usefulness” and “Perceived Ease of Use” to “Continuous Intention.” “Continuous Intention” connects with an arrow labeled “H 9” leading to the final box on the far right labeled “Actual Use.” A text box at the bottom center is labeled “Self-Efficacy.” Two upward arrows labeled “H 10” and “H 11” point to the arrows “H 7” and “H 8,” respectively.Proposed framework
The flowchart shows four text boxes on the left arranged in a vertical series from top to bottom and labeled as follows: “Trust,” “Security,” “Individual Mobility,” and “Facilitating Condition.” Rightward arrows extend from each of these four boxes to a text box at the center labeled “Perceived Usefulness,” via “H 1 a,” “H 2 a,” “H 3 a,” and “H 4 a.” More rightward arrows labeled “H 1 b,” “H 2 b,” “H 3 b,” and “H 4 b” extend from each of these four text boxes to a text box at the bottom center below “Perceived Usefulness,” labeled “Perceived Ease of Use.” A text box at the top center is labeled “Social Influence,” with a downward arrow labeled “H 5” pointing to “Perceived Usefulness.” Another arrow labeled “H 6” extends from “Social Influence” and points to a text box labeled “Continuous Intention” at the center right. Rightward arrows labeled “H 7” and “H 8” extend from “Perceived Usefulness” and “Perceived Ease of Use” to “Continuous Intention.” “Continuous Intention” connects with an arrow labeled “H 9” leading to the final box on the far right labeled “Actual Use.” A text box at the bottom center is labeled “Self-Efficacy.” Two upward arrows labeled “H 10” and “H 11” point to the arrows “H 7” and “H 8,” respectively.Proposed framework
2.5 Hypothesis
2.5.1 Trust (T)
T is a critical determinant of user behavior in service contexts, particularly in mitigating uncertainty during transactions. Grounded in Ajzen's (1991) theory of planned behavior, trust shapes behavioral intentions by influencing attitudes and perceived control. In digital environments like e-payment systems, To and Trinh (2021) highlight trust’s amplified role in overcoming user apprehensions about security and reliability. Empirical studies further reinforce this relationship: Al-Sabaawi et al. (2021) demonstrated that T enhances users’ perceptions of a system’s PU and PEOU, key components of the TAM. Synthesizing these insights, this study posits that T serves as a foundational precursor to user acceptance of e-payment platforms, thereby bridging theoretical frameworks and offering actionable insights into user adoption drivers.
Trust influences perceived usefulness (H1a) and perceived ease of use (H1b).
2.5.2 Security (S)
The S of e-payment systems is crucial in influencing consumers’ decision to use them (Karim et al., 2020). E-payment systems offer additional S measures, such as requiring a PIN or OTP, to mitigate risks associated with carrying cash and cards. S can be categorized into legal security, system security and transaction security (Alshannag et al., 2022). E-payment can be considered secure only when it meets the customer’s security expectations by ensuring a safe and secure process. PU and PEOU are essential for the e-payment system S. They enhance the system’s value and usability, helping to mitigate risks associated with traditional payment methods. This relationship fosters user confidence and encourages the adoption of e-payment solutions.
Security influences perceived usefulness (H2a) and perceived ease of use (H2b).
2.5.3 Individual mobility (IM)
IM is a field of study that focuses on how specific individuals move within a given network or system (Hidayat et al., 2021). The potential for consumers to use e-payment services anytime and anywhere through mobile devices is believed to be an essential factor that may influence their intention to use such services (To and Trinh, 2021). According to the theory that individual mobility affects PU and PEOU, when customers have greater mobility and freedom to use e-payment systems, they are more likely to view them as useful. Individual mobility in the context of e-payment systems is closely linked to the hypotheses of PU and PEOU, as it can improve users’ experiences and aid their adoption and use of these services.
Individual mobility has an impact on perceived usefulness (H3a) and perceived ease of use (H3b).
2.5.4 Facilitating conditions (FC)
The presence of supportive circumstances, such as well-equipped infrastructure and available resources, can encourage the adoption and usage of e-payment systems. Research has found a link between these FC and an increased intention to use e-payment systems (Rahadi et al., 2022; Hossain et al., 2021). When users have access to the necessary infrastructure and resources, they may view e-payment systems as more useful and easier to use (Ebadi and Raygan, 2023). Conditions that facilitate the adoption and use of e-payment systems are critical for improving customer experience and facilitating the adoption and use of e-payment systems. Users’ PU and PEOU of e-payment systems can be favorably influenced when they have access to the necessary infrastructure and resources, eventually increasing their desire to use them.
Facilitating conditions have an impact on perceived usefulness (H4a) and perceived ease of use (H4b).
2.5.5 Social influences (SI)
SI refers to societal pressures on technology adoption. Rahadi et al. (2022) recognize it as a critical component in explaining technology adoption behavior. The opinions and beliefs of significant others can influence an individual’s participation in an activity. Social impact refers to the degree to which a person believes significant others think they should use technology. Research has shown that social pressure can influence behavior and views (Fishbein and Ajzen, 1975). When people lack personal experience with a technology, they may consult their social circle for guidance. Understanding the effect of SI on user perceptions and behavior is therefore critical in promoting the adoption and ongoing use of e-payment systems. By understanding how significant others’ opinions and beliefs can shape users' perceptions, service providers can develop strategies to leverage SI to promote positive attitudes and CI towards e-payment systems.
Social influences have an impact on perceived usefulness (H5) and continuous intention (H6).
2.5.6 Perceived usefulness (PU)
PU pertains to the extent to which individual ascertains that utilizing a particular technology will augment their occupational proficiency. Research indicates that PU positively impacts user adoption of electronic remuneration methods (Alswaigh and Aloud, 2021). TAM has suggested that an individual’s perception of a technology’s usefulness is based on their belief in its ability to improve their performance or productivity (Davis et al., 1989). Research has shown that PU significantly impacts an individual’s motivation to use technology. This research aims to explore the correlation between PU and CI.
Perceived usefulness has an impact on continuous intention (H7).
2.5.7 Perceived ease of use (PEOU)
PEOU pertains to the user’s assurance in the degree of operability of a specific technology. As per copious research, PEOU is an integral component in ascertaining user acceptance and adoption of electronic remuneration systems. These studies indicate that PEOU has a positive and significant impact on users’ inclinations to utilize electronic remuneration systems (Alswaigh and Aloud, 2021). PEOU variable is considered in this research because it is an essential determinant of users’ acceptance and adoption of e-payment systems. PEOU is the degree to which an individual believes using a specific technology is simple and easy. Individuals are more likely to embrace and use a system if it is simple. Users are less likely to adopt and may resist using a system if viewed as complicated or difficult. Understanding the role of PEOU in shaping users’ attitudes toward e-payment systems is critical for promoting the adoption and ongoing use of this technology.
Perceived ease of use has an impacts continuous intention (H8).
2.5.8 Continuous intention (CI)
The concept of CI applies to a person’s propensity to use and accept new technology. There is a well-established correlation between user attitude and conduct in the instance of e-payment adoption. Given this definition, e-payment continuation can be defined as the user’s CI to engage in and continue using e-payment systems. Several factors have been identified in a previous study that predict users’ intentions to continue using information systems (Chen et al., 2022). Because CI is such an important element of technology adoption and usage, it is used as a variable. Adoption of e-payment systems is a continuous process that needs user participation and support. Understanding users’ intentions to continue using e-payment systems is therefore critical for supporting the technology’s long-term viability. CI represents users’ beliefs and opinions about the PU, PEOU and SI associated with e-payment systems, all of which are important in shaping users’ attitudes and behavior toward the technology. Researchers can obtain insights into how to promote the sustained use of e-payment systems and improve their adoption rates by studying the factors that affect CI.
Continuous intention has an impact on Actual Usage (H9).
2.5.9 Self-efficacy (SE)
SE pertains to an individual’s conviction in their capacity to proficiently execute a particular behavior (Bandura, 1986). He suggests that an individual’s perception of their abilities can increase the likelihood of successfully completing a task. In the context of new technology systems, individuals with high SE may perceive a system as easier to use and have a higher intention to adopt it fully. SE is used as a moderator in the correlation between PU and PEOU with e-payment CI because it influences how users view and engage with technology. In the research, SE is utilized as a moderator to provide insight into the influence of users’ perceptions of their ability to use e-payment systems on the relevance between PU, PEOU and CI. By examining SE in this manner, researchers can obtain valuable data that can inform the development of strategies aimed at increasing the adoption and retention of e-payment systems.
The relationship between Perceived Usefulness (H10) and Perceived ease of use (H11) and Continuous intention to use e-payment systems are stronger for user’s high self-efficacy than for users with low self-efficacy.
3. Methodology
3.1 Research design
This study examines the elements that drive Generation Z’s long-term acceptance of e-payment systems and cashless preferences, which are evaluated using the extended technology acceptance model. Trust (T), security (S), individual mobility (IM), facilitating conditions (FC), perceived usefulness (PU), perceived ease of use (PEOU), social influence (SI), self-efficacy (SE), continuous intention (CI) and actual usage (AU) are all important criteria. Building on TAM’s central premise that ease of use and utility impact behavioral attitudes, ETAM broadens this reasoning by including other psychological and contextual aspects to better explain prolonged technology use.
In this study, continuous intention (CI) refers to the user’s continued willingness to continue using an e-payment system, whereas actual usage (AU) represents the actual behavioral outcome of that intention, as evaluated by the frequency and duration of e-payment transactions. As a result, the theory that “Continuous Intention has an impact on Actual Usage” is based on behavioral theories such as the technology acceptance model and the theory of planned behavior (Ajzen, 1991), which state that intention is the most powerful predictor of behavior. This logical linkage maintains theoretical consistency and increases the explanatory ability of the suggested framework. The overall research strategy takes a quantitative, cross-sectional approach, with an emphasis on empirical testing of theoretical links between ETAM concepts. The model was evaluated statistically to confirm the anticipated routes and moderating effects. This design allows for a systematic review of causal links as well as substantial evidence for theory testing and practical applications.
3.2 Instruments
This study adopted a quantitative design to systematically measure variables using a structured questionnaire for data collection. The survey, distributed online (Google Forms) and offline, comprised 34 validated items (reduced from an initial 55 after pilot testing) on a seven-point Likert scale (1 = Strongly Disagree to 7 = Strongly Agree). It was divided into two sections: demographic details (Part A) and participant perceptions of e-payment adoption drivers/barriers (Part B). Each construct (T, S, IM, FC, PU, PEOU, SI, SE, CI and AU) was assessed using previously validated measures modified from earlier research (Davis et al., 1989; Venkatesh and Bala, 2008; Rahadi et al., 2022). To ensure construct clarity and content validity, the instrument was expertly validated three times by a quantitative research faculty professional. A pilot test (n = 50) evaluated the items' internal consistency and reliability, resulting in a Cronbach’s α of 0.962, indicating high reliability.
The data were analyzed using SPSS and Analysis of Moment Structures (AMOS) in an organized manner. Descriptive statistics were employed to summarize demographic information. The reliability analysis (Cronbach’s alpha and composite reliability) confirmed internal consistency. Validity analysis (average variance extracted and discriminant validity) proved the construct’s soundness. Structural equation modeling (SEM) was used to test hypotheses and evaluate model fit indices. Moderation analysis assessed the impact of self-efficacy (SE) on the connection between PU, PEOU and CI. These steps provided analytical rigor while also increasing the transparency and dependability of the findings.
3.3 Respondents
This investigation concentrated on users with prior exposure to e-payment systems in Bangladesh. Data was amassed through various channels, including electronic mail, LinkedIn and Facebook. Links to the survey were disseminated to individuals, resulting in the participation of 475 users between 14 September and 1 November 2022. The SPSS 26 software suite encoded and analyzed the collected data. Out of all the collected questionnaires, only 442 were deemed valid and suitable for statistical analysis. The rest were screened out because they were either incomplete or provided by participants who did not use e-payment systems. The overall demographic information is given in Table 1.
Demographic survey data (n = 442)
| Variable | Items | Percentage |
|---|---|---|
| Gender | Male | 44.30% |
| Female | 55.70% | |
| Age | 10–25 years (1997–2012) | 88.7% |
| 26–41 years (1981–1996) | 6.3% | |
| 42–57 years (1965–1980) | 2.7% | |
| 58–67 years (1955–1964) | 2.3% | |
| Educational qualification | Intermediate | 4.1% |
| Undergraduate | 69.7% | |
| Graduate | 15.6% | |
| Master’s degree | 7.5% | |
| Ph.D. | 3.2% | |
| Occupation | Student | 88.5% |
| Employee | 8.8% | |
| Self-employed | 1.4% | |
| Housewife | 1.4% | |
| Monthly income | Under 5,000 | 72.4% |
| 5,001–15,000 | 16.1% | |
| 15,001–25,000 | 4.1% | |
| 25,001–35,000 | 0.2% | |
| Above 35,001 | 7.2% | |
| Which e-payment systems do you use most? | ATM cards | 11.3% |
| Mobile payment | 82.4% | |
| Internet banking | 5.9% | |
| E-wallet | 0.5% |
4. Data analysis and results
4.1 Descriptive statistics (EFA)
To determine if the data is suitable for factor analysis, the correlations between the measurements were examined and it was found that they had sufficient correlations. The number of factors to be retained was determined by analyzing the eigenvalue parameters. The Kaiser–Meyer–Olkin (KMO) value obtained was 0.8960, which was considered acceptable by Kaiser. Moreover, Bartlett’s sphericity test’s significance level was found to be less than 0.000, suggesting that the correlation matrix was a good fit. The communalities of the 36 measures ranged from 0.864 to 0.966. It also shows descriptive statistics for the relationship between several factors, including trust, security, individual mobility, facilitating conditions, social influence, perceived usefulness, ease of use, continuous intention and actual use. The average scores for all these factors were above the median of the scale. Perceived usefulness had the highest average score (5.91), while security-related practices had the lowest average score (5.46).
4.2 Common method variance (CMV)
According to a study in researchers employed Harman’s single component test to assess CMV and to identify the presence of CMV concerns, Podsakoff et al. (2003) proposed that CMV problems may exist if all survey questions fall under the same component or if a single factor accounts for more than 50% of the variance. The test results indicated that the first component explained only 22.06% of the entire variance and that there were other factors with eigenvalues larger than 1, showing that the data were not influenced by CMV issues.
4.3 Measurement models analysis
Confirmatory factor analysis was conducted to test the measurement model validity and reliability, which is shown in Figure 2. The overall goodness of the model was measured using various fit indices and is given in Table 2. To confirm the model’s validity and dependability, the research analyzed a variety of parameters. Composite reliability (CR > 0.70) and Cronbach’s alpha (>0.70) score were satisfactory (Hair et al., 2010; Masud et al., 2025; Gazi et al., 2025a, b, c). According to Fornell and Larcker (1981), an extracted average variance (AVE>0.50) and factor loads (>0.70) were also acceptable for determining composite dependability. Discriminant validity was determined by comparing intervariable correlations with the square root of AVE, with the greatest correlation value predicted to be smaller than AVE. In addition, the variance inflation factor (VIF) was employed to test for multicollinearity and the expected threshold value was less than 10 (Hair et al., 2010; Yu et al., 2025; Akter et al., 2025; Islam et al., 2025) (see Table 3). The standardized regression weights are shown in Table 4.
The diagram shows a structural equation model diagram with ten oval-shaped latent variables connected by directional arrows and observed variables shown as rectangles. Each latent variable is measured by multiple indicators with standardized loading values. “T” is positioned at the top left. Three leftward arrows from it point to indicators “T 2,” “T 3,” and “T 4,” arranged in a vertical series, with loadings of 0.91, 0.81, and 0.89, respectively. At the far left, small circles labeled “e 4,” “e 3,” and “e 2,” from top to bottom, connect through rightward arrows to “T 2,” “T 3,” and “T 4,” with coefficients of 0.84, 0.65, and 0.78, respectively. Below “T,” “S” connects to “S 1,” “S 2,” “S 3,” and “S 5,” with loadings of 0.90, 0.85, 0.91, and 0.91. At its far left, small circles labeled “e 10,” “e 9,” “e 8,” and “e 7,” from top to bottom, connect through rightward arrows to “S 1,” “S 2,” “S 3,” and “S 5,” with coefficients of 0.81, 0.73, 0.83, and 0.83, respectively. To the right of “T” and “S,” “S E” connects through rightward arrows to “S E 1,” “S E 3,” and “S E 4,” with loadings of 0.74, 0.93, and 0.95, respectively. At its far right, small circles labeled “e 27,” “e 28,” and “e 29,” from top to bottom, connect through leftward arrows to “S E 1,” “S E 3,” and “S E 4,” with coefficients of 0.91, 0.93, and 0.74, respectively. Beneath “S E,” “P E O U” is connected by rightward arrows to indicators arranged in a vertical series and labeled “P E O U 4,” “P E O U 3,” and “P E O U 2,” with loadings of 0.93, 0.92, and 0.93, respectively. At its far right, small circles labeled “e 32,” “e 33,” and “e 34,” from top to bottom, connect through leftward arrows to “P E O U 4,” “P E O U 3,” and “P E O U 2,” with coefficients of 0.86, 0.85, and 0.87, respectively. Beneath “P E O U,” “P U” is connected by rightward arrows to indicators arranged in a vertical series and labeled “P U 4,” “P U 3,” “P U 2,” and “P U 1,” with loadings of 0.89, 0.91, 11.94, and 0.87, respectively. At its far right, small circles labeled “e 37,” “e 38,” “e 39,” and “e 40,” from top to bottom, connect through leftward arrows to “P U 4,” “P U 3,” “P U 2,” and “P U 1,” with coefficients of 0.78, 0.83, 0.88, and 0.75, respectively. Beneath them, “C I” is connected by rightward arrows to indicators arranged in a vertical series and labeled “C I 5,” “C I 4,” “C I 2,” and “C I 1,” with loadings of 0.92, 0.94, 0.95, and 0.91, respectively. At its far right, small circles labeled “e 41,” “e 42,” “e 43,” and “e 44,” from top to bottom, connect through leftward arrows to “C I 5,” “C I 4,” “C I 2,” and “C I 1,” with coefficients of 0.84, 0.88, 0.90, and 0.83, respectively. At the bottom, “A U” is connected by rightward arrows to indicators arranged in a vertical series and labeled “A U 5,” “A U 4,” “A U 3,” and “A U 2,” with loadings of 0.92, 0.90, 0.91, and 0.89, respectively. At its far right, small circles labeled “e 46,” “e 47,” “e 48,” and “e 49,” from top to bottom, connect through leftward arrows to “A U 5,” “A U 4,” “A U 3,” and “A U 2,” with coefficients of 0.84, 0.80, 0.62, and 0.78, respectively. In the middle left, “I M” connects through leftward arrows to “I M 1,” “I M 2,” “I M 3,” “I M 4,” and “I M 5,” with loadings of 0.92, 0.93, 0.93, 0.90, and 0.93, respectively. At its far left, small circles labeled “e 15,” “e 14,” “e 13,” “e 12,” and “e 11,” from top to bottom, connect through rightward arrows to “I M 1,” “I M 2,” “I M 3,” “I M 4,” and “I M 5,” with coefficients of 0.84, 0.86, 0.87, 0.81, and 0.86, respectively. Beneath “I M,” “F C” connects through leftward arrows to “F C 2,” “F C 3,” and “F C 5,” with loadings of 0.95, 0.94, and 0.89, respectively. At its far left, small circles labeled “e 19,” “e 18,” and “e 17,” from top to bottom, connect through rightward arrows to “F C 2,” “F C 3,” and “F C 5,” with coefficients of 0.90, 0.88, and 0.79, respectively. At the bottom left, “S I” connects through leftward arrows to “S I 1,” “S I 2,” “S I 3,” and “S I 4,” with loadings of 0.89, 0.89, 0.90, and 0.91, respectively. At its far left, small circles labeled “e 25,” “e 24,” “e 23,” and “e 22,” from top to bottom, connect through rightward arrows to “S I 1,” “S I 2,” “S I 3,” and “S I 4,” with coefficients of 0.80, 0.79, 0.82, and 0.82, respectively. All ten oval-shaped latent variables — “T,” “S,” “I M,” “F C,” “S I,” “S E,” “P E O U,” “P U,” “C I,” and “A U” — are interconnected by double-headed arrows representing correlations.Measurement models analysis
The diagram shows a structural equation model diagram with ten oval-shaped latent variables connected by directional arrows and observed variables shown as rectangles. Each latent variable is measured by multiple indicators with standardized loading values. “T” is positioned at the top left. Three leftward arrows from it point to indicators “T 2,” “T 3,” and “T 4,” arranged in a vertical series, with loadings of 0.91, 0.81, and 0.89, respectively. At the far left, small circles labeled “e 4,” “e 3,” and “e 2,” from top to bottom, connect through rightward arrows to “T 2,” “T 3,” and “T 4,” with coefficients of 0.84, 0.65, and 0.78, respectively. Below “T,” “S” connects to “S 1,” “S 2,” “S 3,” and “S 5,” with loadings of 0.90, 0.85, 0.91, and 0.91. At its far left, small circles labeled “e 10,” “e 9,” “e 8,” and “e 7,” from top to bottom, connect through rightward arrows to “S 1,” “S 2,” “S 3,” and “S 5,” with coefficients of 0.81, 0.73, 0.83, and 0.83, respectively. To the right of “T” and “S,” “S E” connects through rightward arrows to “S E 1,” “S E 3,” and “S E 4,” with loadings of 0.74, 0.93, and 0.95, respectively. At its far right, small circles labeled “e 27,” “e 28,” and “e 29,” from top to bottom, connect through leftward arrows to “S E 1,” “S E 3,” and “S E 4,” with coefficients of 0.91, 0.93, and 0.74, respectively. Beneath “S E,” “P E O U” is connected by rightward arrows to indicators arranged in a vertical series and labeled “P E O U 4,” “P E O U 3,” and “P E O U 2,” with loadings of 0.93, 0.92, and 0.93, respectively. At its far right, small circles labeled “e 32,” “e 33,” and “e 34,” from top to bottom, connect through leftward arrows to “P E O U 4,” “P E O U 3,” and “P E O U 2,” with coefficients of 0.86, 0.85, and 0.87, respectively. Beneath “P E O U,” “P U” is connected by rightward arrows to indicators arranged in a vertical series and labeled “P U 4,” “P U 3,” “P U 2,” and “P U 1,” with loadings of 0.89, 0.91, 11.94, and 0.87, respectively. At its far right, small circles labeled “e 37,” “e 38,” “e 39,” and “e 40,” from top to bottom, connect through leftward arrows to “P U 4,” “P U 3,” “P U 2,” and “P U 1,” with coefficients of 0.78, 0.83, 0.88, and 0.75, respectively. Beneath them, “C I” is connected by rightward arrows to indicators arranged in a vertical series and labeled “C I 5,” “C I 4,” “C I 2,” and “C I 1,” with loadings of 0.92, 0.94, 0.95, and 0.91, respectively. At its far right, small circles labeled “e 41,” “e 42,” “e 43,” and “e 44,” from top to bottom, connect through leftward arrows to “C I 5,” “C I 4,” “C I 2,” and “C I 1,” with coefficients of 0.84, 0.88, 0.90, and 0.83, respectively. At the bottom, “A U” is connected by rightward arrows to indicators arranged in a vertical series and labeled “A U 5,” “A U 4,” “A U 3,” and “A U 2,” with loadings of 0.92, 0.90, 0.91, and 0.89, respectively. At its far right, small circles labeled “e 46,” “e 47,” “e 48,” and “e 49,” from top to bottom, connect through leftward arrows to “A U 5,” “A U 4,” “A U 3,” and “A U 2,” with coefficients of 0.84, 0.80, 0.62, and 0.78, respectively. In the middle left, “I M” connects through leftward arrows to “I M 1,” “I M 2,” “I M 3,” “I M 4,” and “I M 5,” with loadings of 0.92, 0.93, 0.93, 0.90, and 0.93, respectively. At its far left, small circles labeled “e 15,” “e 14,” “e 13,” “e 12,” and “e 11,” from top to bottom, connect through rightward arrows to “I M 1,” “I M 2,” “I M 3,” “I M 4,” and “I M 5,” with coefficients of 0.84, 0.86, 0.87, 0.81, and 0.86, respectively. Beneath “I M,” “F C” connects through leftward arrows to “F C 2,” “F C 3,” and “F C 5,” with loadings of 0.95, 0.94, and 0.89, respectively. At its far left, small circles labeled “e 19,” “e 18,” and “e 17,” from top to bottom, connect through rightward arrows to “F C 2,” “F C 3,” and “F C 5,” with coefficients of 0.90, 0.88, and 0.79, respectively. At the bottom left, “S I” connects through leftward arrows to “S I 1,” “S I 2,” “S I 3,” and “S I 4,” with loadings of 0.89, 0.89, 0.90, and 0.91, respectively. At its far left, small circles labeled “e 25,” “e 24,” “e 23,” and “e 22,” from top to bottom, connect through rightward arrows to “S I 1,” “S I 2,” “S I 3,” and “S I 4,” with coefficients of 0.80, 0.79, 0.82, and 0.82, respectively. All ten oval-shaped latent variables — “T,” “S,” “I M,” “F C,” “S I,” “S E,” “P E O U,” “P U,” “C I,” and “A U” — are interconnected by double-headed arrows representing correlations.Measurement models analysis
Results of model fit indices for measurement model and structural model
| Model fit indices | Measurement model | Structural model | Suggested value | |
|---|---|---|---|---|
| “The Ratio of Chi-Square to Degrees of Freedom (X2/Df)” | 1.754 | 2.070 | <3 | Hair et al. (2010), Hu and Bentler (1999) |
| “Goodness-Of-Fit Index (GFI)” | 0.888 | 0.868 | >0.90 | |
| “Average GFI” | 0.865 | 0.847 | ≥0.80 | |
| “Comparative Fit Index (CFI)” | 0.973 | 0.965 | ≥0.90 | |
| “Normalized Fit Index (NFI)” | 0.974 | 0.934 | ≥0.90 | |
| “Incremental Fit Index (IFI)” | 0.950 | 0.965 | ≥0.90 | |
| “Tucker–Lewis Index (TLI)” | 0.970 | 0.961 | ≥0.90 | |
| “Root-Mean-Square Error of Approximation (RMSEA)” | 0.041 | 0.049 | ≤0.05 | |
Constructs validity statistics
| Constructs | CR | AVE | MSV | MaxR(H) | T | S | IM | FC | SI | SE | PEOU | PU | CI | AU | VIF |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| T | 0.903 | 0.758 | 0.078 | 0.914 | 0.870 | 1.147 | |||||||||
| S | 0.941 | 0.798 | 0.051 | 0.943 | 0.225 | 0.894 | 1.105 | ||||||||
| IM | 0.965 | 0.847 | 0.052 | 0.966 | 0.122 | 0.170 | 0.920 | 1.088 | |||||||
| FC | 0.946 | 0.853 | 0.057 | 0.951 | 0.169 | 0.164 | 0.162 | 0.924 | 1.105 | ||||||
| SI | 0.943 | 0.805 | 0.091 | 0.943 | 0.280 | 0.194 | 0.128 | 0.138 | 0.897 | 1.178 | |||||
| SE | 0.909 | 0.771 | 0.083 | 0.946 | 0.216 | 0.141 | 0.066 | 0.121 | 0.235 | 0.878 | 1.127 | ||||
| PEOU | 0.948 | 0.859 | 0.107 | 0.948 | 0.140 | 0.062 | 0.094 | 0.135 | 0.180 | 0.193 | 0.927 | 1.147 | |||
| PU | 0.945 | 0.811 | 0.107 | 0.949 | 0.263 | 0.197 | 0.228 | 0.239 | 0.301 | 0.289 | 0.327 | 0.900 | 1.342 | ||
| CI | 0.962 | 0.863 | 0.085 | 0.964 | 0.121 | 0.080 | 0.083 | 0.178 | 0.187 | 0.221 | 0.223 | 0.279 | 0.929 | 1.148 | |
| AU | 0.946 | 0.813 | 0.085 | 0.947 | 0.077 | 0.111 | 0.056 | 0.036 | 0.187 | 0.113 | 0.072 | 0.144 | 0.291 | 0.902 |
Note(s): Italic Diagonal Values Are the Square Root of AVE Value
Standardized regression weights
| Variable | Items | Estimate | S.E. | t-value | Cronbach’s alpha |
|---|---|---|---|---|---|
| T | T2 | 0.914 | 0.903 | ||
| T3 | 0.807 | 0.037 | 21.432 | ||
| T4 | 0.886 | 0.041 | 25.114 | ||
| S | S1 | 0.898 | 0.940 | ||
| S2 | 0.853 | 0.029 | 30.658 | ||
| S3 | 0.913 | 0.031 | 26.142 | ||
| S5 | 0.909 | 0.034 | 29.443 | ||
| IM | IM1 | 0.918 | 0.966 | ||
| IM2 | 0.926 | 0.028 | 31.700 | ||
| IM3 | 0.932 | 0.028 | 35.923 | ||
| IM4 | 0.897 | 0.029 | 35.243 | ||
| IM5 | 0.926 | 0.027 | 34.184 | ||
| FC | FC2 | 0.947 | 0.945 | ||
| FC3 | 0.936 | 0.033 | 31.053 | ||
| FC5 | 0.887 | 0.034 | 31.689 | ||
| Social influence | SI1 | 0.892 | 0.943 | ||
| SI2 | 0.887 | 0.033 | 29.656 | ||
| SI3 | 0.903 | 0.031 | 28.428 | ||
| SI4 | 0.906 | 0.031 | 28.809 | ||
| SE | SE1 | 0.737 | 0.904 | ||
| SE3 | 0.929 | 0.029 | 31.211 | ||
| SE4 | 0.953 | 0.033 | 20.197 | ||
| PEOU | PEOU2 | 0.928 | 0.948 | ||
| PEOU3 | 0.920 | 0.029 | 33.533 | ||
| PEOU4 | 0.932 | 0.029 | 34.687 | ||
| PU | PU1 | 0.866 | 0.944 | ||
| PU2 | 0.938 | 0.040 | 28.865 | ||
| PU3 | 0.909 | 0.037 | 30.980 | ||
| PU4 | 0.886 | 0.038 | 25.938 | ||
| CI | CI1 | 0.912 | 0.962 | ||
| CI2 | 0.951 | 0.028 | 35.149 | ||
| CI4 | 0.936 | 0.029 | 37.047 | ||
| CI5 | 0.916 | 0.028 | 32.511 | ||
| AU | AU2 | 0.885 | 0.946 | ||
| AU3 | 0.906 | 0.029 | 30.282 | ||
| AU4 | 0.897 | 0.030 | 31.137 | ||
| AU5 | 0.918 | 0.030 | 29.273 |
4.4 Structural model analysis
Based on the assessment model’s fitness, we proceeded to conduct SEM aimed at assessing the hypothesized paths. The data show that the model explained (e.g. R2 value) 48, 58, 49 and 54% of the variance in continuous intention to use e-payment and tendency to go toward a cashless society. Table 2 shows that the SEM model has adequate model fit.
4.5 Hypothesis examination results
This study aimed to show the customers’ satisfaction and intention of using e-payment systems and explore the relationship among T, S, IM, FC, PU, PEOU, SI, CI and AU. The study showed some specific correlations through SEM analysis using the software AMOS version 24, and the result of eleven hypotheses (H1a, H1b, H2b, H3a, H4a, H4b, H5, H6, H7, H8 and H9) are statistically significant (Table 5).
Hypothesis results
| Hypothesis path | Estimates (β) | S.E. | t-value | p-value | Decision | |
|---|---|---|---|---|---|---|
| H1a | PU ← T | 0.086 | 0.052 | 1.710 | * | Accept |
| H1b | PEOU ← T | 0.200 | 0.041 | 4.071 | *** | Accept |
| H2a | PU ← S | −0.003 | 0.046 | −0.054 | N.S. | Reject |
| H2b | PEOU ← S | 0.107 | 0.036 | 2.226 | ** | Accept |
| H3a | PU ← IM | 0.055 | 0.046 | 1.115 | N.S. | Reject |
| H3b | PEOU ← IM | 0.168 | 0.037 | 3.522 | *** | Accept |
| H4a | PU ← FC | 0.100 | 0.050 | 2.030 | ** | Accept |
| H4b | PEOU ← FC | 0.174 | 0.039 | 3.620 | *** | Accept |
| H5 | PU ← SI | 0.139 | 0.053 | 2.799 | ** | Accept |
| H6 | CI ← SI | 0.105 | 0.052 | 2.163 | ** | Accept |
| H7 | CI ← PU | 0.142 | 0.049 | 4.240 | *** | Accept |
| H8 | CI ← PEOU | 0.206 | 0.060 | 2.920 | ** | Accept |
| H9 | AU ← CI | 0.288 | 0.048 | 5.928 | *** | Accept |
Note(s): ***p < 0.001; **p < 0.05 and *p < 0.1 and NS = not significant
Table 5 details the research model's findings, including standardized estimates (β), standard errors, t-values, significant values and hypothesis decisions. In particular, T positively affects PU and PEOU, respectively (H1a: β = 0.086, t = 1.710, p = 0.087 & H1b: β = 0.200, t = 4.071, p < 0.001). S positively impacts PEOU (H2b: β = 0.107, t = 2.226, p < 0.05); again, IM positively impacts PEOU (H3b: β = 0.168, t = 3.522, p < 0.001). Further, FC positively affects PU and PEOU (H4a: β = 0.100, t = 2.030, p < 0.05 & H4b: β = 0.174, t = 3.620, p < 0.001 respectively) and SI positively affects PU and C (H5: β = 0.139, t = 2.799, p < 0.05 and H1b: β = 0.200, t = 4.071, p < 0.001). PU and PEOU positively affects CI (H7: β = 0.142, t = 4.240, p < 0.001 and H8: β = 0.206, t = 2.920, p < 0.05). CI positively affects AU (H9: β = 0.288, t = 5.928, p < 0.001). However, two of the thirteen hypotheses were not supported, suggesting that there is no substantial association between S and PU (H2a: β = −0.003, t = −0.054, p-value = 0.957), and there is no significant association between PU and IM (H3a: β = −0.055, t = 1.115, p-value = 0.265).
4.6 Moderation analysis
The research looked at the connection between SE and PU, PEOU and CI of e-payments. Individuals with strong SE are more likely to be influenced by PU and PEOU when deciding to use e-payment, according to the findings (Table 6). This implies that the effect of PU and PEOU on CI on e-payment intention is stronger for those with high levels of SE. As a result, when designing and deploying e-payment systems, it is critical to consider SE. According to the research, high levels of SE can positively moderate the impact of PU and PEOU on e-payment CI.
5. Discussion
This study investigated a number of variables that affect both the actual use of e-payment systems and consumers’ ongoing intention to implement them in Bangladesh. According to the findings, user adoption behavior is highly influenced by the following factors: perceived usefulness (PU), perceived ease of use (PEOU), social influence (SI), self-efficacy (SE), continuous intention (CI), individual mobility (IM), trust (T), security (S) and facilitating conditions (FC).
5.1 Positive connections and theoretical coherence
The findings show that trust (T) has a positive correlation with both PU and PEOU (H1a and H1b), which is consistent with previous research by Liu et al. (2022), who stressed trust as a basis for perceived system reliability and user confidence. Similarly, security (S) has a favorable influence on PEOU (H2b), which supports Zhang et al. (2022) and Paul et al. (2013)‘s findings that felt safety improves ease of adoption. Furthermore, individual mobility (IM) has a positive connection with PEOU (H3b), similar to Sharma et al. (2019), implying that flexibility in access improves perceived convenience.
Facilitating conditions (FC) considerably boost both PU and PEOU (H4a and H4b), consistent with Chen and Aklikokou (2019), who found that technical and organizational support promotes user acceptability. Furthermore, social influence (SI) has a favorable impact on both PU (H5) and CI (H6), as demonstrated by Singh and Sharma (2022), indicating that peer and social pressure can promote adoption behavior. Finally, PU and PEOU have a beneficial influence on CI (H7 and H8), lending credence to Karim et al. (2020) and reinforcing the importance of ease and usefulness in determining continuous intention.
5.2 Contextual interpretation and deviations
Two findings differ from earlier research. Firstly, in contrast to Singh et al. (2020), security (S) has no discernible impact on PU (H2a). This could imply that, while being aware of security threats, Bangladeshi users choose practicality over perceived security when utilizing electronic payments. Secondly, contrary to Yen and Wu (2016), individual mobility (IM) has no discernible impact on PU (H3a). This is because of Bangladesh’s limited infrastructure, which makes mobile flexibility seem less beneficial due to erratic internet availability. These results demonstrate the existence of context-specific trade-offs and the tendency of users to prioritize accessibility and usefulness over theoretical expectations.
5.3 Moderating effects and useful information
Self-efficacy (SE) strongly moderates the link between PU and CI, according to the moderation study, suggesting that users who have greater technical confidence are more inclined to stick with e-payments in spite of usability issues. This is in line with Zhang et al. (2022) and shows how self-efficacy can help users overcome small system obstacles and bridge technology gaps.
5.4 Theoretical and practical implications
Overall, this study builds on the extended technology acceptance model (ETAM) by incorporating context-specific characteristics important to Bangladesh’s e-payment ecosystem. It demonstrates that functional benefits (PU), usability (PEOU) and social encouragement (SI) motivate users to continue using e-payment systems, hence facilitating the transition to a cashless society (RQ1 and RQ2). However, the nonsignificant correlations between S-PU and IM-PU imply that contextual factors in poor economies can influence the strength of conventional TAM linkages. These findings have important implications for policymakers and practitioners, underlining the need to improve infrastructure stability, promote user education and establish trust mechanisms in order to ensure inclusive adoption across varied user groups.
6. Conclusion
This investigation aimed to ascertain the determinants that influence individuals’ inclination to persist in utilizing e-payment systems and transition toward a cashless society. The ETAM is employed in conjunction with external variables to evaluate consumers’ intention to utilize e-payment systems in the future. The proposed model and its analysis addressed all research queries. T, S, IM, FC, PU, PEOU, SI, SE, CI and AU all had a substantial impact on the utilization of e-payment systems. Additionally, the study scrutinized the correlation between PU and CI of e-payment systems in Bangladesh, as well as the relationship between PEOU and CI of e-payment systems in Bangladesh. The results furnish valuable insights into the adoption and utilization of e-payment systems in the country. Policymakers and industry players in the e-payment sector can leverage these findings to expedite adoption by addressing these factors.
6.1 Theoretical implications
This study advances the theoretical understanding of technology adoption and behavioral intention research by extending the extended technology acceptance model (ETAM) in a developing-country environment. The ETAM framework explains consumer behavior toward e-payment systems, highlighting trust (T) and security (S) as key drivers of perceived usefulness (PU) and perceived ease of use (PEOU). Consistent with Liu et al. (2022) and Zhang et al. (2022), the findings demonstrate that users’ perceptions of trust and security significantly improve system reliability and adoption behavior.
Furthermore, individual mobility (IM) and mobile-friendly design improve usability, while facilitating conditions (FC) (e.g. tools, infrastructure and support) amplify PU and PEOU, which is consistent with Chen and Aklikokou (2019). The role of social influence (SI) is also consistent with Singh and Sharma's (2022) findings, which show that social endorsement improves perceived utility and adoption behavior.
However, the study adds to existing theory by demonstrating two context-specific deviations: (a) security (S) has no significant impact on PU and (b) individual mobility (IM) has no significant impact on PU. This shows that traditional TAM interactions may decrease as a result of infrastructural and sociocultural restrictions, reflecting Yen and Wu's (2016) findings. When applied to poor economies, these discrepancies suggest that the ETAM model should include contextual moderators like infrastructure quality and digital literacy. Finally, the moderating effect of self-efficacy (SE) on PU and continuous intention (CI) contributes to theoretical comprehension by confirming that technological confidence improves behavioral persistence, which is consistent with Zhang et al. (2022). This study improves TAM-based theory and adds to the literature on digital inclusion, technology readiness and e-payment uptake in emerging economies by combining technical robustness with contextual flexibility.
6.2 Managerial implications
The findings also offer actionable advice for industry stakeholders, service providers and authorities looking to increase e-payment use in Bangladesh. Initially, establishing user trust is crucial, as the study found that trust has a considerable influence on adoption intention (PU and PEOU). Managers, according to Liu et al. (2022), should maintain openness, data protection, and responsive customer service in order to increase credibility. Secondly, having strong security standards is critical. Although security had no direct affect on PU, users’ perceptions of overall system safety remained a critical confidence factor, as Paul et al. (2013) noted. To increase long-term user confidence, policymakers should tighten compliance measures and enforce data privacy regulations. Third, promoting individual mobility (IM) can increase accessibility. According to Sharma et al. (2019), stakeholders should create mobile-optimized systems that perform well even when connectivity is unreliable. Fourth, improving facilitating conditions (FC) – such as 24-h technical support and user education – can directly increase both PU and PEOU. This is consistent with Chen and Aklikokou (2019), who underlined the significance of organizational and technical support in adoption success. Fifth, utilizing social influence (SI) is critical. According to Singh and Sharma (2022), e-payment providers should leverage positive peer effects, social media advertising and influencer endorsements to normalize e-payment use. Finally, increasing self-efficacy (SE) is critical. Managers should provide user training, tutorials and awareness programs that increase digital confidence and help users solve usability difficulties. Zhang et al. (2022) favor this method and believe it will encourage long-term use. Overall, these managerial implications, based on both empirical data and preceding literature, show how Bangladesh’s e-payment industry can increase adoption through trust-building, user education and legislative assistance.
6.3 Research limitations
This study has notable limitations. First, the sample was overrepresented by younger respondents, limiting generalizability to older demographics with differing motivations and behaviors. Future research should integrate complementary theoretical models and ensure broader age diversity to enhance understanding of e-payment adoption dynamics.
