MINISTRY OF EDUCATION AND TRAINING THE STATE BANK OF VIET NAM BANKING UNIVERSITY OF HO CHI MINH CITY NGUYEN TRAN MY DUYEN THE IMPACT OF PERCEIVED RISKS AND PERCEIVED BENEFITS ON THE INTENTION TO REUSE.
INTRODUCTION
Research Statement
E-wallet is one of the most popular forms of online payment today In 2021, Vietnam is in the top three countries with the percentage of users paying via mobile in Asia with 29.1% As of the end of the first quarter of 2020, Vietnam has more than 13 million e-wallet accounts activated and used, the total wallet balance is about 1.36 trillion VND and there are more than 225 million transactions made (The State Bank of Vietnam, 2020)
Cash—once the most popular payment method among Vietnamese users—has declined since the pandemic began A Visa survey shows that the Covid-19 era accelerated e-wallet adoption, with 57% of users having up to three e-wallet apps on their mobile devices and 55% preferring an all-in-one app that can handle all transactions.
According to a statistical report from the State Bank of Vietnam, Vietnam's market has about 48 e-wallets and officially licensed non-bank payment intermediaries operating in the country, reflecting a sevenfold increase since 2015 and highlighting rapid growth in licensed digital wallets and non-bank payment services within Vietnam's fintech landscape.
MoMo is one of the most attractive e-wallets in Vietnam, with more than 31 million existing customers According to Appota's Mobile Applications 2021 report, MoMo has the highest percentage of regular users among e-wallets, at 61% MoMo's success stems from bringing a series of offline services online, consolidating many functions into a single app With one application, almost all needs are met, making life simpler and easier, especially in the current epidemic situation.
MoMo e-wallets offer fast, convenient transactions, but they expose users to security risks from cybercriminals In recent years, security breaches have drained assets from mobile payment apps, especially when a phone is lost or an attacker gains MoMo credentials These incidents affect consumer psychology and erode trust, reducing willingness to reuse the service Because trust underpins continued use, studying customers' risk perception when using MoMo e-wallets can illuminate adoption patterns and guide improvements in security and user protection.
Although a few drawbacks still hinder users from reusing the MoMo application, the undeniable convenience it offers motivates customers to act The rapid growth in registered MoMo accounts over the years underscores the e-wallet’s importance to consumers As daily life becomes busier and society evolves, people prioritize convenient products and services that save time and reduce costs When perceived benefits outweigh perceived risks, the intention to reuse and actual usage rise Consequently, studying perceived benefits remains essential To broaden MoMo adoption, M_Service should build and leverage a robust database of consumer perceptions—capturing both risks and benefits—to address concerns, retain users, and drive ongoing development of the app.
Around the world, some previous studies have shown that perceived risk and perceived benefit have an influence on the acceptance of mobile payments by Putritama (2019); Sentanu et al (2020); Shafi & Misman (2021) At the same time, the study of risks and benefits in mobile payments is a research direction that attracts many scholars, but in Vietnam, there are currently very few studies talking about the impact of perceived risk and perceived benefit to the intention to reuse e- wallets
In view of the development trend of e-wallets in the world and the current situation of Momo e-wallet payment in Vietnam, the author has chosen the topic of graduation thesis with the title: "The impact of perceived risks and perceived benefits on the intention to reuse Momo e-wallet of Gen Z” to explore what makes users still intend to reuse Momo e-wallet despite being aware of the potential risks surrounding this application.
Research Objectives and Questions
This study investigates the impact of perceived risk and perceived benefit on Gen Z's intention to reuse the Momo e-wallet in Ho Chi Minh City, exploring how these perceptions shape willingness to continue using the service and identifying the key factors driving repeated adoption among young digital-wallet users in Ho Chi Minh City.
In addition, the study also has the following sub-research objectives:
The first is to identify the impact of perceived benefits and perceived risks on the intention to reuse MoMo e-wallet of Gen Z in Ho Chi Minh City
The second is to measure the level of impact of perceived benefits and perceived risks on the intention to reuse MoMo e-wallet of Gen Z in Ho Chi Minh City
The third is to propose implications to increase benefits, reduce risks, and increase Gen Z's intention to reuse Momo e-wallet
Do perceived risks and perceived benefits affect the intention to reuse MoMo e-wallet?
How are the impacts of each perceived risk and perceived benefit factor on the intention to reuse Momo e-wallet?
What managerial implications can be proposed to help businesses increase the intention to reuse Momo e-wallet?
Research Subjects and Scope
This study examines how perceived risk and perceived benefit influence the intention to reuse the Momo e-wallet among Generation Z residents under 27 years old in Ho Chi Minh City It analyzes how these perceptions affect young, urban consumers’ willingness to continue using Momo, highlighting the drivers of continued adoption for the Momo e-wallet in Vietnam's largest city.
The study will be conducted from March 2022 to June 2022.
Research Methodology
Using a quantitative research method, this study conducts a survey to assess how perceived risk and perceived benefit affect Gen Z's intention to reuse the Momo e-wallet in Ho Chi Minh City After data collection, the responses will be analyzed with SPSS 22 and AMOS, following a sequence of steps that typically includes data screening, reliability and validity testing, and hypothesis testing through structural equation modeling to illuminate the relationships between perceived risk, perceived benefit, and reuse intention.
- Descriptive statistics of the research sample
Research Significance
Understanding the impacts of perceived risks and consumer behaviors remains limited, and measuring how perceived risk and perceived benefit influence Gen Z's intention to reuse the Momo e-wallet provides critical insights for shaping adoption strategies By quantifying these factors, Momo can develop targeted solutions to promote the intention to reuse the e-wallet among Gen Z users, informing product development and marketing initiatives This, in turn, helps Momo improve the intention to reuse its existing services, attract user attention, boost satisfaction, and persuade more users to rely on the Momo e-wallet app, driving higher engagement and long-term loyalty.
Thesis structure
This thesis includes front matter such as the cover, declaration, acknowledgments, table of contents, lists of acronyms, lists of figures and tables, references, and appendices, with the content organized into five chapters that structure the study from introduction to conclusion.
Chapter 1 discussed the importance of the research subject, the research goals and tasks, the research difficulties and questions, the research objects and scope of the research, the research methods and data, and the study's contributions
The topic "The Impact of Perceived Risks and Perceived Benefits on the Intention to Reuse Momo E-Wallet" is both scientifically rigorous and practically relevant, establishing a solid foundation for future research This study provides an in-depth examination of the theoretical foundations, research methodologies, and data collection processes used to generate findings and offer actionable solutions.
LITERATURE REVIEW
Theoretical Foundations
An e-wallet is a digital tool that lets a user perform financial transactions on a mobile phone It acts as an intermediary between the payer’s device and the recipient’s device, enabling purchases and bill payments with a few taps and making mobile payments fast and convenient This digital wallet concept underpins modern mobile payments and digital transactions in everyday shopping and utility bill settlement (Halevi, Ma, Saxena, & Xiang).
2012) In other terms, an E-wallet is a smartphone application that enables users to do a variety of financial activities through mobile (Quasim, Siddiqui, & Rehman,
Daily transactions, including buying train tickets and making credit card payments, have been strengthened and improved by the rapid development of electronic wallets in recent years This trend, noted by Shin, highlights how digital wallets enhance the convenience, speed, and security of everyday payments.
According to Normayanti Putri et al (2021), an e-wallet is a prepaid account that allows users to store funds and use them as a payment method This digital wallet is safeguarded by security measures, such as passwords, to protect the user’s money and transactions.
An e-wallet is a product or service that enables the storage of digital money and the execution of payments through internet-connected electronic intermediaries It securely holds funds and can be used to make online and in-store payments, providing convenient, fast, and contactless transactions.
An e-wallet is not restricted to online transactions; its versatility extends to mobile phone-based point-of-sale payments, enabling merchants and customers to easily minimize cash on hand—a practice that reduces the challenges of cash handling and supports cashless transactions.
According to statistics from the the State Bank of Viet Nam, as of May 9,
In 2022, Vietnam had 48 non-banking organizations and enterprises licensed by the State Bank of Vietnam to provide e-wallet services, reflecting a growing and regulated fintech ecosystem for digital payments This licensing, amid expanding e-commerce, helps meet consumers' basic digital payment needs and supports broader online shopping adoption E-wallets bring several benefits, including fast and convenient transactions, secure payments, easy top-ups, wide merchant acceptance, and better transaction tracking and rewards, all contributing to a smoother consumer experience in online commerce.
Whether it's money transfers, mobile top-ups, or quick payments, the platform delivers fast, easy operation that saves you time Users can make payments for purchases and pay for services anytime and anywhere by following a few simple steps and confirming the transaction password With support for multiple transfer methods and seamless payments, this solution enables secure, convenient transactions across devices, helping you manage finances more efficiently.
Our solution helps users save travel time by speeding up payment transactions, making everything easy and fast while on the go It supports fast, secure payments and cashless transactions, so users don't have to carry cash and risk loss or theft In addition, users can query their account information anytime, anywhere, providing 24/7 access to balances and recent activity.
In addition, payment by e-wallet helps secure transactions, allows payment of small fees, is easy to use, popular, and has a wide range of uses
Although e-wallets provide notable benefits, several barriers still affect their use Security concerns continue to erode user trust, and some transactions incur fees that can be higher than those for digital banking services such as Internet banking and mobile banking In addition, personal and account information can be stolen if devices access unreliable websites or are exposed to malware, potentially resulting in financial losses Strengthening security, clarifying fee structures, and improving safe browsing and device protections are essential to enhance user confidence and improve the overall e-wallet experience.
Poliushkevych (2019) identifies the absence of a robust legal framework as the most critical issue facing modern payment methods, especially e-wallets In many countries there is no effective regulation of cryptocurrency circulation, which leaves users’ rights unprotected when risks and fraud occur This regulatory gap helps explain the slow adoption of e-wallets in Vietnam despite a rapidly growing e-commerce market and underscores why regulatory clarity is a prerequisite for broader global development of digital wallet services.
Generation Z, defined as individuals born between 1995 and 2010, are digital natives and the most connected to social media and sophisticated technology (Skinner, Sarpong, & White, 2018) They are distinguished from earlier generations by an obsession with technology, strong individualism, and a preference for convenience, viewing technology as an integral part of daily life from birth (Berkup, 2014) Numerous studies indicate that Gen Z engages in more online transactions than previous generations (Dalimunte et al., 2019; Dewanti & Indrajit, 2018).
Reuse intention refers to a user’s post-use behavior that determines whether they will use a service again Described by Sibona et al (2017), this concept links customers’ experiences after service usage to their future reuse decisions, making post-use evaluation a key predictor of repeat usage and service loyalty.
Yeboah-Asimah et al (2018) define reuse intention as a user’s commitment to continue using a service and to take advantage of all the features offered by the provider In the mobile payments arena, reuse intention refers to the extent to which a person already uses mobile payments to purchase goods or services and plans to continue doing so in the foreseeable future (Humbani).
Reuse intention describes a consumer’s decision to repeatedly repurchase or reuse a product or service from a particular brand and to promote it to friends and family, signaling ongoing loyalty and trust This intent reflects perceived value and satisfaction with the brand and often results in continued purchases and positive word-of-mouth recommendations within the consumer’s social network.
Theoretical Framework
Davis (1989) developed the Technology Acceptance Model (TAM) based on the Theory of Reasoned Action (TRA) to explain how a user’s behavioral intention to use a technology is predicted jointly by perceived usefulness (PU) and perceived ease of use (PEOU) Perceived usefulness is defined as the belief that using the technology will improve performance, while perceived ease of use refers to the belief that the technology will be easy to operate The model also proposes that perceived ease of use is a predictor of perceived usefulness, creating a causal pathway from ease of use to usefulness that shapes overall technology adoption.
Technology adoption is driven by perceived usefulness and perceived ease of use, forming the core of the Technology Acceptance Model (TAM) Mobile-based payment systems are widely used for transactions, and consumers favor this payment method, linking perceived usefulness to behavioral intention Shaw (2014) argues that perceived usefulness significantly influences the desire to use an e-wallet, with data showing that the promise of achieving the intended outcomes motivates adoption In mobile payments, respondents note that transactions can be completed quickly thanks to readily accessible smartphones A broad body of research using perceived usefulness as a variable consistently finds a positive effect on technology adoption, underscoring its critical role in shaping acceptance and improving outcomes in future investigations Consequently, this study should analyze perceived usefulness within the e-wallet context.
Perceived ease of use, a core element of the Technology Acceptance Model, assesses how readily consumers interact with an e-wallet Studies show that e-wallet payments are typically more convenient and faster than traditional banking transactions, delivering savings in both time and money, and these gains are often supported by blockchain technology.
Perceived ease of use refers to how easy a system is to operate, and it exerts a positive and meaningful influence on users’ behavioral intention to adopt technology (Davis et al., 1989; Lee et al., 2017) A consistent pattern across studies shows that greater ease of use boosts behavioral intention to adopt and continue using information systems, and it also enhances perceived usefulness (Agrebi & Jallais, 2015; Indarsin & Ali, 2017; Perkasa & Rustam, 2016; Raza et al., 2017) For instance, Al-Maroof and Al-Emran (2018) found that students who perceived web service technology as simple and user-friendly reported higher perceived usefulness and stronger behavioral intentions The Technology Acceptance Model (TAM) remains widely used due to its applicability across diverse technologies (Davis et al., 1989; Ali et al., 2021; Krisnawati et al., 2021), making it well suited to examining E-Wallet adoption Accordingly, E-Wallet as a modern technology can be effectively studied through TAM, and this research extends TAM with additional variables that influence the behavioral intention to use E-Wallet payment services (Kumar, Sivashanmugam, & Venkataraman, 2017).
Perceived risk in consumer behavior is the possibility that any action may trigger unpredictable outcomes, some of which could be unpleasant, and consumers often cannot fully foresee the implications of their choices with accuracy (Bauer, 1960) The concept is highly multidimensional, encompassing financial, social, psychological, physical, and performance risks (Kaplan, Szybillo & Jacoby, 1974).
Perceived risk refers to the consideration of potential negative outcomes or repercussions when using a service, as defined by Featherman and Pavlou (2003) Building on Gupta and Kim (2010), it represents the user’s assessment of the degree of uncertainty and the adverse consequences associated with a merchant’s transaction.
Perceived risk is a key factor shaping e-wallet adoption, as consumers weigh security and privacy when deciding whether to use digital wallets Many users place greater importance on protecting the wallet itself than on the mobile device, but the potential loss of the phone can have a substantial impact on adoption decisions Risk associated with e-wallets includes data loss, credit card fraud, and skepticism toward new technology, all of which influence consumer attitudes and behavior toward digital payments (Gefen et al., 2003; Teo and Yeong, 2003).
Perceived risk in mobile payments is the anxiety about the security of transactions and is linked to concerns about the negative consequences of adopting new technology It encompasses fears of personal data leakage and potential financial loss associated with fintech use Although fintech adoption carries inherent risk and uncertainty, it also offers the benefit of simplifying consumer interactions compared with traditional financial services.
Wu and Wang (2005) report a Taiwan-based study showing that perceived risk strongly shapes consumers' behavioral intentions to adopt mobile commerce The findings suggest that when customers perceive risk, they become more cautious, drawing on their knowledge and experience with mobile commerce to navigate it Even so, many shoppers who intend to transact online may still proceed if the perceived benefits outweigh the risks, or they can avoid the high-risk aspects of mobile commerce The study also notes several advantages of mobile commerce—competitive pricing, a wider product selection, convenience, and time savings—that can entice online transactions despite lingering risk concerns.
Across e-services, e-commerce, and mobile payments, perceived risk emerges as a key driver shaping users’ intention to adopt digital technologies Featherman et al (2003) identify seven risk dimensions—financial, privacy, efficiency, timing, psychological, social, and general risks—that influence the willingness to use e-services Lim (2003) highlights three risk sources in e-commerce: technology, suppliers, and products Tan et al (2010) show that insufficient or unreliable security technologies heighten perceived risk, leading to lower satisfaction and a reduced intention to continue using e-commerce platforms Likewise, Yang et al (2015) pinpoint three risk aspects—financial, efficiency, and privacy risks—that affect the acceptance of mobile payments.
This study defines risk perception as the potential losses and negative outcomes arising from user uncertainty when using the Momo e-wallet, and it assesses how this risk influences Gen Z's intention to reuse the service To capture these effects, four dimensions of risk perception are employed: financial risk, security risk, time-loss risk, and operational risk.
Per Al-Debei et al (2015), perceived benefit is the relative advantage that signals how strongly people believe an innovation will deliver higher benefits When consumers decide to use a mobile payment service, they weigh multiple factors to reduce negative utility while maximizing positive value, a notion echoed by De Kerviler et al (2016) and Park et al.
(2019), perceived benefit relates to the system's convenience and quickness, as well as its user-friendly payment procedure The utility of mobile payment refers to the degree to which customers perceive that using mobile payment will provide them with benefits (e.g., convenience, simplicity of transaction) in comparison to other payment methods (Bailey et al., 2017)
The term "perceived benefit" relates to customers' opinion that the technical system they are using may improve performance while also increasing efficiency, as well as their belief that the technology they are utilizing gives advantages (Farahdiba, 2019)
Perceived benefit is the belief that using a technological product will yield performance improvements and enhance job performance In essence, it represents the expectation that the device will provide tangible advantages that simplify tasks and increase efficiency, making work easier for the user (Normayanti Putri et al., 2021; Rodiah, 2020).
Previous research
Jaehyeon Jun, Insu Cho and Heejun Park (2018) examined factors influencing continued use of mobile easy payment services in their empirical investigation, "Factors Influencing Continued Use of Mobile Easy Payment Service: An Empirical Investigation." Using a structural equation model with 182 respondents, the study finds that all benefit-side factors positively affect perceived value, and this perceived value subsequently drives the intention to continue using the service In contrast, sacrifice-related factors show no significant impact The researchers conclude that customers tend to focus on anticipated benefits rather than potential drawbacks, suggesting that managers should prioritize delivering additional benefits to sustain ongoing use of mobile easy payment services.
Afrida Putritama (2019) based on the research model of Theory of Reasoned Action (TRA) which introduced by Fishbein and Ajzen (1975) to cunduct the study
With 113 Indonesian participants, this study examines how perceived benefits and perceived risks shape the intention to continue using mobile payment FinTech in Indonesia Using an exploratory sequential mixed-methods approach—beginning with a questionnaire and ending with semi-structured interviews—the research captures both positive and negative factors affecting continuance Results show that both perceived benefit and perceived risk significantly influence continuance intention, though perceived benefit has a greater impact Among benefits, convenience emerges as the strongest driver of perceived benefit, thereby supporting ongoing use, while among risks, financial risk has the largest effect on perceived risk, which in turn affects the intention to continue using mobile payment FinTech.
Norma Diana and Farah Margaretha Leon (2020) conducted the study
Factors Affecting Continuance Intention of Fintech Payments among Millennials in Jakarta investigates how millennials shift from cash to cashless transactions and examines how perceived benefits—economic benefits, seamless transactions, and convenience—along with perceived risks—financial, legal, security, and operational—shape their intention to continue using FinTech payment services Employing Structural Equation Modeling–Partial Least Squares (SEM-PLS) with data from 313 questionnaire respondents, the study finds that convenience is the most influential benefit driving continuance intention, while operational risks have a negligible impact It also reveals disparities in how benefits and risks affect early adopters versus late adopters, underscoring differences in user segments The results contribute to understanding the factors that influence Jakarta millennials’ choice to utilize FinTech payment services and their continued usage.
Widya Sentanu, Stefan A.N Sagala, Dodi Marjuki, and Willy Gunadi (2020) build on Ryu's (2018) analysis of the effects of benefits and risks on e-wallet use by grounding their study in Valence Theory and the Theory of Reasoned Action (TRA), and by identifying the limits of the Benefit–Risk Framework to reconstruct the risk and benefit elements that shape users' continuance intention Using partial least squares structural equation modeling (PLS-SEM) with data from 203 online survey respondents, the study finds that perceived convenience has the strongest positive influence on the continuance intention to use e-wallet services, while financial risk remains the primary consumer concern; nevertheless, such risk does not prevent users from continuing to use the e-wallet service.
Nguyen Hoang Minh, Hua Le Thien Bao, and Le Thi Thuy Vi (2020) conducted a study titled Perceiving benefit-risk and Fintech users’ continuance intention in Ho Chi Minh City, anchored in the TRA model of Fishbein and Ajzen and the TAM model of Davis The study aims to assess benefit–risk perception and continuance intention among Fintech users in Ho Chi Minh City Data were collected from 472 Fintech users under 40 and analyzed with AMOS; scales were tested using Cronbach’s Alpha, exploratory factor analysis (EFA), and structural equation modeling (SEM) Results show that perceived benefits are driven by economic benefits and convenience, while perceived risks are driven by financial risks and security risks; continuance intention to use Fintech is positively affected by perceived benefits and negatively affected by perceived risks The study also identifies differences between non-proficient and proficient Fintech users: for non-proficient users, perceived benefits are shaped by economic benefits and convenience, and continuance intention is positively influenced by perceived benefits and not by perceived risks, with risk perception driven by financial risk.
Among fintech-savvy users, perceived benefits—driven by economic gains and convenience—positively influence the intention to continue using fintech financial services, while perceived risk negatively affects ongoing use, shaped by financial and security risk factors.
Nguyen Vinh Khuong, Nguyen Thi Thanh Phuong, Nguyen Thanh Liem, Cao Thi Mien Thuy, and Tran Hung Son (2022) explore the factors affecting the continuance intention to use financial technology among Vietnamese youth in the COVID-19 era and beyond, examining Perceived Benefit, Perceived Risk, Belief, and Social Influence within the TAM and TRA frameworks, and employing PLS-SEM via SmartPLS on a sample of 161 Gen Z Fintech users; the results show Perceived Benefit as the strongest predictor of continuance intention to utilize Fintech, followed by Belief, while the overall effects are modest, potentially due to Vietnam-specific characteristics, offering implications for service providers, governments, and researchers to calibrate development and future research, and contributing to the literature by expanding the focus on young people and presenting results that differ from earlier studies.
The author of the study The subject of research Factors
Factors influencing continued use of mobile easy payment service: an empirical investigation
- Benefits (Compatibility, Simplicity, Economic Value)
- Sacrifices (Switching Cost, Perceived Privacy Risk)
The Mobile Payment Fintech Continuance Usage Intention in Indonesia
- Perceived Benefit (Economic Benefit, Seamless Transaction, Convenience)
- Perceived Risk (Financial Risk, Legal Risk, Security Risk)
- FinTech Continuance Intention Norma Diana and Factors Affecting Continuance - Perceived Benefit (Economic
Intention of Fintech Payment among Millennials in Jakarta
- Perceived Risk (Financial Risk, Legal Risk, Security Risk, Operational Risk)
- Continuance Intention of Fintech Payment
Analysis of the Effects of Benefit and Risk Factors on the use of E-Wallet
- Perceived Benefit (Economic Benefit, Seamless Transaction, Convenience)
- Perceived Risk (Financial Risk, Legal Risk, Security Risk, Performance Risk, Time Risk, Psychological Risk, Operational Risk, Social Risk)
Perceiving benefit-risk and Fintech users’ continuance intention in Ho Chi Minh City
- Perceived Benefit (Economic Benefit, Seamless Transaction, Convenience)
- Perceived Risk (Financial Risk, Security Risk, Operational Risk)
Factors Affecting the Intention to Use Financial Technology among Vietnamese Youth:
Research in the Time of COVID-19 and Beyond
- Perceived Benefit (Economic Benefit, Seamless Transaction, Convenience)
- Perceived Risk (Financial Risk, Legal Risk, Security Risk, Operational Risk)
After reviewing prior research, the author identified a range of perceived benefits and perceived risks that influence the intention to reuse e-wallets The perceived risk and perceived benefit factors outlined in Table 2.2 are shown to affect the intention to reuse the Momo e-wallet.
Table 2.2 Factors of perceived risk and perceived benefit impact the intention to reuse an E-wallet
Factors identified in previous studies Researchers
Perceived Risk Financial Risk Putritama (2019); Diana, N., & Leon, F M (2020);
Minh et al (2020); Sentanu et al (2020); Khuong et al
Security Risk Putritama (2019); Diana, N., & Leon, F M (2020);
Minh et al (2020); Sentanu et al (2020); Khuong et al
Time-loss Risk Sentanu et al (2020)
Operational Risk Diana, N., & Leon, F M (2020); Minh et al (2020);
Sentanu et al (2020); Khuong et al (2022)
Economic Benefit Jun, J., Cho, I., & Park, H (2018); Putritama (2019);
Diana, N., & Leon, F M (2020); Minh et al (2020); Sentanu et al (2020); Khuong et al (2022)
Putritama (2019); Diana, N., & Leon, F M (2020); Minh et al (2020); Sentanu et al (2020); Khuong et al
Minh et al (2020); Sentanu et al (2020); Khuong et al
Research Hypotheses and Model
2.4.1.1 The relationship between perceived risk and intention to reuse
Perceived risk, as defined by Ryu (2018), captures users’ view of uncertainty and potential adverse outcomes linked to using an e-wallet Evidence suggests that greater perceived risk in mobile payments discourages adoption, with fewer consumers choosing to use mobile wallets (Liu et al., 2019) In this study, perceived risk is structured around four first‑order constructs: financial risk (FR), security risk (SR), time‑loss risk (TR), and operational risk (POR).
Financial risk—defined as the potential for financial loss from using payment systems—stems from system failures, financial fraud, and higher transaction costs (Yang et al., 2015b; Ryu, 2018) In the e-wallet sector, fraud and losses on user accounts are frequent, raising consumer concern about the security and reliability of digital payments.
In Vietnam, e-wallet services are under scrutiny as authorities and users weigh the potential financial risks tied to their operation Despite rising adoption of wallets like Momo, consumers remain skeptical about the safety of using these digital payment tools to buy services such as air tickets and hotel bookings, raising questions about risk management, transparency, and consumer protection in the Vietnamese market.
Security risk is the potential loss of control over personal information, including unauthorized use or disclosure, as described by Luo et al (2010) It also refers to the possibility of financial loss due to hackers’ criminal activities within the payment service system, such as data privacy breaches and monetary loss (Ryu, 2018) In the context of e-wallets, security risks include invasion of privacy—such as abuse of transaction history or hacking of a credit card linked to the user account—which can lead to anxiety and uncertainty about using the service.
In today’s rapidly evolving technology landscape, service providers consistently prioritize protecting customers’ personal information through robust data protection and cybersecurity practices Yet even with widespread perceptions of security in the digital age, ongoing tech changes keep network attackers adapting, leaving providers to continually confront evolving threats that can erode customer trust and create a lingering sense of security risk among users.
Time risk refers to the time lost during the operation of a payment system, including slow access to the service, problems launching the service, and the need to reinstall the service These time losses have been identified in prior research (Yang et al., 2015b; Namahoot & Laohavichien, 2018; Choo et al.).
2016) For e-wallets, repeat transactions, application restarts, or interruptions in the transaction process can also become a time-loss risk factor
Sometimes customers using the Momo e-wallet encounter transaction problems, such as sending funds to the wrong account, system errors, or delays caused by upgrading the app, all of which can extend the refund process In these cases, users must spend time contacting customer support to resolve the issue Adapting to new features in Momo also takes time, and because Momo acts as an intermediary between multiple organizations and apps, any unfavorable situation may require waiting for resolution from partner services.
Operational risk refers to the likelihood of a product failing to perform as planned and promoted, and so failing to provide the anticipated benefits (Luo et al.,
Operational risk represents the potential for loss arising from a company's internal problems, such as failed procedures, system outages, and inadequate operational capabilities (Ryu, 2018) These internal issues can pose barriers for both users and financial institutions—banks and fintech firms alike—leading customers to abandon or discontinue services when the perceived level of operational risk is high.
In some cases, customer complaints directed at the Momo provider are not responded to promptly or effectively, leading to growing customer dissatisfaction and a higher perceived risk among users Prompt and thorough handling of complaints helps maintain trust, protect the platform’s reputation, and reduce users’ perception of risk When support responses lag, users may doubt service reliability and security, underscoring the need for strong customer service processes, clear escalation paths, and proactive communication.
Perceived risk has a detrimental influence on the intention to reuse mobile payment systems, as demonstrated in prior research by Putritama (2019); Diana, N., & Leon, F M (2020); Minh et al (2020); Sentanu et al (2020); and Khuong et al (2022) Based on these results, the author posits the hypothesis that perceived risk negatively affects the intention to reuse mobile payments.
H1: Perceived risk has a negative effect on the intention to reuse Momo e- wallet
2.4.1.4 The relationship between perceived benefit and intention to reuse
Perceived benefit, as defined by Lee et al (2013), is the user’s assessment of the potential advantages that technology can deliver when used In this study, perceived benefit is formed by three primary first-order constructs: Economic Benefit (EB), Seamless Transaction (ST), and Convenience (CV) These components—economic gains, frictionless processes, and everyday ease—collectively shape users’ overall perceived value of the technology and its adoption potential.
In fintech, Ryu (2018) defines economic benefit as cost reduction and financial gain, noting cheaper transaction costs, a greater interest rate for creditors, and a lower interest rate for debtors in some fintech services With regard to e-wallets, an economic benefit example is the reduction of transaction charges levied on consumers by financial service providers (Sangwan et al., 2019; Ozturk et al., 2017; Lee & Kim).
As of 2020, prior research indicates that users often prefer e-wallets to other payment methods due to the added perks they provide, such as discounts, cashback, coupons, and other promotional offers (Chawla & Joshi, 2019; Jun et al., 2018).
Momo e-wallet currently provides money transfer and receipt services, with other financial services offered free of charge In addition, users receive numerous discount codes for Momo e-wallet utilities, such as mobile phone recharge and food ordering As a result, using an e-wallet can help users save a lot of money by cutting unnecessary expenses and taking advantage of these promotions.
Seamless fintech transactions are defined by the benefits of using digital finance tools to complete activities such as purchasing goods, transferring or lending funds, or investing, all without the involvement of traditional banks in the transaction flow In mobile e-wallet contexts, the absence of a conventional financial intermediary signals a truly seamless experience A defining feature is one-stop processing, which allows users to access multiple financial services within a single session—money transfers, balance checks, credit-limit inquiries, and even shopping—simultaneously This perspective is also echoed by Jun et al (2018), who describe seamless transactions as enabling an e-wallet to handle a broad range of financial activities smoothly.
METHODOLOGY
Research process
This study builds a theoretical framework by reviewing local and international literature on perceived risk and perceived benefit to establish the groundwork for understanding consumer acceptance of e-wallets It also analyzes the development process, popularity, and operational mechanisms of e-wallets to map current market dynamics After synthesizing secondary sources, the research formulates a theoretical foundation, develops a testable research model with hypotheses, and conducts empirical testing to validate the proposed relationships between perceived risk, perceived benefit, and e-wallet adoption.
Once the research model has been developed, the next step is to design a rigorous research plan that supports data collection for testing the proposed correlations and hypotheses within the research model This phase outlines the methods, instruments, sampling strategy, and measurement techniques needed to gather reliable data, ensuring alignment with the specified constructs and variables By carefully selecting an appropriate research design, researchers can accurately assess the relationships among variables, validate the hypothesized links, and provide a clear roadmap for data analysis and interpretation A well-structured research design enhances the study’s validity and reliability while guiding the overall execution of the research.
Using a quantitative research design, this study collects data to examine how perceived risk and perceived benefit influence the intention to reuse the Momo e-wallet The analysis reveals how these factors affect user willingness to reuse, providing actionable conclusions and management implications for stakeholders The results, illustrating the relationships among perceived risk, perceived benefit, and reuse intention, are presented in the accompanying figure.
Research design
This study was conducted in two phases: (1) Preliminary research, (2) Formal research
Preliminary research aims to determine the relationship between the dependent variable and the independent variables, while screening the independent variables within the proposed research model It also assesses the adequacy of the scale, making adjustments and supplements to align it with the study concepts The formal study employs quantitative techniques using a questionnaire to collect data The steps to build and evaluate the scale used in the model are presented in Appendix 1.
Research problem Objective of reserach Review the literature
Models and hypotheses Preliminary research
Collecting data Statistical analysis The result, the solution implication
Cronbach’s Alpha Exploratory factor analysis (EFA) Confirmatory Factor Analysis (CFA) Structural Equation Modeling (SEM)
Data collection
An adequately sized sample depends on correct calculation and the level of precision sought Key factors shaping sample size include the expected reliability, the data analysis method, the estimation strategy, and the parameters to be estimated When planning a study, researchers should explicitly align the sample size with these elements to ensure robust results and credible inferences.
Hair et al (1998) state that exploratory factor analysis (EFA) requires a minimum of five observations per variable, a 5:1 subject-to-variable ratio With 37 observed variables in this study, this implies a required sample size of n = 37 × 5 = 185.
According to Tabachnick and Fidell (2007), the minimum sample size for regression is n ≥ 50 + 8m, where m is the number of predictors In this study, with seven independent variables, the required minimum is n ≥ 50 + 8×7 = 106 Therefore, a sample of at least 106 observations is recommended to ensure adequate statistical power for the regression analysis.
Structural equation modeling (SEM) relies on large-sample distribution theory, so it requires a substantial sample size to produce stable covariance estimates, a point emphasized by Raykov and Widaman (1995) Similarly, Anderson et al (1985) identify sample size as SEM’s main limitation, noting that covariances stabilize only with a sufficiently large sample; although 200 participants are often considered the minimum, 300 or more is preferable for robust results.
However, in order to ensure the reliability of the research scales, the author determined the sample size in this study to be n = 350
This study uses a non-probability sampling approach, specifically convenience sampling, to save time and reduce costs while maintaining objectivity in data collection The participants are Gen Z individuals aged 12 to 27 who currently live and work in Ho Chi Minh City and are users of the Momo e-wallet.
According to Anderson and Gerbing (1988), developing a scale for the model's variables is a crucial step in a bridge between theory and theory testing
Likert scales are a practical and accessible method to gather quantitative data Likert scales facilitate the operationalization of complicated phenomena by decomposing abstract themes into observable data This allows for the statistical examination of hypotheses
Typically, Likert scales comprise five or seven items, and the items at either end are referred to as response anchors The middle is often a neutral place with positive and negative elements on each side Each item is either a 1–5 or 1–7 score
This study uses a Likert scale comprising perception-related statements Respondents rate each item on a five-point scale, from "Strongly disagree" to "Strongly agree," based on their assessment of the study object's features These responses provide a standardized measure of attitudes and perceptions, enabling reliable statistical analysis and meaningful interpretation of the data.
The topic assesses the amount of agreement with each observable characteristic using a 5-level Likert scale, as follows:
Strongly disagree Disagree Neither agree nor disagree Agree Strongly agree
In order to ensure objectivity in the study, the measurable variables are encoded as follows APPENDIX 1
To develop a robust survey questionnaire, the author drew on established scales and the observed factors identified in prior work The instrument was designed to align with validated measures and capture the key constructs of interest Before official data collection, the questionnaire underwent piloting to assess clarity, reliability, and validity, and was revised based on feedback to improve item wording and overall usability.
The exact format of the questionnaire is supplied as a survey form with the following three sections:
Section 1: Introduction to the questionnaire and directions for completion
Section 2: The questions focus on the respondents' personal information so that the author may compile statistics and characterize the survey sample
Section 3: The key questions of the research are quantitative, employing a 5- level Likert scale to assess the degree of agreement for each observable characteristic.
Data analysis
Cronbach's alpha, introduced by Lee Cronbach in 1951, is a widely used statistic for assessing the reliability of scales that measure the concepts embedded in a research model It remains the most commonly employed method for evaluating the reliability of multivariable scales, making it a standard tool in scale development and psychometric assessment.
Cronbach's alpha assesses reliability for each observed variable as well as the reliability of the entire scale, so items measuring the same construct should be strongly correlated Researchers typically examine the corrected item-total correlation for each measurement variable If an observed variable has a corrected item-total correlation of at least 0.3 and the Cronbach's alpha coefficient is greater than 0.6, the scale is considered satisfactory (Nunnally et al., 1994).
Many researchers consider Cronbach's alpha values from 0.8 up to near 1.0 to indicate good reliability of a scale Peterson (1994) suggests that a coefficient from about 0.7 to nearly 0.8 is satisfactory, and other authors argue that an alpha of 0.6 or higher can be acceptable when the concept being studied is new to respondents (Slater, 1995).
In summary, observed variables with Cronbach's Alpha coefficient less than 0.6 and corrected item-total correlation less than 0.3 will be excluded from the model
Exploratory factor analysis (EFA) is a member of the interdependent multivariate analysis family, meaning it does not rely on predefined dependent and independent variables but instead focuses on the relationships among observed variables As a data reduction and summarization technique, EFA uncovers the underlying latent factors that explain patterns of correlations across measured variables It is particularly useful for identifying the minimum set of variables needed to address a research problem and for revealing how variables relate to one another, enabling researchers to simplify complex data without imposing a prior structure.
To evaluate the scale value in EFA analysis, researchers often consider five important attributes:
- Factor loading must be used to determine whether to retain or eliminate the observed variable to guarantee that the scale achieves the convergent value
If the loading factor is less than 0.5, the variable is eliminated from the model, and vice versa Factor loadings more than or equal to 0.5 (Hair et al.,
In factor analysis, the number of factors to retain is determined by the eigenvalues: keep only factors with eigenvalues greater than 1 and discard those with eigenvalues less than 1 This Kaiser criterion ensures that retained factors explain more variance than an individual original variable, while the excluded factors do not improve the data’s summary Consequently, using this rule helps define the model’s dimensionality and prioritizes factors with meaningful contributions to the underlying structure.
- KMO index is used to consider the appropriateness of factor analysis, if 0.5
0.90, CFI > 0.90, GFI > 0.90, Cmin/df < 3, and RMSEA < 0.08 to confirm adequate model fit.
Table 3.1 Synthesis of indicators to evaluate the goodness of fit of the model
Name of rating indicato Symbol Critical value References
Chi-square p-value < 0.05 Hair et al (2006)
Goodness of Fit Index GFI GFI > 0.90 Bentler(1990)
Index TLI TLI > 0.90 Hair et al (2006)
Error Approximation RMSEA RMSEA < 0.08 Hair et al (2006)
Source: Compiled from the author
At the same time, CFA also helps to evaluate the scale in three aspects: discriminant validity, convergent validity, and internal consistency
The study evaluated both convergent and discriminant validity to assess the measurement model, with convergent validity indicated when AVE values exceed 0.50 and factor loadings are above 0.50 (Hair et al., 2011) Discriminant validity is demonstrated when inter-construct correlations are less than the square root of AVE and the Maximum Shared Variance (MSV) is below AVE Internal consistency reliability was assessed using composite reliability, expected to exceed 0.60 (Hair et al., 2011).
3.4.4 Structural Equation Modeling (SEM) Analysis
The structural equation modeling method is used to test the research model
In hypothesis testing, structural equation modeling (SEM) allows us to combine latent concepts with measurement and can consider independent measures or a combination of research concepts at the same time
During the assessment of a structural model, the associations between constructs are evaluated using a one-tailed test with a p-value less than 0.05 The link between independent and dependent variables is determined by the magnitude of the path coefficient and the R-squared value, which indicate the model’s predictive accuracy.
Chapter 3 outlines the research design and implementation process used to evaluate the scale, test the research model, and examine the hypotheses, with the study proceeding in three stages: qualitative research to generate insights, preliminary quantitative research to refine measures and models, and finally formal quantitative research to validate the findings.
Drawing on prior work in Chapter 2, the author refined, supplemented, and constructed an expectation scale with observed variables, employing a clear five-point Likert scale for all variables The core question investigates how perceived risk and perceived benefit shape Gen Z's intention to reuse the Momo e-wallet This chapter also outlines the data analysis methods used in quantitative research Based on the research methods described in Chapter 3, a field survey was conducted, and the detailed results are reported in Chapter 4.
RESULT
Descriptive Statistics
Samples were collected using a convenient sampling method through a Google Forms survey After removing invalid answer sheets, 358 valid responses remained for quantitative analysis, and the results reveal the key characteristics of the sample.
Gender distribution among the sample shows 24.9% male and 75.1% female, indicating that women use the Momo e-wallet about three times more than men The study focuses on Gen Z users aged 12–27, a group that tends to have high education but relatively low income As a result, 62.6% of Momo e-wallet transactions are under 200,000 VND The money transfer feature is widely used, selected by 48.9% of users.
Survey results show that over 80% of respondents use the Momo e-wallet at least once a week, signaling strong user familiarity with the platform This high level of regular use suggests that customers arrive at evaluations with substantial firsthand experience of the Momo e-wallet.
This study offers a representative snapshot of the current usage of the Momo e-wallet in Vietnam, revealing that Gen Z is the most familiar with digital wallet technology, while women tend to use Momo more than men.
Occasionally (1 time/week) 129 36,0% Infrequently (1 time/month) 43 12,0%
The average value spent in every Momo e-wallet transaction
For what payment do you use e-wallet most often?
Cronbach’s Alpha
According to the proposed model, four concepts derived from perceived risk and three concepts derived from perceived benefit require measurement and evaluation The reliability of all concepts was assessed using Cronbach's alpha coefficients computed in SPSS, and the results are presented in Table 4.2.
Table 4.2 Cronbach's Alpha coefficient analysis
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
Financial risk (FR): Cronbach’s Alpha: 0,878
Security risk (SR): Cronbach’s Alpha: 0,834
Time-loss risk (TR): Cronbach’s Alpha: 0,870
Operational risk (POR): Cronbach’s Alpha: 0,840
Economic benefit (EB): Cronbach’s Alpha: 0,837
Seamless transaction (ST): Cronbach’s Alpha: 0,825
Intent to use (ITUE): Cronbach’s Alpha: 0,781
As a result of running Cronbach's Alpha for the first time, the variable SR3 was removed because it did not meet the reliability requirements of the scale (APPENDIX 4)
Table 4.2 shows the results of Cronbach's Alpha from the second run, indicating that all scales meet the reliability requirements for internal consistency Cronbach's Alpha coefficients confirm acceptable reliability across the scales, and the corrected item–total correlations for the observed variables exceed the 0.3 threshold, with EB5 at the smallest value of 0.562 Together, these findings support strong overall reliability of the measurement constructs.
Cronbach's Alpha coefficient of the scales is greater than 0.6 (the smallest is Cronbach's Alpha of the scale ITUE is 0.781)
All concepts to be measured have thirty-six observed variables Each variable demonstrates a corrected item-total correlation from 0.562 to 0.724 (above the threshold of 0.3) and Cronbach's Alpha values from 0.781 to 0.878 (above the threshold of 0.6) Therefore, all thirty-six observed variables meet the reliability criteria and are accepted.
Exploratory Factor Analysis
4.3.1 Exploratory factor analysis of factors creating perceived risk
The results of exploratory factor analysis of the scale of factors creating the perceived risk of Gen Z when using the Momo e-wallet are performed with the
Principal Axis Factoring method and Promax rotation
Table 4.3 shows a KMO value of 0.924 (0.5 < KMO ≤ 1) and a Bartlett's test significance of Sig = 0.000, indicating the observed variables are closely related and suitable for factor analysis The total variance explained is 57.075% (>50%), meaning the extracted factors account for over half of the data variability and align with the original factors creating the perceived risk among Gen Z users of the Momo e-wallet, with no disturbance between the variables.
Table 4.3 Exploratory factor analysis of factors creating perceived risk
Financial risk Time-loss risk Security risk Operational risk
4.3.2 Exploratory factor analysis of factors creating perceived benefit
The results of exploratory factor analysis of the scale of factors creating the perceived benefit of Gen Z when using the Momo e-wallet are performed with the
Principal Axis Factoring method and Promax rotation
As a result of the first EFA analysis, the author removed the EB5 variable because it had a factor loading < 0.5 (APPENDIX 5)
From the second analysis in Table 4.4, the KMO value is 0.907 (0.5 ≤ KMO ≤ 1) and Bartlett's test is significant (Sig = 0.000 < 0.05), indicating that the observed variables are closely related The total variance explained is 56.628% (>50%), which is satisfactory and shows that the component variables in the factor scale accounting for the perceived benefit of Gen Z when using the Momo e-wallet can be explained by 56.628% of the data variability and are explained by the same factors as in the original model with no disturbance between variables.
Table 4.4 Exploratory factor analysis of factors creating perceived benefit
Convenience Economic benefit Seamless transaction
4.3.3 Exploratory factor analysis for the scale of the intention to reuse
The results of exploratory factor analysis of the scale of intention to reuse the Momo e-wallet of Gen Z are performed with the Principal Axis Factoring method and Promax rotation
Factor analysis results show a KMO value of 0.697 and a significant Bartlett's test (Sig = 0.000), indicating adequate sampling adequacy and that the observed variables are closely related The total variance explained is 55.038% (above the 50% threshold), demonstrating that the component variables in the Gen Z intention to reuse the Momo e-wallet scale explain 55.038% of the data's variability and are well represented by the original variables.
Table 4.5 Exploratory factor analysis for the scale of the intention to reuse
Confirmatory factor analysis
4.4.1 Confirmatory factor analysis for the scale of factors creating perceived risk, perceived benefit
According to Zainudin, A (2012), to handle second-order variables in CFA, we will evaluate the convergent validity of first-order variables
Gerbing and Anderson (1988) hold that a scale attains convergent validity when all standardized weights exceed 0.5 and are statistically significant (p < 0.05) Building on this, Hair et al (2009) indicate that convergent validity is achieved when composite reliability is above 0.7 and the average variance extracted (AVE) exceeds 0.5.
Table 4.6 Correlations of First-Order Perceived Risks
CR AVE MSV MaxR(H) FR TR SR POR
Note: CR = Composite Reliability; AVE = Average Varience Extracted; MSV = Maximum Shared Varience; FR = Financial Risk; TR = Time-loss Risk; SR = Security Risk; POR = Operational Risk
Analysis of the correlations among the first-order latent variables, as shown in Tables 4.6–4.7, reveals composite reliability (CR) values exceeding 0.7 and average variance extracted (AVE) values above 0.5, which supports convergent validity for the first-order latent-variable scales These results indicate that the measurement model meets standard criteria for reliability and convergent validity, validating the use of the first-order constructs in subsequent analyses.
Table 4.7 Correlations of First-Order Perceived Benefits
CR AVE MSV MaxR(H) CV EB ST
Note: CR = Composite Reliability; AVE = Average Varience Extracted; MSV = Maximum Shared Varience; CV = Convenience; EB = Economic Benefit; ST = Seamless Transaction
4.4.2 Confirmatory factor analysis for the scales of perceived risk, perceived benefit, and intention to reuse
To validate the measurement model prior to testing the structural model, an analysis of reliability and convergent validity was conducted The results in Table 4.8 indicate that all constructs meet internal consistency reliability, with composite reliability values ranging from 0.785 to 0.824 Convergent validity is also established, as all CR values exceed 0.7 and all AVE values exceed 0.5 across the factors.
Table 4.8 Reliability Convergent Validity Results
Analytical results shown in Table 4.9 indicate that inter-construct correlations are lower than the square root of the AVE, and the MSV coefficient is below the AVE, demonstrating discriminant validity for the measurement scales These findings support the distinctiveness of the constructs and the robustness of the measurement model.
Table 4.9 Correlations of Latent Variables
CR AVE MSV ITUE PR PB
Note: CR = Composite Reliability; AVE = Average Varience Extracted; MSV = Maximum Shared Varience; ITUE = Intention to reuse; PR = Perceived risk; PB = Perceived benefit
In short, all the scales in this study meet the requirements of reliability and validity: composite reliability, convergent value and discriminant value
Following satisfactory findings for convergent and discriminant validity, the model’s goodness of fit was assessed As shown in Table 4.10, all fit indices indicate satisfactory model fit: CMIN/DF = 1.257 (less than 3); TLI = 0.974; CFI = 0.976; GFI = 0.902; RMSEA = 0.027.
Fit Index CMIN/DF TLI CFI GFI RMSEA
To evaluate model fit, this article reports several indicators: CMIN/DF, the ratio of chi-square to degrees of freedom, provides a scaled measure of discrepancy between the observed and modeled covariance structures; TLI (Tucker-Lewis Index) and CFI (Comparative Fit Index) compare the specified model with a null baseline to assess improvement in fit; GFI (Goodness-of-Fit Index) reflects how well the model reproduces the observed covariance matrix, and RMSEA (Root Mean Square Error of Approximation) estimates the lack of fit per degree of freedom, with lower RMSEA indicating better fit.
Structural equation modeling
The structural model reflects the predicted relationships between the study constructs and latent variables, with Figure 4.1 summarizing the testing results The analysis confirms that the model hypotheses are statistically significant (P < 0.05).
Specifically, through Figure 4.1 the author extracts the following conclusions:
Hypothesis H1 states that: Perceived risk has a negative impact on Gen Z's intention to reuse Momo e-wallet The estimated results show the relationship between perceived risk and intention to reuse Momo e-wallet is -0.603 with a standard error SE = 0.094 This estimate is statistically significant at P = 0.000
Intention to reuse Momo e-wallet
Figure 4.1 The results of the analysis of the structural equation modeling
Thus, this hypothesis is accepted This shows that perceived risk is a negative factor in the intention to reuse Momo e-wallet That is, a person of Gen Z when using Momo e-wallet, if they perceive high risks, they will tend not to intend to reuse Momo e-wallet
Hypothesis H2 posits that perceived benefit positively influences Gen Z's intention to reuse the Momo e-wallet The estimated path coefficient is 0.771 (SE = 0.110) with a p-value of 0.000, indicating a statistically significant relationship Therefore, Hypothesis H2 is supported: higher perceived benefits increase Gen Z's intention to reuse the Momo e-wallet In practical terms, Gen Z users who perceive greater benefits from using the Momo e-wallet are more likely to intend to reuse it.
Within the measurement model, the square multiple correlation (SMC), also known as R-square, ranges from 0 to 1 and reflects a model’s descriptive power and predictive accuracy Figure 4.1 shows that the R-square for Intention to Reuse (ITUE) is 0.678, indicating that perceived risk (PR) and perceived benefit (PB) together account for 67.8% of the variance in ITUE.
Chapter 4 reports that the scale was validated with SPSS and AMOS, leading to the removal of SR3 due to reliability concerns The remaining items were then evaluated for composite reliability, convergent validity, and discriminant validity, with all constructs meeting acceptable fit criteria for the market data The final stage employed structural equation modeling (SEM) to test the proposed model and hypotheses, revealing that perceived benefit positively influences the intention to reuse the Momo e-wallet, while perceived risk negatively affects this intention.