Research rationale
Non-cash payments are increasingly becoming a global trend in economic development, particularly in Vietnam A 2020 VISA survey indicates that Vietnamese consumers are opting for electronic payment methods, such as contactless cards, mobile banking, and electronic wallets, leading to a decline in cash transactions compared to the previous year.
Consumers are increasingly opting for non-cash payment methods, with 74% of Vietnamese consumers expected to use them more in the next year The State Bank of Vietnam (SBV) reported a significant rise in e-payment during the first four months of 2020, with mobile payments soaring by 198.8% in quantity and 21.9% in value compared to 2019 To further promote non-cash payments, the SBV has urged local branches to implement strategies in key sectors such as utilities, healthcare, education, and public services Currently, around 50 banks have established electronic tax payment connections with authorities across 63 provinces, and 27 banks, along with 10 payment service providers, are collaborating with Vietnam Electricity for bill collection This trend clearly indicates a shift towards non-cash payment methods, highlighting the rapid development and growing significance of e-payment in the economy, which is evolving to meet consumer demands.
The rise of non-cash payments has led to significant attention on electronic wallets (e-wallets) from the government, businesses, and consumers, making them the most favored method of payment Although the Vietnamese government issued e-wallet licenses in 2008, their popularity surged in 2017 due to strong integration with various services According to Le Anh Dung, Deputy Head of the Payment Department of the State Bank of Vietnam (SBV), by the first quarter of 2020, Vietnam had 34 active e-wallets with 13 million registered accounts Additionally, SBV statistics reveal that there were 225.6 million transactions through e-wallets, amounting to a total value of 77.7 trillion VND during the first three months of 2020.
A survey by Cimigo reveals that Momo, Moca, and ZaloPay are the leading e-wallets in Ho Chi Minh and Hanoi These e-wallets are primarily utilized for mobile phone top-ups, utility bill payments, and money transfers On average, Momo users spend approximately 520,000 VND daily, while Moca users spend around 506,000 VND, and ZaloPay users spend about 441,000 VND.
The demand for e-wallet adoption in Vietnam has surged, indicating a significant shift towards non-cash payments Le Thi Thuy Sen, Director of the Communications Department of SBV, emphasized the commitment to ensuring that no one is left behind in accessing financial services, particularly in rural areas Tailored programs will be developed to align with local habits and cultures, making e-wallets more accessible and understandable for remote communities As a result, e-wallets are poised to become the leading non-cash payment method in Vietnam Additionally, the Covid-19 pandemic has prompted the government to encourage reduced cash transactions, further solidifying the e-wallet's status as a preferred payment option in the current climate.
While existing studies on e-wallets in Vietnam primarily focus on urban areas like Ho Chi Minh City and Hanoi, the popularity of e-wallets is rising among residents in provinces, driven by favorable policies from the Vietnamese government and the State Bank of Vietnam (SBV) This shift indicates that consumers in rural areas are emerging as a key target market for e-wallet services Consequently, provinces represent a significant opportunity for e-wallet service providers to explore, prompting the author to conduct an in-depth study on this topic.
“Factors influencing consumers’ adoption intention of e-wallet in Vinh Long province”.
Research background
International research
E-wallets have emerged as a prominent non-cash payment method, attracting significant attention from researchers worldwide In international studies, e-wallets are often referred to as mobile wallets or digital wallets, whereas in Vietnam, they are predominantly known as e-wallets.
Amin (2009) conducted a study on "Mobile wallet acceptance in Sabah: an empirical analysis," examining the factors influencing the intention to adopt mobile wallets in Sabah, Malaysia The findings revealed that perceived usefulness, perceived ease of use, perceived expressiveness, and knowledge of mobile wallets significantly impacted adoption intentions at a 95% significance level Additionally, the author recommended incorporating perceived self-efficacy, perceived financial cost, perceived enjoyment, and normative pressure into the model to enhance its applicability in various situations, addressing a limitation of the study.
Rathore's (2016) research on the "Adoption of Digital Wallet by Consumers" identified three key factors influencing the adoption of digital wallets in India: convenience in online purchasing, the usefulness of digital wallets, and brand loyalty However, a significant barrier to adoption was the reliance on a stable internet connection It's important to note that the study primarily examined consumer behavior regarding online shopping payments, leaving out potential factors that could influence the intention to adopt digital wallets for other uses.
Yadav (2017) investigated the "Active determinants for adoption of mobile wallets" and discovered that perceived usefulness significantly influences the intention to adopt mobile wallets, while factors such as perceived ease of use, perceived risk, trust, and quality of service were found to have an insignificant impact on Indian consumers' adoption intentions This finding contrasts with previous research, as perceived ease of use is typically regarded as a crucial factor in the adoption of new technologies, including mobile wallets, according to the Technology Acceptance Model.
Domestic research
The author discovers that research on e-payment and e-wallets in Vietnam is limited, with Liu and Tai's (2016) study, "A Study of Factors Affecting the Intention to Use Mobile Payment Services in Vietnam," being particularly noteworthy This study highlights that perceived usefulness, perceived ease of use, and trust in safety significantly influence the intention to use mobile payment Additionally, the convenience of mobility directly impacts perceived usefulness and ease of use, which in turn affects the intention to adopt mobile payment While this research is crucial for the development of mobile payment in Vietnam, it notably lacks a thorough investigation into the e-wallet sector, representing a significant limitation.
Regarding e-wallet, Nguyen Thuy Dung and Nguyen Ba Hung (2018) provided an overview of the current situation of e-wallet in Vietnam through a study
The adoption of electronic wallet payments in Vietnam has surged in recent years, becoming a common payment method for consumers due to various topping-up and payment options However, significant challenges remain, including a lack of synchronization among service providers, insufficient information and guidance on e-wallet usage, and low consumer trust This study provides an accurate overview of the e-wallet landscape in Vietnam over the past few years, yet it notably lacks an exploration of the factors influencing Vietnamese consumers' intentions to adopt e-wallets, highlighting a critical gap in the research.
Research on the factors affecting consumers' intention to adopt e-wallets has gained significant attention globally, yet there is a notable lack of related studies in Vietnam Understanding these factors is crucial for service providers to better reach consumers and enhance the growth of e-wallet payment systems Consequently, the author's research aims to address this gap and promote further exploration in this area.
Research aims and objectives
Research aims
This research investigates the factors influencing consumers' intentions to adopt e-wallets and their impact on consumer behavior, ultimately providing recommendations for enhancing e-wallet payment systems in Vinh Long province In today's competitive landscape, these insights will be invaluable for e-wallet service providers striving to grow their market presence and boost subscriber numbers.
Research objectives
The article will first synthesize theoretical models and empirical research related to the intention to adopt new technologies, with a specific focus on e-wallets Following this, a proposed model and measurement scale will be developed to identify the factors influencing consumers' intention to adopt e-wallets in Vinh Long province.
The author plans to conduct a one-month survey to gather data, which will be analyzed using SPSS 20.0 software to assess the significance of the model This analysis will enable the author to identify the factors influencing the adoption intention of e-wallets and evaluate their impact.
The author offers recommendations for e-wallet service providers to enhance their understanding of consumer behaviors in Vinh Long province, aiming to boost their market competitiveness.
Research object and scope
Research object
Based on the aim of this paper, the research object is the intention to adopt e- wallet of consumers in Vinh Long province
The survey targets residents of Vinh Long province who have adopted at least one e-wallet This criterion ensures that respondents possess relevant experience with e-wallets, enabling them to provide accurate and insightful responses.
Research scope
This research focuses on individuals residing or working in Vinh Long province, chosen for its notable e-wallet adoption trends Key establishments such as Vincom Center, CGV movie theater, Coopmart supermarket, and Thegioididong store have incentivized consumers to embrace e-wallets through various promotions, including discounts and vouchers Additionally, the local electricity provider has ceased in-home bill collections, prompting a shift from cash to non-cash payments These factors present significant opportunities for enhancing e-wallet adoption, positioning Vinh Long province as a promising market for further investigation.
In terms of time, the research was conducted within 3 months from September
2020 to November 2020 Particularly, the survey was carried out within one month which was in October 2020 so that the data could be lately updated.
Research questions
This research is implemented to answer three following questions:
(1) What are the factors influencing the intention to adopt e-wallets of consumers in Vinh Long province?
(2) To what extent do these factors influence the intention to adopt e-wallets of consumers in Vinh Long province?
(3) Based on this study, what are the recommendations for e-wallet service providers to expand their services to consumers in the provinces?
Research methods
The research was carried out in accordance with two following phases in order to guarantee the profession of scientific research
In the initial research phase, a qualitative approach was utilized, beginning with a comprehensive review of relevant theoretical frameworks and prior studies The author then identified the most appropriate frameworks to develop a research model and formulate hypotheses Subsequently, a brief interview and pilot survey were conducted with e-wallet users to refine the questionnaire for clarity and ease of understanding.
The official research phase utilized a quantitative method, beginning with the author conducting a survey based on a standardized questionnaire to gather primary data The collected data was then analyzed using SPSS 20.0 software to assess Cronbach's Alpha coefficient, perform Exploratory Factor Analysis, and conduct Pearson correlation and Multiple Linear Regression This analysis aimed to test the reliability of the scale, evaluate the proposed model, and validate the research hypotheses.
Contribution and significance of the research
Contribution of the research
This research uniquely focuses on consumers in a province, contrasting with other studies that typically target major cities like Ho Chi Minh and Ha Noi for easier data collection The author aims to explore the e-wallet adoption landscape in smaller provinces, which have been largely overlooked by researchers, to gain a more comprehensive understanding of the current situation.
The Unified Theory of Acceptance and Use of Technology (UTAUT) traditionally includes four key factors: performance expectancy, effort expectancy, social influence, and facilitating conditions However, the author introduces three additional factors—trust, perceived risk, and self-efficacy—that have been shown to significantly influence adoption intention in prior research Notably, this study presents the first model that integrates all seven factors, marking a significant advancement in understanding technology adoption.
Significance of the research
Despite the growing popularity of e-wallets in recent years, scientific research on this topic in Vietnam remains limited This study significantly enriches the existing literature on e-wallet adoption, particularly in Vinh Long province, by providing insights into the factors influencing consumers' intentions to adopt e-wallets Future researchers can leverage this knowledge as a reference for their studies, potentially encouraging further exploration of e-wallets from various perspectives and increasing interest in this important area.
This study offers e-wallet service providers valuable insights into the market potential in Vinh Long province, serving as a reference for their investment decisions It highlights key consumer behavior factors influencing the adoption of e-wallets, such as performance expectancy, effort expectancy, social influence, facilitating conditions, trust, perceived risk, and self-efficacy By understanding these factors, providers can develop effective strategies to engage and attract customers in this promising market.
This research offers fresh insights into the e-wallet sector, introducing several new factors not previously explored in Vietnamese studies The author aims to enhance understanding of e-wallet adoption in Vinh Long province, enabling service providers to implement more effective strategies for promoting e-wallet payments.
Thesis structure
The graduation thesis consists of 5 chapters:
Chapter 2: Literature review and Research Model
In Chapter 1, the author emphasizes the importance of the chosen topic and outlines the research background, aims, and objectives Three key research questions are proposed, along with a brief overview of the methodology employed in the study The chapter concludes by highlighting the research's contributions and significance, as well as the overall structure of the thesis The following chapter will delve deeper into the theoretical framework and review previous studies.
LITERATURE REVIEW AND RESEARCH MODEL
Overview of e-wallet
E-wallet is defined as an electronic wallet that allows consumers to conduct financial transactions through a mobile device (Amoroso and Magnier-Watanabe,
E-wallets serve as virtual wallets that enable consumers to make payments and top up accounts via smartphones, effectively replacing traditional wallets for functions like storing money and paying utility bills, purchasing products, and transferring funds (Sharma and Kulshreshtha, 2019) They provide easier access to financial services by allowing users to save personal and banking information while tracking payment history, which encourages their adoption for banking transactions (Singh et al., 2017) Additionally, e-wallets offer benefits such as loyalty rewards, lower interest rates, and cash incentives, making them a catalyst for traditional banking by facilitating faster and more cost-effective transactions The rise of e-wallets reduces the reliance on cash and enhances financial transparency for governments (Shin, 2009) Consequently, the integration of e-wallets into daily life presents significant advantages, aiding Vietnam's transition towards a cashless society and aligning with national non-cash payment development strategies.
The Vietnamese market is currently saturated with numerous e-wallets, each offering a range of functionalities However, the majority of these e-wallets share four primary features: the ability to transfer and receive money, store funds,
Once consumers register and activate their e-wallet, they can fund their accounts through various methods, including direct recharges at e-wallet service provider counters, bank counters linked to the e-wallet, or online top-ups from compatible e-wallet accounts and cooperating bank accounts With funds in their e-wallet, users can easily transfer money to other e-wallet accounts within the same system or to any bank accounts associated with the e-wallet service providers.
Consumers can securely and conveniently store money in an e-wallet, which reflects the actual value of the funds topped up or transferred.
Consumers can conveniently make online payments using their e-wallets, provided they have sufficient funds This feature enables easy transactions for purchasing products and services on platforms like Tiki, Lazada, and Shopee Additionally, e-wallets facilitate payments for utility bills, including internet, electricity, and water, as well as mobile phone recharges They also streamline the process of buying online tickets for movies, trains, and flights, saving time and effort Furthermore, some e-wallets offer options for booking hotels, purchasing insurance, and paying tuition fees, enhancing overall consumer convenience.
Consumers can manage their personal information, update passwords, check their balance, and track transaction history through the account query function This feature enhances security and builds trust for users adopting electronic payment tools such as e-wallets.
Since its licensing by the Vietnamese government in 2008, the e-wallet service experienced slow growth, with only 7 providers by 2015 To address this, the government introduced policies to promote e-wallet development, leading to a significant increase in providers to 21 by 2017 This surge intensified competition, transforming the e-wallet market into a lucrative sector characterized by aggressive promotional campaigns among major providers By October 2020, the number of e-wallet providers in Vietnam had risen to 39, indicating a highly competitive landscape A survey by Cimigo identified Momo, ZaloPay, and Moca as the top three e-wallet providers, highlighting the promising growth potential of the e-wallet market in Vietnam.
The Covid-19 pandemic has significantly impacted various industries, but e-wallet services have emerged as a major beneficiary, as individuals increasingly turn to digital payment solutions for safety and convenience Table 2.1 lists the e-wallet providers operating in Vietnam as of October 2020.
Table 2.1 E-wallets in Vietnam until October 2020
No E-wallet No E-wallet No E-wallet
10 VNPT Ebay 23 VinID Pay 36 Bemo
Source: Compiled by the author using SBV’s statistics (2020)
Definition of adoption intention
Adoption intention refers to consumers' willingness to accept and use new technologies, such as e-wallets (Chakraborty and Mitra, 2018) This concept is also known as usage intention or behavioral intention in various international studies Previous research has established a correlation between behavioral intention and actual behavior (Yiu et al., 2007; Al-Maghrabi and Dennis, 2011; Venkatesh et al., 2012) Consequently, examining adoption intention is essential for understanding consumer behavior, as it facilitates the prediction of actual consumer actions.
Various theoretical models have been proposed by researchers to identify the factors that influence the intention to adopt new technologies, including mobile banking, e-payment, e-wallets, and e-commerce Notably, the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology are the primary frameworks utilized in these studies The author will provide a detailed overview of these models in the subsequent sections.
Theoretical models in determining the intention to adopt new technology
Theory of Reasoned Action (TRA) was developed by Fishbein and Ajzen
In 1980, a model was proposed in the psychology field suggesting that individuals' beliefs and outcome assessments shape their attitudes, which subsequently influence their intention to act Subjective norms, defined as the pressure to conform to the judgments of significant others, also play a crucial role in shaping behavioral intentions Consequently, both attitudes and subjective norms impact an individual's intention to behave, ultimately affecting actual behavior Despite its widespread use, the theory faced criticism for its limitations in addressing comprehensive technology adoption research, as it assumed that individuals acted with full control and were not subject to external constraints.
Due to limited resources and unforeseen circumstances, the application of TRA in this research is not feasible; however, it can serve as a foundational framework for other theoretical models.
Figure 2.1 Theory of Reasoned Action
Social Cognitive Theory (SCT), developed by Bandura in 1986, builds on Miller and Dollard's Social Learning Theory from 1941 SCT illustrates the interplay between three key factors: environmental influences, personal factors, and behavior, which collectively shape an individual's actions An individual's cognitive evaluation of their environment informs their behavior, which in turn influences future evaluations In 1995, Compeau and Higgins expanded on SCT by identifying factors affecting technology usage behavior, including performance-outcome expectancy, personal-outcome expectancy, self-efficacy, affect, and anxiety Performance-outcome expectancy relates to the anticipated results of behavior, while personal-outcome expectancy reflects how behaviors impact one's life Self-efficacy measures an individual's belief in their ability to perform a behavior, affect indicates their emotional response to it, and anxiety represents their apprehension about performing it Despite its widespread application, experts caution against using SCT in specific contexts, deeming it too general for technology adoption studies While this study will not rely on SCT, it will serve as a foundation for other theoretical models discussed later.
Considering the increase in the new technologies, Davis (1989) proposed Technology Acceptance Model (TAM) based on TRA model of Fishbein and Ajzen
The Technology Acceptance Model (TAM), developed in 1980, explores the factors that influence an individual's intention to adopt new technologies Widely utilized in research, TAM emphasizes the importance of perceived usefulness and perceived ease of use as key determinants of technology adoption Perceived usefulness refers to the belief that a new technology can enhance job performance, while perceived ease of use indicates that the technology requires minimal effort to adopt These factors shape attitudes towards technology, ultimately affecting both the intention to adopt and the actual adoption of new technologies.
The Technology Acceptance Model (TAM) has been widely used to study technology adoption behavior, but it has notable limitations Sun and Zhang (2006) identified two key issues: the model's predictive power is relatively low, with an average R square of only 40% across 55 journal studies, and the correlations among factors in the model vary across different research fields, often deviating from TAM's original propositions Additionally, Lee et al (2003) pointed out that TAM is limited to analyzing a specific technology, subject, and time frame To address these shortcomings, Venkatesh and Davis (2000) introduced TAM2, which expands the original model by incorporating new variables such as subjective norm, image, job relevance, output quality, and result demonstrability to enhance the measurement of perceived usefulness In TAM2, perceived usefulness remains a strong predictor of technology adoption intention, alongside perceived ease of use and subjective norm.
The Technology Acceptance Model (TAM) has significantly influenced research on technology adoption intentions, yet it has notable limitations, as highlighted by Burton-Jones and Hubuna (2006) They argue that perceived usefulness and perceived ease of use fail to encompass all factors affecting usage intention, particularly those stemming from the external environment Consequently, the author has chosen not to utilize TAM in this study, although it will serve as a foundational element for the subsequent theoretical model.
2.3.4 Unified Theory of Acceptance and Use of Technology (UTAUT)
Venkatesh and his partners (2003) identified that researchers often struggled to select an appropriate model, leading them to combine variables from various models in technology research Recognizing the similarities among many existing theories and models, they proposed a unified model, the Unified Theory of Acceptance and Use of Technology (UTAUT), to streamline the study of technology adoption By integrating eight different theories and models, the developers of UTAUT aimed to simplify future research, encouraging scholars to apply this comprehensive model instead of gathering constructs from multiple sources.
Table 2.2 The theories and models used to propose UTAUT
The theory of reasoned action (TRA) Fishbein and Ajzen, 1980
The technology acceptance model (TAM) Davis, 1989
Social cognitive theory (SCT) Bandura, 1986
Innovation diffusion theory (IDT) Moore and Benbasat, 1991
The theory of planned behavior (TPB) Ajzen, 1991
The model of PC utilization (MPCU) Thompson, Higgins and Howell, 1991 The motivational model (MM) Davis, Bagozzi and Warshaw, 1992
A model combining TAM and TPB
The UTAUT model, as proposed by Venkatesh et al (2003), identifies four key constructs that influence technology adoption: performance expectancy, effort expectancy, social influence, and facilitating conditions Performance expectancy refers to the belief that technology adoption will yield benefits, while effort expectancy relates to the perceived ease of adopting the technology Social influence encompasses the impact of recommendations from significant others on an individual's decision to adopt technology Facilitating conditions denote the perceived support from organizational and technical infrastructure for technology adoption These constructs significantly affect behavioral intention, which is a predictor of actual adoption behavior, with facilitating conditions also having a direct impact Venkatesh et al (2003) demonstrated that these constructs account for 70% of the variance in behavioral intention, underscoring the model's effectiveness in identifying factors that influence new technology adoption.
UTAUT model has been synthesized, built and developed upon which constructs in previous existing theories and models
Table 2.3 Origin of constructs in UTAUT model
Performance expectancy Perceived usefulness (from TAM/TAM2 and C-TAM-
TPB) External motivators (from MM) Relative advantages (from IDT) Job-fit (from MPCU)
Effort expectancy Perceived ease of use (from TAM/TAM2)
Complexity (from MPCU) Ease of use (from IDT)
Social influences Subjective norms (from TRA, TAM2, TPB, C-TAM-
TPB) Social factors (from MPCU) Image (from IDT)
Facilitating conditions Perceived behavioral control (from TPB, C-TAM-TPB)
Facilitating conditions (from MPCU) Compatibility (from IDT)
The UTAUT model, which incorporates four key moderators—age, gender, experience, and voluntariness of use—offers a more precise understanding of technology adoption compared to the original Technology Acceptance Model (TAM) This comprehensive framework has gained popularity among researchers for studying various technology fields, including mobile commerce, mobile payment, and mobile banking services Given its relevance, the author posits that UTAUT is well-suited for investigating the adoption of e-wallets.
Figure 2.5 Unified Theory of Acceptance and Use of Technology
Venkatesh et al (2012) expanded the UTAUT model to create UTAUT2, introducing three new constructs: price value, hedonic motivation, and habit, while removing the voluntariness moderator Despite its widespread application in technology research, numerous studies have shown that these new constructs often have an insignificant impact on behavioral intention Specifically, price value did not significantly influence the adoption of mobile payment (Morosan and DeFranco, 2016) or mobile commerce (Li and Yang, 2016) Similarly, hedonic motivation was found to have no significant effect on the acceptance of mobile commerce (Jaradat and Al Rababaa, 2013) and mobile banking services (Mahfuz, Khanam, and Mutharasu, 2016) Additionally, habit was shown to lack a significant influence on the intention to adopt mobile payment (Slade, Williams, and Dwivdei, 2013) and mobile commerce (Bendary and Al-Sahouly, 2018) Given the substantial evidence of the insignificance of these three factors on adoption intention, the author opts to utilize the original UTAUT model instead of UTAUT2.
The author chooses the UTAUT model for this thesis because it integrates various theories, making it a highly effective framework for understanding technology acceptance Hino (2015) suggests that UTAUT enhances the conceptualization of mobile commerce by incorporating additional constructs, which is relevant to the study of e-wallet adoption To simplify the model, the author will exclude moderators such as age, gender, experience, and voluntariness, focusing instead on the key factors influencing behavioral intention for a more effective evaluation.
Previous empirical researches on factors influencing intention to adopt e-
Numerous studies have explored the factors affecting the adoption intention of e-wallets globally This article will present a selection of research papers that utilize the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) as theoretical frameworks, while also incorporating additional constructs in chronological order.
Thakur and Srivastava (2014) examined the factors affecting the intention to adopt mobile wallets, focusing on adoption readiness, perceived risk, and personal innovativeness They found that adoption readiness, influenced by perceived usefulness, ease of use, social influence, and facilitating conditions, was the most significant factor in adopting e-wallets Perceived risk, which included security, monetary, and privacy risks, negatively impacted adoption intention, with security and privacy risks significantly affecting perceived risk, while monetary risk did not To alleviate consumer risk perception, e-wallet providers were advised to implement secured socket layers and third-party certifications Additionally, personal innovativeness was shown to directly influence both adoption readiness and behavioral intention, with notable differences between users and non-users The study's limitation was its focus on consumers in urban areas with easy internet access, prompting a recommendation for future research to explore e-wallet adoption in rural areas with limited internet access.
Madan and Yadav (2016) explored the factors influencing the adoption intention of e-wallets in their study, “Behavioral intention to adopt mobile wallet: a developing country perspective.” They enhanced the UTAUT model by incorporating five new constructs: perceived value, perceived risk, perceived trust, perceived regulatory support, and promotional benefits Their findings revealed that performance expectancy, social influence, facilitating conditions, perceived risk, perceived trust, perceived value, and promotional benefits significantly impacted the intention to adopt mobile wallets, while effort expectancy did not, diverging from previous research The authors attributed this discrepancy to consumers' ease of use with mobile wallets However, they acknowledged potential selection bias due to a limited sample size of 210 respondents with uneven demographic representation Additionally, they emphasized the importance of considering other stakeholders in the mobile wallet ecosystem, such as providers, merchants, technology firms, financial institutions, and government entities, in future research to gain a comprehensive understanding of mobile wallet adoption.
Chakraborty and Mitra (2018) investigated the research “A study on Consumers
The study on "Adoption Intention for Digital Wallet in India" revealed that factors such as perceived usefulness, perceived ease of use, social influence, perceived self-efficacy, personal innovativeness, individual playfulness, attractiveness of alternatives, and perceived value significantly impact the intention to adopt digital wallets Notably, constructs outside the original UTAUT model, including perceived self-efficacy and personal innovativeness, were found to be crucial in influencing consumer adoption The model explained 81.7% of the variance in adoption intention, indicating its significance Cluster analysis identified three customer segments, allowing digital wallet providers to tailor strategies for optimal outcomes However, the study's limited sample size of 150 responses restricts the generalizability of findings Future research should explore additional factors affecting adoption intention and focus on continuance intention and satisfaction In a related study by Sharma and Kulshreshtha (2019), nine factors were proposed for mobile wallet adoption, with convenience, ease of use, compatibility, and availability of information showing significant influence The findings suggest that mobile wallet providers should enhance language features and innovate to attract younger consumers, who are more inclined to adopt such technology The study also faced limitations due to its small sample size and recommended comparative research between metro and non-metro cities for deeper insights.
Mahwadha (2019) conducted a research on “Behavioral intention of young consumers towards e-wallet adoption: An empirical study among Indonesia users”
The proposed model indicates that perceived usefulness, perceived trust, and attitude toward using significantly influence the behavioral intention to adopt e-wallets Research findings confirm that these factors positively impact e-wallet adoption intentions, with perceived trust notably affecting perceived usefulness and, in turn, influencing attitudes toward usage E-wallet providers and banks can leverage these insights to enhance service facilities, transaction security, and personal data protection However, the study's limitation lies in its focus on Surabaya, East Java, which restricts the generalization of findings to all Indonesian young consumers In a related study, Tun (2020) explored mobile wallet adoption in Myanmar, identifying perceived usefulness and trust as key factors influencing adoption intention, particularly trust due to the country's financial crisis Surprisingly, service quality, user satisfaction, social influence, and facilitating conditions did not significantly affect behavioral intention, contrasting with previous research Tun suggested that future studies should incorporate additional constructs like perceived risk, perceived value, and perceived self-efficacy for a more comprehensive understanding of mobile wallet adoption The study's limitations included a small sample size during the Covid-19 pandemic and a focus solely on potential users, prompting a recommendation to investigate regular users for broader insights.
In Vietnam, research on e-wallet adoption intentions is limited, with only two notable studies identified The first, conducted by Nguyen Thi Linh Phuong in 2013, utilized the UTAUT model and incorporated additional factors such as perceived credibility, perceived costs, government support, and utility community The study found that all factors, including performance expectancy, effort expectancy, social influence, and facilitating conditions, significantly influenced e-wallet adoption intentions, achieving an R square of 64.5% The research methodology involved interviews with professors and group discussions to create a relevant questionnaire Despite being one of the pioneering studies in this area, it faced limitations due to a small sample size and a respondent pool primarily from Ho Chi Minh City, which may not accurately represent the entire Vietnamese population The author recommends that future studies broaden their survey scope for more reliable and comprehensive results.
Tran Phi Hoang, Tran Anh Tuan, and Nguyen Thi Thanh Phuong (2020) explored the factors influencing Vietnamese tourists' intention to use electronic wallets in Ho Chi Minh City Their research identified five key factors: perception of usefulness, ease of use, risk perception, attitude, and social influence, all of which significantly impacted tourists' adoption of e-wallets While consumers recognized the benefits of e-wallets, concerns about potential risks persisted The study recommended that e-wallet providers enhance technical standards, communication systems, and information security to build consumer trust Additionally, it emphasized the need for awareness campaigns to highlight the usefulness of e-wallets for long-term adoption and suggested developing partnerships with banks, restaurants, hotels, and airlines to improve user experiences However, the study's limitation was its focus on Ho Chi Minh City, prompting the authors to call for future research in other popular tourist destinations like Da Lat, Da Nang, Vung Tau, Hue, and Nha Trang.
Research on e-wallets in Vietnam is limited compared to the extensive studies conducted globally To address this gap, the author aims to explore e-wallet adoption using the UTAUT model while incorporating three additional constructs: trust, perceived risk, and self-efficacy These factors have been shown to significantly influence the intention to adopt e-wallets in previous studies (Thakur and Srivastava, 2014; Madan and Yadav, 2016; Chakraborty and Mitra, 2018; Tun, 2020).
Proposed research model and research hypotheses
The author utilizes the UTAUT model by Venkatesh et al (2003) as a foundational framework for analyzing e-wallet adoption To enhance the model's predictive power regarding adoption intentions, it is further augmented with factors such as trust, perceived risk, and self-efficacy.
The official proposed model is illustrated in the figure 2.6
Figure 2.6 Model proposed by the author
Source: Compiled by the author
Performance expectancy refers to the extent to which consumers believe that a technology, such as an e-wallet, will enhance their ability to perform tasks effectively (Venkatesh et al., 2003) It specifically relates to the belief that using an e-wallet will improve the efficiency of daily transactions (Madan and Yadav, 2016) According to Venkatesh and colleagues (2003), performance expectancy is a crucial element of the UTAUT model Numerous studies have confirmed that performance expectancy positively influences the intention to adopt e-wallets (Schierz et al., 2010; Kim et al., 2010; Pham and Ho, 2014; Slade et al., 2015; Yan and Yang, 2015) Therefore, the first hypothesis is proposed as follows:
H1: Performance expectancy positively influences the intention to adopt e-wallet
Effort expectancy refers to the perceived ease of adopting a technology, particularly in the context of e-wallets, where individuals believe that using such technology requires minimal effort in their daily lives Research by Venkatesh et al (2003) and further studies by Madan and Yadav (2016) highlight that this construct is crucial for understanding technology adoption Venkatesh and colleagues (2013) emphasize that effort expectancy is an essential factor in the adoption model, with numerous studies demonstrating its positive influence on the intention to adopt mobile commerce, especially e-wallets (Chong, 2013; Megadewandanu, 2016; Chou, Li, and others).
Ho, 2018) Therefore, the second hypothesis is proposed as following:
H2: Effort expectancy positively influences the intention to adopt e-wallet
Social influence plays a crucial role in the adoption of e-wallets, as individuals often rely on the opinions of significant people in their lives, such as family, friends, and colleagues (Venkatesh et al., 2003) This influence can significantly affect a person's decision to continue or discontinue the use of e-wallets Research has shown that social influence is a key factor in the adoption of mobile technology services, particularly e-wallets (Liébana-Cabanillas et al., 2014; Oliveira et al., 2016; Lwin and Thanabordeekij, 2019; Soodan and Rana, 2020) Consumers are more inclined to adopt e-wallets when encouraged by influential individuals, leading to the formulation of the third hypothesis.
H3: Social influence positively influences the intention to adopt e-wallet
Facilitating conditions refer to the extent to which individuals perceive support from organizational and technical structures for adopting new technologies (Venkatesh et al., 2003) Specifically, this concept relates to an individual's access to essential resources for effectively adopting e-wallets, which fosters a positive environment for financial transactions and enhances consumer behaviors (Chawla and Joshi, 2019) Numerous studies have demonstrated that facilitating conditions positively influence the intention to adopt e-wallets and similar technologies, including mobile banking and mobile commerce (Yang, 2010; Amoroso and Magnier-Watanabe, 2012; Chong, 2013; Lwin and Thanabordee Kij, 2019; Soodan and Rana, 2020) Consequently, the fourth hypothesis is proposed.
H4: Facilitating condition positively influences the intention to adopt e-wallet 2.5.2.5 Trust
Trust is the extent to which a person believes that e-wallet service providers are trustworthy and have reliable security and privacy policies (Madan and Yadav,
Trust is essential for the widespread adoption of e-payment systems, particularly e-wallets, as it enhances users' confidence in the security of their personal information and funds Numerous studies have identified trust as a significant predictor of the intention to adopt e-wallets Consumers who trust service providers are more inclined to embrace e-wallet services Therefore, the fifth hypothesis is proposed.
H5: Trust positively influences the intention to adopt e-wallet
Perceived risk is considered as any financial, social, product-associated risks that a person has to suffer when conducting online transactions (Wu and Wang,
In recent years, the prevalence of smartphones has raised significant concerns regarding security and privacy risks associated with e-wallet transactions Users often worry that their personal information and funds may be compromised during online banking activities Research indicates that perceived risks negatively influence the intention to adopt mobile banking and e-wallet services.
Pham and Ho, 2014; Liébana-Cabanillas et al., 2014; Slade et al., 2015) Hence, the sixth hypothesis is proposed as following:
H6: Perceived risk negatively influences the intention to adopt e-wallet
Self-efficacy, defined as an individual's confidence in adopting new innovations, plays a crucial role in the context of e-wallet usage According to Bandura's Social Cognitive Theory, when individuals believe they possess the necessary knowledge, skills, and abilities to utilize e-wallets, they are more inclined to adopt this technology independently Research has consistently shown that self-efficacy significantly influences the intention to adopt technology services, particularly e-wallets Therefore, it is proposed that self-efficacy is a key factor in enhancing the adoption of e-wallets.
H7: Self-efficacy positively influences the intention to adopt e-wallet
In Chapter 2, the author provides an overview of the e-wallet landscape in Vietnam, followed by a review of theoretical models and prior research in the e-wallet domain She identifies and develops the most appropriate model for this context, presenting a proposed model along with detailed explanations of each construct and the associated hypotheses The subsequent chapter will focus on the methods for data collection and analysis.
METHODOLOGY
Research procedure
The research includes two main stages which are preliminary and official research
Source: Compiled by the author
Define the research objectives, aims, scope
Review previous models and research
Propose the model and develop hypotheses
Conduct short interviews with some users
Design the official measurement scale
Conduct the survey and collect data
Make some recommendations Official research
In the preliminary research, the author employed a qualitative method to gain an in-depth understanding of the topic The research aims and objectives were clearly defined, and the scope was established due to time and resource constraints The author synthesized theoretical models and previous studies on e-wallets to visualize the overall landscape and factors influencing adoption intention This foundational knowledge enabled the proposal of a new model and hypotheses, as detailed in Chapters 1 and 2 Following this, short interviews with e-wallet users were conducted to gather their perceptions A pre-measurement scale was designed based on adapted items from prior research, followed by a pilot survey to ensure clarity of the items for respondents After analyzing the feedback from interviews and the pilot survey, the author refined the official measurement scale and created the official questionnaire for the main survey.
This study employed a quantitative research method to investigate e-wallet usage among consumers in Vinh Long province Data was gathered through an online survey, and analyzed using SPSS 20.0 software to test various hypotheses Key analytical techniques included Descriptive Statistics, Cronbach Alpha, Exploratory Factor Analysis, Multiple Linear Regression, Independent T-test, and Analysis of Variance The findings were discussed in relation to previous research, leading to evaluations and insights The author also provided recommendations aimed at helping e-wallet service providers enhance adoption rates in the region.
Preliminary research
The interviews aimed to explore consumer perceptions regarding the intention to adopt e-wallets The author conducted face-to-face interviews with 10 individuals from Vinh Long province who had experience using e-wallets Each interview lasted approximately 10 minutes, during which open-ended questions were posed to gauge perceptions of various factors, including performance expectancy, effort expectancy, social influence, facilitating conditions, trust, perceived risk, and self-efficacy The specific questions related to each factor are detailed in the study.
• How do you feel about the usefulness of e-wallet in your daily life?
• How do you feel about the effort you have to make to adopt e-wallet?
• Who influences your adoption of e-wallet behavior?
• What resources do you need to adopt e-wallet?
• On what aspects do you trust your e-wallet service providers?
• How do you feel about the risks when adopting an e-wallet?
• How do you believe in yourself when adopting an e-wallet?
All interviewees agreed that the questions regarding e-wallet usage are both meaningful and necessary, highlighting the importance of various factors in studying e-wallet adoption While most responses aligned closely with the measurement items, the author recognizes the need to provide additional explanations for certain items to enhance clarity and specificity.
The author can enhance the measurement items by incorporating key insights from the responses For question 1, respondents highlighted several benefits of e-wallet adoption, including easier and more convenient payments, as well as increased discounts and refunds In question 3, 70% of participants indicated that their relatives and friends significantly influenced their decision to adopt e-wallets, while the remaining respondents cited the impact of superiors and professors Lastly, for question 4, all participants agreed that smartphones, tablets, and internet access are essential resources for adopting e-wallets.
After completing the pre-measurement scale based on prior research, the author conducted a pilot survey with 10 participants from Vinh Long province who used at least one e-wallet Six respondents suggested that examples should be provided to clarify the efficiency of the payment process and the benefits beyond payment The author defined the payment process's efficacy as faster, easier, and more convenient, while benefits included discounts and refunds Additionally, eight respondents expressed confusion regarding social influence questions, struggling to identify who influenced their behaviors and whose opinions they valued The author proposed that relatives, friends, and colleagues could be significant influences, while supervisors and professors might be valued for their opinions Furthermore, seven respondents had difficulty understanding the necessary resources for adopting an e-wallet, prompting the author to provide examples such as smartphones and tablets These clarifications aimed to help respondents in the official survey comprehend the questions better The official measurement scale, developed from previous studies and informed by the pilot survey, is detailed in the following section.
3.2.3.1 Measurement items for Performance expectancy
Performance expectancy refers to the extent to which the adoption of technology, such as e-wallets, improves an individual's task performance (Venkatesh et al., 2003) In this study, the author utilizes measurement items from Morosan and DeFranco (2016) to effectively capture the nuances of performance expectancy Additionally, insights from interviews and a pilot survey led the author to include examples for PE1 and PE3, enhancing their clarity and understanding.
Table 3.1 Measurement scale for Performance expectancy
Code Original Scale Adjusted Scale
Using mobile payments would increase the efficiency of my hotel stay
Using e-wallet would increase the efficiency of my payment process (faster, easier and more convenient payment)
Using mobile payments would allow me to make more accurate purchases, reservations in hotels
Using e-wallet payments would allow me to track the payment process more accurately
Using mobile payments would allow me to obtain benefits beyond the hotel stay (for example, using a preferred credit card)
Using e-wallet would allow me to obtain benefits beyond the payment (vouchers, discounts, refunds)
Overall, I believe that mobile payments are useful when staying in hotels
Overall, I believe that e-wallet is useful when making payment for products/service
Source: Compiled by the author using the scale of Morosan and DeFranco, 2016
3.2.3.2 Measurement items for Effort expectancy
Effort expectancy refers to the ease of adopting technology without significant effort (Venkatesh et al., 2003) This research utilizes the UTAUT2 model's measurement scale (2012) to assess effort expectancy, as it offers greater precision than the original UTAUT model (2003) The author has made specific adjustments to the terminology to better fit the context of e-wallets.
Table 3.2 Measurement scale for Effort expectancy
Code Original Scale Adjusted Scale
EE1 Learning how to use mobile Internet is easy for me
Learning how to use e-wallet is easy for me
EE2 My interaction with mobile Internet is clear and understandable
My interaction with e- wallet would be clear and understandable EE3 I find mobile Internet easy to use I find e-wallet easy to use
EE4 It is easy for me to become skillful at using mobile Internet
It is easy for me to become skillful at using e-wallet
Source: Compiled by the author using the scale of Venkatesh et al., 2012
3.2.3.3 Measurement items for Social influence
Social influence refers to the impact that the opinions of significant individuals have on the intention to adopt e-wallets (Venkatesh et al., 2003) The author utilizes the updated measurement scale from Venkatesh et al (2012) for this construct, ensuring it reflects the latest characteristics of the model Additionally, the author will provide clarifications for SC1, SC2, and SC3 to prevent any misunderstandings among respondents regarding the individuals referenced.
Table 3.3 Measurement scale for Social influence
Code Original Scale Adjusted Scale
People who are important to me think that I should use mobile
People who are important to me (relatives, friends, colleagues) think that I should use e-wallet
People who influence my behavior think that I should use mobile Internet
People who influence my behavior (relatives, friends, colleagues) think that I should use e-wallet
People whose opinions that I value prefer that I use mobile
People whose opinions that I value (superiors, professors) prefer that I use e-wallet
Source: Compiled by the author using the scale of Venkatesh et al., 2012
3.2.3.4 Measurement items for Facilitating condition
Facilitating conditions refer to the extent to which individuals perceive the availability of organizational and technical support for adopting new technologies, such as e-wallets (Venkatesh et al., 2003) The author utilizes the UTAUT2 scale from Venkatesh et al (2012) for this construct but has made a slight modification to FC1 based on insights from interviews and a pilot survey The term "resources" was deemed too vague, prompting the author to provide specific examples to clarify its meaning.
Table 3.4 Measurement scale for Facilitating condition
Code Original Scale Adjusted Scale
FC1 I have the resources necessary to use mobile Internet
I have the resources necessary to use e wallet (for example: smartphone, tablet)
FC2 I have the knowledge necessary to use mobile Internet
I have the knowledge necessary to use e- wallet
FC3 Mobile Internet is compatible with other technologies I use
E-wallet is compatible with other technologies I use
I can get help from others when
I have difficulties using mobile Internet
I can get help from others when I have difficulties using e-wallet
Source: Compiled by the author using the scale of Venkatesh et al., 2012
Trust in e-wallet service providers is defined by users' perceptions of their security and privacy policies (Madan and Yadav, 2016) To address the absence of a trust construct in the original UTAUT model, the author adapted insights from research on e-payment adoption in restaurants (Khalilzadeh et al., 2017) to better fit the e-wallet context.
Table 3.5 Measurement scale for Trust
Code Original Scale Adjusted Scale
T1 I believe mobile payment service providers keep their promise
I believe e-wallet service providers keep their promise
I believe mobile payment service providers keep customers' interests in mind
I believe e-wallet service providers keep customers' interests in mind
T3 I believe mobile payment service providers are trustworthy
I believe e-wallet service providers are trustworthy
I believe mobile payment service providers will do everything to secure the transactions for users
I believe e-wallet service providers will do everything to secure the transactions for users
Source: Compiled by the author using the scale of Khalilzadeh et al., 2017
3.2.3.6 Measurement items for Perceived risk
Perceived risk refers to the potential dangers individuals face during online transactions (Wu and Wang, 2005) The author adopts the measurement scale developed by Khalilzadeh et al (2017) to assess perceived risk, while also modifying certain terms to better align the scale with the context of e-wallets.
Table 3.6 Measurement scale for Perceived risk
Code Original Scale Adjusted Scale
The probability that something will go wrong with the performance of mobile payment is high
The probability that something will go wrong with the performance of e-wallet is high
Mobile payment might not perform well and create problems with my payment process in restaurants
E-wallet might not perform well and create problems with my payment process during purchasing
The chances of using the mobile payment and losing control over my personal information privacy is high
The chances of using the e-wallet and losing control over my personal information privacy is high
My signing up and using mobile payment would lead me to a loss of privacy because my personal information would be used without my knowledge
My signing up and using e-wallet would lead me to a loss of privacy because my personal information would be used without my knowledge
Source: Compiled by the author using the scale of Khalilzadeh et al., 2017
3.2.3.7 Measurement items for Self-efficacy
Self-efficacy refers to an individual's confidence in their ability to embrace new innovations, such as e-wallets (Koksal, 2016) The measurement scale for self-efficacy is adapted from Makanyeza's (2017) research on the intention to adopt mobile banking services, necessitating some modifications to ensure its relevance to the e-wallet context.
Table 3.7 Measurement scale for Self-efficacy
Code Original Scale Adjusted Scale
I have confidence to use mobile banking even if there is no one around to show me how to do it
I have confidence to use e-wallet even if there is no one around to show me how to do it
SE2 I easily understand how mobile banking works
I easily understand how e-wallet works
SE3 I feel comfortable using mobile banking on my own
I feel comfortable using e-wallet on my own
Source: Compiled by the author using the scale of Makanyeza, 2017
3.2.3.8 Measurement items for Adoption intention
Adoption intention refers to consumers' willingness to embrace and use new technologies, particularly e-wallets (Chakraborty and Mitra, 2018) To investigate this concept, the author employs the measurement scale developed by Venkatesh et al (2012), making necessary adjustments to ensure its relevance to the e-wallet context.
Table 3.8 Measurement scale for Adoption intention
Code Original Scale Adjusted Scale
AI1 I intend to continue using mobile
I intend to continue using e-wallet in the future
AI2 I will always try to use mobile Internet in my daily life
I will always try to use e-wallet in my daily life
AI3 I plan to continue to use mobile
I plan to continue to use e-wallet frequently
Source: Compiled by the author using the scale of Venkatesh et al., 2012
The questionnaire, adapted from prior research, has been modified to fit the context of this thesis To effectively investigate consumers in Vinh Long province, it will be translated from English to Vietnamese, ensuring that respondents can comprehend the questions and are more inclined to provide answers The translation will be reviewed by the author's supervisor to guarantee its accuracy.
The questionnaire consists of four key sections, with the initial part featuring three filtering questions designed to exclude unsuitable responses Only respondents who meet the established criteria are allowed to proceed with the survey, ensuring that the author collects reliable data that contributes to accurate results.
The second section features three key questions aimed at understanding e-wallet adoption behaviors, providing insights into respondents' experiences and habits These inquiries focus on the duration of e-wallet usage, the specific e-wallets adopted, and the reasons behind their adoption.
Official research
3.3.1 Method of data collection and sampling
The primary data for this study was collected through an online survey using a Google Form link, which was distributed to target respondents via Zalo and Facebook in October 2020 Participation was limited to individuals residing in Vinh Long province who use at least one e-wallet, as detailed in Chapter 1.
Due to the large population size, the author employs a non-probability convenient sampling technique in this thesis This method is favored by many researchers for its speed, cost-effectiveness, and ease of access to subjects The author targets individuals who are readily available and willing to participate in the questionnaire, including relatives, friends, and acquaintances.
This research employs Exploratory Factor Analysis, necessitating a sample size of at least 3-6 times the number of observed variables, which totals 29 Consequently, the minimum sample size required ranges from 87 to 174 Additionally, Bollen (1989) recommends a data collection principle of 5:1, indicating that at least 145 responses are needed for this study For regression analysis, Green (1991) suggests a minimum sample size of 50 + 8*n, where n represents the number of independent variables Given that this study includes 7 independent variables, the sample size should be no less than 106.
After collecting data, the author cleans them by eliminating the inappropriate responses Then, qualified data will be processed by the software SPSS 20.0 for the next analysis steps
Descriptive statistics summarize the fundamental characteristics of collected data, providing a clear overview of the sample and the measures utilized in the study For demographic data, these statistics are presented in ratios to enhance visualization of respondent features Conversely, measurement data outcomes are displayed through maximum, minimum, mean values, and standard deviation, allowing for a comprehensive evaluation of respondents' perceptions regarding the constructs examined in the study.
Cronbach Alpha is a key metric for assessing internal consistency and scale reliability, determining if observed variables effectively measure the same constructs The Corrected Item-Total Correlation evaluates the contribution of each observed variable to a construct, guiding decisions on variable elimination According to Nunnally and Bernstein (1995), a Cronbach Alpha coefficient of 0.6 or higher is deemed acceptable, while a Corrected Item-Total Correlation of 0.3 or more is also acceptable Variables with a Corrected Item-Total Correlation below 0.3 should be considered for removal, especially if the Cronbach Alpha coefficient improves after their elimination.
Exploratory Factor Analysis (EFA) is a statistical method designed to condense a set of inter-correlated observed variables into a smaller number of factors (Hair, 2010) This approach allows researchers to focus on key factors, thereby saving time and effort To determine the suitability of EFA for a model, certain indicators must be evaluated, with the Kaiser-Meyer indicator being one of the primary considerations.
The Olkin-Kaiser-Meyer-Olkin (KMO) measure assesses the sampling adequacy for each variable and the overall model, with a KMO value close to 1 indicating that Exploratory Factor Analysis (EFA) is suitable, while a value below 0.5 suggests it should not be used The acceptable KMO range is between 0.5 and 1 Bartlett’s test evaluates whether the correlation matrix is an identity matrix, with a significance level below 0.05 indicating that the variables are correlated, thus supporting the use of EFA The author employs Principal Component Analysis to explore correlations between observed variables and factors, retaining only those factors with an eigenvalue greater than 1 To ensure the appropriateness of EFA, the total variance explained must be at least 50% Additionally, Varimax rotation is applied to examine factor correlations, with factor loadings of 0.5 or higher indicating a strong relationship between observed variables and factors.
Pearson correlation analysis is essential for assessing the linear relationship between independent and dependent variables prior to regression analysis A Pearson coefficient close to 1 indicates a strong correlation, with this study considering a coefficient greater than 0.3 and a significance level below 0.05 as acceptable, following Nguyen Dinh Tho (2011) Additionally, this analysis aids in identifying multicollinearity issues when independent variables exhibit high correlation, prompting further examination using the Variance Inflation Factor (VIF) during regression analysis.
The author performs a multiple linear regression analysis to explore the relationship between various factors and consumers' intention to adopt e-wallets in Vinh Long province A regression equation is constructed, utilizing R-squared to assess the model's fit with the data, while adjusted R-squared is employed to avoid overestimation Additionally, the F-Test is applied to evaluate the model's suitability, confirming its significance if the significance level is below 0.05 (Hoang Trong, Chu Nguyen Mong Ngoc).
This study examines the relationship between independent variables derived from the EFA step and the dependent variable, which is the intention to adopt e-wallets The author employs t-tests to evaluate hypotheses regarding these relationships A significance level of 0.05 or lower indicates that an independent variable significantly influences the adoption intention, leading to the acceptance of the corresponding hypothesis; otherwise, the hypothesis is rejected.
The author employs Variance Inflation Factor (VIF) to assess multicollinearity within the model, with a threshold of VIF values below 2 indicating no multicollinearity (Nguyen Dinh Tho, 2011) In addition to examining multicollinearity, the author tests key assumptions such as linearity, normal distribution, and autocorrelation of residuals to ensure the integrity of the regression model.
3.3.2.6 Independent T-test and Analysis of Variance (ANOVA)
The Independent T-test and Analysis of Variance (ANOVA) are utilized to examine how demographic factors such as gender, age, educational level, occupation, and income influence the intention to adopt e-wallets This analysis seeks to identify variations in adoption intentions among different demographic groups.
The Independent T-test is utilized when comparing two groups based on a demographic variable, such as gender The Levene Test assesses the equality of variances between these groups; a significance level below 0.05 indicates differing variances, necessitating the use of the significance level for "Equal variances not assumed." Conversely, a significance level of 0.05 or higher suggests no variance difference, allowing for the "Equal variances assumed" significance value In the Independent T-test, the significance level from the Levene Test is compared to a critical significance level, typically set at 0.05 A significance level below 0.05 reveals a significant difference in the means of the two groups, while a level of 0.05 or higher indicates no significant difference in their means.
ANOVA is utilized when analyzing more than two groups based on demographic variables such as age, education level, occupation, and income The Levene test assesses the equality of variances among these groups; a significance level of 0.05 or lower indicates unequal variances, making ANOVA inapplicable Conversely, a significance level above 0.05 suggests equal variances, allowing for the use of ANOVA In the context of the ANOVA test, a significance level of 0.05 or lower provides sufficient evidence of a significant difference among groups, while a level above 0.05 indicates insufficient evidence to confirm any differences.
In Chapter 3, the author outlines the research procedure, which consists of two key stages: preliminary research and official research The preliminary research details the execution of interviews and a pilot survey, along with the design of the measurement scale and questionnaire In the official research phase, the author elaborates on data collection methods, sampling techniques, and data analysis methods, including Descriptive statistics, Cronbach Alpha, EFA, Pearson analysis, Multiple linear regression, Independent T-test, and ANOVA The results of these analyses will be discussed in the subsequent chapter.