INTRODUCTION
Research gap
Since Vietnam is still in search of its solution for the future of cashless payments (Internation Finance Corporate, 2014), mobile securities trading (hereinafter referred to as
M-Trading, like other forms of mobile and electronic commerce in Vietnam, is still in its early stages of development Since 2008, the Vietnamese government has encouraged all securities firms in the country to establish their own websites, allowing investors to execute trading orders online at any time Prominent securities firms such as Saigon Securities Inc., Hochiminh City Securities Corp., and VNDirect Securities have embraced this shift towards website-based trading.
Several securities firms in Vietnam, including FPT Securities Corp and VPbank Securities Corp., have developed smartphone applications to facilitate mobile trading for clients Despite this advancement, mobile-based transactions face limitations due to inherent risks and customers' cautious approach to risk assessment As a result, the adoption of M-Trading among Vietnamese securities investors remains low Consequently, researchers are increasingly focused on understanding the motivations behind investors' willingness or reluctance to utilize M-Trading and the factors that influence their behavioral intentions in this context.
Numerous studies have examined the adoption of Information and Communication Technology (ICT) in the financial industry, focusing on various aspects such as internet banking (Wang & Shan, 2012; Chong et al., 2010; Kim et al., 2007; Laforet & Li, 2005), mobile banking (Yu, 2012; Aboelmaged & Gebba, 2013), internet securities trading (Teo et al., 2004; Ramayah et al., 2009; Singh et al., 2010), online securities trading (Abroud, Choong & Muthaiya, 2010), and M-Trading (Tai & Ku, 2013).
In Vietnam, numerous studies have been conducted on various aspects of digital finance and technology, including online banking (Chong et al., 2010), e-banking (Nguyen Duy Thanh & Cao Hao Thi, 2011; Nguyen et al., 2014), and e-payment systems (Nguyen & Lin, 2011) Research has also explored mobile money transfers (Le Van Huy & Tran Nguyen Phuong Minh, 2011), mobile learning (Ngo & Gwangyong, 2014), personal internet banking (Hoang, 2015), mobile payments (Pham & Liu, 2015), and mobile shopping (Ngo et al.) These studies highlight the growing significance of digital financial services and their impact on consumer behavior in Vietnam.
2015), but only few on e-trading of securities were conducted
Understanding the behavioral intentions of Vietnamese investors regarding M-Trading is crucial, as the factors influencing their adoption remain unclear Gaining insights into these intentions can significantly benefit brokerage firms by enhancing their M-Trading implementation and service quality Additionally, it can assist the Vietnam State Securities Commission in formulating effective policies for managing the securities market Therefore, developing and empirically testing a comprehensive model that explores the reasons behind investors' willingness or reluctance to engage with M-Trading is essential.
In Vietnam, M-Trading refers to mobile commerce transactions conducted through devices like smartphones, tablets, and PDAs, excluding laptops, and necessitates internet access similar to online shopping This study builds its theoretical framework on the Unified Theory of Acceptance and Use of Technology (UTAUT), integrating essential elements from the Technology Acceptance Model.
The extended UTAUT model has been effectively utilized to understand users' behavioral intentions in various contexts, including mobile securities trading (Tai & Ku, 2013), mobile banking (Yu, 2012), and internet banking (Yee et al., 2015) This study aims to address the existing gap in research within Vietnam by applying the extended UTAUT model (Venkatesh et al., 2003), alongside multi-faceted perceived risks (Tai & Ku, 2013) and privacy concerns (Zhou, 2012), to examine how these factors influence the behavioral intention of Vietnamese users to engage in mobile trading (M-Trading).
Research objectives and research questions
The objective of this study is to investigate the factors influencing securities investors’ behavioral intention Particularly, the study aims at answering the following questions:
Question 1: which factors based on the modified UTAUT model influence securities investors’ behavioral intention to use M-Trading in Vietnam?
Question 2: Which factors of perceived risks influence securities investors’ behavioral intention to use M-Trading in Vietnam?
Question 3: Whether do the privacy concerns affect securities investors’ behavioral intention to use M-Trading in Vietnam or not?
Research methodology and research scope
This study employs questionnaires to gather data, initially developed in English and subsequently translated into Vietnamese To refine the Vietnamese version, in-depth interviews were conducted with eight individuals prior to the mass survey implementation Data analysis is performed using SPSS software, following three key stages: testing the measurement scale's reliability with Cronbach’s Alpha, assessing validity through Exploratory Factor Analysis (EFA), and employing structural equation modeling (SEM) and path analysis to explore relationships among factors in the research model The survey is conducted in Ho Chi Minh City, Vietnam's largest metropolitan area and economic center, which hosts the Ho Chi Minh City Securities Exchange (HOSE) The research targets Vietnamese individual securities investors aged 18 and older, including those familiar with internet securities trading and e-financial services, as well as those who have not yet utilized such services, to investigate their behavioral intention to adopt M-Trading Additionally, potential investors within the same age group are invited to participate in the study.
Research structure
The research is divided into five chapters
The first chapter introduces about background, research problems, research questions, research purpose, scope of research and research structures
The second chapter covers literature review of the previous research and shows hypotheses, as well as the conceptual model of the research
The third chapter presents the research process, sampling size, measurement scale, main survey, and data analysis method
The fourth chapter concentrates on preparation data, descriptive data, assessment measurement scale and hypotheses testing
The fifth chapter points out research overview, research findings, managerial implications, research limitations and directions for future research.
LITERATURE REVIEW& HYPOTHESES DEVELOPMENT
Theoretical background
Extensive studies on the acceptance and use of Information and Communication Technology (ICT) have led to the development of various models from diverse theoretical disciplines, including psychology, sociology, and information systems One significant area of research, based on the motivational model, examines how both extrinsic and intrinsic motivations impact users' acceptance of ICT (Davis et al., 1992).
Another stream based on TAM model to explore the role of perceived usefulness and perceived ease of use on usage intentions and actual usage (Davis, 1989) Venkatesh et al
The UTAUT model, as noted in 2003, accounts for 69% of the intention to use ICT, significantly outperforming earlier models that explained only about 40% Its comprehensive nature has led to its adoption in numerous studies aimed at predicting user intentions in e-commerce and e-financial services By extending the UTAUT model to include factors such as financial risk, economic risk, functional risk, and privacy concerns, it provides a solid foundation for examining technology acceptance in the context of Vietnam.
2.1.1 Unified Theory of Acceptance and Use of Technology (UTAUT)
The Unified Theory of Acceptance and Use of Technology (UTAUT), developed by Venkatesh et al in 2003, synthesizes eight existing theories, including the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT) UTAUT identifies four key constructs—performance expectancy, effort expectancy, social influence, and facilitating conditions—that significantly impact user adoption of Information and Communication Technology (ICT).
The theory postulates that four core constructs – performance expectancy, effort expectancy, social influence, and facilitating conditions – are direct determinants of ICT behavioral intention and ultimately behavior (Venkatesh et al., 2003)
Performance expectancy, akin to perceived usefulness and relative advantage, encompasses five key performance-related elements: perceived usefulness, extrinsic motivation, job fit, relative advantage, and outcome This construct highlights the importance of these factors in determining how users perceive the effectiveness and benefits of a system or technology.
Effort expectancy, akin to perceived ease of use and complexity, plays a crucial role in understanding ICT adoption Social influence, resembling subjective norm, highlights how individuals' acceptance of technology is shaped by the expectations of others Facilitating conditions, similar to perceived behavioral control, emphasize the importance of having the necessary resources and knowledge to effectively use ICT systems The UTAUT model integrates various factors from competing theories and identifies behavioral intention and facilitating conditions as key determinants of adoption behavior Ultimately, users must possess mobile internet knowledge to successfully adopt ICT systems; without this knowledge, their ability to engage with technology is significantly hindered.
Figure 2.1: the UTAUT model (Venkatesh et al., 2003)
The UTAUT model, in its original form, is not suitable for researching user acceptance of mobile commerce, as it was primarily designed for PC and fixed-line Internet applications Since its introduction in 2003, while many studies reference UTAUT, only a limited number fully utilize all of its constructs.
Research has utilized the extended UTAUT model to analyze user adoption in various sectors, including online securities trading (Wang & Yang, 2005), internet banking (Yee et al., 2015; El-Qirem, 2013; Yu, 2012), and health information technology (Kijsanayotin et al., 2011).
2009), in digital library (Nov & Ye, 2009), and in e-government services (Suha & Anne,
The UTAUT model has been utilized to analyze the adoption of various mobile services, including mobile banking (Yu, 2012), mobile wallets (Shin, 2009), mobile payments (Kim et al., 2009), and mobile technologies (Park et al., 2007) Many of these studies either applied the UTAUT framework directly or modified it by integrating constructs from the Technology Acceptance Model (TAM) or incorporating additional independent variables to enhance the understanding of intention adoption.
Figure 2.2: Basically generalized model of extant researches
Unlike previous studies that primarily utilized the UTAUT model to assess a single construct of users' risk perceptions, Zhou (2012) took a more comprehensive approach by analyzing the usage of location-based services through the lenses of UTAUT alongside perceived risk, privacy concerns, and trust.
In 2013, Ku examined the factors influencing securities investors' intentions to use mobile trading platforms by enhancing the UTAUT model This extended model incorporates the symmetry axis, highlighting how usage intention is affected by UTAUT constructs and perceived risks.
Figure 2.3: Basically generalized extended UTAUT model of extant researches
The extended UTAUT model has been effectively utilized in various studies to predict users' intentions to use mobile-based services; however, its application in Vietnam remains limited By incorporating enablers like performance expectancy, effort expectancy, and social influence, along with inhibitors such as financial risk, economic risk, functional risk, and privacy concerns, the extended UTAUT provides a solid framework for empirically examining the behavioral intention to adopt M-Trading in Vietnam.
Behavioral intention, risk perceptions and privacy concern
Ajzen (1991) posits that intentions reflect the motivational factors influencing behavior, indicating the level of effort individuals are willing to exert to perform a specific action This concept aligns with theories such as the Theory of Reasoned Action (TRA), the Theory of Planned Behavior (TPB), and the Decomposed Theory of Planned Behavior (DTPB), as well as intrinsic and extrinsic motivation models Usage intention regarding the adoption of Information and Communication Technology (ICT) systems measures an individual's motivation to engage in a particular behavior, highlighting the strength of their intention to act (Fishbein & Ajzen, 1975).
Reactions to use ICT Actual use of ICT
Usage Intention of mobile-based services
Positive Effects to use mobile-based services
Negative effects to use mobile-based services
Demographics serve as a precursor to actual behavior, aligning with psychological theories that suggest individual behavior is both predictable and shaped by personal intention The UTAUT model highlights that usage intention significantly impacts the adoption of Information and Communication Technology (ICT) (Venkatesh et al., 2003; Venkatesh & Zhang, 2010).
Concerning the acceptance of mobile-based mode in financial services, Tai & Ku
(2013) indicated that risk perceptionsare important determinant of behavioral intention
Perceived risks are recognized as significant barriers to mobile usage, as highlighted in various studies (Chen, 2008; Luo et al., 2010; Hsu et al., 2011) Mobile users often experience apprehension due to uncertainties related to data input errors, software malfunctions, connection disruptions, and potential privacy breaches (Mallat et al., 2008).
M-Trading, a mobile-based financial service, requires users to register with personal information, which raises concerns about security The risk of opportunistic hackers accessing trading accounts, deleting data, or executing unauthorized trades may deter investors from reaping the benefits of M-Trading.
Numerous studies have demonstrated that users' perception of risk significantly affects their intention to utilize mobile-based financial services Mallat (2007) identified perceived risk as the primary barrier to adopting mobile payment systems, while Mallat et al (2008) highlighted it as a crucial factor influencing the use of mobile ticketing services.
Consistent to Mallat (2007) and Mallat et al (2008), Cruz et al (2010) and Koenig-Lewis et al (2010) indentified high perceived risk as a key inhibitor of mobile banking
Users of mobile-based financial services often perceive risks from multiple dimensions, including security, economic, and functional risks Security risk is primarily associated with concerns over electronic fraud and hacker attacks, while economic risk stems from fears of financial loss due to transaction errors or operational failures Additionally, functional risk relates to worries about the reliability and accessibility of the services provided.
Information privacy refers to the claim of ICT’s users to determine for themselves when, how, and to what extent their information is communicated to others (Malhotra et al.,
Privacy concerns among ICT users vary due to cultural differences, regulatory laws, past experiences, and personal characteristics, leading to diverse levels of apprehension regarding personal information disclosure (Li, 2011; Malhotra et al., 2004) Users with heightened privacy concerns often perceive service providers as opportunistic, resulting in reluctance to share personal information with securities firms (Dinev & Hart, 2006) or providing inaccurate details (Teo et al., 2004) From the UTAUT perspective, these privacy concerns act as barriers to usage (Bansal et al., 2010) Additionally, Malhotra et al (2004) identified various aspects of privacy concerns, including collection, control, and awareness Research has demonstrated that privacy concerns significantly influence perceived risk (Zhou, 2012; Junglas et al., 2008; Bansal et al., 2010) and affect user adoption across multiple domains, such as instant messaging (Lowry et al., 2011), web-based healthcare services (Bansal et al., 2010), electronic health records (Angst & Agarwal, 2009), software firewalls (Kumar et al., 2008), and ubiquitous commerce (Sheng et al., 2008).
Venkatesh et al.'s (2003) UTAUT serves as the foundational theoretical framework for analyzing securities investors' acceptance of M-Trading However, due to the unique characteristics of M-Trading compared to traditional ICT, certain UTAUT constructs may not be applicable Thus, it is essential to incorporate risk perceptions into an extended UTAUT model for this research Given that M-Trading is still emerging in Vietnam, the number of securities investors utilizing this mobile application is minimal Consequently, this study focuses on behavioral intention to use M-Trading as the dependent variable, while omitting two UTAUT constructs: use behavior and experience.
The UTAUT model's developer noted that the "facilitating conditions" construct loses significance in predicting user intention when both "performance expectancy" and "effort expectancy" are present Facilitating conditions indicate that users must possess the necessary skills and resources to engage in M-Trading, including mobile internet knowledge and the ability to cover communication and service fees However, in Vietnam, few securities investors utilize M-Trading, and brokerage firms offer this application without fees Consequently, facilitating conditions, which typically influence usage behavior but show no significant correlation with behavioral intention, have been excluded from the research model for this study.
Hypotheses Development
“voluntariness” is also not included
Research indicates that users' concerns about risk significantly influence the adoption of mobile-based financial services (Tai & Ku, 2013; Laukkanen & Kiviniemi, 2010; Luo et al., 2010) Perceived risk reflects an individual's awareness of potential uncertainties and negative outcomes associated with an activity (Forsythe et al., 2006; Littler & Melanthiou, 2006; Bland et al., 2007; Im et al., 2008) Even when users recognize the benefits of a service, their willingness to adopt it may be hindered by perceived risks The M-Trading platform, which integrates mobile Internet, devices, and systems, presents unique challenges such as data entry errors, electronic data interception, and unstable wireless connections that are not present in traditional formats Consequently, securities investors may hesitate to use M-Trading due to their risk perceptions.
Previous studies using the UTAUT model have shown that privacy concerns significantly affect perceived risk In their research on M-Trading adoption in Taiwan, Tai & Ku (2013) identified three types of perceived risks—security, economic, and functional—but did not investigate the relationship between privacy concerns and these risks This study aims to address that gap by integrating perceived risks and privacy concerns into the UTAUT model, providing a more comprehensive understanding of the factors influencing securities investors' adoption or resistance to M-Trading.
In this study, behavioral intention is identified as an endogenous variable, specifically within the context of M-Trading This construct reflects the degree to which securities investors believe that M-Trading can enhance their transaction performance Performance expectancy, as defined by Venkatesh et al (2003), refers to an individual's belief that using a specific ICT system will lead to improved performance In the case of M-Trading, performance expectancy encompasses benefits such as increased trading efficiency and greater convenience, which are likely to impact the intention to use the platform Consequently, the following hypothesis is proposed.
Hypothesis 1: Securities investors with high performance expectancy for M-Trading will have greater behavioral intention to use it
Effort expectancy refers to the belief that learning to use a specific ICT system, such as M-Trading, will not demand substantial effort (Venkatesh et al., 2003) Users' perceptions of the effort required to learn and engage with M-Trading significantly influence their behavioral intention to adopt the platform Consequently, if investors perceive M-Trading as user-friendly, their intention to utilize the system is likely to rise.
Research indicates that the effort expectancy associated with using an ICT system significantly influences users' intention to adopt the technology (Venkatesh & Morris, 2000; Wang et al., 2009; Deng et al., 2011) Therefore, we propose the following hypothesis:
Hypothesis 2: Securities investors with high effort expectancy for M-Trading will have greater behavioral intention to use it
Social influence is defined as the degree to which an individual perceives that its important peers expect his/her to employ a certain ICT system (Venkatesh et al., 2003)
Social influence plays a crucial role in shaping securities investors' intentions to adopt M-Trading, particularly in Vietnam where the platform is still emerging Users are likely to be guided by their peers' perceptions regarding the quality and functionality of M-Trading Additionally, individuals gather information, assess alternatives, and make decisions based on their experiences, preferences, and the external environment Previous research indicates that social influence significantly predicts the intention to use specific information systems It is anticipated that users' behavioral intentions to engage with ICT-based services are swayed by their peers' opinions Therefore, the following hypothesis is proposed.
Hypothesis 3: Securities investors who perceive a high degree of positive social influence (i.e., supportive of M-Trading) from their peers will have a greater behavioral intention to use M-Trading
2.3.2 Hypotheses Derived From Risk Perceptions
Tai & Ku (2013) proved three-facet perceived risks (i.e security risk, economic risk, and functional risk) positively influence behavioral intention to use M-Trading, and Dai et al
In 2014, research highlighted that multi-dimensional perceptions of risk play a crucial role in understanding online shopping behaviors This study specifically examines the impacts of security risk, economic risk, and functional risk on the intention to use M-Trading in Vietnam.
Security risk refers to the concerns that securities traders have regarding potential harm from electronic fraud or hacking incidents while utilizing M-Trading This perception of security risk is identified as a significant barrier to the widespread adoption of mobile financial services and is considered one of the greatest challenges facing mobile financial service providers.
Security risks, including potential fraud and misrepresentation, are significant concerns for Internet users (Miyazaki & Fernandez, 2001) Research indicates that many individuals feel vulnerable to identity theft when utilizing mobile financial services (Mallat, 2007; Wessels & Drennan, 2010) Consequently, this leads to the formulation of the following hypothesis.
Hypothesis 4: Securities traders perceiving high security risk in M-Trading will have less behavioral intention to use it
Economic risk refers to investors' concerns about potential financial losses stemming from transaction errors or operational mistakes while utilizing M-Trading Research indicates that individuals are often hesitant to adopt mobile-based financial services, as highlighted by studies from Koenig-Lewis et al (2010) and Wessels.
M-Trading presents unique challenges compared to traditional trading channels like website-based and telephone-based securities trading The small touch screens with limited display resolution can result in frequent input errors and typos that are hard to detect Unlike other formats where investors or brokers can manually verify transaction accuracy, M-Trading often lacks such safeguards, creating feelings of uncertainty and fear among users Therefore, the following hypothesis is proposed:
Hypothesis 5: Securities investors who perceive a high economic risk for M-Trading will have less behavioral intention to use it
Functional risk refers to investors' concerns about the potential unavailability or malfunction of mobile financial services Research indicates that many individuals avoid using these services due to fears of system failures or mobile Internet disconnections during transactions Studies by Shen et al (2010) and Wessels & Drennan (2010) highlight that users are apprehensive about the reliability of mobile devices and networks, leading to worries about interruptions or delays in transactions As a result, it is hypothesized that these concerns significantly impact the adoption of mobile financial services.
Hypothesis 6: Securities investors who perceive a high functional risk for M-Trading will have less behavioral intention to use it
2.3.3 Hypotheses Derived From Privacy Concerns
When using M-Trading, users must provide personal information such as their username, password, location, verification code, and account number during the signup process This requirement may raise privacy concerns among investors, who may be apprehensive about how mobile application developers handle the collection, storage, and use of their information.
Securities traders often express concerns about their personal information being shared with third parties without their consent, leading to anxiety over potential losses from information leakage and unauthorized sales This apprehension can hinder their intention to adopt M-Trading services Research has shown a negative correlation between privacy concerns and the behavioral intentions related to information and communication technology (ICT) usage Therefore, it is hypothesized that privacy concerns negatively impact securities traders' willingness to engage with M-Trading platforms.
Hypothesis 7: Securities investors with high privacy concerns in M-Trading will have less behavioral intention to use M-Trading
Noticeably, privacy concerns and security risk are indicated as two clearly distinct constructs (Miyazaki &Fernandez, 2001; Román 2007; Román &Cuestas 2008, Riquelmi &
Román, 2014) However, there exists interactive influence on each other (Belanger et al
In 2002, research highlighted by Riquelmi & Román (2014), Schlosser et al (2006), and Hu et al (2010) indicates that a strong concern for personal information privacy can lead to negative perceptions regarding the security of smartphone applications Additionally, investors who are not well-versed in online security and third-party security measures may feel anxious about sharing personal information while engaging in mobile-based trading.
Fernandez (2001) concluded that both privacy concerns and security risk are the major obstacles in the development of online shopping Accordingly, the following hypothesis is proposed:
Hypothesis 8: Privacy concernsare positively correlated to security risk
In addition, Malhotra et al (2004) indicated that Internet users with a high degree of information privacy concerns are likely to be high perceptions of risk Nepomuceno et al
Conceptual model
Based on the hypotheses above, the below research model (Figure 2.4) is proposed and evaluated empirically in M-Trading’s settings
Chapter summary
This chapter outlines the theoretical framework of the model, highlighting that behavioral intention to use M-Trading is influenced by seven key factors: performance expectancy, effort expectancy, social influence, security risk, economic risk, functional risk, and privacy concerns These factors were chosen due to their established relationships in prior research Consequently, ten hypotheses have been formulated for this study The following chapter will detail the methodology employed to analyze the data and test the research model's hypotheses.
Behavioral Intention to use M-Trading
METHODOLOGY
Research design
The study was conducted in two primary phases: a pilot survey and a main survey, utilizing both qualitative and quantitative methods in the pilot and focusing on quantitative methods in the main survey The primary participants were individual securities investors located in Ho Chi Minh City Drawing from prior research and the specific context of Vietnam, the initial questionnaire featured eight measurement scales, including performance expectancy, effort expectancy, social influence, security risk, economic risk, functional risk, privacy concerns, and behavioral intention Subsequently, the draft questionnaire was translated from English into Vietnamese.
A pilot test utilizing both qualitative and quantitative methods was conducted from May to July 2015 to refine the questionnaire for a larger data collection This involved a small group of two securities broking professionals and four experienced senior securities investors familiar with website-based trading and mobile financial applications in Vietnam The primary goal was to explain the purpose of the pilot test and share the questionnaires and related documents with the participants A subsequent discussion aimed to identify items for elimination, addition, or revision to better fit the Vietnamese context After adjusting the initial questionnaire, it was distributed to a sample of fifteen colleagues and clients to identify any unclear items Feedback from this process led to the final version of the questionnaire, which was then subjected to a quantitative pilot test to assess item reliability using Cronbach Alpha and exploratory factor analysis (EFA) The main survey was conducted in Ho Chi Minh City from June to July 2015, with the questionnaire emailed to all staff at VNDirect.
The survey, conducted in collaboration with securities brokers from VNDS, SSI, and HSC, involved distributing questionnaires to securities investors via email, ensuring automatic return of results to the author Additionally, in-depth interviews were carried out on trading floors, with the collected data analyzed by CFA and hypotheses tested using SEM (as illustrated in Figure 3).
Figure 3: Research process (adopted from Nguyen Dinh Tho & Nguyen Thi Mai Trang, 2007)
- Eliminate corrected item - total correlation
- Eliminate the variables with low EFA
- Evaluate the validity and the correlation among variables to identify underlying factors or define number of extracted factors
- Composite reliability, extracted variances, uni-dimensionality test, convergent validity and discriminant validity
The draft questionnaire comprises eight measurement scales: performance expectancy, effort expectancy, social influence, security risk, economic risk, functional risk, privacy concerns, and behavioral intention Each variable in the research model is assessed using a five-point Likert scale, where responses range from 1 (strongly disagree) to 5 (strongly agree).
Performance expectancy refers to the extent to which users believe that utilizing M-Trading will enhance their trading performance in securities According to the research model established by Tai & Ku (2013), this concept is measured through four specific items.
1 Using M-Trading would enhance my securities trading efficiency PE1
2 I feel M-Trading is useful PE2
3 Using M-Trading would increase the convenience of securities trading PE3
4 Using M-Trading would enable me to accomplish securities trading more quickly PE4
Effort expectancy mentions the degree that an individual believes that learning to use M-Trading will not require significant effort Basing on the research model developed by Tai
& Ku (2013), this construct comprises four items:
Effort expectancy (adopted from Venkatesh et al., 2003) Coding
1 Learning how to use M-Trading would be easy for me EE1
2 I expect to find M-Trading clear and understandable EE2
3 It would be easy for me to become skillful at using M-Trading EE3
4 Learning how to use M-Trading would be easy for me EE4
Social influence refers to how much an individual believes that their key peers expect them to engage in M-Trading According to the research model established by Tai & Ku (2013), this concept includes four key components.
Social influence (adopted from Venkatesh et al., 2003) Coding
1 I feel people around me would encourage me to use M-Trading SI1
2 People who are important to me would think that i should use M-Trading SI2
3 I will discuss M-Trading with my peers SI3
4 In my environment, people encouraged me to use M-Trading SI4
Tai & Ku (2013) identify perceived risks in M-Trading, which encompass security risks and economic risks, highlighting concerns such as personal information disclosure, account loss, hacker attacks, malfunctions, and transaction errors These risks are assessed through twelve items, with four items dedicated to each of the three constructs.
Security risk (adopted from Tai & Ku, 2013) Coding
1 I would not feel secure conducting securities trades via M-Trading systems SR1
2 I am worried that others might be able to access my M-Trading account SR2
3 I would not feel secure sending sensitive information across M-Trading systems SR3
4 I would not feel totally safe providing personal information over M-Trading systems SR4
Economic risk (adopted from Tai & Ku, 2013) Coding
1 I am uneasy about using M-Trading because I may lose money due to incorrect operation ER1
2 I am uneasy about using M-Trading because I may lose money due to a careless mistake ER2
3 I am uneasy about using M-Trading because I may lose money due to system processing errors ER3
4 When transaction errors occur, I am concerned that the securities broker may not compensate my loss ER4
Functional risk (adopted from Tai & Ku, 2013) Coding
1 M-Trading systems may not perform well because of the limited processing power of mobile devices FR1
2 M-Trading systems may not perform well because of system failure FR2
I am uneasy about using M-Trading because securities transactions may fail due to the unstable nature of mobile devices, mobile operating systems or mobile networks
4 I am concerned that M-Trading services cannot meet my needs due to poor functionality or system malfunctions FR4
Privacy concerns reflect M-Trading users’ concern on personal information disclosure to securities firms Basing on the research model developed by Zhou (2012), this construct comprises four items:
Privacy concerns (adopted from Zhou, 2012) Coding
1 I am concerned that the information I disclosed to the service provider could be misused PC1
2 I am concerned that a person can find private information about me on Internet PC2
3 I am concerned about providing personal information to the service provider, because of what others might do with it PC3
4 I am concerned about providing personal information to the service provider, because it could be used in a way I did not foresee PC4
Behavioral intention mentions the users’ intention to use M-Trading Basing on the research model developed by Tai & Ku (2013), this construct comprises four items:
Behavioral intention(adopted from Venkatesh et al., 2003) Coding
1 I intend to use M-Trading in the future UI1
2 I predict I would use M-Trading in the future UI2
3 I plan to use M-Trading in the future UI3
4 I will use M-Trading for my securities trading needs UI4
Measurement refinement.…
In this qualitative study, the draft questionnaire was translated into Vietnamese, and in-depth interviews were conducted with six participants, as listed in Appendix A All feedback from these interviews is documented in Appendix B, where modifications were made to ensure accuracy and clarity in the Vietnamese version Despite the widespread use of most scales in prior research, this study was essential for adapting the questionnaire to the Vietnamese context before launching the quantitative survey The finalized questionnaire surveys are available in Appendix C for the English version and Appendix D for the Vietnamese version.
Measurement scales after being modified through in-depth interviews includes thirty four items as depicted as Table 3.1
1 I think that using M-Trading would enhance my securities trading efficiency PC1
2 I feel M-Trading is useful PC2
3 I think that using M-Trading would increase the convenience of securities trading PC3
4 I think that using M-Trading would enable me to accomplish securities trading more quickly PC4
5 With my ability, learning how to use M-Trading would be easy for me EE1
6 I expect that M-Trading would be displayed understandably and easy to utilize as same as website-based trading EE2
7 I would attempt to use M-Trading skillfully EE3
8 I would find M-Trading easy to use as same as website-based trading or other mobile-based applications EE4
9 I feel people around me would encourage me to use mobile-based financial applications (banking, securities trading, electronic payment) SI1
10 People who are important to me would think that I should use mobile-based financial applications (banking, securities trading, electronic payment) SI2
I will use mobile-based financial applications (banking, securities trading, electronic payment) to be correspondent to my peers since they used/are about to use
The mass media often mobile-based financial applications (banking, securities trading, electronic payment) are often covered by the mass media, I use it on trial basis
13 My school, my company and community encourage me to use mobile-based financial applications (banking, securities trading, electronic payment) SI5
14 I would not feel secure conducting securities trades via mobile securities trading systems SR1
15 I am worried that others might be able to access my M-Trading account SR2
16 I would not feel secure sending sensitive information across mobile securities trading systems SR3
17 I would not feel totally safe providing personal information over M-Trading systems SR4
18 I (would) feel uneasy about using M-Trading because I may lose money due to incorrect operation ER1
19 I (would) feel uneasy about using M-Trading because I may lose money due to a careless mistake ER2
20 I (would) feel uneasy about using M-Trading because I may lose money due to system processing errors ER3
21 When transaction errors occur, I (would) be concerned that the securities broker may not compensate my loss ER4
22 M-Trading systems may not perform well because of the limited processing power of mobile devices FR1
23 M-Trading systems may not perform well because of system failure FR2
I (would) feel uneasy about using M-Trading because securities transactions may fail due to the unstable nature of mobile devices, mobile operating systems or mobile networks
25 I (would) be concerned that M-Trading services cannot meet my needs due to poor functionality or system malfunctions FR4
26 I am concerned that the information I disclosed to the service provider could be misused PC1
27 I am concerned that a person can find private information about me on Internet
28 I am concerned about providing personal information to the service provider, because of what others might do with it PC3
29 I am concerned about providing personal information to the service provider, because it could be used in a way I did not foresee PC4
30 I intend to use M-Trading in the future UI1
31 I predict I would use M-Trading in the future UI2
32 I plan to learn skillfully the usage of M-Trading in the future UI3
33 I will refer M-Trading to other people UI4
Sample
In Ho Chi Minh City, a convenience sampling approach was utilized due to time constraints, employing a self-administered survey that encompassed seven factors and thirty-three variables The sample was selected through a non-probability sampling technique, ensuring a focused study within the urban landscape of Ho Chi Minh City.
Chi Minh City is home to Vietnam's largest securities exchange, where individual investors often rely on securities brokers and investment advisers for guidance and advice to achieve their financial goals.
To ensure the reliability and validity of the variables, Cronbach’s Alpha, Exploratory Factor Analysis (EFA), and Confirmatory Factor Analysis (CFA) will be employed, followed by Structural Equation Modeling (SEM) to test the model and hypotheses A minimum sample size of 100 is essential, adhering to the guideline of having at least five times the number of items (Hair et al., 2010), resulting in the requirement that n must exceed 100 and n equals 5k, where k represents the number of items.
Thus, the minimum sample size was 5x33 = 165 samples
To achieve a sample size of approximately 300, 400 questionnaires were distributed to participants, resulting in 321 responses and an impressive response rate of 80.5 percent No questionnaires were deemed invalid, as all respondents provided varied and plausible answers Ultimately, 244 valid questionnaires were utilized for this research, which meets the minimum sample size requirement and is considered satisfactory for analysis.
Data analysis and interpretation
A total of 244 responses were analyzed using SPSS 22 and Amos 22 to evaluate the model Initially, Cronbach’s Alpha was employed to assess the reliability of each measurement component, while Exploratory Factor Analysis (EFA) tested the validity of the entire item scale Items deemed inappropriate based on convergent and discriminant validity were eliminated as necessary In the second phase, Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) using Amos 22 were conducted to enhance the model's value.
To evaluate the reliability of scales within a specific sample and to examine their internal consistency, it is essential to utilize Cronbach’s Alpha coefficient, which should exceed 0.6 (Devellis, 2003) Additionally, corrected item-total correlation values must be a minimum of 0.3 to confirm that each item effectively measures the same construct as the overall scale (Pallant, 2011).
3.4.2 Validity measure by EFA (Exploratory Factor Analysis)
To assess the validity and relationships among variables and to identify underlying factors, Exploratory Factor Analysis (EFA) was conducted using the oblique Promax method It is essential to meet specific prerequisites for EFA, as outlined by Pallant (2011).
To ensure statistical validity, the minimum sample size should be at least 165 cases, calculated by multiplying the number of items in the conceptual model (33) by a required rate of five observations per item This means that a sample size of at least 100 is essential, with five cases needed for each item to achieve reliable results.
- The correlations of r of the correlation matrix should show at least 0.3
- Kaiser-Meyor-Olkin (KMO) test must be equal or above 0.6 (Tabachnick & Fidell, 2007)
- Barllett’s test of sphericity should have significant less than 5%
- To extract factors, the eigenvalue of factors must be greater than 1 (Kaiser, 1956)
The CFA results indicate model fit when CMIN/DF is below 3 with a p-value exceeding 5%, while GFI, RFI, and CFI values should be above 0.9, and RMSEA should be less than 10% The author assessed the reliability of the measurement scale using composite reliability (CR) and determined convergent validity through average variance extracted (AVE), alongside evaluating discriminant validity via item correlations (r) Subsequently, structural equation modeling (SEM) was employed to test the hypothesized model and estimate path coefficients for each proposed relationship within the structural model.
Pilot test
Before conducting the official survey, a pilot quantitative study was performed to evaluate the constructs of the conceptual model This pilot study utilized a convenient sample size of 120 participants (n = 120) and employed two key tools: Cronbach’s Alpha for reliability assessment and Exploratory Factor Analysis (EFA) for data analysis.
In this research model, the Cronbach’s Alpha coefficient was utilized to assess the internal consistency reliability of each scale This coefficient typically ranges from 0 to 1, with George & Mallery (2003) offering guidelines for interpretation: a score greater than 0.9 indicates excellent reliability, above 0.8 signifies good reliability, above 0.7 is considered acceptable, above 0.6 is questionable, and above 0.5 is deemed poor.
