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 create their own websites, enabling investors to execute trading orders online Notable 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, such as FPT Securities Corp and VPbank Securities Corp., have developed smartphone applications to facilitate mobile trading Despite this innovation, the adoption of mobile trading (M-Trading) among Vietnamese investors remains limited due to inherent challenges and customers' cautious approach to risk assessment Consequently, researchers are increasingly focusing on understanding the reasons behind investors' willingness or reluctance to engage in M-Trading and the factors that influence their behavioral intentions in this context.
Numerous studies have explored the adoption of Information and Communication Technology (ICT) in the financial industry, focusing on areas such as internet banking, mobile banking, and online securities trading Research has highlighted significant advancements in Vietnam, with investigations into online banking, e-banking, e-payment, and mobile money transfer Key studies include those on mobile learning, personal internet banking, mobile payment, and mobile shopping, reflecting the growing integration of ICT in Vietnam's financial sector.
2015), but only few on e-trading of securities were conducted
Understanding the behavioral intentions of Vietnamese investors towards M-Trading is crucial, as the factors influencing their adoption remain unclear This insight is essential for brokerage firms aiming to enhance M-Trading implementation and service quality, as well as for the Vietnam State Securities Commission to formulate effective market regulations Therefore, developing and empirically testing a comprehensive model of securities investors' behavioral intentions regarding M-Trading is imperative to identify the reasons behind their willingness or reluctance to adopt this technology.
In Vietnam, M-Trading refers to mobile commerce conducted through devices like smartphones, tablets, and PDAs, excluding laptops, and it necessitates Internet access similar to online shopping This study develops its theoretical framework based on a literature review of the Unified Theory of Acceptance and Use of Technology (UTAUT), which integrates essential elements from the Technology Acceptance Model.
The extended UTAUT model has been effectively utilized to understand 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 research gap in 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 explore 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 later translated into Vietnamese To refine the Vietnamese version, in-depth interviews were conducted with eight individuals prior to mass implementation of the survey Data analysis is performed using SPSS software, following a three-stage process: first, Cronbach’s Alpha tests the reliability of the measurement scale; second, Exploratory Factor Analysis (EFA) assesses its validity; and finally, Structural Equation Modeling (SEM) and path analysis examine the relationships among factors in the research model The survey is conducted in Ho Chi Minh City, Vietnam's largest city and economic hub, which hosts the country's primary securities exchange, 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 utilized these services, to explore their behavioral intentions towards M-Trading Additionally, potential investors of the same age group will also be invited to participate.
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 ICT users' acceptance and usage have been conducted due to the widespread adoption of technology, leading to the development of various models from disciplines such as psychology, sociology, and information systems One prominent approach, based on the motivational model, examines how extrinsic and intrinsic motivations impact acceptance (Davis et al., 1992) Another significant framework, the Technology Acceptance Model (TAM), investigates the influence of perceived usefulness and perceived ease of use on users' intentions and actual usage (Davis, 1989; Venkatesh et al.).
The UTAUT model, as noted in 2003, effectively explains 69% of the intention to use ICT, significantly outperforming earlier models that accounted for only about 40% Its comprehensive nature has made it a popular choice in previous studies for predicting user intentions in areas such as e-commerce and e-financial services By extending the UTAUT model to include factors like financial risk, economic risk, functional risk, and privacy concerns, it provides a strong foundation for examining technology acceptance within the Vietnamese context.
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 prominent theories, including the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) UTAUT identifies four key constructs—performance expectancy, effort expectancy, social influence, and facilitating conditions—that directly influence users' intention to adopt information and communication technology (ICT) These constructs serve as critical determinants of both behavioral intention and actual usage behavior, highlighting the factors that drive successful technology adoption (Venkatesh et al., 2003).
Performance expectancy, akin to perceived usefulness and relative advantage, encompasses five key performance-related constructs: perceived usefulness, extrinsic motivation, job-fit, relative advantage, and outcome This aggregate reflects the overall expectation of performance in relation to a given task or technology.
Effort expectancy, akin to perceived ease of use and complexity, plays a crucial role in understanding technology acceptance Social influence, comparable to subjective norm, suggests that individuals' acceptance of information and communication technology (ICT) is shaped by their perceptions of others' expectations Additionally, facilitating conditions, which align with perceived behavioral control, highlight the necessity of having the right resources and knowledge to adopt ICT effectively The UTAUT model synthesizes various factors from competing theories, demonstrating that both behavioral intention and facilitating conditions are key determinants of technology adoption Ultimately, users must possess mobile internet knowledge and resources to successfully engage with ICT systems.
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 designed primarily for PC and fixed-line Internet systems Since its introduction in 2003, only a limited number of studies have utilized all of its constructs, highlighting the need for a more tailored approach to understand mobile commerce adoption.
Recent studies have utilized an extended version of the UTAUT model to analyze user adoption across various sectors, including online securities trading in financial markets (Wang & Yang, 2005), internet banking (Yee et al., 2015; El-Qirem, 2013; Yu, 2012), and health information technology (Kijsanayotin et al.).
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) These studies primarily concentrated on applying the UTAUT framework or modifying it by integrating constructs from the Technology Acceptance Model (TAM) and incorporating additional independent variables that facilitate intention adoption.
Figure 2.2: Basically generalized model of extant researches
Unlike previous studies that primarily focused on a single construct to assess users' risk perceptions using the UTAUT framework, Zhou (2012) explored the usage of location-based services through a comprehensive lens that included UTAUT, perceived risk, privacy concerns, and trust.
In 2013, Ku examined the factors influencing securities investors' intention to engage in mobile securities trading by creating an extended UTAUT model This 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, which effectively predicts users' intention to use mobile-based services, has seen limited application in Vietnam By incorporating enablers like performance expectancy, effort expectancy, and social influence, alongside inhibitors such as financial risk, economic risk, functional risk, and privacy concerns, this model provides a strong foundation for empirically testing the behavioral intention to use M-Trading in the Vietnamese market.
Behavioral intention, risk perceptions and privacy concern
Ajzen (1991) posits that intentions reflect the motivational factors influencing behavior, indicating the effort individuals are willing to exert to perform specific actions This concept aligns with theories such as the Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), and the Decomposed Theory of Planned Behavior (DTPB), as well as intrinsic and extrinsic motivation models The intention to adopt or accept an ICT system quantifies an individual's motivation to engage in that behavior, highlighting the relative strength of their intention (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 play a crucial role as a precursor to actual behavior, aligning with psychological theories that suggest individual behavior is predictable based on intentions The Unified Theory of Acceptance and Use of Technology (UTAUT) highlights that usage intention significantly impacts Information and Communication Technology (ICT) usage, as demonstrated in research by Venkatesh et al (2003) and Venkatesh & Zhang (2010).
Concerning the acceptance of mobile-based mode in financial services, Tai & Ku
Risk perceptions significantly influence behavioral intentions, as highlighted by various studies (2013) Perceived risks are often seen as barriers to adoption (Chen, 2008; Luo et al., 2010; Hsu et al., 2011) Mobile users frequently identify risks related to uncertainties from data input errors, software failures, connection issues, and privacy concerns (Mallat et al., 2008; Cruz et al., 2010; Koenig-Lewis et al., 2010) M-Trading, a mobile-based financial service, requires users to provide personal information, which raises the risk of opportunistic hackers accessing trading accounts, deleting data, or executing unauthorized trades Consequently, investors may choose to forgo the potential advantages of M-Trading due to these risks.
Numerous studies have shown that users' perceptions of risk significantly influence their intention to utilize mobile-based financial services Mallat (2007) identified perceived risk as a primary barrier to the adoption of mobile payment systems, while Mallat et al (2008) highlighted it as a crucial factor in the use of mobile ticketing services Supporting these findings, Cruz et al (2010) and Koenig-Lewis et al (2010) also recognized high perceived risk as a major obstacle to mobile banking adoption.
Users of mobile-based financial services often perceive risks from various angles, primarily including security, economic, and functional risks Security risks are associated with potential harm from electronic fraud or hacking incidents Economic risks stem from concerns about possible financial losses due to transaction errors or operational failures Meanwhile, functional risks relate to the perceived reliability and accessibility of the service.
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 significantly due to cultural differences, regulatory laws, past experiences, and individual characteristics (Li, 2011; Malhotra et al., 2004) Users with heightened privacy concerns often perceive service providers as opportunistic, leading them to withhold or provide inaccurate personal information in response to requests from securities firms (Dinev & Hart, 2006; Teo et al., 2004) From the UTAUT perspective, these privacy concerns are recognized as inhibitors to usage (Bansal et al., 2010) Additionally, Malhotra et al (2004) identified various dimensions of privacy concerns, including collection, control, and awareness Research has shown that privacy concerns significantly impact perceived risk (Zhou, 2012; Junglas et al., 2008; Bansal et al., 2010) and influence user adoption across various platforms, 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).
This study adopts Venkatesh et al.'s (2003) UTAUT as the primary theoretical framework to explore securities investors' acceptance of M-Trading Given the unique characteristics of M-Trading compared to traditional ICT contexts, certain UTAUT constructs may not be applicable Therefore, it is essential to integrate risk perceptions into an extended UTAUT model for this research As M-Trading is still emerging in Vietnam, there is a limited number of securities investors who have utilized this mobile application Consequently, the study focuses on behavioral intention to use M-Trading as the dependent variable, excluding two UTAUT constructs: use behavior and experience.
The UTAUT model's developer noted that the "facilitating conditions" construct loses significance in predicting 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, which includes having mobile internet knowledge and covering communication and service fees Despite this, in Vietnam, few securities investors utilize M-Trading, and brokerage firms often offer this application free of charge, leading to the exclusion of facilitating conditions from the research model as it shows no significant correlation with behavioral intention This study aims to explore M-Trading usage among investors.
Hypotheses Development
“voluntariness” is also not included
Research indicates that users' concerns about risks significantly influence their adoption of mobile-based financial services (Tai & Ku, 2013; Laukkanen & Kiviniemi, 2010; Luo et al., 2010) Perceived risk refers to 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 intention to adopt it may waver due to perceived risks M-Trading, which integrates mobile internet, devices, and systems, presents unique challenges such as increased data entry errors, electronic data interception, and unstable wireless connections that are not typically found in traditional formats Consequently, investors' willingness to use M-Trading may be hindered by their risk perceptions.
Previous studies utilizing the UTAUT model have shown that privacy concerns significantly influence perceived risk In their research on M-Trading adoption in Taiwan, Tai & Ku (2013) identified security, economic, and functional risks as components of perceived risk within the UTAUT framework but did not investigate the connection between privacy concerns and these risk types This study aims to address this gap by integrating perceived risks and privacy concerns into the UTAUT model, providing a clearer understanding of the factors that affect 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, where it reflects securities investors' belief in the platform's ability to enhance their transaction performance Performance expectancy, as defined by Venkatesh et al (2003), refers to the degree to which individuals perceive that utilizing a specific ICT system will enhance their performance In the case of M-Trading, this encompasses the instrumental benefits such as improved trading efficiency and increased convenience, which ultimately shape the behavioral intention to adopt the platform Therefore, we propose the following hypothesis.
Hypothesis 1: Securities investors with high performance expectancy for M-Trading will have greater behavioral intention to use it
Effort expectancy refers to the perceived ease of learning to use a specific ICT system, influencing users' evaluations of the effort needed to engage with it (Venkatesh et al., 2003) In the context of M-Trading, if investors perceive the system as user-friendly, their intention to adopt it is likely to increase Research consistently shows that effort expectancy is a crucial factor in shaping behavioral intentions toward ICT systems (Venkatesh & Morris, 2000; Wang et al., 2009; Deng et al., 2011) Therefore, we propose the hypothesis that higher effort expectancy will lead to greater behavioral intention to use M-Trading.
Hypothesis 2: Securities investors with high effort expectancy for M-Trading will have greater behavioral intention to use it
Social influence refers to the extent to which individuals feel pressured by their peers to adopt a specific ICT system (Venkatesh et al., 2003) In the context of M-Trading, which is relatively new in Vietnam, users are likely to be swayed by their peers' perceptions of its quality and functionality Usage intention is shaped by personal experiences, preferences, and the external environment, guiding users in gathering information, assessing options, and making decisions (Zeithaml, 1988; Dodds et al., 1991) Previous research indicates that social influence significantly predicts the intention to use information systems (Baron et al., 2006; Wang et al., 2009) Therefore, it is anticipated that individuals' intentions to utilize ICT-based services are affected by their peers' opinions (Karahanna et al., 1999; Venkatesh & Davis, 2000), leading to the following hypothesis.
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, it was highlighted that multi-dimensional perceptions of risk play a crucial role in online shopping research This study specifically examines how security risk, economic risk, and functional risk influence the behavioral intention to use M-Trading in Vietnam.
Security risk, defined as the perceived threat of electronic fraud or hacker attacks during M-Trading, poses a significant barrier to the adoption of mobile financial services Research indicates that security concerns, including potential fraud and misrepresentation, are primary issues for Internet users Many individuals express a belief in their vulnerability to identity theft while utilizing mobile financial platforms Consequently, addressing security risks is crucial for mobile financial service providers to enhance user trust and adoption rates.
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 due to transaction errors or operational mistakes while using M-Trading Research indicates that individuals are hesitant to adopt mobile-based financial services (Koenig-Lewis et al., 2010; Wessels & Drennan, 2010; Hsu et al., 2011) Unlike traditional trading methods, such as website and telephone trading, M-Trading's small touch screens with limited display resolution can easily lead to input errors and typos that are hard to detect Furthermore, while investors and brokers can manually verify transaction information through other formats, such safeguards are often lacking in M-Trading systems, resulting in increased feelings of uncertainty and fear 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 services in mobile financial transactions Research by Shen et al (2010) and Wessels & Drennan (2010) highlights that many individuals avoid mobile financial services due to fears of service failures or internet disconnections during transactions Additionally, users who resist adopting these systems often believe that mobile devices, operating systems, and networks are inherently unstable, leading to worries about interruptions, delays, or cancellations of transactions (Mallat et al., 2008; Cruz et al., 2010; Koenig-Lewis et al., 2010) Therefore, the following hypothesis is proposed:
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
Using M-Trading requires users to provide personal information, including username, password, location, verification code, and account number, raising privacy concerns among investors Traders are apprehensive about the potential misuse of their data by mobile application developers, fearing that their personal information may be shared with third parties without consent, leading to risks such as data leakage and financial losses These privacy worries can negatively impact securities traders' willingness to use M-Trading, as supported by research indicating a significant relationship between privacy concerns and users' behavioral intentions in the context of information and communication technology Therefore, it is hypothesized that privacy concerns will adversely affect the adoption of M-Trading.
Hypothesis 7: Securities investors with high privacy concerns in M-Trading will have less behavioral intention to use M-Trading
Privacy concerns and security risks are recognized as two separate constructs, yet they interactively influence one another This relationship has been highlighted in various studies, emphasizing the importance of understanding both elements in the context of user trust and online behavior.
Research indicates that a strong concern for personal information privacy leads to negative perceptions of smartphone application security Investors lacking knowledge about online security and third-party identification often fear disclosing personal information during mobile trading Miyazaki and Fernandez (2001) identified privacy concerns and security risks as significant barriers to the growth of online shopping Therefore, we propose the following hypothesis:
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
Research from 2012 highlighted that privacy concerns significantly elevate perceived risks among North American households when shopping online Previous studies, including those by Zhou (2012), Eastlick et al (2006), and Bansal et al (2010), further support the notion that privacy issues influence perceived risks Consequently, this leads to the formulation of two hypotheses regarding the multi-faceted nature of perceived risks in online purchasing.
Hypothesis 9: Privacy concerns are positively correlated to economic risk
Hypothesis 10: Privacy concerns are positively correlated to functional risk.
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 foundations of the model, revealing 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 demonstrated in prior research Consequently, ten hypotheses are proposed for this study The subsequent 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 is structured in two key phases: a pilot survey and a main survey The pilot phase utilized both qualitative and quantitative methods, while the main survey focused solely on quantitative analysis The primary participants were individual securities investors located in Ho Chi Minh City Drawing from previous research and tailored to the Vietnamese context, the draft questionnaire included eight measurement scales: 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 to Vietnamese.
In a pilot test conducted from May to July 2015, both qualitative and quantitative methods were employed to refine a questionnaire intended for a larger data collection effort A small group of two securities broking professionals and four experienced senior securities investors, all familiar with website-based trading and mobile financial applications in Vietnam, participated in this initial phase The pilot aimed to clarify the questionnaire and accompanying documents, followed by discussions to identify items for elimination, addition, or revision to better fit the Vietnamese context After adjusting the questionnaire, it was distributed to a sample of fifteen colleagues and clients for feedback on clarity and potential misunderstandings The final version was then prepared for the main survey, which took place in Ho Chi Minh City from June to July 2015 The quantitative pilot test assessed the reliability of the questionnaire items using Cronbach's Alpha and exploratory factor analysis (EFA), with the questionnaire subsequently emailed to all staff at VNDirect.
The survey conducted by Securities Corporation (VNDS) involved numerous brokers from Saigon Securities Inc (SSI) and Ho Chi Minh City Securities Corporation (HSC), along with professionals from Mirae Asset Corporation, VPBank Securities Corp., and Vietcapital Securities Corp The collaboration with VNDS, SSI, and HSC facilitated the distribution of questionnaires to securities investors via email, ensuring that all responses were automatically collected by the author Additionally, in-depth interviews were conducted on trading floors, and the collected data was analyzed by CFA, with hypotheses tested using SEM (see 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 key 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, with responses ranging from 1 (strongly disagree) to 5 (strongly agree).
Performance expectancy refers to an individual's belief that using M-Trading will enhance their trading performance According to the research model by Tai & Ku (2013), this concept is defined by four key components.
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 significant peers anticipate their use of M-Trading According to the research model established by Tai & Ku (2013), this concept consists of 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
According to Tai & Ku (2013), the perceived risks associated with M-Trading encompass security risks and economic risks, which include concerns such as the disclosure of personal information, account loss, hacker attacks, system malfunctions, and transaction errors The study identifies twelve items that measure these three constructs, with four items dedicated to each risk category.
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 detailed in Appendix A All feedback from these interviews is recorded in Appendix B, where modifications were made to enhance accuracy and clarity in the Vietnamese version Although the scales used have been widely applied in previous research, this study is crucial for adapting the questionnaire to the Vietnamese context before proceeding with the quantitative survey The final revised questionnaire is presented in Appendix B, while the official versions are available in Appendix C for English and Appendix D for Vietnamese.
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 research relied on a non-probability sampling technique to select participants for the study.
Chi Minh City hosts Vietnam's largest securities exchange, making it a key hub for individual investors These investors often turn to securities brokers and investment advisers for guidance and advice to achieve their financial goals.
The reliability and validity of variables will be assessed using Cronbach’s Alpha, Exploratory Factor Analysis (EFA), and Confirmatory Factor Analysis (CFA), followed by the application of Structural Equation Modeling (SEM) to test the model and hypotheses A minimum sample size of 100 is required, adhering to the guideline of being at least five times the number of items (Hair et al., 2010), ensuring that n > 100 and n = 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 a total of 321 responses and an impressive response rate of about 80.5 percent None of the questionnaires were discarded due to invalid responses, such as selecting the same option for all questions or providing implausible answers Ultimately, 244 questionnaires were deemed valid for this research, surpassing the minimum sample size requirement and ensuring satisfactory data for analysis.
Data analysis and interpretation
A total of 244 responses were analyzed using SPSS 22 and Amos 22 to test the model Initially, Cronbach’s Alpha assessed the reliability of each measurement component, while Exploratory Factor Analysis (EFA) evaluated the validity of the entire item scale Items deemed inappropriate based on convergent and discriminant validity were eliminated if necessary In the second phase, Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were conducted using Amos 22 to enhance the model's value.
To evaluate the reliability of each scale for a specific sample and examine 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 is effectively measuring the same construct as the overall scale (Pallant, 2011).
3.4.2 Validity measure by EFA (Exploratory Factor Analysis)
To assess the validity and correlation among variables for identifying underlying factors or determining the number of extracted factors, Exploratory Factor Analysis (EFA) was conducted using the oblique Promax method It is essential to meet specific requirements for EFA, as outlined by Pallant (2011).
To ensure robust statistical analysis, a minimum sample size of 165 cases is essential, calculated by multiplying the number of items in the conceptual model (33) by a required rate of five observations per item This means that for accurate results, researchers should aim for at least 100 samples, with each item in the model adequately represented.
- 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's assessment of the measurement scale's reliability is based on composite reliability (CR), and convergent validity is determined using average variance extracted (AVE) Discriminant validity is evaluated through the correlation between items (r) Structural equation modeling (SEM) is then 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, the constructs of the conceptual model were assessed through a pilot quantitative study involving a convenient sample of 120 participants (n = 120) This pilot evaluation utilized two key tools: Cronbach’s Alpha for reliability assessment and Exploratory Factor Analysis (EFA) to analyze the data.
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: values greater than 0.9 indicate excellent reliability, those above 0.8 signify good reliability, above 0.7 are considered acceptable, above 0.6 are questionable, and above 0.5 are deemed poor.
