Behavioral intention to use mobile stock trading , evidence from vietnams securities investors

UNIVERSITY OF ECONOMICS HO CHI MINH CITY
International School of Business
------------------------------

NGUYEN PHUC BINH

BEHAVIORAL INTENTION TO USE
MOBILE STOCK TRADING:
EVIDENCE FROM VIETNAM’S
SECURITIES INVESTORS

MASTER OF BUSINESS (Honours)

Ho Chi Minh City – 2015


UNIVERSITY OF ECONOMICS HO CHI MINH CITY
International School of Business
------------------------------

Nguyen Phuc Binh

BEHAVIORAL INTENTION TO USE
MOBILE STOCK TRADING:
EVIDENCE FROM VIETNAM’S
SECURITIES INVESTORS
ID: 22130010

MASTER OF BUSINESS (Honours)
SUPERVISOR: Dr. TRAN PHUONG THAO

Ho Chi Minh City – 2015


i
ACKNOWLEDGMENTS
Firstly, I would like to express my deepest appreciation to my supervisor Dr. Tran
Phuong Thao for her professional guidance, valuable advices, continuous
encouragement, and support that made this thesis possible.
I would like to extend deep senses of gratitude to Prof. Nguyen Dinh Tho, Dr. Tran
Ha Minh Quan, and lecturers who have taught and transferred me valuable
knowledge and experiences during my time at the International School of Business,
special thanks to all of my dear friends in Mbus4 class who gave me useful
materials, responses and experiences to conduct this study.
I would also like to express my grateful thanks to my managers, my friends, and
my colleagues who participated in filling the questionnaires and/or helped send the
questionnaires to their peers; to securities investors, and provided valuable
information and comments for this study.
Personally, I wish to express my deep gratitude to my colleagues and friends
working at Vietcombank Fund Management (VCBF); Saigon Securities Inc. (SSI);
VNDirect Securities Corporation (VND); Hochiminh City Securities Corporation
(HSC); Maybank Kim Eng Securities Limited; Mirae Asset Wealth Management
Securities (VN) JSC, and Vietcapital Securities Corporation (VCSC).
Ho Chi Minh City, Vietnam,
January 27, 2016

Nguyen Phuc Binh


ii
ABSTRACT

The purpose of this study is to investigate the determinants of securities investors’
behavioral intention towards using mobile stock trading. Based on a modified UTAUT
(Unified Theory of Acceptance and Use of Technology) with multi-facet perceived risks
and privacy concerns, a comprehensive research model was proposed. An empirical
survey with a valid sample of 244 securities investors was conducted in Vietnam to test
the research model. The analysis results of SEM indicated that three enablers of
adopting mobile stock trading in Vietnam are UTAUT constructs (i.e., performance
expectancy, effort expectancy and social influence), and the inhibitors are the
perceptions of risk (i.e. security risk, economic risk) and privacy concerns. This
implies that to facilitate the behavioral intention to use mobile stock trading, securities
firms need to consider securities investors’ technological perceptions, risk perceptions
of this type of trading, and concerns on the disclosure of personal information upon
logging in the application. The findings of this study not only have important
implications for mobile commerce research, but also provide insights for securities
firms and developers of mobile stock trading systems.

Keywords: mobile stock trading (m-trading), UTAUT, security risk, economic risk,
privacy concerns, behavioral intention to use.


iii
TABLE OF CONTENTS
ACKNOWLEDGMETS................................................................................................................. i
ABSTRACT....................................................................................................................................... ii
TABLE OF CONTENT................................................................................................................ iii
LIST OF FIGURES......................................................................................................................... v
LIST OF TABLES.......................................................................................................................... vi
LIST OF ABBREVIATIONS..................................................................................................... vii
CHAPTER 1: INTRODUCTION............................................................................................... 1
1.1. Background of the study........................................................................................................ 1

1.2. Research gap.............................................................................................................................. 2
1.3. Research objectives and research questions.................................................................... 4
1.4. Research methodology and research scope...................................................................... 4
1.5. Research structure................................................................................................................... 5
CHAPTER 2: LITERATURE REVIEW& HYPOTHESES DEVELOPMENT.......6
2.1. Theoretical background......................................................................................................... 6
2.1.1. Unified Theory of Acceptance and Use of Technology (UTAUT)............................ 6
2.1.2. The extended UTAUT............................................................................................................ 7
2.2. Behavioral intention, risk perceptions and privacy concern..................................... 8
2.2.1. Behavioral Intention............................................................................................................. 8
2.2.2. Risk Perception....................................................................................................................... 9
2.2.3. Privacy Concern..................................................................................................................... 9
2.3.

Hypotheses Development................................................................................................ 10

2.3.1. Hypotheses Derived From UTAUT................................................................................. 11
2.3.2. Hypotheses Derived From Risk Perceptions................................................................ 12
2.3.3. Hypotheses Derived From Privacy Concern................................................................ 13
2.4. Conceptual model................................................................................................................... 15
2.5. Chapter summary.................................................................................................................. 15
CHAPTER 3: METHODOLOGY........................................................................................... 16
3.1. Research design...................................................................................................................... 16
3.1.1. Research process.................................................................................................................. 16


iv
3.1.2. Measurement scales............................................................................................................ 17
3.2. Measurement refinement.…............................................................................................... 20
3.3. Sample........................................................................................................................................ 21

3.4. Data analysis and interpretation....................................................................................... 22
3.4.1. Reliability measure.............................................................................................................. 22
3.4.2. Validity measure by EFA (Exploratory Factor Analysis).......................................... 22
3.4.3. CFA & SEM........................................................................................................................... 23
3.5. Pilot test..................................................................................................................................... 23
3.5.1. Cronbach’s Alpha................................................................................................................ 23
3.5.2. Exploratory factor analysis............................................................................................... 24
3.6. Chapter summary.................................................................................................................. 26
CHAPTER 4: RESEARCH FINDINGS................................................................................ 27
4.1. Data description..................................................................................................................... 27
4.2. Confirmatory factor analysis (CFA)................................................................................ 28
4.2.1. Saturated model.................................................................................................................... 28
4.2.2. Composite reliability and variance extracted............................................................... 29
4.3. The structural equation model analysis (SEM)........................................................... 29
4.4. Discussion................................................................................................................................. 32
4.4.1. UTAUT constructs............................................................................................................... 32
4.4.2. Perceived risks...................................................................................................................... 33
4.4.3. Privacy concerns.................................................................................................................. 34
4.5. Chapter summary.................................................................................................................. 35
CHAPTER 5: CONCLUSION, IMPLICATION& LIMITATIONS............................ 36
5.1. Key findings............................................................................................................................. 36
5.2. Managerial Implications...................................................................................................... 38
5.2.1. UTAUT constructs............................................................................................................... 38
5.2.2. Perceived risks...................................................................................................................... 40
5.2.3. Privacy concerns.................................................................................................................. 40
5.3. Research contribution.......................................................................................................... 41
5.4. Limitation and further study............................................................................................. 42
REFERENCE…............................................................................................................................. 44

v
APPENDICES
Appendix A: List of in-depth interview participants......................................................... 52
Appendix B: In-depth interview’s refinement measurement scale............................... 53
Appendix C: Questionnaire (English Version)..................................................................... 58
Appendix D: Questionnaire (Vietnamese Version)............................................................. 61
Appendix E: Discriptive Statistics............................................................................................ 64
Appendix F: CFA results............................................................................................................. 65
Appendix G: In-depth interview for “security risk” and “privacy concerns”.........66


vi
LIST OF FIGURES
Figure

Name

Page

2.1
2.2

The UTAUT model
Basically generalized UTAUT model of extant researches

7
8

2.3

Basically generalized extended UTAUT model of extant researches

8

2.4

Conceptual framework model

15

Research process

17

4.1

The saturated model (standardized)

28

4.2

SEM & path analysis

30

4.3

The research results estimated by SEM

31

3


vii
LIST OF TABLES
Table

Name

Page

3.1
3.2

Final measurement scales
Cronbach’s Alpha test

20
23

3.3

EFA – KMO and Bartlett’s Test

25

3.4

EFA – Total Variance Explained

25

3.5

EFA – Pattern matrix

25

4.1

Descriptive Statistics

27

4.2

The results for reliability and variance extracted test

29

4.2

Summary of results of testing hypotheses by SEM

32


viii

LIST OF ABBREVIATIONS

No.

Abbreviation

Name

1

PE

Performance expectancy

3

EE

Effort expectancy

4

SI

Social influence

5

SR

Security risk

6

FR

Functional risk

7

ER

Economic risk

8

PC

Privacy concern

9

BI

Behavioral intention

10

ICT

Information communication technology

11

UTAUT

Unified theory of acceptance and use of technology

12

TAM

Technology acceptance model

13

TFSA

Toronto Financial Services Alliance

14

SEM

Structural Equation Model

15

EFA

Exploratory Factors Analysis

16

CFA

Comfirmatory Factors Analysis

17

PDAs

Personal Digital Assistants

18

PC

Personal Computer

19

MOIT

Ministry of Industry & Trade

20

M-Trading

Mobile Stock Trading


1
CHAPTER 1: INTRODUCTION
1.1. Research Background
“Information technology is not the cause of the changes we are living through. But
without new information and communication technologies none of what is changing our lives
would be possible” (Castells, 1999, p.2). In the most recent decades, we have witnessed mobile
technology, an important and most innovated component information communication
technology (ICT), has achieved a virtual revolution. It has transformed a pure means of
communication through a full set of mobile services in finance, shopping, payment, social
networking, remote diagnostics, tourism, and etc., which added billions of dollars to the world
economy. Therefore, bringing new technologies to improve life and business performance
seems not new at all (Chong et al., 2010). Actually, mobile technology has helped many
financial institutions in various economic sectors with many opportunities to reach new and
potential markets, and customers. The development of mobile applications has provided
financial service providers with new channels of reaching their customers and in turn its
customers have more chance to access their financial information than ever before (TFSA,
2013). One of the outstanding advantages of mobile technology is its ability to give users
instant, new, and useful information at appropriate time regardless of locations (Liang
& Yeh, 2009). Without being so, mobile commerce will lose its significant economic value. As
mobile-commerce uninterruptedly grows, brick-and-mortar or online financial services will be
demanded to be available in mobile devices by the consumers (TFSA, 2013).
Typically, securities trading, a component of financial service has also been sharply
changed since first taking shape in the world due to the widespread adoption of ICT. Since
introduced in 1995, online trading which is a newly innovated mode of securities trading has
increased dramatically (Li, Lee & Cude, 2002). Upon implementation of online trading, no
longer do securities investors need to get out of their houses to the trading floors located at
busy streets in big cities thanks to the Internet-based trading systems that covered every

corner of the world. Online securities trading opened a new chapter in the establishment and
development of global securities market, and it is expected to continue being a valued choice
for investors (American Banker, as cited in Li et al., 2002). Online trading (i.e. website-based
trading and mobile-based trading) as a substitute to the traditional trading (i.e. direct trading
at floors, phone-based trading) has unique characteristics which help the investors handle
their portfolio without or less being contingent on brokers as well as preserve the personal
privacy. Online trading helps brokerage firms reduce costs by eliminating human interaction


2
(Bakos et al., 2000). With online trading, buying or selling securities is only a single
click on a computer mouse (Li et al., 2002).
Although the Vietnamese financial market in general; and specifically the securities
market have not been significantly developed, it was categorized as an attractive emerging
market. Obviously, more and more foreign prominent financial institutions such as
Citibank, HSBC, ANZ bank, Commonwealth bank, Bank of Tokyo, Franklin Templeton,
Blackhorse, AIG, Prudential, Manulife, …. have made entry in Vietnam market and have
found success. This proves the true potential of the Vietnam’s financial market. In the
integration with global playground, the Vietnamese financial institutions have been also
adopting and developing online services for years. Currently there is a very limited
number of financial institutions in Vietnam (only banks develop mobile banking and
securities brokerage firms develop mobile securities trading) conducting their services on
the basis of mobile technology. There are some reasons that people believe in the wide and
rapid adoption of mobile commerce in Vietnam. According to MOIT (2014), Vietnam is in
the group of countries having high rate of mobile purchase, and the potential for mobile
shopping has been realized and has great opportunities for growth. Ngo et al. (2015)
indicated that researches into mobile shopping in Vietnam too few to profoundly
understand the determinants of usage adoption.
Despite the benefits of mobile technology in contemporary finance, there are potential
risks in association with financial transactions via mobile devices, such as hacking, misusage,

and privacy concerns. Those disadvantages of mobile devices that are vulnerable to lose
consumers’ money and prestige are why the usage of e-finance (i.e. e-banking and electronic
securities trading) has not widely adopted yet. That is why behavioral intention to use mobile
technology, the antecedent of actual usage, in financial service is widely studied over the world
recently in tandem with the striking development of smart-phones and its applications.

1.2. 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”) as same as other types of mobile and electronic commerce in Vietnam is still in
its infancy (Ngo et al., 2015). Indeed, since 2008, as encouraged by Vietnamese government, all
of Vietnam’s securities firms develop its own website, in which website-based trading is always
available for the investors to execute their trading orders. Some well-known securities firms
(i.e. Saigon Securities Inc., Hochiminh City Securities Corp., VNDirect Securities


3
Corp., FPT Securities Corp., MB Securities Corp., VPbank Securities Corp., BaoViet
Securities Corp., etc), even developed smart-phone-based application for its clients to
conduct securities trading everywhere. However, there are some limitations while
carrying out mobile-based transactions due to its nature (Tai & Ku, 2013) as well as
careful consideration of customers with regard to the risk assessment (Zhou, 2012),
thus only few Vietnamese securities investors actually use M-Trading. In other words,
the questions on rationales of securities investors willing or reluctant to use MTrading and factors influencing their behavioral intention to use M-Trading in
Vietnam have received increasing concerns of researchers.
In the literature, many prior studies on ICT’s adoption in financial industry has
been conducted up to now 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); M-Trading (Tai &

Ku, 2013). Remarkably, in Vietnam’s context, there have been also many researches online
banking (Chong et al., 2010); e-banking (Nguyen Duy Thanh & Cao Hao Thi, 2011;
Nguyen et al., 2014); e-payment (Nguyen & Lin, 2011), mobile transfer of money (Le Van
Huy & Tran Nguyen Phuong Minh, 2011); mobile-learning (Ngo & Gwangyong, 2014),
personal internet banking (Hoang, 2015), mobile payment (Pham & Liu, 2015), mobile
shopping (Ngo et al., 2015), but only few on e-trading of securities were conducted.
Because Vietnamese investors' behavioral intention to use M-Trading have not been
well indicated, which factors deter or encourage their adoption remain unknown, a better
understanding of their behavioral intention would have great practical implications, not only
for brokerage firms seeking to manage more effectively the implementation of M-Trading and
improve their services, but also for the authorities of the Vietnam’s State Securities
Commission that would have proper policies on administering securities market. Obviously,
understanding why securities investors are willing or reluctant to use M-Trading by
developing and empirically examining a comprehensive model of securities investors’
behavioral intention to use M-Trading is strongly needed.
In Vietnam, M-Trading as other types of mobile commerce is defined as conducting
transactions using mobile devices such as smart phones, tablets, PDAs (Personal Digital
Assistants), and other mobile devices (except for laptops). As same as Internet shopping, it
requires Internet access. In this study, the theoretical background of the research is developed
with the concept of literature review of the unified theory of acceptance and use of technology
(UTAUT) that combines key constructs from Technology Acceptance Model


4
(TAM). Recently, the UTAUT model or extended UTAUT model have successfully employed to
explain behavioral intention to usemobile securities trading (Tai & Ku, 2013), users’ intention
and behavior of mobile banking (Yu, 2012), and intention to use internet banking (Yee et al.,
2015). Therefore, to fulfill the gap in the context of Vietnam, this study is about to employ
extended UTAUT model (Venkatesh et al., 2003) liaised with multi-facet perceived risks (Tai &
Ku, 2013) and privacy concerns (Zhou, 2012) to investigate the influential level of these factors

on Vietnamese’s behavioral intention to use M-Trading.

1.3. 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?
1.4. Research methodology and research scope
This study uses questionnaires to collect data. The survey is originally developed in
English and then translated into Vietnamese. In-depth interviews are then conducted with
eight people in order to modify the Vietnamese version of the questionnaire before the survey
is implemented in mass. The next step is analyzing the collecting data. The data of this
research is processed using SPSS software with three main stages. First, Cronbach’s Alpha is
used to test the reliability of the measurement scale. Then, the validityof the measurement
scale will be checked by Exploratory Factor Analysis (EFA). Finally, structural equation model
(SEM) and path analysis are employed as the main method for investigating the relationships
among factors in the research model. Ho Chi Minh City is the largest city and the metropolitan
area in Vietnam. It is also the economic center of Vietnam and accounts for a large proportion
of the economy of Vietnam. Moreover, Ho Chi Minh City has been chosen to conduct the
survey for this study since it is one of the biggest cities in Vietnam in which the largest
securities exchange of Vietnam is located (Ho Chi Minh City Securities Exchange
– HOSE). The research’s subjects are Vietnamese individual securities investors with the age
range above 18 years old. They might either be used to use internet securities trading, and or
internet/mobile banking service, or never use any e-financial services because the study’s goal
is to find out the behavioral intention to use M-Trading instead of actual use. Moreover,

5

since investigating the behavioral intention, potential investors at the same age level
will be also invited to participate.
1.5. 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.


6
CHAPTER 2: LITERATURE REVIEW & HYPOTHESES DEVELOPMENT
The chapter 2 is to present the theories associated with behavioral intention to use
mobile technology, the acceptance of mobile-based financial services and the models
testing the adoption of mobile technology over the world. Moreover, a conceptual model is
built resulting from the hypotheses generating from extant literature, simultaneously, its
constructs and relationship hypothesized among these constructs are also discussed.

2.1. Theoretical background
Studies on ICT users’ acceptance and use have been conducted extensively

because ICT have been in wide usage, and several models originated from different
theoretical disciplines (i.e. psychology, sociology and information systems) have been
developed to explain the acceptance and usage. One stream developed on motivational
model has focused on how extrinsic and intrinsic motivations influence the acceptance
(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. (2003) indicated that the UTAUT model is able to
explain sixty nine percent of intention to use ICT (technology acceptance) while other
previous model explained approximately forty percent of technology acceptance. Since
UTAUT is a comprehensive model which has been adopted by several previous studies
to successfully predict users’ usage intention toward e-commerce, e-financial services,
an extended UTAUT (incorporating financial risk, economic risk, functional risk, and
privacy concerns) serves as a robust basis for doing the same in Vietnam context.
2.1.1. Unified Theory of Acceptance and Use of Technology (UTAUT)
UTAUT is developed by Venkatesh et al. (2003) to integrate eight theories, which
include the technology acceptance model (TAM), innovation diffusion theory (IDT), the
motivational model, the theory of reasoned action (TRA), the theory of planned behavior
(TPB), a model combining the TAM and TPB, the model of PC utilization and social cognitive
theory. UTAUT proposes that four constructs including performance expectancy, effort
expectancy, social influence, and facilitating conditions affect user adoption of an 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 is similar to perceived usefulness and relative advantage. The
construct of performance expectancy is aggregated from five performance-related constructs:
perceived usefulness, extrinsic motivation, job-fit, relative advantage and outcome


7

expectations. Effort expectancy is similar to perceived ease of use and complexity. It is
similar to constructs included in previous models or theories, namely, perceived ease of
use, complexity, and ease of use. Social influence is similar to subjective norm. This
construct proposes that people’s ICT acceptance behavior is affected by if they believe
others expect them to be willing or reluctant to a certain behavior. Facilitating conditions
are similar to perceived behavioral control. In UTAUT, facilitating conditions are
integrated thirty two factors used in eight competing models into five constructs and
empirically identified that behavioral intention and facilitating conditions were two direct
determinants of adoption behavior. This construct reflects that users need to be equipped
with mobile internet knowledge in order to use ICT system. Without owning these
knowledge and resources, they cannot adopt ICT system.

Performance
Expectancy
Effort
Expectancy

Behavioral
Intention

Social
Influence

Use
Behavior

Facilitating
Conditions

Gender

Age

Experience

Voluntariness
of Use

Figure 2.1: the UTAUT model (Venkatesh et al., 2003)
2.1.2. The extended UTAUT
The UTAUT model without modification cannot be applied to the research on user’s

acceptance of mobile commerce since all ICT adoption theories or models, including
UTAUT, were developed for PC and/or fixed line Internet systems/applications. Since 2003,

among many studies citing UTAUT, very few employ all if its constructs (Williams et al.,
2011). Extant researches have used extended UTAUT to explain user adoption in online
securitiesing in the financial market (Wang & Yang, 2005), internet banking (Yee et al.,
2015; El-Qirem, 2013; Yu, 2012), in health information technology (Kijsanayotin et al.,
2009), in digital library (Nov & Ye, 2009), and in e-government services (Suha & Anne,
2008). Further, the UTAUT has also been employed to examine user adoption of mobile


8
services, such as mobile banking (Yu, 2012), mobile wallet (Shin, 2009), mobile payment
(Kim et al., 2009), and mobile technologies (Park et al., 2007). These studies mainly
focused on employing the UTAUT or revising UTAUT by combining TAM’s construct, or
by adding up a few independent variables as the role of enabler of intention adoption.

Reactions to use ICT

Intention to use ICT

Actual use of ICT

Figure 2.2: Basically generalized model of extant researches
However, other than most previous studies employing UTAUT only adopted a single
construct to evaluate users’ risk perceptions, Zhou (2012) examined location-based services
usage from the perspectives of UTAUT and perceived risk, privacy concerns and trust. Tai &
Ku (2013) investigated the determinants of securities investors’ intention towards using mobile
securities trading by developing an extended UTAUT model incorporating symmetry axis of
which usage intention influenced by UTAUT’s constructs and perceived risks.

Positive Effects to use
mobile-based services

Usage Intention of
mobile-based services
Demographics

Negative effects to use
mobile-based services

Figure 2.3: Basically generalized extended UTAUT model of extant researches
Since (extended) UTAUT is a comprehensive model which has been adopted by
several previous studies to successfully predict users’ usage intention toward mobilebased service, but very few studies were implemented as the same manner in Vietnam.
An extended UTAUT (incorporating enablers as performance expectancy, effort
expectancy, and social influence; and inhibitors such as financial risk, economic risk,
functional risk, and privacy concerns) will serve as a robust basis for an empirically
testing the behavioral intention to use M-Trading in Vietnam.

2.2. Behavioral intention, risk perceptions and privacy concerns
2.2.1. Behavioral Intention
Ajzen (1991) argues that ‘‘Intentions are assumed to capture the motivational factors
that influence a behavior. They are indications of how hard people are willing to try, of how
much of an effort they are planning to exert in order to perform the behavior’’ (p. 181). It is
similar to attitude towards behavior (TRA, TPB, DTPB) and extrinsic and intrinsic motivation
(MM) derived from previous models or theories. Usage intention to adopt/accept ICT system
measures an individual’s relative strength of intention to perform a behavior (Fishbein &
Ajzen, 1975). It indicates a person’s motivation to perform a specific behavior,


9
and is viewed as the antecedent of actual behavior. Consistent to all models portraying from
psychological theories, which argue that individual behavior is predictable and influenced by

individual intention, UTAUT argued and demonstrated usage intention to have significant

influence on ICT usage (Venkatesh et al., 2003; Venkatesh & Zhang, 2010).
2.2.2. Risk Perception
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 usually considered as one of the potential barriers (Chen, 2008; Luo et
al., 2010; Hsu et al., 2011). Noticeably, mobile users see risk in prospect uncertainty
arising from data input errors, software failures, connection loss, and privacy loss (Mallat
et al., 2008; Cruz et al., 2010; Koenig-Lewis et al., 2010). M-Trading is a variant of mobilebased financial service, of which while using, the users are required to sign up with certain
information. Hence, there is an anticipated risk of exposure to opportunistic hackers who
can access their trading accounts, delete data or make unauthorized trades. As a result,
investors may elect to forgo the potential benefits of using M-Trading.
Many previous researches have found that the intention to use mobile-based financial
services is influenced by users’ perception of risk. For instance, Mallat (2007) indicated that

perceived risk is the main obstacle of the adoption mobile payment sytems. Futher, Mallat et
al. (2008) found that perceived risk is a key determinant of using mobile ticketing service.
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.
In the perspective of mobile-based financial services, users perceive risk from several
facets, the most common of which are security risk, economic risk, and functional risk (Cruz et
al., 2010; Koenig-Lewis et al., 2010; Wessels & Drennan, 2010). Perceived security risk

of mobile financial services lies in the
perception of potential harm due to electronic
fraud or hacker attacks. Perceived economic risk arises from the perception of possible
economic loss due to transaction error or faulty operation. Perceived functional risk lies
in the perception of possible lack of service reliability or accessibility.
2.2.3. Privacy concerns
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.,
2004), and privacy information concernsexhibit ICT users’ concern on what extent their
personal information to be disclosed (Li, 2011). Due to the differences in culture, regulatory
laws, past experiences, and personal characteristics, ICT’susers exhibit dissimilar degrees of
concerns on information privacy (Malhotra et al., 2004). The users with high levels of


10
privacy concerns believe that service providers generally tend to behave opportunistically
with their personal information. Therefore, in response to a request from securities firms
for personal information, the securities traders will likely to refuse to provide personal
information (Dinev & Hart, 2006) and/or to provide incorrect personal information (Teo
et al., 2004). From the UTAUT perspective, privacy concerns are viewed as usage
inhibitors (Bansal et al., 2010). Besides, Malhotra et al. (2004) indicated that privacy
concerns of Internet users including collection, control, and awareness in addition to the

general concern and specific concern proposed by Li (2011). Previous researches
evidenced that privacy concerns significantly influences perceived risk (Zhou, 2012,
Junglas et al., 2008, Bansal et al., 2010). Moreover, privacy concerns has significant effects
on user adoption of 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).

2.3. Hypothesis development
Venkatesh et al.’s (2003) UTAUT is adopted as a primary theoretical framework to
examine securities investors’ acceptance of M-Trading. However, since the M-Trading context
differs in some ways from the traditional ICT context, not any single constructs of UTAUT
may fit the specific M-Trading’s context. Hence, it is necessary to integrate the risk perceptions
into the extended UTAUT model to propose our research model. Because M-Trading in
Vietnam is still in its infancy, as the matter of fact, there is very limited number of securities
investors having actually used this mobile application. Therefore, this study considers
behavioral intention to use M-Trading as a dependent variable, and excludes from the
proposed model two constructs pertaining to UTAUT, i.e. use behavior and experience.
The developer of UTAUT model also stated that “facilitating conditions” construct
becomes non-significant in predicting intention when both “performance expectancy”
construct and “effort expectancy” construct exist in the research model. Furthermore,
facilitating conditions reflect that users have ability and resources necessary to use M-Trading
(Venkatesh et al. 2003). This means securities traders need to be equipped with mobile internet
knowledge in order to use M-Trading, and need to pay communication fees and service fees
associated with that usage (Zhou, 2012). However, in the real settings of Vietnam, only few
securities investors have used M-Trading and brokerage firms are providing its clients with
this application on the fee-free basis, so facilitating conditions, which is the antecedent of use
behavior and has no significant association with behavioral intention, is excluded from the
research model. Besides, since this study is to investigate M-

11
Trading’s adoption in a voluntary usage context, the UTAUT’s moderating variable
“voluntariness” is also not included.
Extant researches on mobile-based financial services usage behavior have had
findings which showed that user’s concerns about risk issues are key determinants for the
adoption (Tai & Ku, 2013; Laukkanen & Kiviniemi, 2010; Luo et al., 2010). Perceived risk
is regarded as a person’s awareness of prospective uncertainty and adverse consequences
of engaging in a given activity (Forsythe et al., 2006; Littler & Melanthiou, 2006; Bland et
al., 2007 ; Im et al., 2008). In spite of perceiving the benefit of a given service, people’s
intention to adopt the service may be hesitant due to their perceived risks with respect to
service’s usage. M-Trading’s platform is an ICT artifact composed of mobile Internet,
mobile devices and mobile systems, so upon implementing a securities transactions
through mobile devices, some negative results including increased data entry errors;
electronic data interception and unstable wireless connections may be occurred while not
being found in other traditional formats. Securities investors’ intention to use M-Trading
may be impeded resulted from their perceptions of the risks.

Many previous studies employing UTAUT model found that privacy concerns
strongly impacts on perceived risk in general. While finding M-Trading’s adoption in
Taiwan, Tai & Ku (2013) specified perceived risks as security risk; economic risk; and
functional risk, and included in UTAUT model but failed to explore the relationship
amongst privacy concerns and these three types of risks. This research tries to fill the
gap. Taking the context in which M-Trading occurs into account, this study
incorporates perceived risks and privacy concerns of which influences the perceptions
of risk, into UTAUT model to produce a more precise explanation of the antecedent of
securities investors’ adoption or resistance of M-Trading.
2.3.1. Hypotheses Derived From UTAUT
In this study, behavioral intention is an endogenous variable. In M-Trading’s
context, this construct is conceptualized as the extents to which securities investors believe
that M-Trading will improve their transaction performance. Performance expectancy is

defined as the extent to which an individual believes that usage of certain ICT system will
help improve their performance (Venkatesh et al., 2003). Performance expectancy is the
instrumental value of using M-Trading such as the improvement of trading efficiency, the
increment of convenience in trading. Such benefits will influence the behavioral intention
to use M-Trading. Thus, the following hypothesis is proposed:

Hypothesis 1: Securities investors with high performance expectancy for MTrading will have greater behavioral intention to use it.


12
Effort expectancy is defined as the extent to which individuals believe that learning to
use a certain ICT system will not require significant effort (Venkatesh et al., 2003). Effort
expectancy of using M-Trading is the users’ evaluation of how much effort is required to learn
how to use and engage with the system. Therefore, behavioral intention to use M-Trading is
anticipated to increase if the investors believe that M-Trading is easy to handle. Extant studies
have broadly found that effort expectancy for using an ICT system is a significant antecedent
of behavior intention to use the ICT system (Venkatesh & Morris, 2000; Wang et al., 2009;
Deng et al., 2011). Thus, the following hypothesis is proposed:

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 is conceptualized as the extent in that securities investors are
encouraged to use M-Trading by their peers. Since M-Trading is still too new to be
popularly used in Vietnam, the users are only expected to be influenced by their peers’
perceptions of the quality and capabilities of M-Trading. Moreover, usage intention
indicates that users will follow their experience, preference and external environment
to collect information, evaluate alternatives, and make usage decision (Zeithaml, 1988;
Dodds et al., 1991). Prior studies have found that social influence is an important

predictor of usage intention to use a certain information system (Baron et al. 2006;
Wang et al. 2009). It is expected that people’s behavioral intention to use a given ICTbased service is influenced by their peers’ opinion of that service (Karahanna et al.
1999; Venkatesh & Davis, 2000). Thus, 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. (2014) also indicated multi-dimensional perceptions of risk are among
the most critical variables in the study of online shopping. In this study, security risk,
economic risk, and functional risk are investigated its effects on behavioral intention
to use M-Trading in Vietnam.
Security risk is securities traders’ perception of prospective harm caused by electronic
fraud or hacker attacks while using M-Trading. Security risk is found the main obstacle to the


13
adoption of mobile financial services and has been suggested being the greatest challenge to

the mobile financial service provider (Luarn & Lin, 2005; Misra & Wickamasinghe, 2004;
Mallat et al., 2008). Miyazaki & Fernandez (2001) identified security risk (i.e. potential
fraud, misrepresentation) as a key concern for Internet users. Previous researches have
indicated that many people believe that they are susceptible to identity theft while using

mobile financial services (Mallat, 2007; Wessels & Drennan, 2010) . Thus, the following
hypothesis is proposed:
Hypothesis 4: Securities traders perceiving high security risk in M-Trading will
have less behavioral intention to use it.
Economic risk is investors’ perception of the possibility of economic loss due to

transaction error or incorrect operation when using M-Trading. Extant researchers found
that people resists using financially mobile-based services (Koenig-Lewis et al., 2010;
Wessels & Drennan, 2010; Hsu et al., 2011). Other than the traditional trading channels
such as website-based and telephone-based securities trading, while using M-Trading, data
entry via small touch screen with limited display resolution may bring about input errors
and typos easily made and difficult to detect. In addition, if using other formats, the
investors or their securities-brokers can manually verify the accuracy of the transaction
information, in contrast, rarely is such safeguards available in M-Trading system, leading
to feelings of uncertainty and fear. Thus, the following hypothesis is proposed:

Hypothesis 5: Securities investors who perceive a high economic risk for MTrading will have less behavioral intention to use it.
Functional risk is securities investors’ perceived possibility of service unavailability or
malfunction. Many researchers found that many people forgo using mobile financial services
due to concerns for such failures. Shen et al. (2010) and Wessels & Drennan (2010) found that
many people are frightened that there would have occurrence of a failure of service systems or
disconnection from the mobile Internet while conducting financial transactions via mobile
devices. Moreover, many resisters of system usage tend to suppose that mobile devices, mobile
operating systems and networks are intrinsically unstable and worry that transactions may be
interrupted, ceased, or delayed (Mallat et al., 2008; Cruz et al., 2010; Koenig-Lewis et al.,
2010). Thus, the following hypothesis is proposed:

Hypothesis 6: Securities investors who perceive a high functional risk for MTrading will have less behavioral intention to use it.
2.3.3. Hypotheses Derived From Privacy Concerns
Upon using M-Trading, users’ personal information (i.e., username, pass-code,
location, verified code, account number) needs signing up to log in the system. This


14
disclosure may arouse investors’ concern about their privacy. They may worry about their
mobile-based application developers’ practice on information collection, storage and usage.

For instance, the securities traders are doubtful about their personal information being shared
with other third parties without their prior approval or knowledge by the service providers,
and may be anxious about the potential losses associated with information disclosure, such as
information leakage and sales. Due to this concern, securities traders’ behavioral intention to

use M-Trading may be impeded. Extant researches (Bansal et al., 2010, Sheng et al., 2008,
Miyazaki & Fernandez, 2001) indicated this negative relationship between privacy concerns

and ICT’s behavioral intention. Hence, the following hypothesis is proposed:
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. 2002, as cited in Riquelmi & Román, 2014; Schlosser et al. 2006;
Hu et al. 2010). For example, a high concern for personal information privacy would
directly produce negative attitudes toward the security of smart-phone application,
and the securities investors who lack knowledge about online security and the third
party security identification would worry about disclosing personal information
during the process of mobile-based trading. Miyazaki & 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. (2012) indicated that the perceptions of risk are increased by
privacy concerns when North American households conduct purchases in an online
environment. Other previous researches have also indicated the effect of privacy
concerns on perceived risks (Zhou, 2012; Eastlick et al., 2006; Bansal et al., 2010).
Thus, the following two hypotheses about multi-facet perceived risks are proposed:

Hypothesis 9: Privacy concerns are positively correlated to economic risk.
Hypothesis 10: Privacy concerns are positively correlated to functional
risk. 2.4. Conceptual model
Based on the hypotheses above, the below research model (Figure 2.4) is
proposed and evaluated empirically in M-Trading’s settings.


15

Privacy
Concerns

Performance
Expectancy

H7H1+
H4-

Effort

H2+

Behavioral

Intention to use
M-Trading

Expectancy

Social

Influence

Security
Risk

H3+

H8+

H5-

H6

Economic
Risk

H9+

Functional

H10+

-

Risk

Figure 2.4: Conceptual Model

2.5. Chapter summary
This chapter presents theoretical background of each concept in the model.

Based on discussion of literature review, behavioral intention to use M-Trading is
affected by seven factors, these are: performance expectancy, effort expectancy, social
influence, security risk, economic risk, functional risk and privacy concerns. Such
factors are selected to build the model because their relationship has already tested by
many previous researchers through their studies. Hence, there are ten hypotheses
proposed for this research. The next chapter will discuss methodology that used to
analyze the data and test hypotheses of the research model.