INTENTION
TO USE BIKE-BOOKING APPLICATION: THE CASE OF STUDENTS IN HO CHI MINH CITY
Phuong
Viet Le-Hoang
Ho Chi Minh City Open University, Vietnam
E-mail: lehoangvietphuong@gmail.com
Submission: 11/26/2019
Revision: 1/7/2020
Accept: 1/21/2020
ABSTRACT
This study aims to determine the factors that influence the intention to use the bike-booking application and measure the influence of the elements on the behavior intention to use the bike-booking application for university students in Ho Chi Minh City. Based on the research results, the author gives some managerial implications to increase customers' choice of bike-booking applications to increase the attractiveness of customers to choose apps. With the number of surveyed questionnaires is 200, the author collected 177 valid respondents, and the study results showed that the scales used in the model meet the requirement about reliability and validity. Research has shown that factors such as trust, perceived ease of use, subjective norm, perceived usefulness, perceived price level positively influence the intention to use a bike-booking application for university students in Ho Chi Minh City. In which the perceived usefulness has the most substantial impact, the subjective norm has the lowest effect on behavior intention. The second most effective one is the perceived price level; the third most effective is the ease of use; the fourth most effect is trust.
Keywords: bike-booking; trust; perceived ease of use; subjective norm; perceived usefulness; perceived price level
1.
INTRODUCTION
Ten years ago, the image of
foreigners who hold a paper map to explore tourist destinations in Vietnam was
ubiquitous. However, at present, this image is scarce, because visitors have a
more powerful tool than before; specifically, the smartphone. With the Google
Map or another map app on a smartphone, getting directions is easier than ever.
Along with that, the paper map is increasingly narrowing the number of users,
which means this old navigation method is ending with the rapid development of
technology. Especially the popularity of smartphones and the wave of
application of technology 4.0 is happening quickly around the world.
The development of e-commerce has
led to intense competition between technology applications (Lee and Wong,
2016). An example of e-commerce is the online transport of services that can be
accessed via smartphones, such as Uber, Go-Jek, Grab, and Lyft. The Uber app is
a new technological advancement in transportation invented by Travis Kalanick
and Garrett Camp in 2009.
The first Uber app was initially
accessible only via iPhone and then accessible to Android 3 years later. The
application has expanded internationally and operates in 377 countries
worldwide (MOHAMAD et al., 2016). It has become an increasing trend among those
who request to book the bike (or to put it another way, it has another name
such as call-bike, technology bike) through smartphones.
Over the past two years, online
transportation services in Indonesia are experiencing significant growth. Some
applications dominate the online transportation market, namely Uber and Go-Jek.
Moreover, according to the research results of Septiani, Handayani and Azzahro
(2017) on online call-bike-booking by application, Go-Jek is the first rank and
becomes the most popular online transport service inside and outside Java
island in Indonesia.
Grab
is one of the first bike-booking apps in Vietnam, so Uber and Grab have
pioneered the development of online booking applications today, and surely this
is the most popular application today. Following the success is the launching
of some booking applications such as Vato and Bee. The technology taxi service
has become an increasing trend via smartphone. Users need to download the
bike-booking application and request a ride with one-touch. For students, the
need for travel is to meet learning, playing, and part-time jobs.
So
through smartphones, students can easily call the bike and get to their
destination quickly. It has opened up opportunities for businesses to invest in
the ride-booking application. Furthermore, as a result, this research explores
and measures the factors that impact the behavior of customers, precisely the
behavior intention of students when they use the app.
2.
LITERATURE REVIEW
Watanabe, Naveed and Neittaanmäki
(2016) explain that ride-sharing is on-demand for
connecting passengers and vehicle owners (drivers) in real-time using online
mobile technology. Ride-sharing or bike-booking services are now becoming a
popular service for people to fulfill their travel needs. Bike-booking applications
usually include online shipping service, and the shipping service is the part
of an e-commerce service defined as a transaction made in a mobile network.
In mobile commerce, customers or
users can order products or services over the internet without using a personal
computer (CLARKE III, 2001). In short, the use of mobile phones for services in
general as well as bike reservations, in particular, brings many benefits for
both users and drivers.
The experienced and reliable drivers
offer a new way of booking for users or students using a free downloadable
application from smartphones. Users can book and monitor the bike-booking
service via their mobile application. Services include booking and delivery (TANIMUKTIA
et al., 2016). It provides many benefits, such as drivers and customers can
know each other's exact location. Customers can view driver and vehicle
information, and customers can easily find transportation to go to other places
(time-efficient) (FARIN et al.,
2016).
This research uses the theory of
reasoned action (TRA) of Fishbein and Ajzen (1975), the theory of planned
behavior (TPB) behavioral theory of Ajzen (1991), and the technology
acceptance model (TAM) of Davis (1989). The technology acceptance model is
based on the principles that applied from the attitude model of Fishbein and
Ajzen (1975) from psychology, which specifies how to measure the components
related to behavior. Besides, it can distinguish between beliefs and attitudes
and specify how external stimuli, such as the objective features of an attitude
that have a causal relationship to beliefs, attitudes, and behaviors.
Davis (1989) did not consider the
norm in behavior prediction because Fishbein and Ajzen (1975) concede that
subjective norms are the least understood concept about TRA and have
theoretical uncertainty. Second, instead of considering some outstanding
personal beliefs, Fishbein and Ajzen (1975) focused on only two aspects:
Perceived usefulness and perceived ease of use. The perceived usefulness is
considered to be the degree to which a person believes that using a specific
system will enhance their work performance. Perceived ease of use is defined as
the degree to which an individual believes that using a particular system will
not require physical and mental exertion (Davis, 1989). Therefore, in the
technology acceptance model (TAM), an attitude towards technology usage is
proposed. Moreover, the attitude toward use is defined as the degree level in
which an individual is associated with the use of the target system in his or
her job.
3.
HYPOTHESES DEVELOPMENT
According to Tanimuktia et al.
(2016), e-commerce is still overshadowed by user doubts about reliability,
security, and privacy. They are concerned about the ability of the seller to
provide their personal information and money to others without their consent.
Not only that, the customer must depend on the driver, so in the future, the
customer must bear the risks of driver uncertainty.
Also, belief is the relationship
between the two sides. Meanwhile, reliability is a quality that is being
possessed by one party. An exchange partner is trustworthy when it is worthy of
the trust of another party (SEPTIANI et al., 2017). The tendency to
trustworthy, also known as the tendency to the reliability, is generally an
individual trait defined as general willingness based on expanded socialization
to depend on other attractive people. Trust trends have been shown to have a
direct effect on the formation of beliefs. Trust is an essential factor
influencing consumer behavior (KOUFARIS; HAMPTON-SOSA, 2004), and it determines the success of
adopting technology such as e-commerce (Holsapple and Sasidharan, 2005).
· H1:
Trust has a positive effect on
the intention of using bike-booking application.
Although
mobile penetration rates are considered high in developing countries,
e-commerce is still considered as a new technology by its users. Many people
find e-commerce quite useful, they also feel that e-commerce is quite
challenging to use (DAVIS, 1989). According to Rogers (1995), the complexity of
a particular system becomes a frustrating inhibitor through innovation. Much
research also provides evidence of the significant impact perceived ease of use
for the intended purpose (AGARWAL;
PRASAD, 1999; DAVIS; BOGOZZI; WARSHAW, 1989; VENKATESH; MORRIS, 2000).
· H2:
Perceived ease of use has
a positive effect on the
intention of using bike-booking application.
The subjective norm refers to the
perceived social pressure to perform or not to perform that behavior (AJZEN,
1991). Hsu and Lu (2004) suggest that subjective norms have a significant
influence on the intentions and attitudes of individuals involved in certain
types of behavior. Many consumers decide to use the bike-booking app if their
friends also use it and are advised by them. Mobile applications seem to be
very useful if they are user-friendly (WENG el al., 2017). Fan et al. (2005) also
suggest that users are more likely to recommend services to other users if they
are satisfied with the service.
· H3:
Subject
norm has a positive effect on the intention of using bike-booking application.
Product usefulness is often referred
to as a product's benefits, features, attributes, or functions (GATIGNON; XUEREB, 1997; HONG; LIN;
HSIEH, 2017; RENKO; DRUZIJANIC, 2014).
Consumers often judge products based on their usefulness. When the product
meets their expectations, it will naturally bring positive results (DODDS; MONROE; GREWAL,
1991; THAICHON;
QUACH, 2015).
Users need to use the booking app as
a useful tool to use transportation more accessible and faster with lower
prices, and users can know the payment price and the navigation, license plate
number, and driver information via the application. So technology products
based heavily on the useful structure. The previous studies have shown that
usefulness is significant effect technology adoption of users (MATHIESON, 1991;
RAMAYAH; JAAFAR, 2008). Many
studies provide evidence that there is a relationship between usefulness and
intention (AGARWAL;
PRASAD, 1999; DAVIS et al., 1989; VENKATESH;
MORRIS, 2000).
· H4:
Perceived
usefulness have a positive effect
on the intention of using
bike-booking application.
Price is defined primarily as the
total amount charged to a goods or service or the total value that consumers
are willing to exchange for the benefits of using or owning a product (GRACIOLA
et al., 2018; KIM, 2019; KOTLER;
ARMSTRONG, 2017). Price is one of the main factors that affect everyone if
people feel that it is affordable using the bike-booking app. When customers
book a bike, and a price will appear. That action can avoid the driver may add
unnecessary expenses that the customer does not know.
Price is the perceived value of the
goods or services at the time of the transaction. Prices can change quickly
(especially compared to features and commitments) (THAICHON et al., 2016), and
the price can influence people's buying decisions. (FERRIS;
HAUGEN;
MAKHIJA, 1988; GRACIOLA et al., 2018; GODEY et
al., 2012; LICHTENSTEIN; BLOCK; BLACK, 1988; THAICHON et al.,
2016).
H5: Perceived
price level
has a positive effect on the intention of using bike-booking application.
Figure 1: Proposed research model of the author
4.
METHODOLOGY
The study
applies the mix method, including qualitative and quantitative research
methods. The qualitative approach to explore and adjust the scale; also,
quantitative research methods to measure the factors affecting the intention to
use the bike-booking application in Ho Chi Minh City, Vietnam.
This research
uses the qualitative research method via group discussions and expert
discussions to build research models, scales, questionnaires, and preliminary
surveys to complete research models before issuing the questionnaire. The
author surveyed the vice chairman of the Vietnam E-commerce Association (VECOM)
and surveyed three members of the Executive Committee of VECOM to complete the
group discussion.
Quantitative
research method based on information collected from the students of many
universities in Ho Chi Minh City (HCMC). The research uses five levels of
Likert scale, namely strongly disagree, disagree, neutral, agree, and strongly
agree to measure the impact of factors affecting bike-booking adoption, and
this research uses the convenient sampling method. Hair et al. (2014) pointed
out that when the study uses the Likert scale five levels with the n variables,
the study should ensure a minimum sample size of 5*n=5n. To ensure the quality
of the sample, the author decided to distribute a total of 200 questionnaires.
In particular,
this research surveyed five prestigious and reputable universities in economics
major in Ho Chi Minh City, such as the Industrial University of HCMC, the
University of Economics of HCMC, the University of Economics and Law,
University of Economics and Finance, HCMC Open University. For each university,
the author directly distributed the survey questionnaires, and the number of
questionnaires for each university was 40. So after screening data, there were
177 valid questionnaires to be used in the quantitative analysis (accounting
for 88.5%). In quantitative research, the author uses descriptive statistical
methods and test reliability through Cronbach's Alpha coefficients. Besides,
the author applies the EFA method and regression to determine factors affecting
the adoption of bike-booking application.
5.
ANALYSIS AND RESULTS
5.1.
Data description:
After the
three months to conduct the survey from August to October in 2019 and perform data
analysis in over three weeks of November, the author collected 177 valid
respondents out of 200 questionnaires, accounting for 88.5% and the data
description is as the following table:
Table 1: Data
description
|
|
Frequency |
Percent (%) |
Accumulation percent (%) |
|
|
Gender |
Male |
108 |
61.0 |
61.0 |
|
Female |
69 |
39.0 |
100.0 |
|
|
Student |
First-year |
59 |
33.3 |
33.3 |
|
Second-year |
52 |
29.4 |
62.7 |
|
|
Third-year |
42 |
23.7 |
86.4 |
|
|
Fourth-year |
24 |
13.6 |
100.0 |
|
|
Number of uses |
< three times |
72 |
40.7 |
40.7 |
|
From three to five times |
81 |
45.8 |
86.4 |
|
|
> five times |
24 |
13.6 |
100.0 |
|
According to the results of the
descriptive statistics, the difference in the number of people using the
bike-booking application is quite high among men and women. Among 177 people,
108 men use bike-booking applications, accounting for 61%, and 69 women are
accounting for 39%.
The results show that the number of
first-year students is 59 people, accounting for 33.3%, the highest proportion.
Next is a second-year student with 52 people, accounting for 29.4%. As for
3rd-year students, 42 people are accounting for 23.7%. The rest are fourth-year
students with 24, it is the lowest rate and accounting for 13.6%. In general,
most people who use the bike booking application will most likely be students
in the first year and second year.
Because, at this age, these students
have just come to Ho Chi Minh City, students’ families may not have bought a
bike yet, or the students are not familiar with the road, the bike-booking app
is the best service for students. The app is convenient, so most of the first
and second-year students choose what is most convenient and easy for students.
As for students in the third and fourth years, they may have gone to work a
lot, so the selection of the bike-booking application is in case of urgency so
that the usage rate will be less.
The analysis results show that the
number of using the bike-booking application in the recent month is less than
three times with 72 people, accounting for 40.7%. Next is the number of times
using the app from three to five times with 81 people, accounting for the
highest rate of 45.8%. Finally, the number of times used over five times in
recent months has 24 people. It is the lowest target rate, and it is accounting
for 13.6%. In general, the number of people using the booking app is quite
large, from three to five times a month because travel need is always an
important issue. It shows that the bike-booking application is trendy and
convenient.
5.2.
Reliability test: Cronbach’s Alpha
According to Nunnally and Bernstein
(1994), the condition to accepting variables is that Corrected Item - Total
Correlation is equal or greater than 0.3 and Cronbach’s Alpha if item deleted
is equal or greater than 0.7. According to Hair et al. (2014), new studies can
accept that Cronbach’s Alpha if item deleted is equal or greater than 0.6.
Therefore, all of the
items satisfy the requirement, so this can use the Exploratory Factor
Analysis (EFA).
Table 2: Constructs, corrected item – total correlation and Cronbach Alpha
|
Constructs |
Corrected Item – Total
Correlation |
Cronbach’s
Alpha if item deleted |
|
|
Trust -
Cronbach’s Alpha = 0.791 |
|||
|
TR1 |
I feel
secure with the information I fill out in the application |
0.615 |
0.733 |
|
TR2 |
I feel
safe to be driven by drivers through the app (because an organization
supervises the drivers) |
0.618 |
0.732 |
|
TR3 |
I believe in the information that the company provides
me |
0.664 |
0.683 |
|
Perceived ease of
use - Cronbach’s Alpha = 0.886 |
|||
|
PEU1 |
The
bike-booking app is easy for me to use |
0.626 |
0.904 |
|
PEU2 |
The bike-booking
app does not need to spend too much effort to learn how to use it |
0.728 |
0.863 |
|
PEU3 |
The
process of using the bike-booking application is straightforward |
0.832 |
0.824 |
|
PEU4 |
I think
the bike-booking application is easy to use for everyone |
0.840 |
0.882 |
|
Subject norm -
Cronbach’s Alpha = 0.897 |
|||
|
SN1 |
Many
people around me think that I should use the bike-booking application instead
of choosing the traditional way of calling |
0.771 |
0.876 |
|
SN2 |
Many of
my friends and colleagues are using the bike-booking application |
0.794 |
0.856 |
|
SN3 |
The
environment makes me feel that using the ride-booking app is the future trend |
0.826 |
0.828 |
|
Perceived usefulness -
Cronbach’s Alpha = 0.891 |
|||
|
PU1 |
I think
a bike-booking app is a useful tool |
0.621 |
0.895 |
|
PU2 |
The
bike-booking application can help me to book a bike more easily |
0.804 |
0.852 |
|
PU3 |
I think
the bike reservation application is required for use |
0.765 |
0.861 |
|
PU4 |
The
bike-booking application is more convenient for the ride |
0.788 |
0.855 |
|
PU5 |
The
bike-booking app can help me book the bike faster |
0.709 |
0.874 |
|
Perceived price
level - Cronbach’s
Alpha = 0.841 |
|||
|
PLL1 |
Using
the bike-booking app puts me at a high data rate |
0.666 |
0.803 |
|
PLL2 |
Using
the bike-booking app made me pay more money for my phone |
0.678 |
0.797 |
|
PLL3 |
It is cheaper
to book a bike than a traditional bike |
0.699 |
0.787 |
|
PLL4 |
I think
the price I paid was in line with the distance |
0.656 |
0.807 |
|
Intention to use
bike-booking application (behavior intention) Cronbach’s
Alpha = 0.923 |
|||
|
BI1 |
I will
continue to use the bike-booking application in the near future |
0.875 |
0.882 |
|
BI2 |
I will
use the bike-booking service via application when I need to move |
0.787 |
0.912 |
|
BI3 |
If I am
satisfied, I will use the bike-booking application regularly |
0.779 |
0.916 |
|
BI4 |
I will
say/recommend bike-booking application to my friends |
0.852 |
0.890 |
5.3.
Exploratory Factor Analysis (EFA)
Exploratory Factor Analysis (EFA)
is an analytical technique which is aimed to reduce data, so it is beneficial
for identifying variables by the group. In the exploratory factor analysis, the
author used Principal Component Analysis and Varimax rotation to group the
components.
5.3.1.
Independent variables
The results
show that KMO is 0.874 and can make sure the requirement 0.5<KMO<1.
Bartlett is 2141.284 with sig = 0.00<0.05, so all of the variables are
correlation together in each component. Total variance explained equals 74.034%,
and it is greater than 50%; as a result, it can meet the requirement of
variance explained. From this one, this research can conclude that variables
can explain 74.034% in changing factors. Also, eigenvalues equal 1.111 >1,
and it is the fluctuation that can explain for each factor, so the extracted
factors have a significant summarize in the best way. The rotated matrix in EFA
show that the loading factor is higher than 0.55 and it can divide into five
components by the following table:
Table 3: Rotated matrix
|
Concepts |
Items |
Component |
||||
|
1 |
2 |
3 |
4 |
5 |
||
|
Perceived usefulness |
PU2 |
0.832 |
|
|
|
|
|
PU4 |
0.821 |
|
|
|
|
|
|
PU3 |
0.778 |
|
|
|
|
|
|
PU5 |
0.757 |
|
|
|
|
|
|
PU1 |
0.705 |
|
|
|
|
|
|
Perceived ease of
use |
PEU3 |
|
0.868 |
|
|
|
|
PEU4 |
|
0.855 |
|
|
|
|
|
PEU2 |
|
0.765 |
|
|
|
|
|
PEU1 |
|
0.681 |
|
|
|
|
|
Perceived price
level |
PLL4 |
|
|
0.762 |
|
|
|
PLL1 |
|
|
0.723 |
|
|
|
|
PLL3 |
|
|
0.712 |
|
|
|
|
PLL2 |
|
|
0.689 |
|
|
|
|
Subject norm |
SN3 |
|
|
|
0.866 |
|
|
SN2 |
|
|
|
0.838 |
|
|
|
SN1 |
|
|
|
0.838 |
|
|
|
Trust |
TR1 |
|
|
|
|
0.847 |
|
TR3 |
|
|
|
|
0.820 |
|
|
TR2 |
|
|
|
|
0.797 |
|
|
KMO |
0.874 (sig=0.000) |
|||||
|
Bartlett's |
2141.284 |
|||||
|
Eigenvalues |
7.732 |
1.998 |
1.709 |
1.516 |
1.111 |
|
|
Total Variance Explained |
19.025 |
34.949 |
49.084 |
62.669 |
74.034 |
|
5.3.2.
Dependent variable:
The results
show that KMO is 0.775 and can make sure the requirement 0.5<KMO<1.
Bartlett is 638.168
with sig = 0.00<0.05, so all of the variables are correlation together in
each component. Total variance explained equals 81.415%, and it is greater than
50%; as a result, it can meet the requirement of variance explained. Besides,
eigenvalues equal 3.257>1, and it is the fluctuation that can explain for
each factor, so the extracted factors have a significant summarize in the best
way. Finally, all of the variables have the loading factor that is greater than
0.55 and meet requirement.
Table 4: Dependent variable, and testing
|
Dependent variable |
Component |
|
|
1 |
||
|
Behavior intention |
BI1 |
.936 |
|
BI2 |
.922 |
|
|
BI3 |
.877 |
|
|
BI4 |
.872 |
|
|
KMO |
0.775 (sig=0.000) |
|
|
Bartlett's |
638.168 |
|
|
Eigenvalues |
||
|
Total
Variance Explained |
81.415 |
|
|
Cronbach’s
Alpha |
0.923 |
|
5.4.
Regression
Regression
analysis finds out what is the factors that affect the intention of using
bike-booking application and measure the affecting levels of these factors. Before
doing the regression analysis, the author does compute the mean value of these
factors. Whereas:
BI: Behavior intention to use bike-booking application
(BI1, BI2, BI3, BI4)
PLL: Perceived price level (PLL1,
PLL2, PLL3, PLL4)
PU: Perceived usefulness (PU1, PU2, PU3, PU4, PU5)
SN: Subject norm (SN1,
SN2, SN3)
PEU: Perceived ease of use (PEU1,
PEU2, PEU3, PEU4)
TR: Trust (TR1, TR2, TR3)
The following formula can describe regression analysis model in this
research:
BI = β0 + β1*PLL + β2*PU + β3*SN + β4*PEU +
β5*TR
Whereas, BI is
dependent variable and it can measure the intention of bike-booking adoption in
Ho Chi Minh City, and PLL, PU, SN, PEU, TR are independent variables which can
measure the perceived price level, perceived usefulness, subject norm, perceived
ease of use and trust respectively.
Table 5: Regression results
|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
Collinearity |
|||
|
Beta |
Sd. Error |
Beta |
Tolerance |
VIF |
||||
|
1 |
(Constant) |
-0.010 |
0.229 |
|
-.044 |
0.965 |
|
|
|
PLL |
0.242 |
0.062 |
0.263 |
3.924 |
0.000 |
0.505 |
1.982 |
|
|
PU |
0.289 |
0.062 |
0.286 |
4.646 |
0.000 |
0.599 |
1.669 |
|
|
SN |
0.154 |
0.055 |
0.162 |
2.825 |
0.005 |
0.691 |
1.446 |
|
|
PEU |
0.153 |
0.053 |
0.176 |
2.852 |
0.005 |
0.598 |
1.671 |
|
|
TR |
0.139 |
0.041 |
0.170 |
3.368 |
0.001 |
0.890 |
1.124 |
|
|
R2 |
0.612 |
|||||||
|
Adjusted R2 |
0.600 |
|||||||
|
Sig. |
0.000 |
|||||||
|
Durbin Watson |
1.634 |
|||||||
The results of the regression model show that the sig of
PLL, PU, SN, PEU, and TR are smaller than 0.05. So it means that the perceived
price level, perceived usefulness, subject norm, perceived ease of use, and trust
affect the behavior intention to use bike-booking. From the standardized beta,
the perceived usefulness is the most influential factor (beta = 0.286), and the
subject norm is the least influential factor (beta = 0.162). While, the order
of three other factors that affect behavior intention is perceived price level,
perceived ease of use, and trust, respectively.
The R2 value
is that the percentage of all independent variables can explain the dependent
variable (Tabachnick and Fidell, 2014). From the regression, the R2 value is
0.612, and it means that 61.2% of the intention to use the bike-booking
application is from 5 factors, and 38.8% of that is from other factors that are
outside of the model. The sig value is 0.000, and it is less than 0.05, so the
research model is fit, and the variables which use in the model have a
significant statistic. Besides, Durbin – Watson is 1.634, and as a result,
there is no autocorrelation between the residuals in the model. What is more,
variance inflation factors (VIF) are too small (from 1.124 to 1.982), and these
point out that there is no multicollinearity in this model, so all of the
independent variables do not correlate together.
The multiple
regression model by unstandardized coefficients can be identified:
BI = 0.242*PLL + 0.289*PU + 0.154*SN + 0.153*PEU +
0.139*TR
5.5.
Hypothesis testing:
Table 6: Hypothesis testing
|
Hypothesis |
Content |
Result |
|
H1 |
Trust has a positive effect on the intention of
using bike-booking application |
Accepted |
|
H2 |
Perceived ease of use has a positive effect on the intention of using bike-booking application |
|
|
H3 |
Subject norm has a positive effect
on the
intention of using bike-booking application |
Accepted |
|
H4 |
Perceived usefulness have a positive
effect on the intention of using bike-booking application |
Accepted |
|
H5 |
Perceived price level has a positive
effect on the intention of using bike-booking application |
Accepted |
Figure 2: Factors
affecting to the intention of using bike-booking application
6.
CONCLUSION AND MANAGERIAL
IMPLICATION
The purpose of this research is to
find the factors that influence the intention to use the bike-booking
application for university students in Ho Chi Minh City and provide some
managerial implications to improve the quality of the booking application and
attract customers. Through the process of analyzing the survey data of 177
university students in Ho Chi Minh City, the research uses the survey results.
It applies quantitative analysis techniques such as statistics and reliability
tests, exploratory factor analysis, regression analysis. From this study, it
can be concluded that consumers' intention to use the ride-booking application
is influenced by perceived ease of use, trust, perceived usefulness, perceived
price level, and subjective norm.
Through
data analysis, all of the independent variables can use to explain the
influence on the ride-booking application. Each variable has a positive
influence on the behavior intention of students. In which the perceived usefulness
is the most substantial impact because the coefficient of factor impact is
highest and equals 0.286. The subjective norm has the lowest coefficient of
0.162. The second most affected factor is the perceived price, with a beta
coefficient of 0.263. The third most impact factor is perceived ease of use
with a coefficient of 0.176. The beta coefficient of trust is 0.170, so this
factor is the fourth most powerful factor that affects intention to use a
rike-booking application.
Today, safety is a severe problem in
all of country in the world, so the users must pay attention to their security
and other issues such as the case of robbery or related issues. Not only that,
but the purchase of consumer information is also causing discomfort to
customers. Therefore, businesses need to improve the confidentiality of
customer information as well as improve the quality of drivers. Drivers need to
have a driver's license and transparent information on the application.
Besides, companies do double-check for drivers who do not drive anymore to
avoid fraud that causing damage to customers.
There are many ways to improve the
usefulness of the application. The developer of the company needs to improve in
many aspects of the use, such as finding drivers quickly, build applications
that do not crash. Besides, the company must expand the market to the other
provinces; the reason is that the bike-booking application is only accessible
in Ho Chi Minh City and Hanoi. Because of the impact of usefulness, it is
necessary to maintain the facilities and complete the development of a few
other functionalities to support the customers better.
Customers have to face a problem
that is the location of picking up. The application has not been updated and
locating exactly where the customer is, so it is not convenient for customers
to wait or call the driver for the address of this situation. Businesses need
to improve location positioning, as well as upgrade their
applications so that customers do not have to spend a lot of time on finding
the right location.
The price to book the ride on the
application fluctuates greatly. At the peak, the price is very high, much
higher than the traditional ride. Meanwhile, technology company promotions are
on the decline, and students feel dissatisfied and lead to less use of the
ride-booking app. Currently, there are quite some new bike-booking applications
to market. Therefore, businesses want to survive and develop; they need an
appropriate discount strategy to attract customers in particular and students
in general. Also, companies need to adjust prices to match the distance
traveled through the artificially intelligent system of the business.
Businesses need to improve
promotions and service quality. The reason is that in the current technological
age, electronic word of mouth is fast spread, young people or users can share
their feeling by posting on websites or social network pages such as Facebook,
Zalo, Instagram, and other ones. It leads to the application can spread fastly
and widely if it is excellent. Therefore, this is a significant factor that
needs improvement and needs to thrive.
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