Assessment of influencer’s effects on customers’ online purchasing behavior in Vietnam
Pham Minh et al. HCMCOUJS-Economics and Business Administration, 11(2), 81-96
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Assessment of influencer’s effects on customers’ online purchasing
behavior in Vietnam
Pham Minh1*, Dang Thao Yen1, Ngo Thi Huong Quynh1, Hoang Thi Hong Yen1,
Tran Thi Thanh Nga1, Nguyen Van Quoc1
1Ho Chi Minh City Open University, Vietnam
*Corresponding author: minh.p@ou.edu.vn
ARTICLE INFO
ABSTRACT
DOI:10.46223/HCMCOUJS.
econ.en.11.2.1419.2021
Today, the development of the Internet and social networks
has changed the lives of human beings. The ability of these
technologies to connect people in real-time expands the influence
of some people in the community. Therefore, this study is
conducted to test whether customers change purchasing behavior
in online environments under the impact of those influencers by
using Technology Acceptance Model (TAM). The study conducted
a survey of 503 Vietnameses on Google Form from November
2020 to mid-January 2021. The collected data were analyzed using
AMOS 24 with CB-SEM analysis method. The results showed a
positive relationship between influencers and customers’ online
purchasing behavior. More specifically, customers are more likely
to buy online if they trust influencers and their advertisements.
This is the most influential factor among the three influencer traits
(as source credibility): trustworthiness, expertise, and
attractiveness. A remarkable point in this study is that Vietnamese
people are more concerned with perceived ease of use when
buying online than other factors in the TAM model. This is the
basis for businesses to implement influencer marketing strategies
and improve the competitiveness of their online business.
Received: January 27th, 2021
Revised: April 06th, 2021
Accepted: April 28th, 2021
Keywords:
behavior; influencer; online
purchasing source credibility;
technology acceptance model
1. Introduction
Over the past decades, the procurement process has changed. Customers’ purchasing
behavior has been influenced by the advent of Information and Communication Technology
(ICT), as well as the popularity of social media. The exponential adoption of social media is
evident in the fact that there are 2,789 billion social media users globally, with a penetration rate
of about 37% (Kemp, 2017). The rapid growth of social media has provided marketers new
approaches to attract their customers (Bernhardt, Mays, & Hall, 2012). Fauser, Wiedenhofer, and
Lorenz (2011) suggested that social media can be used to influence customers in all stages of the
purchase decision process. On the other hand, traditional advertising is losing effectiveness and
online influencers have become a powerful marketing weapon (Schivinski & Dabrowski, 2016).
The social media platforms also provide the opportunity for customers to engage in two-way
feedback with individuals they normally don’t have face-to-face contact with, especially
celebrities.
Internet or online shopping offers many benefits in terms of both the information search
and the buying process (Rose & Samouel, 2009). Nowadays, people prefer to search and collect
information passively through their influencers (Pham & Bui, 2020). According to Schickel
(2000), people are always excited about celebrities. Celebrities are seen as a special part of the
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virtual community, spreading information through various social media channels by sharing
stories, photos, experiences, or opinions about many objects, services, and products, etc.
(Alhidari, Iyer, & Paswan, 2015; Alsulaiman, Forbes, Dean, & Cohen, 2015). Atkin and Block
(1983) argue that there are many reasons why influencers are so dominant. Among them,
businesses use celebrities to help customers identify their brands and create a competitive
advantage (Ilicic & Webster, 2015). In addition, celebrities play the role of “initiator” and
“influencer” in the consumer purchasing process. They create awareness, develop the
community’s interest in the brand and link it to the product (Malik & Guptha, 2014). People tend
to believe a product that is endorsed by an influencer is a good product. In Vietnam, it is very
easy to see the image of a celebrity linked with brands such as Son Tung M-TP and Oppo.
There is a significant relationship between celebrity endorsement and purchasing
behavior, with celebrity trust-worthiness having the strongest impact. The research by Malik and
Guptha (2014) shows that a product that is endorsed by a credible source can influence
purchasing behavior and celebrity trust-worthiness is the most essential trait of a celebrity. In
Vietnam, Nguyen and Nguyen (2017) demonstrated that customer attitudes towards the brand are
positively influenced by three factors, including celebrity’s trust-worthiness. Therefore, Vietnam
is a great market for applying online influencer marketing strategies. When the number of social
media users is 65 million, the rate of searching for information about brands is very high at 85%
and the online shopping rate is up to 75% (Kemp, 2020). Besides, the number of times a
Vietnamese person over 18 years old clicks on ads on Facebook is 17 times per month. However,
when there are too many brands/products used by the same influencers to promote on social
networks, it can make customers doubt the reliability of the product. In addition, using a
celebrity can overshadow products and influence purchasing behavior.
The Technology Acceptance Model (TAM) has been widely used in technology research
and successfully applied as a theoretical framework for predicting online purchasing intention
and behavior (Gefen, Karahanna, & Straub, 2003a, 2003b; Pavlou, 2003; Shim & Lee, 2011).
However, TAM is still constantly developing and improving. This study proposes a research
model developed from TAM and extended by combining the Theory of Planned Behavior (TPB)
and source credibility theory. This study examines the relationship between the components of
influencer’s credibility, including attractiveness, trust-worthiness, and expertise (Erdogan, 1999;
McCracken, 1989; Ohanian, 1991; Petty, Cacioppo, & Schumann, 1983) and TAM components.
Next, this article also examines the factors of TAM that affect consumers’ online attitudes and
purchasing behaviors in the Vietnamese context. The goal is to assess how well celebrity source
credibility factors influence internet purchasing behavior, thereby helping select the brand’s right
celebrity. Also, it supports in determining how to operate online shopping websites and how to
motivate consumers to adopt online purchasing behavior in Vietnam. Since then, the article also
tested whether influencers have the effect of changing purchasing behavior from traditional
methods to online. The research concepts and hypotheses are presented in the next section.
2. Literature review
2.1. Source Credibility theory (SC)
Source credibility is a term commonly used to refer to the positive characteristics of the
influencer that affect the recipient’s acceptance of a message (Ohanian, 1990). The information
provided by these people can change the behavior of those affected by them. In this study, the
mentioned sources are influencers, more specifically celebrities. Up to now, celebrities have
always had a great attraction to the community. Celebrity image is widely used in marketing
because marketers believe that celebrities can attract customers’ attention (Chung & Cho, 2017).
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SC theory argues that the effectiveness of a message depends on the level of expertise and trust-
worthiness of the endorser (Ohanian, 1991). Information from a credible source can influence
behavior through a process known as penetration, which occurs when receivers accept a source
that influences their attitudes and their living values. Rieh and Belkin (1998) argue that, on the
Internet, perceptions of source reliability play an essential role in people’s judgments. Zhang and
Watts (2008) affirmed that SC is an important factor of message evaluation and information
adoption in the online community. This study proposes three aspects of SC in the research
model: trust-worthiness, expertise, and attractiveness developed by Ohanian (1990).
Trust-worthiness refers to the honesty, integrity, and trust of an endorser. M. J. Kim,
Chung, Lee, and Preis (2016) found that customers experience more usefulness in an online
shopping environment when they get information from a trusted source. Expertise is defined as
the degree to which a provider is considered to be a source of valid claims. It refers to the
knowledge, experience, or skills they possess. It does not really matter whether the endorsers are
experts or not, but how the target audience perceives them (Ohanian, 1991). Attractiveness is
defined by many researchers as a combination of similarity, familiarity, and likability of
credibility sources (Maddux & Rogers, 1980; Steadman, 1969). Numerous studies have shown
that celebrity’s attractiveness has a positive effect on brand image, attitudes toward the brand and
purchasing intention (Joseph, 1982; Mello, Garcia-Marques, Briñol, Cancela, & Petty, 2020;
Ozanne, Liu, & Mattila, 2019); and it also becomes more favorable if the attractiveness of the
endorser has increased (Kamins, 1990). Besides, attractiveness is not only about appearance but
also requires the spiritual skills, personality, lifestyle, and talent of celebrities (Erdogan, 1999).
2.2. Technology Acceptance Model (TAM)
The Technology Acceptance Model (TAM) was proposed by Davis (1985) and expanded
in 1989. TAM seeks to explain users’ adoption of information technology. Based on the Theory
of Reasoned Action (TRA), the main purpose of TAM is to provide a basis for investigating the
impact of external factors on internal factors such as beliefs, attitudes, and intentions of users
(Davis, 1989). According to TAM, there is a causal relationship between user attitudes,
intentions, and behavior. The mediating factors used to evaluate these relationships are perceived
ease of use and perceived usefulness. TAM is constantly developing and perfecting over time by
scientists. The results of the researches have shown a strong relationship between intention to use
and perceived usefulness and actual usage behavior (Davis, Bagozzi, & Warshaw, 1989). TAM
has been used extensively in studies. More specifically, the researchers used TAM to predict
online purchasing intention and behavior for products such as books (Jin, 2014; Lin, 2007),
clothing (Ha & Stoel, 2009), tourism (Assaker, 2020), health care (Kamal, Shafiq, & Kakria,
2020) and financial or banking services (Munoz-Leiva, Climent-Climent, & Liébana-Cabanillas,
2017; Suh & Han, 2003). In this article, SC components are used as external factors of TAM to
demonstrate influencer’s effects on customer purchasing behavior.
Influencer marketing is one of the most effective methods of attracting thecommunity’s
attention towards the business’s goals. Marketers take advantage of the value of trust-worthiness
by choosing endorsers that many consider to be honest and trustworthy (Shimp, 1997).
According to the research of Friedman, Santeramo, and Traina (1978), marketers choose trusted
celebrities and are expected to be brand ambassadors. Oni and Ayo (2010) proposed that source
trust-worthiness impacts perceived usefulness and ease of use. Then, Li (2013) demonstrated that
source trust-worthiness induces cognitive and emotional responses, such as usefulness and ease
of use, in adopting new technology systems. C. W. Chen, Chen, and Chen (2014) reported that
source trust-worthiness positively affects perceived usefulness. Marketers need to choose high-
trustworthy celebrities to increase the brand’s credibility, therefore, increase awareness for the
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online sales page. Furthermore, L. Chen and Aklikokou (2020) have demonstrated that trust-
worthiness has perceived ease of use effect, even stronger than its effect on perceived usefulness.
Therefore, the first hypotheses are stated as follows:
H1: Celebrity trust-worthiness has a positive effect on perceived usefulness
H2: Celebrity trust-worthiness has a positive impact on perceived ease of use
Expertise sources influence the perceived quality of product. A celebrity endorsed social
media site that assures customers of its product quality. On the other hand, Speck, Schumann,
and Thompson (1988) found that expert celebrities create a higher probability of product
information retrieval than amateur ones, but the difference was not statistically significant. Li
(2015) pointed out that companies use experts to strongly recommend the ease of use and
usefulness of a new system so that employees can change their thinking according to experts’
recommendations (Bhattacherjee & Sanford, 2006). Sussman and Siegel (2003) found that
sources of expertise positively affect the usefulness of information systems. Companies provide
reliable sources with a compelling message that motivates their employees to understand the
usefulness and ease of using technology based on trusted expert recommendations. Liaw and
Huang (2003) argued that when users prefer to use search engines to find information, they
consider search engines to be easy to use. From the above studies, the expertise of the celebrity
has influenced the user’s perceived usefulness and ease of use of the technology system. A more
highly specialized source/celebrity is more persuasive (Aaker & Myers, 1987) and aroused more
purchasing intention by that brand (Ohanian, 1991). Information sharing about products and
websites is also interesting by customers and has involved the making decision process. The
hypotheses given here are:
H3: Celebrity expertise has a positive effect on perceived usefulness
H4: Celebrity expertise has a positive impact on perceived ease of use
The attractiveness of the source is closely related to the influencer’s appearance that can
enhance persuasion based on similarity, familiarity, likability, or desire for target customers
(Kapitan & Silvera, 2016). The message from an attractive source is shown to be more
convincing (Chaiken, 1979). Celebrities need to convey a message that is appealing to the user
so that customers find the website useful and easy to use. Two next hypotheses are proposed:
H5: Celebrity attractiveness has a positive effect on perceived usefulness
H6: Celebrity attractiveness has a positive impact on perceived ease of use
TAM suggests that the intention to use new technologies will lead to the actual behavior
of the customer. Several previous studies have experimentally confirmed the relationship
between perceived ease of use, perceived usefulness, and attitudes in many organizational
contexts (Schepers & Wetzels, 2007; Venkatesh & Brown, 2001; Venkatesh & Davis, 1996).
They suggest that perceived ease of use and usefulness are the two most important factors in
explaining the system’s adoption. Studies have shown that perceived usefulness promotes users’
attitudes and intentions (Ayeh, Au, & Law, 2013; Morosan, 2012). They also assessed perceived
usefulness in influencing online purchases (Chiu, Huang, & Yen, 2010). High-quality posts and
discussions allow customers not only to receive useful information but also to receive advice on
a particular topic (Zheng, Zhao, & Stylianou, 2013). M. J. Kim et al. (2016) demonstrated that
when online shoppers receive higher quality information, they can perceive the information as
helpful. Potential customers who find a website helpful are more likely to have a more positive
and sympathetic response to online shopping than those who don’t. So the hypothesis was
formed:
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H7: Perceived usefulness has a positive effect on attitudes
H8: Perceived usefulness has a positive effect on purchasing behavior
In addition, Li (2013) found that when users of online shopping websites receive a
compelling message with a higher level of information quality, they consider these pages as easy
to use. Previous research has shown that ease of use influences attitudes (Castaneda, Frias, &
Rodriguez, 2009). Similarly, Yu and Tao (2009) and Chiu et al. (2010) confirmed that ease of
use directly affects attitude. Also, it is often believed that a system is considered more valuable if
it is easy to use. For example, studies by Morosan (2012) and D. Y. Kim, Park, and Morrison
(2008) provide strong empirical evidence for the positive relationship between perceived ease of
use and attitudes in online systems. Therefore, the research hypothesis is proposed that:
H9: Perceived ease of use has a positive impact on customer attitude
2.3. Theory of Planned Behavior (TPB)
Theory of Planned Behavior (TPB) is designed to explain almost every human behavior
and has been shown to be successful in predicting and interpreting human behavior across
different application contexts (Davis et al., 1989). According to the TPB, human behavior is
guided by behavioral intention as well as perceived behavioral controls. In contrast, behavioral
intention is generally determined by attitudes toward behavior, subjective norms, and perceived
behavioral control. The TPB has proven that customer attitudes impact their intentions, and in
turn, impact the behavior they will take. Bentler and Speckart (1979) state that attitude not only
influences intention, but also directly affects behavior without having intentions act as an
intermediary influence. The concept of attitude presented is the evaluation of performing a
specific behavior in relation to the attitude object, such as buying a product (Blackwell, Paul, &
James, 2006).
According to Afendi, Azizan, and Darami (2014), attitude is considered to have a direct
relationship with behavioral intention. Pavlou and Fygenson (2006) argue that attitude plays an
important role in the formation of an intention to contribute to online shopping. From previous
studies, attitude is the degree of an individual’s positive perception of a behavior. A customer
with a more positive attitude towards a behavior is more likely to do this. Accordingly, in the
online shopping context, customers with more positive attitudes are considered to be more likely
to shop together as a group. The final hypothesis is stated as follows:
H10: Attitude has a positive impact on purchasing behavior of customers
3. Methodology
3.1. Measurement and data collection
The proposed research model is built from source credibility theory and TAM expanded
by combining TPB and the research above. The scales of trust-worthiness, expertise, and
attractiveness are inherited from Dwivedi and Johnson (2013). The perceived usefulness scale
for evaluating the influence of celebrities on online shopping is designed based on research by
Zhu, Chang, and Luo (2016). The perceived ease of use scale is combined from the studies of
Kucukusta, Law, Sahli, and Legoherel (2015), Kang and Namkung (2019) and Hwang (2009).
The Attitude scale is based on the study of George (2004). Meanwhile, the purchasing
behavior scale is inherited from Shah, Zahoor, and Qureshi (2019) research. All these scales
have 03 observed variables.
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The survey subjects are all adults living in Vietnam who are aware and make purchasing
decisions themselves, and have exposure to the Internet and social media. Data was collected via
questionnaires designed on Google Form and links were sent through Facebook groups. Because
the survey was conducted during the Covid-19 pandemic, so, to ensure the safety and speed of
this research, the technique of sample collection was a convenient sampling method. The
collection period was from November 2020 to mid-January 2021. The collected data were
analyzed using AMOS 24 software and CB-SEM analysis method.
3.2. Descriptive statistics
Table 1
Descriptive statistics of the sample
Classification
Age (year old)
Categories
Frequency
464
30
Percentage
92.9
6.0
18 - 25
26 - 40
Over 40
9
1.8
Male
Female
111
392
87
22.1
77.9
17.3
12.1
70.6
24.7
6.6
Gender
North
Central
61
Location
South
355
124
33
High school and below
College/Professional high school
Bachelor
Education
326
20
64.8
4.0
Postgraduate
Below 10
448
55
89.1
10.9
Income
(million VND)
10 and over
Source: Data analysis
This study has collected 503 valid questionnaires and the target respondents were
identified through a filter question: Have you ever purchased online? Among the participants, up
to 77.9% of respondents are female. The majority of respondents are between 18 and 25 years
old, accounting for 92.9%. Respondents mainly live in the South (70.6%), the other 02 areas
account for a lesser proportion: Central (12.1%) and North (17.3%). The majority of the survey
respondents have Bachelor’s education, accounting for 64.8%, the rest are from high school and
below, College/Professional high school and Postgraduate with respectively 24.7%, 6.6%, and
4.0%. Because most respondents are newly graduated students, their income will not be high,
around 10 million VND (89.1%).
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4. Research results
4.1. Confirmatory Factor Analysis
Table 2
Reliability and Convergent validity
Variables
Trust-worthiness
Expertise
CR
CA
AVE MSV Standardized Regression Weight
0.845 0.852 0.645 0.441
0.881 0.884 0.712 0.357
0.886 0.889 0.721 0.412
0.856 0.876 0.664 0.654
0.749 - 0.827
0.797 - 0.885
0.757 - 0.901
0.728 - 0.792
0.754 - 0.810
0.681 - 0.821
0.651 - 0.855
Attractiveness
Perceived Usefulness
Perceived Ease Of Use 0.865 0.875 0.682 0.654
Attitude
0.844 0.805 0.644 0.630
0.817 0.815 0.600 0.556
Purchasing Behavior
Source: Data analysis
In this study, we use the two-step modeling approach for SEM suggested by Anderson and
Gerbing (1988). First, data was analyzed by Confirmatory Factor Analysis (CFA). Reliability
testing was performed with an assessment of two indicators: Composite Reliability (CR) and
Cronbach’s Alpha (CA). Hair, Black, Babin, and Anderson (2010) suggested that to satisfy the
reliability of the scale, the CR indexes should be greater than 0.7. Similarly, Nunnally (1994)
confirmed that the CA indexes also needed to be greater than 0.7 for the scale to be considered
good. The results in Table 2 show that the CR indexes range from 0.817 to 0.886, while the CA
indexes are from 0.805 to 0.889. Thus, both indicators are satisfied and the scales are considered
reliable.
Next, this study checked the validity of the data through convergent validity and
discriminant validity. Convergent validity is considered through Average Variance Extracted
(AVE) and standardized regression weight. Chahal and Mehta (2013) suggested that the
standardized regression weights should be at least 0.5, preferably above 0.7. These indexes in
Table 2 have a minimum value of 0.651, so all of them satisfy the requirement. On the other hand,
Table 2 also showed that the AVE indexes were greater than 0.5 (Hair, Ringle, & Sarstedt, 2011)
with the smallest index of 0.644 of the attitude scale. This shows that the scales in this study satisfy
the convergent validity of research concepts. The scales are rated as good and suitable for further
analysis.
Table 3
Fornell - Larcker criterion
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Trust-worthiness (1)
Expertise (2)
0.803
0.533
0.844
Attractiveness (3)
Perceived Usefulness (4)
0.642 0.316
0.849
0.664 0.531 0.592
0.815
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(1)
(2)
(3)
(4)
(5)
(6)
(7)
Perceived Ease Of Use (5)
0.634 0.573 0.507 0.809
0.826
Attitude (6)
0.615 0.510 0.505 0.729 0.794
0.802
Purchasing Behavior (7)
Source: Data analysis
0.584 0.597 0.451 0.727 0.731 0.746
0.775
The discriminant validity assessment needs to compare Maximum Shared Variance
(MSV) with AVE. According to Hamari and Koivisto (2014), the MSV of the scales should be
less than their AVE. All the values in Table 2 are satisfactory and demonstrate that the data are
discriminant. The discriminant validity is also assessed by the criterion of Fornell and Larcker
(1981), comparing the square root of AVE of each concept with its correlation values. If the
square root of AVE is greater than the correlation values, then the discriminant validity is
satisfied. The results in Table 3 are satisfied. Thus, the data is confirmed to be discriminant, so it
is valid for further analysis at the next stage.
Table 4
Model fit summary
Criteria
χ2/df
Values
2.684
0.922
0.948
0.959
0.936
0.038
0.058
Cut-off
< 3
Source
GFI
> 0.9
TLI
> 0.9
L. T. Hu and Bentler (1999)
CFI
> 0.95
> 0.9
NFI
RMR
< 0.08
< 0.08
Taylor, Sharland, Cronin, and Bullard
(1993)
RMSEA
Source: Data analysis
Finally, the study conducted model fit testing. The goodness-of-fit of the model was done
through values such as χ2/df (chi-square/degree of freedom), RMR (root mean-square residual),
GFI, NFI, TLI, CFI, and RMSEA (root-mean-square error of approximation) (Table 4). According
to L. T. Hu and Bentler (1999), the indexes considered for goodness-of-fit assessment have
achieved good results in accordance with the set criteria: χ2/df < 3; GFI > 0.9; CFI > 0.95; TLI >
0.9 and NFI > 0.9. Table 4 shows that the indexes such as χ2/df = 2.684; GFI = 0.922; CFI = 0.959;
TLI = 0.948 and NFI > 0.9 achieved good results, so the model is fit. According to Taylor et al.
(1993), 02 indexes (RMR and RMSEA) are less than 0.08, meaning the model is fit. The results of
the study showed that RMR = 0.038 and RMSEA = 0.058; this confirms that all data are
appropriate.
4.2. Structural Equation Modeling
After implementing CFA, the article evaluated the research model using the CB-SEM
method (Covariance Based - Structural Equation Modeling). Figure 1 shows that all of the path
coefficients (β) are positive. Table 5 provides that all of p-values are 0; therefore, the β
coefficients are statistically significant. Thus, all proposed research hypotheses are accepted.
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This research has demonstrated the relationship between influencers and customers’ online
purchasing behavior.
Figure 1. Research model evaluation
Source: Data analysis
Next, this study evaluated the relationship between the concepts in the model. This study
shows that the celebrity’s trust-worthiness is the most important factor affecting the perceived
usefulness and ease of use of a customer for a website. According to Figure 1, the relationship
between trust-worthiness and usefulness (β = 0.440) and ease of use (β = 0.426) is rated the
highest. Therefore, Vietnamese businesses need to choose reputable celebrities in order to
increase the effectiveness of their marketing campaigns. Obviously, source trust-worthiness
positively affects the usefulness and ease of use of a customer’s thinking about a product in C2C
communication (Zhu et al., 2016).
Expertise is also highly appreciated when it correlates with usefulness β = 0.365 and ease
of use β = 0.418 (Table 6). We can see the requirement of choosing a celebrity; besides being
highly trust-worthy, it requires a high level of expertise. Therefore, online shopping businesses
that are appreciated by highly expert influencers will strongly influence the perceived usefulness
and ease of use of the customer for their website. The index of the relationship between
attractiveness and usefulness (β = 0.361) and ease of use (β = 0.255) is rated the lowest. So while
attractiveness contributes to the increase in the number of customers, it is not a strong factor. In
general, the study results are similar to the study of Kang and Namkung (2019), when the
components of influencer (as source credibility) tended to influence the perceived usefulness of
customers more strongly than perceived ease of use.
Table 5
SEM results
Hyp.
H1
Path
β
S.E.
0.045
0.037
C.R.
8.860
7.832
P-value
0.000
Evaluation
Accepted
Accepted
PU
PU
TR
EX
0.402
0.290
H3
0.000
90
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Hyp.
H5
Path
β
S.E.
C.R.
7.647
8.864
8.998
5.743
10.027
5.567
6.059
7.523
P-value
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
Evaluation
Accepted
Accepted
Accepted
Accepted
Accepted
Accepted
Accepted
Accepted
PU
PEU
PEU
PEU
AU
AT
TR
0.304
0.404
0.345
0.224
0.535
0.268
0.338
0.488
0.040
0.046
0.038
0.039
0.053
0.048
0.056
0.065
H2
H4
EX
AT
PEU
PU
H6
H9
H7
AU
H8
PH
PU
H10
PH
AU
(PU: Perceived Usefulness; PEU: Perceived Ease of Use; TR: Trust-worthiness; EX: Expertise; AT: Attractiveness;
AU: Attitude; PH: Purchasing Behaviour)
Source: Data analysis
The results in Table 6 show a strong effect of ease of use versus usefulness when
influencing customer attitudes. Specifically, the relationship between ease of use and attitude is
higher (β = 0.597), while usefulness affects attitude at β = 0.287. This is a very interesting finding
because it is completely contrary to previous studies such as Ayeh et al. (2013) and Li (2015).
Obviously, the Vietnamese are more concerned with ease of use than usefulness. Therefore,
businesses must pay more attention to designing websites that are easier to use than just focusing
on content.
Table 6
Standardized total effects
AT
EX
TR
PEU
PU
AU
Perceived Ease Of Use
Perceived Usefulness
Attitude
0.255
0.361
0.256
0.256
0.418
0.365
0.354
0.305
0.426
0.440
0.381
0.346
0.597
0.292
0.287
0.503
Purchasing Behaviour
0.488
(PU: Perceived Usefulness; PEU: Perceived Ease of Use; TR: Trust-worthiness; EX: Expertise; AT: Attractiveness;
AU: Attitude)
Source: Data analysis
According to Table 7, the indirect impact between trust-worthiness and attitude is the
highest level (β = 0.381). On the contrary, celebrity attractiveness influences shoppers’ attitudes
at the lowest level (β = 0.256). This shows that trust-worthiness is an extremely important factor
that influencers need to put on the top, especially in the online environment. From there, it helps
to create a genuine, trustworthy image that helps reinforce consumer attitudes. In addition,
although the level of the influence of the expertise and the attractiveness affecting the attitude is
lower, it does not mean that these factors will be ignored.
Similarly, the indirect effect of trust-worthiness on purchasing behavior is also highest (β
= 0.346). From there, we can see that trust plays the most important role in deciding celebrities’
influence on customers’ purchasing decision-making behavior. The results of indirect effects
between attractiveness and expertise also reached at a lower intensity. Especially, attractiveness
Pham Minh et al. HCMCOUJS-Economics and Business Administration, 11(2), 81-96
91
has an even weaker effect than perceived ease of use (0.256 versus 0.292). Therefore, when
making a decision to choose an influencer as a brand ambassador, businesses need to select
whom with high self-trustworthy for increasing the influence on customer attitudes and
behaviors.
Table 7
Standardized indirect effects
AT
EX
TR
PEU
PU
0.256
0.256
0.354
0.305
0.381
0.346
Attitude
0.292
0.140
Purchasing Behaviour
(PU: Perceived Usefulness; PEU: Perceived Ease of Use; TR: Trust-worthiness; EX: Expertise; AT: Attractiveness)
Source: Data analysis
Finally, this research assessed the influence of these factors on purchasing behavior.
Figure 1 shows that attitude is the most important factor in purchasing decisions (β = 0.488).
However, the relationship between usefulness to purchasing behavior is just as important with β
= 0.503. Apparently, customers’ attitudes had made usefulness’s relationship with purchasing
behavior stronger. This indicates that attitudes play a more important role in shaping the vast
majority of Vietnamese online purchasing behavior.
5. Conclusions
E-commerce has grown rapidly with the advancement of technology. Along with that,
social media and websites were born to serve the needs of human life. Changing habits from
traditional to online shopping requires significant outside impacts such as influencers. Celebrities
need to possess qualities including trust-worthiness, expertise, and attractiveness for attracting
business investment as well as consumer attention towards online purchasing shops. Among the
three factors above, the results of this study show that celebrity trust-worthiness plays a very
important role in customer perception, contrary to Ohanian (1991). Therefore, priority should be
given to celebrities to make a reputation for themselves, but do not forget to improve their
expertise and attractiveness. Marketers can use this research as a reference when making
decisions about the right brand representative.
Based on these results, online websites need to easily be designed to perform operations
such as search, payment, etc. Celebrities need to convey clear messages about features of the
site’s ease of use and increased user awareness of its usefulness. The interesting finding of this
study is that an easy-of-use website will attract more customers and create a friendly look for
increasing the rate of visits. However, this is in contrast to studies by Koufaris (2002); Davis
(1989); P. J. Hu, Chau, Sheng, and Tam (1999), which suggests that the perceived usefulness is a
more important predictor of using the system. Therefore, businesses need to consider suitable
ways of operating their website to attract more customers.
6. Limitation and further studies
The proposed model of this study proves the influence of celebrities on online purchasing
behavior. This is a factor that changes traditional buying habits to online shopping. However, this
study was done in Vietnam at the time that the Covid-19 pandemic was complicated. Therefore, the
data collection was difficult and the convenient sampling method is not highly reliable. In addition,
some necessary information has not been collected from the respondents. Specifically, information
about the type of purchased products, the frequency of buying as well as specific information about
online shopping websites through celebrity influence has not been collected due to the limitations of
online surveying. However, this limitation is not too critical because the survey has gathered the
92
Pham Minh et al. HCMCOUJS-Economics and Business Administration, 11(2), 81-96
necessary information and selected the right target objects. In addition, another limitation is that the
research model can only develop a certain number of components when combining SC theory,
TAM, and TPB without studying the remaining variables in the original model. Future studies can
supplement these components to help expand the research model.
References
Aaker, D. A., & Myers, J. G. (1987). Advertising management. New York, NY: Prentice Hall.
Afendi, N. A., Azizan, F. L., & Darami, A. I. (2014). Determinants of halal purchase intention:
Case in perlis. International Journal of Business and Social Research, 4(5), 118-123.
Alhidari, A., Iyer, P., & Paswan, A. (2015). Personal level antecedents of eWOM and purchase
intention, on social networking sites. Journal of Customer Behaviour, 14(2), 107-125.
Alsulaiman, K., Forbes, S. L., Dean, D. L., & Cohen, D. A. (2015). Relationships between
perceived product values and three word of mouth variables. Journal of Customer
Behaviour, 14(4), 277-294.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review
and recommended two-step approach. Psychological Bulletin, 103(3), 411-423.
Assaker, G. (2020). Age and gender differences in online travel reviews and User-Generated-
Content (UGC) adoption: Extending the Technology Acceptance Model (TAM) with
credibility theory. Journal of Hospitality Marketing & Management, 29(4), 428-449.
Atkin, C., & Block, M. (1983). Effectiveness of celebrity endorsers. Journal of Advertising
Research, 23(1), 57-61.
Ayeh, J. K., Au, N., & Law, R. (2013). Predicting the intention to use consumer-generated media
for travel planning. Tourism Management, 35(1), 132-143.
Bentler, P., & Speckart, G. (1979). Models of attitude-behavior relations. Psychological Review,
86(5), 452-464.
Bernhardt, J. M., Mays, D., & Hall, A. K. (2012). Social marketing at the right place and right
time with new media. Journal of Social Marketing, 2(2), 130-137.
Bhattacherjee, A., & Sanford, C. (2006). Influence processes for information technology
acceptance: An elaboration likelihood model. MIS Quarterly, 30(4), 805-825.
Blackwell, R. D., Paul, W. M., & James, F. E. (2006). Attributes of attitudes. In Consumer
behavior (pp. 235-243). New York, NY: Thompson Press.
Castaneda, J. A., Frias, D. M., & Rodriguez, M. A. (2009). Antecedents of internet acceptance
and use as an information source by tourists. Online Information Review, 33(3), 548-567.
Chahal, H., & Mehta, S. (2013). Modeling patient satisfaction construct in the Indian health care
context. International Journal of Pharmaceutical and Healthcare Marketing, 7(1), 75-92.
Chaiken, S. (1979). Communicator physical attractiveness and persuasion. Journal of
Personality and Social Psychology, 37(8), 1387-1397.
Chen, C. W., Chen, W. C., & Chen, W. K. (2014). Understanding the effects of eWOM on
cosmetic consumer behavioral intention. International Journal of Electronic Commerce
Studies, 5(1), 97-102.
Chen, L., & Aklikokou, A. K. (2020). Determinants of e-government adoption: Testing the
mediating effects of perceived usefulness and perceived ease of use. International Journal
of Public Administration, 43(10), 850-865.
Pham Minh et al. HCMCOUJS-Economics and Business Administration, 11(2), 81-96
93
Chiu, C. M., Huang, H. Y., & Yen, C. H. (2010). Antecedents of trust in online auctions.
Electronic Commerce Research and Applications, 9(2), 148-159.
Chung, S., & Cho, H. (2017). Fostering parasocial relationships with celebrities on social media:
Implications for celebrity endorsement. Psychology & Marketing, 34(4), 481-495.
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user
information systems (Doctoral dissertation). Massachusetts Institute of Technology, Sloan
School of Management, Cambridge, MA.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of
information technology. MIS Quarterly, 13(3), 319-340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology:
A comparison of two theoretical models. Management Science, 35(8), 982-1003.
Dwivedi, A., & Johnson, L. W. (2013). Trust-commitment as a mediator of the celebrity
endorser-brand equity relationship in a service context. Australasian Marketing Journal
(AMJ), 21(1), 36-42.
Erdogan, B. Z. (1999). Celebrity endorsement: A literature review. Journal of Marketing
Management, 15(4), 291-314.
Fauser, S. G., Wiedenhofer, J., & Lorenz, M. (2011). Touchpoint social web: An explorative
study about using the social web for influencing high involvement purchase decisions.
Problems and Perspectives in Management, 9(1), 39-45.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and
measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382-388.
Friedman, H. H., Santeramo, M. J., & Traina, A. (1978). Correlates of trust-worthiness for
celebrities. Journal of The Academy of Marketing Science, 6(4), 291-299.
Gefen, D., Karahanna, E., & Straub, D. W. (2003a). Trust and TAM in online shopping: An
integrated model. MIS Quarterly, 27(1), 51-90.
Gefen, D., Karahanna, E., & Straub, D. W. (2003b). Inexperience and experience with online
stores: The importance of TAM and trust. Engineering Management, 50(3), 307-321.
George, J. F. (2004). The theory of planned behavior and Internet purchasing. Internet Research,
14(3), 198-212.
Ha, S., & Stoel, L. (2009). Consumer e-shopping acceptance: Antecedents in a technology
acceptance model. Journal of Business Research, 62(5), 565-571.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis.
Upper Saddle River, NJ: Pearson Prentice Hall.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of
Marketing Theory and Practice, 19(2), 139-152.
Hamari, J., & Koivisto, J. (2014). Measuring flow in gamification: Dispositional flow scale-2.
Computers in Human Behavior, 40, 133-143.
Hu, L. T., & Bentler, P. M. (1999). Cut off criteria for fit indexes in covariance structure
analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A
Multidisciplinary Journal, 6(1), 1-55.
Hu, P. J., Chau, P. Y., Sheng, O. R. L., & Tam, K. Y. (1999). Examining the technology
acceptance model using physician acceptance of telemedicine technology. Journal of
Management Information Systems, 16(2), 91-112.
94
Pham Minh et al. HCMCOUJS-Economics and Business Administration, 11(2), 81-96
Hwang, Y. (2009). The impact of uncertainty avoidance, social norms and innovativeness on
trust and ease of use in electronic customer relationship management. Electronic Markets,
19(2/3), 89-98.
Ilicic, J., & Webster, C. M. (2015). Consumer values of corporate and celebrity brand
associations. Qualitative Market Research, 18(2), 164-187.
Jin, C. H. (2014). Adoption of e-book among college students: The perspective of an integrated
TAM. Computers in Human Behavior, 41, 471-477. doi:10.1016/j.chb.2014.09.056
Joseph, W. B. (1982). The credibility of physically attractive communicators: A review. Journal
of Advertising, 11(3), 15-24.
Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services
through an extended Technology Acceptance Model (TAM). Technology in Society, 60,
Article 101212. doi:10.1016/j.techsoc.2019.101212
Kamins, M. A. (1990). An investigation into the “match-up” hypothesis in celebrity advertising:
When beauty may be only skin deep. Journal of Advertising, 19(1), 4-13.
Kang, J. W., & Namkung, Y. (2019). The information quality and source credibility matter in
customers’ evaluation toward food O2O commerce. International Journal of Hospitality
Management, 78, 189-198. doi:10.1016/j.ijhm.2018.10.011
Kapitan, S., & Silvera, D. H. (2016). From digital media influencers to celebrity endorsers:
Attributions drive endorser effectiveness. Marketing Letters, 27(3), 553-567.
Kemp, S. (2017). Digital in 2017: Global overview. Retrieved January 24, 2021, from
Kemp, S. (2020). Digital in 2020: Vietnam. Retrieved January 24, 2021 from
Kim, D. Y., Park, J., & Morrison, A. M. (2008). A model of traveller acceptance of mobile
technology. International Journal of Tourism Research, 10(5), 393-407.
Kim, M. J., Chung, N., Lee, C. K., & Preis, M. W. (2016). Dual-route of persuasive
communications in mobile tourism shopping. Telematics and Informatics, 33(2), 293-308.
Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online
consumer behavior. Information Systems Research, 13(2), 205-223.
Kucukusta, D., Law, R., Sahli, A. B., & Legoherel, P. (2015). Re-examining perceived
usefulness and ease of use in online booking: The case of Hong Kong online users.
International Journal of Contemporary Hospitality Management, 27(2), 185-198.
Li, C. Y. (2013). Persuasive messages on information system acceptance: A theoretical extension
of elaboration likelihood model and social influence theory. Computers in Human
Behavior, 29(1), 264-275.
Li, C. Y. (2015). The effects of source credibility and argument quality on employees’ responses
toward information system usage. Asia Pacific Management Review, 20(2), 56-64.
Liaw, S. S., & Huang, H. M. (2003). An investigation of user attitudes toward search engines as
an information retrieval tool. Computers in Human Behavior, 19(6), 751-765.
Lin, H. F. (2007). Predicting consumer intentions to shop online: An empirical test of competing
theories. Electronic Commerce Research and Applications, 6(4), 433-442.
Maddux, J. E., & Rogers, R. W. (1980). Effects of source expertness, physical attractiveness, and
supporting arguments on persuasion: A case of brains over beauty. Journal of Personality
Pham Minh et al. HCMCOUJS-Economics and Business Administration, 11(2), 81-96
95
and Social Psychology, 39(2), 235-244.
Malik, G., & Guptha, A. (2014). Impact of celebrity endorsements and brand mascots on
consumer buying behavior. Journal of Global Marketing, 27(2), 128-143.
McCracken, G. (1989). Who is the celebrity endorser? Cultural foundations of the endorsement
process. Journal of Consumer Research, 16(3), 310-321.
Mello, J., Garcia-Marques, T., Briñol, P., Cancela, A., & Petty, R. E. (2020). The influence of
physical attractiveness on attitude confidence and resistance to change. Journal of
Experimental Social Psychology, 90(2020), Article 104018. doi:10.1016/j.jesp.2020.104018
Morosan, C. (2012). Theoretical and empirical considerations of guests’ perceptions of biometric
systems in hotels: Extending the technology acceptance model. Journal of Hospitality &
Tourism Research, 36(1), 52-84.
Munoz-Leiva, F., Climent-Climent, S., & Liébana-Cabanillas, F. (2017). Determinants of
intention to use the mobile banking apps: An extension of the classic TAM model. Spanish
Journal of Marketing-ESIC, 21(1), 25-38.
Nguyen, M. H., & Nguyen, H. L. (2017). The effects of celebrity endorsement on customer’s
attitude toward brand and purchase intention. International Journal of Economics and
Finance, 9(1), 64-77.
Nunnally, J. C. (1994). Psychometric theory (3rd ed.). New York, NY: Tata McGraw-hill
Education.
Ohanian, R. (1990). Construction and validation of a scale to measure celebrity endorsers’ perceived
expertise, trust-worthiness, and attractiveness. Journal of Advertising, 19(3), 39-52.
Ohanian, R. (1991). The impact of celebrity spokespersons’ perceived image on consumers’
intention to purchase. Journal of Advertising Research, 31(1), 46-54.
Oni, A. A., & Ayo, C. K. (2010). An empirical investigation of the level of users’ acceptance of
e-banking in Nigeria. Journal of Internet Banking and Commerce, 15(1), 1-13.
Ozanne, M., Liu, S. Q., & Mattila, A. S. (2019). Are attractive reviewers more persuasive?
Examining the role of physical attractiveness in online reviews. Journal of Consumer
Marketing, 36(6), 728-739.
Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the
technology acceptance model. International Journal of Electronic Commerce, 7(3), 69-103.
Pavlou, P. A., & Fygenson, M. (2006). Understanding and predicting electronic commerce
adoption: An extension of the theory of planned behavior. MIS Quarterly, 30(1), 115-143.
Petty, R. E., Cacioppo, J. T., & Schumann, D. (1983). Central and peripheral routes to
advertising effectiveness: The moderating role of involvement. Journal of Consumer
Research, 10(2), 135-146.
Pham, M., & Bui, N. T. A. (2020). The relationship between celebrity endorsement and brand
equity: What’s happening on the social network? Journal of Science Ho Chi Minh City
Open University, 10(2), 164-178.
Rieh, S. Y., & Belkin, N. J. (1998). Understanding judgment of information quality and
cognitive authority in the WWW. Proceedings of the ASIS Annual Meeting, 35, 279-289.
Rose, S., & Samouel, P. (2009). Internal psychological versus external market-driven
determinants of the amount of consumer information search amongst online shoppers.
Journal of Marketing Management, 25(1/2), 171-190.
96
Pham Minh et al. HCMCOUJS-Economics and Business Administration, 11(2), 81-96
Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model:
Investigating subjective norm and moderation effects. Information & Management, 44(1),
90-103.
Schickel, R. (2000). Intimate strangers: The culture of celebrity in America. Chicago, IL: Ivan
R. Dee Publisher.
Schivinski, B., & Dabrowski, D. (2016). The effect of social media communication on consumer
perceptions of brands. Journal of Marketing Communications, 22(2), 189-214.
Shah, A. M., Zahoor, S. Z., & Qureshi, I. H. (2019). Social media and purchasing behavior: A
study of the mediating effect of customer relationships. Journal of Global Marketing,
32(2), 93-115.
Shim, S. I., & Lee, Y. (2011). Consumer’s perceived risk reduction by 3D virtual model.
International Journal of Retail & Distribution Management, 39(12), 945-959.
Shimp, T. A. (1997). Advertising, promotion, and supplemental aspects of integrated marketing
communications. La Jolla, CA: South Western Educational Publishing.
Speck, P. S., Schumann, D. W., & Thompson, C. (1988). Celebrity endorsements-scripts,
schema and roles: Theoretical framework and preliminary tests. Advances in Consumer
Research, 15(1), 69-76.
Steadman, M. (1969). How sexy illustrations affect brand recall. Journal of Advertising
Research, 9(1), 15-19.
Suh, B., & Han, I. (2003). The impact of customer trust and perception of security control on the
acceptance of electronic commerce. International Journal of Electronic Commerce, 7(3),
135-161.
Sussman, S. W., & Siegal, W. S. (2003). Informational influence in organizations: An integrated
approach to knowledge adoption. Information Systems Research, 14(1), 49-65.
Taylor, S. A., Sharland, A., Cronin, J. J., Jr., & Bullard, W. (1993). Recreational service quality
in the international setting. Journal of Service Management, 4(4), 68-86.
Venkatesh, V., & Brown, S. A. (2001). A longitudinal investigation of personal computers in
homes: Adoption determinants and emerging challenges. MIS Quarterly, 25(1), 71-102.
Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use:
Development and test. Decision Sciences, 27(3), 451-481.
Yu, C. S., & Tao, Y. H. (2009). Understanding business-level innovation technology adoption.
Technovation, 29(2), 92-109.
Zhang, W., & Watts, S. A. (2008). Capitalizing on content: Information adoption in two online
communities. Journal of the Association for Information Systems, 9(2), 73-94.
Zheng, Y., Zhao, K., & Stylianou, A. (2013). The impacts of information quality and system
quality on users’ continuance intention in information-exchange virtual communities: An
empirical investigation. Decision Support Systems, 56(1), 513-524.
Zhu, D. H., Chang, Y. P., & Luo, J. J. (2016). Understanding the influence of C2C
communication on purchase decisions in online communities from a perspective of
information adoption model. Telematics and Informatics, 33(1), 8-16.
Pham Minh et al. HCMCOUJS-Economics and Business Administration, 11(2), 81-96
97
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