Exploring factors affecting the impulse buying behavior on social networks in Vietnam: The case study of Thai Nguyen
Nguyen Thi Thai Ha, Vu Minh Duc/ MICA 2018 Proceedings
International Conference on Marketing in the Connected Age (MICA-2018), October 6th,
2018 Danang City, Vietnam
Exploring Factors Affecting the Impulse Buying Behavior on
Social Networks in Vietnam: The Case Study of Thai Nguyen
Nguyen Thi Thai Haa*, Vu Minh Ducb
aUniversity of Economics and Business Administration, Tan Thinh Ward, Thai Nguyen, 250000, Vietnam
b
National Economics University, 207 Giai Phong Street, Dong Tam Ward, Hai Ba Trung District, Hanoi, 100000, Vietnam
ABSTRACT
This study provides detailed information about impulse buying on social networking by exploring the
relationship of these characteristics to the impulse buying behavior. Based on the SOR and CIFE models, we
developed a theoretical model and demonstrated empirically that demonstrates online store stimuli, personalized
advertising, and reliance on online word of mouth, impulse buying tendency, and on-line trust effect to impulse
buying behavior. 198 questionnaires were collected from consumers using Facebook, mainly in Thai Nguyen.
The data was analyzed according to exploratory factor analysis (EFA) and simple regression method to find the
relationship between the variables. The result is aimed at improving the understanding of impulse buying
behavior and improving online store operations from a management perspective.
Keywords: Impulse buying behavior; impulse buying tendency; urge to buying impulsively; social networking;
impulse buying on social networks
1. Introduction
Impulse purchase, which refers to consumers’ unplanned and sudden purchasing behavior, has received
considerable attention in offline retail environments [18, 50]. So far, behavioral and marketing researchers have
focused on the factors that drive the impulse buying behavior in the store environment. These factors can be
divided into four categories: consumer characteristics, characteristics of the place of purchase, situational
characteristics and the characteristics of the product.
At present, the online environment increasingly attracts the interest of consumers, theo According to User
Interface Engineering (2001) [14], 40% of online consumer spend is due to improper purchases, so researchers
of impulsively buying tend to study more in this environment. Three groups of factors that academics believe
affect online impulsive buying include: marketing - mix, situational characteristics, and consumer characteristics.
Most of the previous studies primarily focused on marketing – mix variables. Beatty and Ferrell (1998) [5]
found that consumers' consideration of in-store goods increased, leading to increased purchasing power. Mattila
và Wirtz [33] argued that a comfortable and comfortable shopping environment reduces the ability of shoppers
to control themselves, resulting in higher impulsive buying behavior. In addition, Van der Heijden [52] also
pointed out that online sellers improve the customer's attention to their website using visual elements.
So far, few studies have addressed the situational factors surrounding consumers [5, 13]. Beatty and Ferrell
(1998) [5] have identified factors such as product availability, availability of time, credit card use, product
transportability... is associated with impulse buying behavior. All of these situational factors have been shown to
have a positive impact on the impulse buying tendency. Rook và Gardner [51] argued that the combination of
states of pleasure, excitement, and power can also lead to impulse buying behavior..
* Corresponding author. E-mail address: thaihamarket[email protected]
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Nguyen Thi Thai Ha, Vu Minh Duc/ MICA 2018 Proceedings
Some researchers argued that personality traits of consumers may affect impulse behavior more than
demographic characteristics [5, 52]. Studies suggested that personality traits can help determine an individual's
improvisation were more likely to be affected by marketing stimuli such as advertising, gifts, product images ...
resulting in in-store browse, they tend to react more often to stimuli and purchase intentions.
It's also an online environment, but the social networking environment has its own characteristics, where
members can build profiles and connect with other members [9]. Social media also allows members to create
communities where ideas and information can be shared among people with similar interests. [24, tr. 670, 36, tr.
An overview of impulsively buying in the online environment shows that there is a lot of research into
consumer impulse buying behavior in e-commerce websites, but the ability to illustrate user behavior on the
social networking environment is very limited. Unlike traditional ecommerce websites, the social commerce
environment attracts consumers primarily because they can promote a close user relationship with many other
users. [42]. According to the author's understanding, the number of studies on impulsively buying on social
networking environment in the world is very limited.
There are a few studies on impulse buying in social network in some countries such as China [68], Taiwan
[11], Sweden [6], no research done in Vietnam.
As of 2/2018, Vietnam has nearly 64 million internet users, accounting for over 67% of the population, 53%
According to Nielsen's Shopper Trend 2017 [40], 82% of traditional shoppers regularly purchase the product
as planned. This makes brands difficult to attract new consumers. However, more than half (52%) of online
shoppers will compare different brands before deciding. Secondly, traditional Vietnamese rarely buy large
volume reserves (19%). While this figure in online shoppers is 44%. Third, only 6% of Vietnamese shoppers
want to make use of promotions. In contrast, 57% of online consumers decide to go shopping because of
promotions. These findings can prove that the impulse buying behavior of Vietnamese consumers in the online
environment may be higher than the traditional environment.
In online consumer shopping, social shopping has been a popular activity, with 95.8% of respondents saying
In today's most used social networks in Vietnam, Facebook has the largest number of users. By the end of
February 2017, Facebook reported 1.59 billion users worldwide. Vietnam has 64 million active Facebook users,
ranking seventh out of all Facebook users globally [15].
Together with the development of the whole country, Thai Nguyen province is the political and economic
center of the Northeast mountainous midland and serves as a focal point for cultural and educational activities in
the northern mountainous region. This locality is also the third largest training center in the country with about
140 thousand students. The economic growth rate in 2017 will be 12.75% of total income per capita in 2017
reaching 65 million VND per person per year in average compared to other provinces in the country (national
average 53.5 million VND) [7]. Due to the encouraging signs in economic development, e-commerce activities
and online shopping of people have been very active. With the geographic location, socio-economic and
intellectual characteristics of Thai Nguyen compared to the North and all country as above, the author selected
Thai Nguyen as the sampling place for this study.
Therefore, the goal of the article is to first, explore the impact of marketing stimulis, personal characteristics,
situational characteristics, and social networking features on impulse buying behavior of consumers; Second,
suggest marketing solutions for online entrepreneurs.
2. Theoretical background
Theoretical background of current research is based on Rook's (1987) impulsive purchase theory [50] and
many other researchers. A model of research based on stimulus-response (SR) theory is expanded into a
stimulus–organism–response (S–O–R) model and the Consumption impulse formation and enactment model
(CIFE).
2.1 Impulse buying
Prior to 1982, the definition of impulse buying was more focused on products than on consumers. Katona và
Mueller [26] defined improvisation as an unplanned act after the consumer enters a shopping center. In contrast,
Nesbitt (1959) (quoted in Chen and Zhang [7]) considers impulse purchasing a sensible act as it is affirmed after
consumers enter a shopping center.
Stern [55] Stern [48] provided a basic definition of impromptu behavior, in which the planned action
classification is unplanned. According to this classification, planned buying behavior involves finding time-
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Nguyen Thi Thai Ha, Vu Minh Duc/ MICA 2018 Proceedings
consuming information, followed by rational decision-making [47, 55]. Unplanned purchases refer to all
purchases made without plans, including impulsive purchases, characterized by the immediate nature of the
purchase decision taking place.
After 1982, when researchers focused on impulse buying, they began to explore the behavioral aspects of
impulsive buying. After that, they agreed to buy improvisation as a temporary or sensory act [8, 47, 50, 52, 65].
Rook [50] redefined impulse buying: “Impulse buying occurs when a consumer experiences a sudden, often
powerful and persistent urge to buy something immediately.” Many researchers have associated unplanned or
unintended purchasing with impulsive purchasing, and an unplanned purchase is necessary for categorizing a
A consumer’s impulse to buy may occur once or recur more than once for the same consumer [12]. Impulse
buyers are less likely to consider the consequences of buying on impulse [50] and less likely to thoroughly
evaluate their purchase decisions than a typical informed shopper [25]. They are more willing to accept
spontaneous buying ideas [22], and are more focused on the immediate gratification of purchasing the product.
Impulse buyers are often drawn to a mysterious attraction of the product which motivates the buyer to purchase
2.2
Stimulus–organism–response (S–O–R)
Stimulus–organism–response model (S–O–R) (Figure 1) has been widely used to explain impulse buying. As
introduced by Woodworth [59], this model was extended and improved from the stimulus-response (S-R) pattern
in behavioral studies [39, 29]. The classic S-R theory deals with human behavior as a response to stimuli, the
process by which humans process stimulis such as the activity of conscious black boxes. Mehrabian và Russell
[35] have built the S-O-R framework by combining the concept of "personal" to explain the inner process of
human cognition and emotions. According to the S-O-R model, environmental or situational variables (stimuli)
can affect endogenous mechanisms (eg, cognition and emotion), thereby leading to responses (behaviors).
Environmental Stimuli
(Stimulus)
Emotional states
(Organism)
Appropriate behavior
(Response)
Organism
Fig. 1. S-O-R model of Mehrabian and Russell in 1974
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Nguyen Thi Thai Ha, Vu Minh Duc/ MICA 2018 Proceedings
Consumption
impulse
enactment
Marketing
stimuli
Constraining
factors?
No
Consumption
impulse
Situational
factors
Marketing
stimuli
Yes
Dissonant
impulsive
behavior
Impulsivity
trait
Volitional System
Resistance Strategies
Non-Behavior
Consumption
impulse
dissipation
Fig. 2. The Integrated Model of Consumption Impulse Formation and Enactment
(from Dholakia 2000)
2.3 An Integrated Model of Consumption Impulse Formation and Enactment
Dholakia (2000) [12] proposed a more detailed theoretical model than previous researchers on the impulse
purchase process. This model explains the impulse behavior related to motivational, will, and cognition (Figure
2).
The model points out three premise of impulse buying behavior: stimulation of marketing, impulse
characteristics and situational factors. One of the vital factors in the impulse buying process is the exposure to
the marketing stimulus [50]. In fact, marketers increase the likelihood of impulse purchases by manipulating
store atmospherics [12]. Moreover, physical and temporal proximity to the product also increases the consumer’s
impulsiveness [22]. Situational factors refer to both environmental and personal factors that are present when the
consumer makes the impulse purchase such as: the amount of money they own, their moods [5], the imitation of
The third precursor of the CIFE model is the impulsivity trait, widely studied in the social sciences, in various
contexts. (For example: Plutchik & van Praag, 1995 [48]; Watson & Clark, 1993 [64]). The impulsivity trait has
been defined as the tendency to react quickly and unreflectively, and are characterized by fast response times,
lack of foresight, and a tendency to act without careful planning.
3. Model of research and hypotheses
From the S-O-R and CIFE model theoretical framework, the research model of this paper has been built as
showing in below figure 3. The stimulus consists of 3 groups including stimulation of marketing (Commodity
stimuli and Online store stimuli), situational factors (Personalized advertising, Reliance on online word of
mouth) and elements of Individuals (impulse buying tendency and online trust) through consumer perceptions of
consumer interest and moods (Perceived usefulness and positive effect) that urge to buying impulsively
(emotional state) resulting in impulse buying behavior. The definitions and correlations of the factors in this
model are explained as follows:
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Nguyen Thi Thai Ha, Vu Minh Duc/ MICA 2018 Proceedings
Commodity stimuli
Marketing
stimuli
H2
H4
Online store stimuli
H1
H3
Personalized
advertising
Perceived
usefulness
H5
H6
H7
H8
Situational
characteristics
H11
H12
Reliance on Electronic
word of mouth
H13
Urge to buying
impulsively
Impulse buying
behavior
Positive effect
H9
Impulse buying
tendency
Personal
H10
consumption
characteristics
Online trust
Organism
Response
Stimulus
Fig. 3. Research Model
3.1 The effects of commodity stimuli and online store stimuli to perceived usefulness and positive effect
Consumer's impulse buying tendency varies with consumer involvement and different types of products [25].
Other studies suggested that impulse buying behavior may be caused by air factors in the shopping environment.
Beatty and Ferrell (1998) [5] found that consumers' consideration of in-store goods increased, leading to
increased impulsively purchasing.
Mattila và Wirtz [33] argued that a comfortable and convenient shopping environment reduces the ability of
shoppers to control themselves, leading to higher impulse purchase behavior. In addition, Van der Heijden [60]
also pointed out that online sellers improve the customer's attention to their website using visual elements, for
example, the ingenious use of Different colors will increase visual impact on the customer. Moreover, according
to Parboteeah [45] convenience shopping, web site design and visual impact are positively correlated with
consumer impulse buying behavior. Thus, the following hypotheses are proposed:
H1: Commodity stimuli and perceived usefulness have a positive relationship.
H2: Commodity stimuli and positive effect have a positive relationship.
H3: Online store stimuli and perceived usefulness have a positive relationship.
H4: Online store stimuli and positive effect have a positive relationship.
3.2 The effects of personalized advertising to perceived usefulness and positive effect
Online personalization refers to providing customers with tailored content and services based on knowledge
obtained through service and user interactions [43, tr. 194]. In this sense, personalization is geared towards
meeting the needs of consumers; Collect data of consumers to make appropriate product [56, tr. 301].
"personalization" as similar interchangeable variables. The broad field of personalization in e-commerce has
been researched extensively. Zhou et al. (2007) [72] have posited that personalized services may increase
customer loyalty to acertain company, Chang and Chen (2009) [10] have found that personalized services
increase customer satisfaction with online shopping, and Adolphs and Winkelmann (2010) [2] have provided a
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Nguyen Thi Thai Ha, Vu Minh Duc/ MICA 2018 Proceedings
vast overview on the subject. The main goal of personalization is to satisfy customers based on their needs and
wants. Previous studies have shown that personalization affects customers’ purchase intentions. Specifically, Ha
et al. (2010) [21] have found that providing customized information to customers facilitates their behavioral
intentions. Thongpapanl and Rehman Ashraf (2011) [58], who examined the effect of information content on
purchase intention, have found that it is moderated by website personalization. Thus, there are grounds for the
following two hypotheses:
H5: Personalized advertising and perceived usefulness have a positive relationship.
H6: Personalized advertising and positive effect have a positive relationship.
3.3 The correlation between reliance on electronic word of mouth and perceived usefulness, positive effect
According to Bass (1969) [4], word of mouth (WOM) is an interplay of individuals, created by a network of
connected consumers. Engaging in social media like Facebook and Twitter allows users to interact more often
with the people they have a close relationship with. A recent study by Katona and colleagues in 2011 [27] also
shows that WOM influence is stronger in well-connected networks. Moreover, opinions from close relationships
in social networks are more interested in or particularly important to them [67], which may affect the
consumption of goods.. Amonrat Thoumrungroje (2014) [59] also demonstrated that consumer dependence (or
reliance) on online word of mouth (REWOM) affects product consumption. Based on the above the following
hypotheses is formulated:
H7: Reliance on electronic word of mouth and perceived usefulness have a positive relationship.
H8: Reliance on electronic word of mouth and positive effect have a positive relationship.
3.4 The effects of impulse buying tendency to urge to buying impulsively
The relationship between impulse buying tendency and urge to buying impulsively has received attention in
both offline and online contexts (eg, Beatty & Elizabeth Ferrell, 1998 [5]; Adelaar et al., 2003 [1]; Zhang et al.,
2007 [71], Wells et al., 2011 [66]). In the context of offline shopping, Beatty & Elizabeth Ferrell (1998) [5] have
shown that impulse buying tendency influence the impulse purchase, and they also demonstrate that individuals
tend to impulse higher levels are more likely to lead to increased urgency. Similarly, in an online context, the
relationship between impulse and intentional impulse buying was proposed in the study by Adelaar et al. (2003)
[1], Wells et al. (2011) [66], Xiang Li et al. (2016) [68] affirmed the positive relationship between impulse
buying tendency and urge to buying impulsively. So that, the following hypothesis is proposed:
H9. Impulse buying tendency and urge to buying impulsively have a positive relationship.
3.5 The correlation between online trust and urge to buying impulsively
In addition to the reliance in online word of mouth, consumers are also influenced by their trust in online
merchants. Kimery and McCord [28] defined customer trust as the willingness to accept the downside of an
online transaction based on their positive expectations about the behavior of the online store in the future.
According to Barber [3], belief is the expectation of an individual's behavior in the society in which he or she
lives. Belief can be given to a person, an object, an organization, an organization, or a role. Trust plays an
According to McCole and Palmer [34], online shopping requires the trust of customers. Gefen [19] assertsed
that consumer trust makes them think that online retailers will not have opportunistic behavior. Many studies
have concluded that consumers with high trust will lead to higher online purchase intent [61, 30, 31]. Online
trust has an impact on impulse buying behavior or is no longer a mystery. In this study, the author puts this
variable into the research paradigm as a new point in the impulse buying research in the social networking
environment. The following assumption is made:
H10. Online trust and urge to buying impulsively have a positive relationship.
3.6 Impact of perceived usefulness on Positive Affect
According to Holbrook [23], perceptions have the potential to influence behavior. Weinberg and Gottwald
[65] point out that cognition plays an important role in impulse buying. In the context of online casual buying,
Parboteeah et al. [44] concluded that perceptions of shopping sites significantly influenced consumer interest
when interacting with web sites. In addition, Zhang et al. (2014) [70] have demonstrated that perceived
usefulness of consumer are positively correlated with their positive affect. From this, the following hypothesis is
given in this study:
H11: Perceived usefulness and positive Affect have a positive relationship.
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3.7 The relationship between positive affect and urge to buying impulsively
Watson and Tellegen [63] have shown that there are two types of effects: positive and negative affect on
impulse buying behavior. Positive affect refers to feelings of excitement, enthusiasm, and pleasant feeling, while
negative influences refer to feelings of distress and discomfort [5]. The study by [38] distinguishes positive
effects that motivate consumers to purchase impulse, while negative effects have no significant effect. Recent
research shows that positive effects, such as joy, excitement, enjoyment, and instant gratification, also affect
consumer’s online impulse buying behavior. [17, 32, 41]. Therefore, the following hypothesis is proposed:
H12: Positive affect and urge to buying impulsively have a positive relationship.
3.8 The effects of urge to buying impulsively to impulse buying behavior
The urge to buy improvisation comes from Rook's study of sudden, spontaneous urge or urge to buy
something. However, the urge or desire [22, 49], can be very powerful, sometimes irresistible, but not always
lead to action (Rook and Fisher, 1995). In fact, individuals use a lot of strategies to control this desire [22]. So,
the urge to buy impulse is a desirable state to experience when encountering an object in the environment, it is
clear before the actual impulse buying behavior. Therefore, the following hypothesis is given:
H13: Urge to buying impulsively to impulse buying behavior have a positive relationship.
4.
Research method
4.1
Data collection
We conducted an online survey on Facebook by means of convenient sampling with 198 questionnaires. The
questionnaire is sent directly to individuals who have purchased the product (leaving a phone number, mailing
address), or individuals recommended to recall the most recent purchase on Facebook. The questionnaire was
built using the Google Form tool under Google Driver. Link the questionnaire with referrals sent to consumers
through Facebook Messenger. Some online stores on facebook in the fields of business such as cosmetics,
fashion, food, fast food, home appliances ... were selected to survey for this study. Description of the sample has
been shown as below:
Table 1: Description of the sample
Quantity
Percentage
(%)
Gender
Age
Female
Male
≤ 20
21-30
31-40
41-50
166
32
68
75
46
9
83.8
16.2
34.3
37.9
23.2
4.5
≥ 51
0
0
Education background
Job
High school
College
Bachelor
Master or above
Students
Employee office in business
82
8
60
48
127
6
41.1
4.0
30.3
24.2
64.1
3.0
Government officials and employees
Free labor
Other
58
2
5
29.3
1.0
2.5
Income
(million VNd/month)
< 3
3-7
8-15
16-25
117
55
22
2
59.1
27.8
11.1
1.0
≥26
2
1.0
Living area
Thai Nguyen
Others
≤ 30 minutes
145
53
10
73.2
26.8
5.1
Time spent on Facebook
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during the day
30 minutes– 1 h
1h-2h
2-3h
33
43
46
66
16.7
21.7
23.2
33.3
>3h
Source: surveys by authors
4.2
Measures
All factors in the research model were measured using a multivariate scale used from previous studies,
with modifications to ensure consistency with the social and contextual of Vietnam. The questionnaire consists
of three parts: a filter section consisting of three questions to select impulse purchases, to eliminate planned
purchasing decisions; the main information and consumer information is finally located (see Appendix for
details). The questionnaire was tested with 10 students to check the semantics, the content of the questions
before sending them to the respondents. The 5 point Likert score from "1 = Strongly Disagree" to "5 = Strongly
Agree" was used for this study. The scales have been translated downstream and upstream to ensure the accuracy
of the original scale.
5. Data analysis
Investigate the relationship between the independent and dependent variables and examine research
hypotheses using the SPSS software.
Table 2: Quality control of the scale
Variable
name
Number of
observed
variables
Cronbach’s
Constructs
Commodity stimuli
Alpha
1
2
3
4
5
6
7
8
9
COMS
ONSS
PEAD
RWOM
IBT
ONTR
PUSE
POEF
UTBI
IBB
2
3
3
6
6
3
3
4
3
3
0.43
Online store stimuli
0.726
0.701
0.899
0.753
0.862
0.695
0.878
0.861
0.747
Personalized advertising
Reliance on Electronic word of mouth
Impulse buying tendency
Online trust
Perceived usefulness
Positive effect
Urge to buying impulsively
Impulse buying behavior
10
Source: Calculated based on surveys by authors
Remove the COMS variable because it has a Cronbach’s Alpha coefficient of less than 0.5. The ONSS
after dropping ONSS4, ONSS5 the Cronbach’s Alpha coefficient was 0.726. Thus, through the analysis of
measuring the scale, the model has 9 quality scale with 34 items. The exploratory factor analysis gives the
following results:
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Nguyen Thi Thai Ha, Vu Minh Duc/ MICA 2018 Proceedings
Table 3. Pattern Matrixa
Factor
1
2
3
4
5
6
7
RWOM3
RWOM2
RWOM5
RWOM4
RWOM1
RWOM6
POEF1
POEF3
POEF4
POEF2
PUSE3
UTBI2
UTBI3
UTBI1
IBB2
,881
,806
,784
,779
,670
,628
,786
,774
,734
,730
,668
,888
,827
,608
,546
,502
IBB1
IBT2
,835
,753
,727
,593
,554
IBT3
PUSE1
PUSE2
IBT1
ONTR2
ONTR1
ONTR3
ONSS2
ONSS1
PEAD1
PEAD2
,853
,846
,640
,622
,560
,727
,669
Extraction Method: Principal Axis Factoring.
Rotation Method: Promax with Kaiser Normalization.
a. Rotation converged in 7 iterations.
Table 3 shows the characteristic variables that have a Factor loading factor greater than 0.5. There are seven
factors that represent the impulse buying behavior with the variable characteristics of the rearranged factor
different from the original theoretical model.
Component 1 includes variables: RWOM1, RWOM2, RWOM3, RWOM4, RWOM5, RWOM6. Name this
factor as RWOM
Component 2 includes variables: POEF1, POEF2, POEF3, POEF4, PUSE3. Variable PUSE3 is originally
based on the PUSE factor, but the EFA is placed in conjunction with the POEF component. Name this factor as
POEF
Component 3 includes variables: UTBI1, UTBI2, UTBI3, IBB1, IBB2. The IBB1, IBB2 is originally based
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Nguyen Thi Thai Ha, Vu Minh Duc/ MICA 2018 Proceedings
on the IBB but the EFA is placed in conjunction with the UTBI component. Name this element UTBI.
Component 4 includes the IBT1, IBT2, IBT3, PUSE1, PUSE2 variables. Variables PUSE1, PUSE2 are
originally based on the PUSE factor, but the EFA is placed in conjunction with the IBT component. Name this
factor as IBT.
Component 5 is ONTR1, ONTR2, ONTR3. Name this factor ONTR.
Component 6 is ONSS1, ONSS2. Name the factor as ONSS
Component 7 consists of 2 variables: PEAD1, PEAD2. Name the element PEAD
As a result, through the EFA model qualification scales and validation, there are 6 scales representing the
factors that influence the impulse buying behavior and the 1 that represents the impulse buying behavior with a
total of 28 variables observed. Synthesize the results as follows:
Table 4: Rename the variable
No
1
Scale
Characteristic variables
Explain the scale
RWOM
RWOM1, RWOM2, RWOM3, RWOM4,
RWOM5, RWOM6
Reliance on Electronic word of
mouth
2
POEF
UTBI
IBT
POEF1, POEF2, POEF3, POEF4, PUSE3
UTBI1, UTBI2, UTBI3, IBB1, IBB2
IBT1, IBT2, IBT3, PUSE1, PUSE2
ONTR1, ONTR2, ONTR3
ONSS1, ONSS2
Positive effect
3
Urge to buying impulsively
Impulse buying tendency
Online trust
4
5
6
ONTR
ONSS
PEAD
7
Online store stimuli
Personalized advertising
7
PEAD1, PEAD2
Total
32
Online store stimuli
Personalized
advertising
H6
H12
Reliance on Electronic
word of mouth
Urge to buying
impulsively
Positive effect
H8
H9
Impulse buying
tendency
H10
Online trust
Organism
Response
Stimulus
Fig. 3. Research after EFA
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Nguyen Thi Thai Ha, Vu Minh Duc/ MICA 2018 Proceedings
Inferential
Investigate the relationship between the independent and dependent variables and examine research
hypotheses using the SPSS software. And using Regression analysis to examine the correlation independent and
dependent variables.
Table 5. Hypotheses Analytical Results
Independent
Variables
Dependent
Variables
R
R Square
F
Sig
Result
H4
H6
Online store stimuli Positive effect
0.362
0.383
0.131
0.146
22.754
25.899
0.000
0.000
Accepted
Accepted
Personalized
advertising
Positive effect
H8
H9
Reliance on
Electronic word of
mouth
Impulse buying
tendency
Positive effect
0.261
0.068
10.998
0.001
Accepted
Urge to buying
impulsively
Urge to buying
impulsively
Urge to buying
impulsively
0.517
0.265
0.425
0.267
0.070
0.181
54.973
11.398
33.289
0.000
0.001
0.000
Accepted
Accepted
Accepted
H10 Online trust
H12 Positive effect
Source: Calculated based on surveys by authors
6. Results
Evaluation of conceptual research model all of hypotheses have been confirmed strongly.
Research results are shown below:
Table 3 shows the result of testing Regression for all hypotheses is meaningful in 0.05 level of detection. So,
these hypotheses confirmed with 0.95 confidence level.
1) Online store stimuli have a direct positive effect on Positive effect (R=0.362). So that H4 hypotheses are
confirmed.
2) Personalized advertising has a direct positive effect on Positive effect (R=0.383). So that H6 hypotheses
are confirmed.
3) Reliance on Electronic word of mouth have a direct positive effect on Positive effect (R=0.261). So that
H8 hypotheses are confirmed.
4) Impulse buying tendency have a direct positive effect on Urge to buying impulsively (R=0.517) so H9
hypotheses are confirmed.
5) Online trust has a direct positive effect on Urge to buying impulsively (R=0.265) so H10 hypotheses are
confirmed.
6) Positive effect has a direct positive effect on Urge to buying impulsively (R=0.425) so H12 hypotheses are
confirmed.
7. Conclusions and recommendations for online retailers
This research was checked the relationship between the reserved variable commodity stimuli, online store
stimuli, personalized advertising, reliance on electronic word of mouth, impulse buying tendency, online trust
effect through intermediate variables perceived usefulness, positive effect, urge to buying impulsively to impulse
buying behavior. Research model was strongly confirmed by the collected data. Among, all hypotheses were
confirmed with %95 confidence. From the results of our study, we have provided some suggestions for retailers
on social networks:
Online store stimulation has a positive impact on consumer perceptions and emotions, which in turn affects
impulse buying behavior. Online retailers therefore need to regularly introduce new products, new uses,
advanced product introduction techniques, detailed descriptions of merchandise, development of promotions and
customer service.
Through personalized advertising, consumers have access to products that fit their needs. The results show
that this has a positive effect on the impulse buying behavior. Retailers should therefore reach out to customers
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Nguyen Thi Thai Ha, Vu Minh Duc/ MICA 2018 Proceedings
through the tools of social networking vendors to make their product introduction more effective.
The results of the study also show that consumers' impulse buying tendency motivates them to buy
impulsively. So, online retailers need to find them to show their products, possibly through customized
advertising.
Online trust can make them buy impulsively, so online retailers need to enhance their credibility,
accountability, and promise.
Finally, research results also show that consumers buy impulsively due to their belief in the comments, the
introduction of friends and family on social networks. Consequently, online sellers should encourage consumers
to leave positive comments, encouraging them to introduce products to their closed ones.
8. Limitations and future research
Although the results of this study have discovered some of the unique features of social networking that
influence impulse buying behavior, there are still some limitations and a need for additional research.
First, about sampling method, one possible concern with this study might be the question of generalizability,
due to the use of a convenient online sample. The sample was collected using non-probabilistic method and may
not represent the general Vietnam online consumer. The results are limited by online consumers who visited the
selected pages on Facebook. The research has examined consumers using Facebook social network, so the
representative sample is not guaranteed. The sample size is quite small (198 observations), most respondents are
female, over 75% under 30 years of age, most them are students (64%) living in Thai Nguyen. Further research
should investigate other social networks such as Instagram, Twitter ... in more diverse areas, provinces and cities
to represent Vietnamese consumers.
Second, in terms of data processing methods, the research tools have not used strong tools (e.g. SEM) to
analyze the relationships of variables in the model.
Third, our research has not yet explored other features of social networks such as spread, social interaction,
social effect ... This could be space for further research.
Thank you
We would like to thank the organizations, individuals and consumers for their assistance in providing this
data so that we can complete this study.
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Appendix
Link the research questionnaire
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