Contemporary Management Research
Pages 97-126, Vol. 17, No. 2, 2021
Analysis of Factors Influencing Impulse Buying Behavior towards
Md Wasiul Karim*
Department of Business Administration, International Islamic University Malaysia
Mohammad Abdul Matin Chowdhury
Department of Finance, International Islamic University Malaysia
Md Abdullah Al Masud
Department of Business Administration, International Islamic University Malaysia
Graduate School of Management, International Islamic University Malaysia
Impulse buying behavior among consumers was previously examined within the
brick-and-mortar stores. The internet revolution is the shift from a traditional retail
environment to electronic commerce where products and services are offered online and
consumers benefit from buying impulse towards e-tailing sites. By applying the S-O-R
model, this study seeks to evidently identify the influencing factors of impulse buying
behavior towards e-tailing sites. The survey data were analyzed using partial least squares
structural equation modeling (PLS-SEM). The result indicates that website stimulus has no
significant positive relationship with online impulsive buying behavior, but an indirect
relationship is found to be associated with impulse buying behavior. Results also
highlighted that website stimulus, marketing stimulus, and product variety positively
influence perceived enjoyment, where perceived enjoyment was a strong predictor of
online impulse buying behavior. The study contributes to the existing literature by
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employing external stimuli of the research framework. It was thus recommended to the
online e-tailers to maximize their relationship with consumers by providing valuable
products and services through online platforms.
Keywords: Impulse buying behavior, e-Tailer, Consumer behavior, Internet marketing, S-
O-R framework, PLS-SEM, Malaysia
Following the rising number of consumers who prefer to buy impulsively from e-
tailing sites, academic research focuses on online impulse buying as a research topic. To
date, most studies on this trend have examined how online store elements represented and
experienced by consumers influence buying impulses and thus offer retailers insight into
how online store buying impulses can be stimulated (Park et al., 2012). E-commerce sites
in today’s era played a significant role by providing vast opportunities for companies to
interact with their clients on a real-time basis (Arbaina & Suresh, 2018). It is therefore
incumbent on companies to foresee a plan to make their presence known on e-tailing sites.
Although some companies have begun reorganizing advertising strategies, it is important
that their pages are readily accessible to consumers to be competitive.
Impulse buying is described as the unplanned, sudden, and spontaneous buying
behavior of the consumers. Li (2015) also described impulsive behavior as unintentional,
unreflective buying due to physical proximity and emotional intimacy to the desired
product, resulting in personal gratification. However, the internet has diverted consumers’
attention towards online shopping sites worldwide (Floh & Madlberger, 2013). According
to Wu et al. (2016), roughly eighty percent of consumer retail purchases are impulsive.
That could be a reason why retailers and vendors are targeting impulsive buyers in view of
its importance. The online buying phenomenon is becoming popular worldwide with the
emerging field of e-commerce. Research shows that users online are more impulsive than
those offline (Mwencha et al., 2014). There is an array of information about e-commerce
sites. If the service delivery does not follow a reasonable standard, the sites and companies
will quickly lose their reputation. A structured web presence with a defined workforce
helps create customer relationships and helps to effectively introduce the brand to potential
customers (Arbaina & Suresh, 2018).
In the process of online transactions, buyers first gather information about the items
they wish to buy, then place the order and make the payment before delivery (Hashmi et
al., 2019). In some cases, trust and the quality of the websites are found to be attractive
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among consumers (Floh & Madlberger, 2013; Wu et al., 2016). In addition to the factors
which refer to the external and internal environment of markets, promotions, and discounts,
also influence buying impulse behavior. It thus results in brand loyalty and a successful
relationship (Al-Salamin & Al-Hassan, 2016). It has been claimed that prolonged sales and
promotions have a negative impact on the product’s brand image and can lead to
organizational losses (Al-Salamin & Al-Hassan, 2016).
Muruganantham and Bhakat (2013) developed an impulse buying behavior
framework by employing external and internal stimuli additionally, situational and
demographic factors and suggested those factors to be employed in the future study.
However, Muruganantham and Bhakat (2013) highlighted external stimuli as the most
challenging factors, especially in the field of marketing stimuli and store-related factors. In
terms of an online impulse purchase, consumers’ confidence in the e-commerce platform
is crucial because several risks are associated with transactions online. It is, therefore, an
important element to be analyzed. It is further suggested by Muruganantham and Bhakat
(2013) that retailers should exploit external stimuli by formulating effective marketing
strategies to reach the potential buyers within the store.
Website stimuli have been widely studied in Malaysia in the context of online
buying behavior (Hasanov & Khalid, 2015; Lee et al., 2016). However, impulse buying
behavior was not critically reviewed or investigated in previous studies in a Malaysian
context. Furthermore, less attention has been paid to the variety-seeking behavior among
consumers who like to buy impulsively online in Malaysia. It is against this background
that it is deemed necessary to investigate variety-seeking behavior in the case of online
impulse buying. The current study aims to fill this research gap by investigating the factors
which mostly influence consumers to indulge in impulsive shopping from e-tailing stores.
Stimulus Organism Response Model
The Stimulus Organism and Response Model (S-O-R) introduced by Mehrabian and
Russell (1974) are widely applied as a theoretical foundation for studies relevant to
consumers’ behavior (Zhu et al., 2015). Stimuli, in the SOR model, are believed to have
an impact on individuals’ internal state (Eroglu et al., 2001). An organism is defined as an
internal process or condition which mediates the relationship between the stimulus and the
final response of the person. The response is the ultimate outcome that determines
consumer behavior or avoidance (Mehrabian & Russell, 1974). According to the S-O-R
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model, consumers’ feelings become an imperative part of responding to the revealing
environmental stimulus (Hetharie et al., 2019). These feelings are also influenced by
sensible and insensible perceptions and environmental understandings (Donovan &
Rossiter, 1982). So, several factors relevant to products/items’ characteristics can be
included in the S-O-R model of consumer behavior, for example, price, promotions,
branding, and quality (Buckley, 1991).
Price and quality both carry some influence on consumers’ choices (Monroe &
Bitta, 1978). The reduction in the price of a sovereign brand is considered as good value
for the spending while consumers considered the sovereign brand for the quality (Buckley,
1991), lowering the price for a brand is considered as resetting the fair price, not as a
promoting quality at a lower price (Bemmaor, 1984). The promotions such as coupons,
special price reductions, advertisements, and in-store displays influence consumers’
behavior (Chevalier, 1975).
Moreover, convenient locations, merchandize quality, price levels, variety of
choices, service, atmosphere, adequate advertisements, and salespersons have a different
impact on consumer behavior (Buckley, 1991).
Donovan and Rossiter (1982) applied the SOR model in the context of consumer
behavior for the first time. Latter, Peng and Kim (2014) applied the SOR model to
investigate the online shopping behavior of consumers and found that the web environment
plays an important role in online buying. Shen and Khalifa (2012) studied online impulse
purchases by employing the SOR model, and results revealed the importance of website
design as stimuli to predict unplanned purchases online. Liao et al. (2016) explained the
role of the SOR model and examined product type and online store display as one of the
key determinants and stimuli of emotions that trigger consumers’ desire to buy impulsively.
Sultan et al. (2018) define impulse buying behavior by implementing the SOR model where
window display, promotional activities, and store atmosphere were key triggers for
determining a consumer’s impulsive nature through positive emotion.
However, many researchers (Ahmad et al., 2019; Bharathi & Sudha, 2017; Graa &
Dani, 2012; Hashmi et al., 2019) have studied consumers’ stimuli, emotional responses,
and impulse purchase by adapting the SOR model.
Impulse Buying Behavior
Impulse buying behavior is an instant and swift purchase deprived off of any
preceding intention to shop towards an exact product type to meet certain needs (Beatty &
Ferrell, 1998). Such behavior occurs after customers experience a swift incidence of a very
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sturdy urge to buy and are obligated to make an impulsive purchase without adequate
contemplation (Hausman, 2000). The motive to make impulsive buying is a multifaceted
hedonic aspect that frequently stimulates emotive conflict, which could be impending from
psychological and emotional (internal) or from the influence of marketers (external) (Rook
& Fisher, 1995). Piron (1991) conducted an in-depth analysis of the impulse purchasing
literature, introduced a more precise and detailed impulse purchasing concept that includes
four components: unplanned, the result of exposure to a stimulus, on-the-spot decision, and
involvement of emotion. Previous research has categorized buying impulses into four
types: pure, suggestion reminded, and planned (Stern, 1962). A pure impulse purchase is
when a purchase is made suddenly without any calculations and thoughts. This category is
predominantly reliant on emotions. Suggestion impulse purchase refers to a person who is
motivated by self-suggestion to try out new products because the customer has no prior
knowledge of the products. Thus, he/she wants to try something new. A reminded impulse
purchase is when customers notice the products they have seen before, which triggers them
to purchase. Planned impulse purchases occur when shoppers are preparing to buy but do
not precisely know the particular items they wish to purchase.
Most popular impulse buying research has previously concentrated on brick-and-
mortar stores (Bhuvaneswari & Krishnan, 2015; Choudhary, 2014; Jones et al., 2003;
Karbasivar & Yarahmadi, 2011; Verplanken & Herabadi, 2001). Due to the revolution of
web 2.0, traditional brick-and-mortar stores have experienced a great demise and have seen
many companies transition to online platforms. Currently, many researchers have
conducted research to examine consumers’ impulse buying behavior online, where virtual
atmosphere plays a crucial role in attracting consumers (Akram et al., 2018), additionally
price, discounts, and bonuses (Park et al., 2012; Xu & Huang, 2014).
Impulse Buying Behavior vs. Unplanned Buying Behavior
A significant and critical type of shopping behavior has been considered unplanned
or impulse purchasing, but today researchers continue to discover numerous aspects and
predictors that trigger unplanned purchasing. Impulse buying was described by Engel and
Blackwell (1982) as a buying activity carried out when the need to purchase was not
previously recognized, nor was a purchasing intention established prior to entering the
shop. Another description provided by (Beatty & Ferrell, 1998) claimed that in a retail
sense, impulse buying was a sudden and spontaneous purchase without pre-shopping
intentions either to purchase the particular product category or to fulfill a particular
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purchasing task. Unplanned or intentional purchasing is an important part of contemporary
shopping habits, and hedonic gratification is closely associated with the purchase of
impulses. When customers are unexpectedly stimulated by the shopping experience and
buy goods or services impulsively, unplanned purchases occur (Lu & Wu, 2019). The study
of (Liu et al., 2013) also demonstrates that unplanned buying and impulse buying are the
same in nature as in both occurrences, the buying behavior occurs without prior planning
Table1 Impulsive Buying between Offline and Online Retailing Environments
Buying impulse often occurs
based on an encounter of
certain stimuli, such as layout
design, lighting, music, and
product assortments (Summers
& Hebert, 2001)
Buying impulse occurs when
discounts and promotions are given
online. Visual appeal such as
website design and color also
attracts consumers (Akram et al.,
stores where consumers look
around and ask if they have
any questions and if consumers
are satisfied with an answer
they end up purchasing that
product (Bhuvaneswari &
Krishnan, A review of
literature on impulse buying
behaviour of consumers in
brick & mortar and click only
Products are displayed in different
online platforms where price,
promotions and payment methods
are properly specified (Vonkeman
et al., 2017).
Consumers can experience the
product in their hands and
ensure its quality is up to par
with their standards
(Aragoncillo & Orus, 2018)
Consumers are being able to buy
from anywhere in the world
whenever they think they need that
product the most (Zhao et al.,
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Enjoyment refers to an individuals’ emotional state wherever it plays an important
role in buying behavior. A perceived enjoyment can be described as the pleasure that a
consumer may get from the shopping activities (Mohan et al., 2013). Consumers’ positive
emotions entail excitement, enjoyment, relaxation, and inspiration (Verhagen & van Dolen,
2011). With a growing sense of pleasure when using online platforms, the possibility of
the consumer making an impulse buying decision increases. Therefore, in developing e-
commerce support systems, a tacit understanding of the factors influencing customer
enjoyment is necessary to stimulate impulse purchasing behavior (Do et al., 2020). Sohn
and Lee (2017) posited that consumers’ emotional experience impacted their impulse
buying decisions positively. Hasima et al. (2020) found from the study that perceived
enjoyment did mediate the relationship between online store environment, promotions, and
impulse purchase and had a direct positive and significant association with an online
impulse purchase. Baskaran et al. (2019) examined impulse buying behavior and found
that impulse purchases among wen are genuinely based on the stimulation effects of
perceived enjoyment. Xiang et al. (2016) studied perceived enjoyment as a mediator to
examine impulse buying urge from e-commerce sites, and results indicated the positive
relationship between enjoyment and online impulse buying. A prior study found the
mediating relationship between perceived enjoyment and online impulse buying (Floh &
Madlberger, 2013; Saad & Metawie, 2015). According to Ingham et al. (2015), perceived
enjoyment can be obtained through visiting the websites, and the experience will enable
them to perform certain actions such as online purchasing activities. Sometimes viewers
from different vlogs pursue enjoyment and are satisfied with the content shown in vlogs
that are insightful and influence them to decide to buy it immediately. Based on the
abovementioned discussion, the following hypothesis is proposed:
H1: There is a positive and significant relationship between perceived enjoyment and
impulse purchase towards e-tail stores.
The website stimulus refers to the characteristics of a website. Website stimulus
explains the appeal offered to consumers by the user interface design (Maiyaki & Mokhtar,
2016). Hence, website design efficiency is critical and should be a crucial factor in
attracting customers for any online merchandise because customers obtain information
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about products and services through social media and online websites (Turkyilmaz et al.,
2015). Previous research indicates that websites’ esthetic presence is critical in attracting
customers and inducing impulsive behavior (Wolfinbarger & Gilly, 2003). It was also
found that the store design aspect directly affects the purchasing of impulses. This involves
ease of website use, visual presentation, safety, and security, etc. (Lo et al., 2016). Akram
et al. (2018) have studied impulse buying behavior among Chinese online shoppers in
China. The results revealed that the quality of the website is an essential aspect that
influences Chinese online shoppers when impulsively purchasing online. Thus, a
significant positive relationship between website quality and impulse buying online was
found in the study of Akram et al. (2018). The characteristics of a website, such as visual
appeal, transaction security, and navigation, are all specific signs which have a direct
impact on impulse buying among consumers (Wells et al., 2011). Website quality attracts
and retains new customers, and it is the quality of the website that influences consumers’
preferences for a specific website (Sharma & Lijuan, 2015). Earlier literature indicates that
their many characteristics can improve the quality of websites. The quality of the website
may influence consumers to attempt impulse purchase (Matharaarachchi et al., 2016).
Specific website personality traits such as dynamism, enthusiasm, genuineness,
sophistication, and enjoyment are positively linked to purchasing impulse activity (Rezaei
et al., 2016). Consumers’ impulse purchase can be determined by few attributes, such as
contemplated content, variety, effortless navigating, and design of the websites, which
motivates them to attempt purchasing action (Wadera & Sharma, 2018). Consumers will
enjoy browsing a website if the quality of the website meets their expectations. Xiang et
al. (2016) found that visual representation of e-commerce websites positively and
significantly affects consumers’ emotional states thus, inducing pleasure and enjoyment.
Based on the literature discussed above, the following hypothesis is formed:
H2: There is a positive and significant relationship between website stimulus and
H3: There is a positive and significant relationship between website stimulus and impulse
purchase towards e-tail stores.
Marketing stimulus is described as the type of product, price, promotion, and
bonuses offered to attract a consumer. Price is an important factor under marketing
stimulus that has widely being exercised to examine impulse buying behavior (Park et al.,
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2012; Sari & Pidada, 2020; Xu & Huang, 2014). Price reductions are offered with a
discount to encourage consumers’ sudden urge to buy, and it can be an initial factor to
attempt the buying behavior (Yue & Abd Razak, 2018). When prices are lowered,
consumers spend less than the original price, and the perceived price discount pleases and
arouses the buyer, which is a contributing factor to their impulse buying behavior
(Koschate-Fischer et al., 2012). Being able to buy a product at a discounted price, making
shoppers feel more optimistic about having to take the opportunity, rather than letting it go
(Sundström et al., 2013). Price discount is a significant factor in the purchase of impulses,
and it is one of the most common marketing promotional strategies as it is capable of
delivering positive effects, resulting in buying opportunities (Park et al., 2012; Sundström
et al., 2013). More precisely, when there are sales or price promotions, buyers appear to be
more impulsive (Rizwan et al., 2014). Consequently, the lower price of a good is the key
determinant of impulse buying (Lim & Yazdanifard, 2015). Sales promotions are
structured as a series of various promotional resources to encourage customers to buy
multiple goods or services within a limited timeframe (Lo et al., 2016). Price discounts,
bonus packs, and coupons are the most growing promotional efforts of online retailers (Tu
et al., 2017). and it results in hedonic browsing among consumers. Park et al. (2012) studied
consumer’s hedonic product browsing and found that consumers feel happy and relaxed
when the prices are reasonable and affordable. Based on the abovementioned literature, the
following hypothesis is formed:
H4: There is a positive and significant relationship between marketing stimulus and
H5: There is a positive and significant relationship between marketing stimulus and
impulse purchase towards e-tail stores.
Online buyers tend to be variety-seekers because they would like to explore the wide
range of selections offered on websites (Lim & Dubinsky, 2004; Moe, 2003). According
to Moe (2003), a vast number of category-level pages are likely to get hedonic browsing
visits, indicating that offerings from a variety of shopping malls allow customers to browse
for hedonic reasons such as diversion, pleasure, or enjoyment. Furthermore, finding a
variety of products improves shopping productivity by increasing access to comparable
items and allowing the better choice of products by prolonged internet browsing (Sharma
et al., 2006). In terms of shopping experiences, the variety of selections offers a change in
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routine and escape from boredom, which is usually a function of exploratory searches
(Blakeney et al., 2010).
According to Hawkins et al. (2007), buyers will switch brands spontaneously if they
discover anything new and variations of choices, and these variations will encourage
customers to quickly buy or purchase without giving it a second thought. Few researchers,
such as Sharma et al. (2010), revealed that variety-seeking behavior would allow
consumers to engage in impulsive buying behavior because variety-seeking is directly
linked to unplanned buying. Research by (Amos et al., 2014) indicated customers choosing
to buy online often depends on their variety-seeking behaviours. Based on the discussion
above, the following hypothesis is proposed:
H6: There is a positive and significant relationship between product variety and impulse
purchase towards e-tail stores.
H7: There is a positive and significant relationship between product variety and impulse
purchase towards e-tail stores.
Figure 1 Conceptual Model: Proposed Model for this Study
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PLS-SEM (Partial Least Square Structural Equation Model)
Social science researchers have adopted partial least squares structural equation
modeling (PLS-SEM) as a standard methodology for analyzing multifaceted
interrelationships between exogenous and endogenous variables (Sarstedt et al., 2020).
This method provides various beneficial features which enhanced its applicability into
several research sectors in the current decade (Sarstedt et al., 2020), such as the ability of
handling complex models for relatively small data (Hair et al., 2017), assessing formative
definite extent models (Sarstedt et al., 2016), and identify the scores for the determinate
endogenous variable (Rigdom et al., 2019). According to the past published papers, PLS-
SEM is being emphasized for its appealing method, particularly for applied science, by
promoting the testing for hypothesized relationships through a forecast emphasis in the
estimation of the model (e.g., Carrión et al., 2016; Evermann & Tate, 2016). Furthermore,
PLS-SEM asphyxiates the outward contrast between explanations that generally academic
research underlines and forecasts, which is necessary to develop managerial implications
(Hair et al., 2019; Shmueli et al., 2016, 2019). However, several debates were
acknowledged in regards to the merits and limitations of this method in different research
areas (Khan et al., 2019). Hence, Hair et al. (2012) recorded 204 marketing-related studies
that adopted PLS-SEM in the period of 1981-2010.
One significant implication of this method is that it provides a complementary
method for robustness assessment (Latan, 2018). Researchers in regression-based studies
generally check those robustness assessments to estimate the behavior of core regression
coefficient when regression measurement is reformed in some way, usually with addition
and confiscation regression (Lu & White, 2014). The recent PLS-SEM guidelines include
nonlinear effects, endogeneity, and unobserved heterogeneity as compulsory for any
analysis (Latan, 2018). PLS-SEM is a popular methodology in recent studies relevant to
consumer/users’ behavior (Blasco Lopez et al., 2018; Chiang et al., 2019; Hossen et al.,
2020; Marmaya et al., 2019). Therefore, this study adopted PLS-SEM to examine the
influential factors on impulse buying behavior.
The deductive approach was preferred for this analysis, which concentrated on the
formation of the hypotheses based on existing theory. This was followed by the appropriate
research strategy which was selected to test the hypotheses (Bryman, 2008). Five
constructs were assessed to test the hypotheses, including website stimulus, marketing
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stimulus, product variety, perceived enjoyment, and impulse buying behavior. However,
six items of website stimulus were mainly adapted from Wells et al. (2011). Five items of
the marketing stimulus were adapted from (Ali et al., 2019). Four items of product variety
were adapted from Park et al. (2012). Three items of the perceived enjoyment were
extracted from Sun and Zhang (2006), and for impulse purchase behavior, five items were
extracted from Park et al. (2012) and then adapted for the current study. A total of twenty-
three items of the five variables were measured using a five-point Likert scale (e.g., 1=
Strongly disagree to 5= Strongly agree) to express the degree of an agreement. Each item
of the questionnaire was developed using the English language. With the aim of attaining
comments and feedback from the respondents, twenty questionnaires were distributed for
pilot testing (n= 20). The questionnaires were subsequently modified in order to bring
Sample size and technique
A sample is defined as a subgroup of the population that has been selected for
participation in the research. The target respondents for this study were e-commerce users
in Malaysia. However, the accessible populations for this study were the e-tail users at
Klang Valley area in Malaysia. The total population was recorded at 7,996,830 based on
the report of world population view 2020 (worldpopulationreview.com). The current study
is aimed at getting 385 respondents based on the sample size formula shown. The study
selected a non-probability sampling method which is judgment sampling, or it is also
known as purposive sampling. Purposive sampling refers to a specific person who can
provide information which the author needs or meets the researcher’s requirements to be
selected as a sample (Sekaran & Bougie, 2010). In the current study, researchers asked the
respondents through qualifying questions whether they make use of e-tailing sites or not.
𝑍2×𝑃×(1 − 𝑃)
1 + 𝑍2× 𝑃(1 − 𝑃)
1.962× 0.5 × (1 − 0.5)
1 + 1.962× 0.5(1 − 0.5)
𝑛 = 384
Data for this current study were collected in a span of one month, between
September and November of 2019. A total of 450 questionnaires were distributed with the
aim of getting 384 for further analysis. However, only 439 responses were received from
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the respondents living at Klang Valley in Malaysia. After the data screening and cleaning
processes were completed, 392 responses were unattained.
ANALYSIS AND FINDINGS
Data were analyzed using statistical software SPSS version 25.0 and PLS-SEM
version 3.0. Table 1 represents the demographic profile of the participants from Klang
Valley area. For this study, respondents were asked their gender, age, marital status,
educational qualification, monthly income, and online buying experience.
Table 1 Demographic Profile
50 and above
less than RM1000
RM4000 and above
Online buying experience
Less than 1 year
More than 3 years
Note. Malaysian Ringgit (RM) = 0.24 United States Dollar (USD)
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Convergent and discriminant validity tests were performed to assess the
measurement model. To test convergent validity, each construct of composite reliability
and Cronbach'α values should be higher than or equal to 0.7 (Chin, 1998). The current
study shows the composite reliability (CR) values ranging from 0.897 to 0.970 and
Cronbach’α values from 0.848 to 0.961. The guideline for loadings is to be at least 0.5 as
conferred by Hair et al. (2006). Although loading of more than 0.7 reflects more variance
(Neupane et al., 2014). The loadings for each indicator are way higher than 0.7, ranging
from 0.746 to 0.954 indicates good loading. The AVE value equal to or above 0.50 implies
that on average, the construct clarified more than half the variance of its indicators. By
comparison, an AVE of less than 0.50 suggests that there is still more error in the items
than the average variance described by the constructs. The rule of thumb therefore is that
an AVE value greater than or equal to 0.50 is appropriate (Hair et al., 2013). AVE for the
current study ranging from 0.685 to 0.865. The test result of the current study may therefore
infer the strong reliability of all of the items.
Discriminant validity concerns the uniqueness of a construct, whether the phenomenon
captured by a construct is special and not reflected in the model by the other constructs
(Hair et al., 2013). Discriminant validity can indeed be evaluated by comparing cross loads
among constructs, using the Fornel-Larcker criterion and the Heterotrait-Monotrait
(HTMT) correlation ratio. Discriminant validity of a construct can also be measured by
comparing the square root of the AVE values with the latent variable correlations (Fornell
& Larcker, 1981). Table 2 shows the strong discriminant validity as it shows that each
factor’s square AVE is greater than all of its correlations with the other factors.
∗ 𝐴𝑉𝐸 = ∑𝜆𝑖
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Table 2 Convergent Validity and Reliability
Note. AVE= Average variance extracted, CR= Composite reliability
We use the bootstrap re-sampling technique (5000 re-sample) and then test the path-
coefficient to investigate the significance of the hypotheses. The t-value> 1.96 is significant
at p<0.05 and t-value> 2.58 is significant at p< 0.01 (Hair et al., 2017).
Figure 2 represents the PLS structural equation modeling technique. The model fit
was adequate based on SRMR and Chi-Square values (Table 3). Only NFI value was below
the standard threshold level. The hypothesized path coefficients are presented in Table 4.
Figure 2 and Table 3 describe the path coefficient (β), t-statistics, and p-value of
each hypothesis. According to the findings in Table 4, all proposed hypotheses were
supported except H3, which indicates a direct relationship. H1 (β= 0.380, t= 2.717)
indicates the path between perceived enjoyment of using e-tailing sites (WS) and impulse
buying behavior (OIBB), describing an existence of a significant positive relationship in
between. H2 (β= 0.208, t= 2.701) shows the path between website stimulus and online
impulse buying behavior, which shows a significant positive relationship between WS of
e-tailing stores and OPBB. H3 (β= 0.089, t= 0.785) describes that website stimulus (WS)
has no positive and significant influence on OIBB. H4 (β= 0.300, t= 3.168) ascertains the
direct, positive, and significant relationship between marketing stimulus (MS) and
perceived enjoyment (PE). H5 (β= 0.221, t= 1.982) indicates the relationship between
marketing stimulus (MS) from online commerce sites and OIBB, which were found to have
positive and significant thus, supported for the current study. H6 (β= 0.324, t= 4.022)
alludes that there was a significant positive association between product variety (PV) and
online impulse buying behavior (OIBB). Lastly, H7 (β= 0.229, t= 2.260) was found to be
significant where it shows a positive association between product variety and online
impulse buying behavior.
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Figure 2 PLS-SEM Structured Model
Table 3 Model Fit
Upper is Better
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Table 4 Hypothesis Testing
Note. *p<0.05; **p<0.01; ***p<0.001
In PLS analysis, the prediction power of a specific construct to determine the standard
path coefficient for each relationship between exogenous and endogenous variables is
evaluated using the endogenous variables’ R-squared (R2) values. The values of R2 in PLS
are interpreted similarly to those obtained from multiple regression analysis. R2 values of
0.75, 0.50, and 0.25 define substantial, moderate, and weak levels to predict the accuracy,
according to Hair et al. (2014). The degree of R2 (determination coefficient) is evaluated
subsequently. Website stimulus (WS), marketing stimulus (MS), and product variety (PV)
accounted for 32.7% (R2= 0.327) of variance in explaining perceived enjoyment.
Meanwhile, perceived enjoyment along with WS, MS, and PV accounted for 45% (R2=
0.450) of the variance in impulse buying behavior.
This study was conducted to investigate the key factors of impulse buying behavior.
Factors such as website stimulus, price and promotion, and varieties of selection were
examined to establish the relationship with impulse buying behavior. However, perceived
enjoyment was employed to see the mediating effects between independent and dependent
variables. Firstly, perceived enjoyment was found to have a significant influence on online
impulse buying behavior (H1 was supported). In this study, respondents perceive that
getting pleasure triggers them to indulge in spontaneous buying behavior. A prior study by
Baskaran et al. (2019) found that perceived enjoyment plays a significant role in buying
intention. Secondly, a positive and significant relationship is associated between website
stimulus (WS) and perceived enjoyment (H2 was supported), but a negative relationship
was found between WS and OIBB (H3 was rejected). Results indicate that website stimulus
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has no direct impact on impulse buying behavior, but previous studies asserted that the
visual appeal of a website is pleasing among e-commerce consumers. Although impulse
purchase is widely made online, Malaysians are still prudent and cautious when selecting
websites for purchasing products because of fraud issues. Adopting web-based purchasing
is increasing, and only a few of the customers who have successfully placed an online order
did not receive all their ordered items.
Another reason worthy of highlighting is the fact that the visually attractive product
that the customer paid for online was different from the one that was eventually delivered
to them (Katrina & Benedict, 2019). Visual appeal in pictures, graphics, and motions was
the key determinant of a consumer’s instant gratification. Thus, H2 was in line with the
previous study of Liu et al. (2013) and Hasima et al. (2020). Thirdly, the current study’s
marketing stimulus was significant in determining perceived enjoyment and impulse
purchase behavior towards e-tailing sites (H4 and H5 were supported); thus, the findings
of this study are consistent with the prior literature of Yue and Abd Razak (2018). Lastly,
both perceived enjoyment and online impulse buying were influenced by a variety of
selections (H6 and H7 were supported). Bringing more varieties in the products stimulates
online shoppers’ emotional state; therefore, consumers urge to buy impulsively.
Table 5 Mediation Effects Analysis
Bootstrapping 95% confidence
Note. *p<0.05; **p<0.01; ***p<0.001 (p value)
Three mediating effects in this study were examined. Firstly, the study examined the
mediating effect of perceived enjoyment on the relationship between marketing stimulus
and online impulse buying behavior. Then the study investigates perceived enjoyment on
the relationship between product variety and online impulse buying behavior and, finally,
perceived enjoyment on the relationship between website stimulus and OIBB. With 95%
confidence intervals, 5000 simulations were bootstrapped and found no 0 straddle in
between (Efron & Tibshirani, 1993). The study indicates that perceived enjoyment has
Contemporary Management Research 115
significant and positive impacts on website stimulus, price and promotion and online
impulse purchase behavior.
The findings can be important for both practical and theoretical implications on
consumers’ impulse buying behavior. At the practical level, the transition from consumers’
planned purchase to impulse buying behavior influenced by marketing stimulus and
product varieties provides an important direction to the retailers. Therefore, these findings
can be useful to retailers and companies to design their strategies for attracting customers.
Retailers and marketers should acknowledge during the development of their strategies that
Malaysian consumers are concern over market stimulus and product varieties. Further,
retailers and companies should keep their websites vibrant and update from time to time
with accurate information. Theoretically, the findings may enhance the understanding of
individuals’ differences in impulse buying behavior, thus expanding knowledge related to
the relationships among antecedents and impulse buying behavior patterns.
Limitation and Future Research Direction
This research has its limitations. Firstly, due to the time constraints, the data were
collected through online platforms and social media, and respondents were mainly from
Klang Valley area. Thus, the study should be inclusive of the entire Malaysia, including
Sabah and Sarawak areas, respectively. Secondly, due to the movement control order
(MCO) imposed in Malaysia to curb the spread of COVID-19, face-to-face questionnaire
distribution was interrupted, and alternative data collection was carried out online and via
social media. Therefore, a high possibility of getting false and unexpected answers was a
remaining spectacle. Thus, it is recommended that the study should be conducted through
the physical distribution of research questionnaires. Though website stimulus had widely
being exercised in the impulsive purchase studies, especially in terms of online or e-
commerce purchase, variety of selection was comparatively less concentrated in the area
of impulse buying online, and few more factors are to be applied in order to study impulsive
purchase via e-retailing stores.
However, it is further recommended that future studies should be carried out in the
entire country (Malaysia), including rural and urban areas. Therefore, the study should also
focus on employed citizens or salaried workers because the impulse purchase behavior may
vary based on situational factors such as time and availability of money. Similarly, the
study would focus on the buying behavior between genders. However, further study could
Contemporary Management Research 116
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Mr. Md Wasiul Karim (Corresponding author) has completed his MBA, focusing on
marketing. The primary research focuses on the factors affecting impulse purchase behavior
among generation Y in Malaysia. His research interests include consumer behavior, marketing
research, Islamic finance and banking, quantitative research, and digital marketing.
Dr. Mohammad Abdul Matin Chowdhury recently completed a Ph.D. in Business Administration with
a major in finance. He has an MSc in finance, especially in the microfinance sector. His research interests
include behavioral finance, financial planning, Islamic finance and banking, consumer behavior, and
banking performance and efficiency.
Contemporary Management Research 126
Mr. Md Abdullah Al Masud is currently pursuing his doctorate in business administration, focusing on
marketing. The primary research focuses on the influence of e-marketing, e-WOM, perceived risk, and
perceived usefulness through online trust on online purchase intentions. He completed his MSc in
marketing while focusing on customer loyalty in the fast-food industry in Malaysia. His research interests
include consumer behavior, Islamic marketing, digital branding, social media marketing, digital customer
experience, brand management, relationship marketing, business environment, and corporate social
Mr. Md Arifuzzaman is currently pursuing a Master of Business Administrative (MBA) at Graduate
School of Management (GSM), International Islamic University Malaysia, focusing on finance. In
addition, his primary research areas are financial derivatives, risk management, and future and option.