Journal of Retailing and Consumer Services 79 (2024) 103853
Contents lists available at ScienceDirect
Journal of Retailing and Consumer Services
journal homepage: www.elsevier.com/locate/jretconser
Understanding impulse buying in short video live E-commerce: The
perspective of consumer vulnerability and product type
Yundi Zhang , Tingting Zhang *, Xiangbin Yan
School of Economics and Management, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing, China
A R T I C L E I N F O
A B S T R A C T
Keywords:
Impulse buying
Live e-commerce
Short video platform
Consumer vulnerability
Product type
Live stream activity
Live-streaming is increasingly becoming a popular e-commerce business model. Although impulse buying is a
traditionally common consumption phenomenon, there is little discussion on the underlying mechanism of
impulse buying behavior from the perspective of consumer protection. Therefore, this study follows the stimulus
organism response framework to explore how short video live streaming influences consumers’ impulse pur­
chases. We analyzed questionnaire data from 411 short video live-streaming consumers using partial least
squares structural equation modeling. The findings suggest that anchor characteristics, time pressure, and livestreaming activity can induce impulsive purchases by stimulating consumer vulnerability. Additionally, the study
validated the moderating effect of product type. The findings of this study enrich the research of consumer
impulse purchase behavior in the context of short video live streaming from the perspective of consumer
vulnerability and product type.
1. Introduction
In recent years, the rapid development of live e-commerce (LE) has
played an essential role in promoting digital economy. According to
Iresearch, LE has brought in more than $423 billion in global revenue in
2022 and is anticipated to grow to $500 billion by 2023 (Iresearch,
2022). By the end of 2022, there were 515 million active users of LE in
China, or 48.2% of all Internet users (CNNIC, 2022). As a development
trend, LE has enormous marketing potential and subtly influences con­
sumers’ consumption habits and behaviors.
LE allows viewers and anchors to interact in real time, helping
consumers better understand goods and improving their shopping
experience. LE can also prompt consumers’ impulsive shopping more
easily than traditional e-commerce (Feng, 2022; Zheng, 2019). Ac­
cording to Imedia Research, 49.5% of LE viewers admit that their pur­
chase behavior is irrational and impulsive occasionally (Imedia
Research, 2023). More impulse buying inevitably leads to regrets and
other issues as well (Grigsby et al., 2021). According to relevant reports,
the general return rate of LE is much greater than that of traditional
e-commerce, ranging from 30% to 50% (Kr36, 2020). A high return rate
can not only increase retailers’ post-sale costs for handling returned
products but also decrease consumers’ shopping experience. Therefore,
it is important to effectively manage consumers’ impulsive shopping
behavior.
Existing research on impulse buying behavior has achieved many
meaningful results in the brick-and-mortar retailing and traditional ecommerce contexts (Amos et al., 2014). Nevertheless, study on the
variables affecting impulsive buying by the context of LE is still at the
initial stage (Huang and Suo, 2021; Sun and Bao, 2023; Yi et al., 2023).
The few studies mostly focus on LE in general or the traditional
e-commerce platform (Dong et al., 2023; Lou et al., 2022; Deng et al.,
2023), with little discussion on live e-commerce of short video platforms
(LESV) (Zhang et al., 2022). Nevertheless, LESV, such as TikTok, follows
the content logic with social attributes, in which case, the user shopping
process involves social attributes and is inductive in a way (Redine et al.,
2023; Li et al., 2022). Consequently, it becomes easier for consumers to
engage in unplanned purchasing behavior than traditional e-commerce
platforms (Liu, 2020; Rahma and Ridanasti, 2023). In addition, given
the unique icon and model of short video social platforms, the mecha­
nisms underlying consumers’ impulse buying behavior may also differ
from traditional e-commerce platforms (Gao et al., 2022; He, 2022).
Therefore, this study focuses on LESV to investigate its consumers’ im­
pulse buying behavior.
Furthermore, Current research on impulsive purchasing in LE is
conducted from the perspectives of merchants and platforms, with an
emphasis on how to promote consumers’ impulsive purchases (D. Wang
* Corresponding author. 30 Xueyuan Road, Haidian District, Beijing, China.
E-mail address: tzhang@ustb.edu.cn (T. Zhang).
https://doi.org/10.1016/j.jretconser.2024.103853
Received 13 December 2023; Received in revised form 16 March 2024; Accepted 4 April 2024
Available online 13 April 2024
0969-6989/© 2024 Elsevier Ltd. All rights reserved.
Y. Zhang et al.
Journal of Retailing and Consumer Services 79 (2024) 103853
et al., 2022; Emily and Sharinah, 2022), and with little studies from a
consumer protection viewpoint. However, although not all impulse
buying behaviors are unreasonable and some reasonable impulse buying
can bring consumers a higher sense of well-being (Spiteri-Cornish,
2020), excessive impulse buying can lead to negative consumer issues
like goods dissatisfaction, post-purchase dissonance, and financial dif­
ficulties (Grigsby et al., 2021). Studies have confirmed that external
stimuli in traditional shopping environments can stimulate consumer
vulnerability which can lead to impulse purchases, and consumer
vulnerability can be an intrinsic mechanism of irrational consumption
behavior (Shi et al., 2017; Zeng et al., 2022). Thus, it is essential to
examine impulsive purchasing in LE from the perspective of consumer
vulnerability.
In addition, product type can affect how customers search for in­
formation online, how they make purchases, and what they decide to
buy (Girard and Dion, 2010). The effect of various product features on
consumption behavior has garnered significant attention from
numerous professionals and academics (Li et al., 2016). Nevertheless,
studies on product type in the area of consumer impulsive consumption
in LE contexts are currently scarce, especially its moderating role in
impulse buying (Hao and Huang, 2023). Therefore, this paper in­
troduces product type as a moderating factor to verify its moderating
role between consumer vulnerability and impulse buying.
The following questions are intended to be addressed by this study:
(1) What variables influence impulsive buying in LESV? (2) What part
does consumer vulnerability play in driving impulsive buying? (3) Does
product type moderate between consumer vulnerability and impulse
buying?
within live e-commerce contexts is critical. While existing research has
explored various factors influencing online impulsive buying behavior
(Jeffrey and Hodge, 2007; Lo et al., 2016), including consumer traits and
retailer strategies (Iyer et al., 2020), studies focusing on live e-commerce
are scarce and seldom address consumer protection perspectives. This
study aims to investigate impulsive buying in the context of short-video
live e-commerce, emphasizing consumer vulnerability to minimize un­
necessary impulsive purchases and mitigate potential harm.
2.2. Consumer vulnerability
Research on consumer vulnerability originally referred to a personal
trait of consumers, where consumers are considered fragile and sensitive
to economic, physical, or psychological harm because they are restricted
from certain individual characteristics (Craig and Elizabeth, 1997). With
the evolution of the marketing environment, some researchers started to
criticize such a way of defining an individual as a vulnerable consumer
(Teresa and Marlys, 2014). They pointed out that, instead of treating
some consumers as always being vulnerable because they possess such a
personal trait, consumer vulnerability should be understood within
certain contexts (Teresa and Marlys, 2014). As suggested by Baker
(2005), internal and external factors that an individual consumer is
experiencing during shopping may contribute to vulnerability. In con­
sumption situations, consumer vulnerability may occur when a con­
sumer makes purchase decisions due to powerlessness or loss of control
(Stacey et al., 2005). It means that a consumer should be considered
vulnerable by defining vulnerability as a state that consumers
experience.
For consumer vulnerability, researchers have reached a consensus
that consumer vulnerability is situational and a state of being (Mansfield
and Pinto, 2008). In current consumption situations, consumer vulner­
ability is usually considered that consumers lose self-control, fail to
accomplish desired consumer goals and experience a state of power­
lessness causing by the impact of marketing strategies, social-cultural
aspects, and consumer demographics (Ren et al., 2020). Such a con­
sumption circumstance may lead to consumer behaviors that are inap­
propriate or even contrary to their interests, such as impulsive buying.
2. Literature review
2.1. Impulse buying in live e-commerce
Impulsive buying is a typical consumer behavior corresponding to
planned buying (Karbasivar and Yarahmadi, 2011; Mohan et al., 2013).
It is characterized by spontaneous and unconscious purchasing actions
triggered by external stimuli and environmental factors (Fisher, 1995;
Tirmizi and Saif, 2009). The propensity for impulsive purchases is
notably higher in online shopping compared to traditional retail, largely
due to diverse product presentations and marketing strategies (Qin,
2020; Verhagen and van Dolen, 2011).
Advancements in technology have facilitated consumer decisionmaking processes (Renko and Druzijanic, 2014), with e-commerce
platforms evolving to incorporate more sophisticated displays of
merchandise. Initially, e-commerce relied on static images and textual
descriptions to convey product information. However, with the
upgrading and improvement of e-commerce platform, the integration of
short videos has emerged as a prevalent method for showcasing prod­
ucts, offering a more dynamic and comprehensive view that mitigates
uncertainty and enhances purchasing intent (Flavián et al., 2017; Orús
et al., 2017). Despite these advancements, challenges remain in
achieving effective consumer interaction. Live e-commerce addresses
these challenges by fostering a sense of authenticity and engagement
among consumers by leveraging real-time interaction and precise
product information (Madanaguli et al., 2021; Wongkitrungrueng and
Assarut, 2020). By means of strong interactivity and timely feedback,
online live shopping helps users save consumption decision-making time
and improve shopping efficiency (Barta et al., 2023a; Zheng et al.,
2023). But at the same time, these features of live e-commerce also
promote the formation of impulsive consumer shopping behavior to a
great extent (Ye et al., 2022; Luo et al., 2024; Huang et al., 2024; Ni and
Ueichi, 2024). The quick decision-making encouraged by live e-com­
merce environments can lead to post-purchase regret, impacting con­
sumer satisfaction and potentially resulting in negative feedback and
returns (Barta et al., 2023b,c; Petcharat et al., 2023; Tsiros & Mittal,
2000). Given these considerations, examining impulsive purchases
2.3. Product type
There are different ways of classifying products by different scholars.
Batra and Ahtola (1990) divided products into functional products and
hedonic products according to consumers’ pursuit of functional value or
hedonic value of products. In addition, Nelson (1970), that is, divided
products into experience products and search products based on the
characteristics of the product. Specifically, while experience products
refer to products that require customers to feel or use them personally to
gain an understanding of product information, search products are those
that full details or attributes are obtained by customers by various means
(such as recommendations from others or online browsing) without
experiencing or feeling them personally. Nelson’s (1970) classification
of products is now widely recognized and considered practical and thus
is adopted by the current study.
It is found that consumers who purchase experience products tend to
search for more information about the products before making purchase
judgments than those who purchase search products (Bei et al., 2004).
Jin et al. (2021) confirmed that for search products, a positive infor­
mation frame can significantly enhance customers’ attention to the
product’s functional attribute information and stimulate their desire to
purchase; However, for experience products, Concerns and purchasing
intentions of customers under various information frameworks do not
significantly differ from one another. Within the realm of research on
online impulse purchases, Li et al. (2016) confirmed that product type is
a moderating factor in the impact of the atmosphere on online impulsive
purchasing intentions in virtual communities. However, Hao (2023)
finds that the influence of time scarcity on impulse purchasing is not
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Y. Zhang et al.
Journal of Retailing and Consumer Services 79 (2024) 103853
moderated by the type of product. Therefore, more research is required
to investigate the moderation impact of product types from LE impulse
buying context.
Anchors are central to live-streaming commerce, engaging directly with
consumers to enhance product understanding (Liu et al., 2023). Given
their position as a primary source of product information, the attributes
of anchors significantly influence consumer psychology, shaping their
perceptions and purchase decisions in the live streaming e-commerce
environment (Zhu et al., 2021). On live streaming platforms, anchors
leverage their expertise, attractiveness, and amiability to significantly
contribute to the promotion of products and the augmentation of brand
image for retailers. (Dang et al., 2023; He and Jin, 2022). Some studies
posit that the characteristics of information sources in live streaming can
directly influence consumers’ impulse buying intentions within the
e-commerce environment facilitated by live streaming (Dong et al.,
2023). Thus, the following hypothesis is formulated.
3. Theoretical background and hypothesis development
3.1. Theoretical background
Derived from environmental psychology, the stimulus-organismresponse model (S–O-R) holds that the behavioral responses of in­
dividuals (R) are due to changes in their internal states (O) which are
caused by stimuli from external factors (S) (Teh et al., 2014). As con­
sumption scenarios have generally shifted from brick and mortar stores
to online purchases, the S–O-R theory is extensively applied in mobile
commerce and e-commerce (Alanadoly and Salem, 2022; Cai, 2022;
Sohaib et al., 2022). For example, Li et al. (2023) investigated how
buying patterns were influenced by hedonic and utilitarian factors at the
user experience level of mobile commerce. The validity and significance
of S–O-R for studying how situational factors lead to certain behaviors
by affecting people’s inner state and cognition has been proved by prior
studies on online consumer behaviors, e.g., Adwan et al. (2022), Floh
and Madlberger (2013), and X. Wang et al. (2022).
S–O-R was also used to analyze consumers’ impulsive purchase
behavior (Tang et al., 2023). For example, Floh (2013) adopted S–O-R to
study the impact of contextual factors in online e-stores on consumers’
shopping impulsivity and browsing behavior through the mediating role
of shopping enjoyment. Yi et al. (2023) verified that viewing frequency
and purchasing frequency influence consumers’ purchasing behavior by
making them have impulse tendencies. Thus, S–O-R is considered suit­
able for guiding the current study.
Given the unique context of LESV, the mechanisms and influencing
factors that cause impulsive purchasing are likely to be diverse from
traditional e-commerce. Thus, based on S–O-R theory, we consider that
consumers are stimulated by exterior considerations like anchor char­
acteristics, time pressure, and live stream activity while watching live
shopping broadcasting, which can lead to consumer vulnerability and
eventually impulsive purchase. The moderating impact of product type
regarding the relationship between consumer vulnerability and impulse
purchasing in LESV is investigated. In Fig. 1, the suggested research
model is displayed.
H1. Anchor characteristics have a positive effect on consumers’ im­
pulse buying.
In addition, studies have shown that the persuasion of salespersons in
offline shopping situations can arouse the vulnerability of consumers
(Shi et al., 2017). Therefore, it is believed that anchors as the sales­
persons in the context of LESV can cause consumer vulnerability during
live-stream broadcasting. Based on this, we put forward the following
hypothesis.
H4. Anchor characteristics have a positive impact on consumer
vulnerability.
3.2.2. Live stream activity
Live stream activity is an attribute specific to the LE context, which
refers to the level of activity of viewers in the live stream, including both
viewers who are watching the live stream and consumers who are pur­
chasing goods from that live stream (Li et al., 2022). The more live
stream activities, the more interactions between viewers or consumers
and in the live streaming (Lin and Wang, 2021). In a live broadcast,
consumers can learn in real-time how many people are watching the
same live broadcast by such data as “viewer count”. Co-watching
behavior is a special interaction between consumers when watching a
live broadcast (Lu et al., 2021). The more people watch the live stream
together, the greater the effect of the herding effect, where consumers
may follow the group to make irrational consumption decisions (Dong
et al., 2023). Based on this, the following hypothesis is proposed.
H2. There is a positive effect of live stream activity on consumers’
impulse buying.
3.2. Hypothesis development
In addition, studies have confirmed that in traditional shopping en­
vironments, different external stimuli can stimulate consumers’
vulnerability (Shi et al., 2017). Based on this, this study believes that as
an important atmosphere clue in LE situations, live stream activity can
arouse consumers’ vulnerability. Thus, the following hypothesis is
proposed.
3.2.1. Anchor characteristics
In the context of LESV, anchors play a pivotal role by trialing prod­
ucts, providing introductions, and interacting with the audience through
real-time Q&A sessions to facilitate product sales (Huang and Li, 2023).
H5. Live stream activity has a positive impact on consumer
vulnerability.
3.2.3. Time pressure
Time pressure refers to the feeling of anxiety and stress that decisionmakers experience as they get closer to the deadline for task completion
(Sharma , 2020). In the context of promotional decisions, Customers’
perception of their time constraints when making decisions about what
to buy based on product or promotional information is known as time
pressure (Sebastian, 2021). The shorter the promotion time left, the
greater the pressure for consumers to decide what to buy (Lv et al., 2022;
Zhang, 2023). Consumers often get an emotional feeling of “you will
regret if you don’t buy it” before the deadline (Cui et al., 2022). Pressure
to decide quickly can instill a sense of urgency in consumers, leading
them to focus more on promotional messages and less on the risks
associated with purchase (Kauffman et al., 2010). In LESV, time pressure
is likely to make consumers willing to buy without much consideration
Fig. 1. Research model.
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Y. Zhang et al.
Journal of Retailing and Consumer Services 79 (2024) 103853
and perhaps sometimes immediate purchases. Thus, this study proposes
the hypothesis.
H3.
studies by appropriately modifying each item based on the circum­
stances of the current research. The measurement items of anchor
characteristics (AC) were adopted from Ohanian (1991), which con­
tained 12 items after adjustment. The measurement items of live stream
activity (LSA) were based on Lin et al. (2021), which included three
items. The measurement items of time pressure (TP) were based on Lin
and Chen (2013) which used four items. The measurement items of
consumer vulnerability (CV) were adopted from Sharma and Ren
(2020), which contained 13 items. The measurement items of impulsive
buying (IB) were based on Beatty (1998) and used three items. In Ap­
pendix A, every measurement item is displayed.
Since the survey was completed in Chinese, we developed the
questionnaire using a back-and-forth translation approach. First, every
item was translated into Mandarin from English., which included some
modifications of some items to adapt to our research situation. Next, we
invited 30 users who had made purchases on live-streaming platforms to
review the items on the questionnaire for clarity and to provide feedback
on how they should be worded. Lastly, we reverse-translated the ques­
tionnaire from Chinese into English. A 5-point Likert scale was used for
all measurement items in the final questionnaire.
Time pressure has a positive effect on impulse buying.
In addition, prior research has confirmed that time pressure may
serve as an external stimulus to cause consumer vulnerability when
shopping offline (Shi et al., 2017). Based on this, this study believes that
in the context of LE, time pressure can also cause consumer vulnerability
as an important external stimulus. Thus, the following hypothesis is
proposed.
H6.
Time pressure has a positive effect on consumer vulnerability.
3.2.4. The role of consumer vulnerability
Consumer vulnerability describes a person’s propensity when under
the influence of external stimuli or temptation during a consuming
scenario, to make decisions that could lower his or her welfare. Prior
studies have shown that consumer vulnerability can be applied as an
intrinsic mechanism for illogical decision-making practices, such as
impulse buying, materialism, and etc. (Shi et al., 2017). In LESV, con­
sumers intuitively feel stimuli from anchors, live stream activity, and
time pressure, which are likely to activate consumer vulnerability and
thus generate impulse purchase behavior. As a result, this study pro­
poses the following hypotheses.
H7.
4.2. Data collection
There were three components in the questionnaire. Within the first
segment, since the subjects are people who have experience in LESV
purchasing, screening questions were set at the beginning of the ques­
tionnaire to exclude people who have no such experience. Then, the
definitions of the two types of products were introduced. Next, Re­
spondents were firstly requested to recollect a time when purchasing a
product while watching a live broadcast and then answer the question of
which type of that product was. After that, questions for all the mea­
surement items were presented. The third segment contained questions
about participants’ demographics, such as gender, occupation, income,
education level, age and years of using live-streaming shopping
platforms.
This study collected data using a web-based questionnaire from users
who have purchased products while watching LE broadcasts. Wen­
juanxing, a specialized Chinese data acquisition platform, was used to
issue the questionnaire. Through an internet link, participants can reach
the homepage of our questionnaire. A “snowball” sampling strategy was
adopted to reach out to potential respondents with the hope that par­
ticipants would send the questionnaires to their relatives and friends.
Data was collected between July 1, 2023 to August 1, 2023. A total of
479 questionnaires were returned. After eliminating respondents with
too short completion times, having obviously consistent options, and
filling-in random answers, an 85.8% response rate was attained from the
411 valid respondents that were found. Table 1 displays the sample’s
descriptive statistics. 60.58% of the respondents are female. Most re­
spondents are 18–30 years old, accounting for 70.35%. With a bache­
lor’s degree or more, 89.3% of the respondents are qualified. Regarding
the level of monthly income, respondents with a monthly income of
RMB 5000–10,000 and RMB 3000 and below, and had a monthly in­
come of are two groups with the highest percentage, accounting for
30.66% and 29.93%.
The study mainly reduces the impact of common method bias
through processing methods such as anonymous filling, filling in at
different periods, and multi-channel collection. When creating the
questionnaire, we also made effort to match the linguistic expression of
the customers. Additionally, a potential common method bias issue was
tested using Harman’s single-factor test. Five factors with eigenvalues
greater than one were obtained by principal component analysis by
using SPSS 25 software. The first factor explained 39.848% of the total
variance which is under 40%. Thus, In the dataset, common method bias
is not a problem (see Appendix B).
To detect possible non-response bias, an independent sample t-test
was carried out by comparing early and advanced respondents. The
Consumer vulnerability has a positive effect on impulse buying.
According to the S–O-R theory, Consumers will respond emotionally
or cognitively to external stimuli, which will lead to the corresponding
consuming actions. Thus, it is believed that anchor characteristics, live
stream activity and time pressure, as special stimulating factors in the
live-streaming context, can induce consumers to make impulse pur­
chases by stimulating consumer vulnerability. Thus, the following hy­
potheses are proposed.
H8. Consumer vulnerability mediates between anchor characteristics
and impulse buying.
H9. Consumer vulnerability mediates between live stream activity and
impulse buying.
H10. Consumer vulnerability mediates between time pressure and
impulse buying.
3.2.5. The moderating effect of product type
This study uses Nelson’s classification method to separate products
into experience products and search products based on academic
research and LE sales characteristics (Nelson, 1974). While search
products are those about which consumers may obtain abundant infor­
mation through various pre-purchase channels without experiencing or
feeling them personally, experience products require customers to
handle or use them personally to obtain such experience (Nelson, 1970).
Existing research has pointed out that different types of products
may bring different feelings and experiences to consumers which can
affect consumer purchasing behavior, especially in e-commerce live
broadcast shopping (Huang et al., 2014). Since consumers can only
evaluate experiential effects after they have such experiences, they tend
to increase their rationality and create rational decisions when pur­
chasing experience products. Based on this, the study sets product type
as a moderating factor and proposes the following hypothesis.
H11. Product type moderates between consumer vulnerability and
impulse buying.
4. Research methodology
4.1. Measurement development
This study used existing measurement items validated by prior
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Y. Zhang et al.
Journal of Retailing and Consumer Services 79 (2024) 103853
Table 1
Demographic statistics of survey samples (n = 411).
Table 2
The results of factor loading, AVE, CR and Cronbach’s alpha.
Category
Item
Frequency
Percentage
(%)
Gender
Male
Female
Under 18
18~25
26~30
31~40
41~50
51~60
Over 60
Unmarried
Married
High school or
below
Junior college
Bachelor
Master
PhD
0-3000 yuan
3001-5000 yuan
5001-10000 yuan
10001-20000 yuan
More than 20000
yuan
Less than 1 year
1–2 years
2–3 years
More than 3 years
162
249
11
149
137
65
32
12
5
322
89
10
39.42
60.58
2.68
36.25
33.33
15.82
7.79
2.92
1.22
78.35
21.65
2.43
34
277
78
12
123
87
126
45
30
8.27
67.4
18.98
0.2.92
29.93
21.68
30.66
10.95
7.3
84
124
144
61
20.44
30.17
35.04
14.84
Age
Marital status
Education
Income (Monthly, CNY)
Contact live shopping
years/year
Construct
Item
Loading
CR
AVE
Cronbach’s alpha
AC
AC1
AC2
AC3
AC4
AC5
AC6
AC7
AC8
AC9
AC10
AC11
AC12
TP1
TP2
TP3
TP4
LSA1
LSA2
LSA3
IB1
IB2
IB3
CV1
CV2
CV3
CV4
CV5
CV6
CV7
CV8
CV9
CV10
CV11
CV12
CV13
0.823
0.816
0.826
0.797
0.825
0.799
0.814
0.817
0.792
0.804
0.798
0.800
0.877
0.848
0.886
0.864
0.850
0.827
0.860
0.921
0.922
0.910
0.815
0.813
0.802
0.821
0.806
0.792
0.818
0.835
0.812
0.832
0.814
0.821
0.822
0.962
0.681
0.952
0.929
0.766
0.892
0.897
0.743
0.800
0.941
0.843
0.907
0.963
0.666
0.958
TP
LSA
IB
CV
findings revealed there was no apparent variation in responding be­
tween the two groups (p > 0.05), indicating that there was no nonresponse bias (see Appendix C).
The computation of variance inflation factors (VIFs) was done in
order to evaluate multicollinearity. Every construct has a score that falls
from 1.228 to 1.398. Thus, this study did not exhibit significant
multicollinearity.
Table 3
Discriminant validity.
5. Data analysis and results
AC
IB
LSA
CV
TP
PLS-SEM, or partial least squares structural equation modeling, was
chosen to analyze the data (Fornell and Bookstein, 1982; Joe et al.,
2014). This study used Smart PLS3.0 for PLS-SEM analysis and SPSS25
for the moderating effect testing.
AC
IB
LSA
CV
TP
0.809
0.407
0.355
0.438
0.367
0.918
0.520
0.420
0.475
0.845
0.414
0.404
0.816
0.354
0.869
5.1. Measurement model assessment
have adequate discriminant validity.
Cronbach’s alpha and Composite Reliability (CR) were used to test
the reliability of measurement scales (Fornell and Larcker, 1981). From
Table 2, for each of the five variables, the Cronbach alpha values are
more than 0.8., and the CR of all the constructs is more significant than
0.8, suggesting that each variable has good reliability.
Factor loadings and the average extracted variance (AVE) were used
to measure the convergent validity. Each factor loading was larger than
0.7 based on each construct, and the AVEs of all the constructs were
greater than 0.5. The findings demonstrated the acceptability of the
convergent validity (Bagozzi and Yi, 1988).
Discriminant validity is the measure of the degree to which a latent
variable is different from other latent variables. This study assessed the
discriminant validity by Fornell-Larcker criterion. The Fornell-Larcker
criterion uses the square root of the average extracted variation (AVE)
as the criterion for judgment, requiring that the correlation coefficient
between each dimension should be less than the square root of the
average extracted variation (Fornell and Larcker, 1981). Table 3 shows
that all of the AVE values are higher than the correlation coefficients
between the variables. In addition, HTMT values between the latent
variables are less than 0.85. Overall, these constructs were considered to
5.2. Structural model assessment
This study used effect size coefficient (f2), determination (R2), and
predictive relevance (Q2) to assess the explanatory power. The out-ofsample predictive ability was tested by Q2 value of the Stone-Geisser
test (Sarstedt et al., 2020). Q2 is evaluated by the following criteria:
When the value of Q2 is greater than 0, it indicates that the exogenous
construct is predictively relevant to the endogenous construct (Sarstedt
et al., 2020). The results showed that Q2 of IB and CV was 0.329 and
0.187, respectively, stating that external factors are predictive of the
internal components within the model.
R2 of IB is 0.397, which indicates that the explanatory ability of AC,
LSA, TP, and CV for IB is at a medium level (Sarstedt et al., 2020). In
addition, the R2 of CV is 0.287, which indicates that the explanatory
ability of AC, LSA and TP for CV is at a weak level. Therefore, the
explanatory ability of independent variables to dependent variables in
the research model is acceptable.
According to Hew et al. (2017), f2 values below 0.02 indicate no
effect. Table 4 demonstrates that each f2 in this study was higher than
0.02, which suggests that the effect sizes of the paths in this study were
5
Y. Zhang et al.
Journal of Retailing and Consumer Services 79 (2024) 103853
Table 4
Path coefficient test.
AC - > IB
LSA - > IB
TP - > IB
AC - > CV
LSA - > CV
TP - > CV
CV - > IB
Path coefficient
SD
T-statistic
P-value
LL
UL
f2
Hypothesis testing
0.144
0.310
0.247
0.296
0.251
0.144
0.141
0.039
0.048
0.042
0.046
0.053
0.052
0.046
3.667
6.477
5.930
6.380
4.716
2.778
3.090
0.000
0.000
0.000
0.000
0.000
0.005
0.002
0.068
0.214
0.164
0.203
0.144
0.046
0.051
0.221
0.401
0.330
0.388
0.355
0.248
0.232
0.026
0.118
0.077
0.099
0.069
0.023
0.024
H1(supported)
H2(supported)
H3(supported)
H4(supported)
H5(supported)
H6(supported)
H7(supported)
deemed acceptable. The standardized root mean square residual (SRMR)
value was 0.038, which demonstrated that the model is well-fitted (Hu
and Bentler, 1999).
(β = 0.655, t = 12.367, p < 0.05). When it’s experience product, the
positive effect is attenuated.
The findings of the regression and coefficient difference test (see
Table 7 and Fig. 3) show that the regression coefficients of the two
product types are significantly different (t = 5.770, p = 0.000 < 0.05),
which indicates that product type moderates from consumer vulnera­
bility to impulse buying. Thus, H8 is supported.
5.3. Path coefficient
The results of the path coefficients (see Fig. 2 & Table 4) indicate AC
plays a positive effect on IB, supporting H1. LSA plays a positive effect
on IB, supporting hypothesis H2. TP plays a positive effect on IB, sup­
porting H3. CV plays a positive effect on IB, supporting H7. AC plays a
positive effect on CV, supporting H4. LSA plays a positive effect on CV,
supporting H5. TP plays a positive effect on IB, supporting H6.
The importance of the overhead effects of the specific paths was
tested using the Specific Indirect Effects. The overhead effect of the
mediated path “AC - > CV - > IB” is significant (see Table 5). Thus, H8 is
supported. The indirect effect of “LSA - > CV - > IB” is significant., Thus,
H9 is supported. The indirect effect value of “TP - > CV - > IB” is sig­
nificant. Thus, H10 is supported. Since the direct effects of LSA, TP, and
AC on IB are also proved, CV partially mediates the effects of LSA, TP,
and AC on IB.
5.5. Robustness test
Due to the possible intrinsic variation across genders, we used PLS
multi-group analysis to examine the robustness by separating and
comparing the results of the two gender samples to the results of the
overall sample (see Appendix D). The outcome showed no significant
difference between the male and female samples (p > 0.05). Overall, our
results show strong robustness (Shi et al., 2018).
5.6. Difference analysis
Prior research in traditional sales contexts has found that younger
consumers exhibit a greater tendency towards impulse purchasing
compared to older consumers (Beatty and Ferrell, 1998). As live
streaming e-commerce represents a new form of online retail, blending
interactivity, immediacy, and entertainment, age may significantly in­
fluence impulse buying intentions within this context, especially given
the differing levels of digital platform familiarity and engagement be­
tween younger and older consumers (Xin et al., 2023). To explore this,
age was divided into two groups in this research: those below and above
30 years old. An independent samples T-test was conducted to assess the
differences in impulse buying intentions between these groups. The
5.4. Moderating effect test
Product type is a categorical variable with two options. In the
dataset, the option “search products” is denoted as 0 and “experience
products” as 1. The moderating effect was tested using group regression
analysis.
Table 6 shows that in the overall model, consumer vulnerability has a
significant impact on impulse buying. When it’s search product, the
positive effect of consumer vulnerability on impulse buying is enhanced
Fig. 2. Structural equation modeling results.
6
Y. Zhang et al.
Journal of Retailing and Consumer Services 79 (2024) 103853
Table 5
Mediating effect test.
AC - > CV - > IB
LSA - > CV - > IB
TP - > CV - > IB
Indirect effects
Standard deviation
T-statistic
P-value
Lower limit
Upper limit
Hypothesis testing
0.042
0.035
0.020
0.016
0.015
0.010
2.675
2.409
2.111
0.007
0.016
0.035
0.014
0.011
0.005
0.075
0.068
0.042
H8(Supported)
H9(Supported)
H10(Supported)
Table 6
Grouped regression model.
Overall
Constants
Consumer vulnerability
R2
Adjust R2
F-value
Search products
Experience products
B
t
β
B
t
β
B
t
β
2.327
0.488
0.175
0.173
86.808***
12.700***
9.317***
–
0.418
0.969
0.834
0.428
0.426
152.948***
3.680***
12.367***
–
0.655
3.263
0.181
0.019
0.014
3.971*
11.777***
1.993*
–
0.139
Dependent variable: impulse buying, *p < 0.05, **p < 0.01, ***p < 0.001.
Table 7
Regression coefficient difference test.
Name
Item 1
Item 2
Regression coefficient b1
Regression coefficient b2
Difference
t value
p value
Consumer vulnerability when
Search products
Experience products
0.834
0.181
0.653
5.777
0.000
consumer vulnerability, and consumers’ impulse buying. The moder­
ating role of product type is also examined. Empirical results from 411
valid responses support all the hypothesized relationships and derive
answers to the research questions raised in the Introduction section.
Anchor characteristics is found to affect consumers’ impulsive pur­
chasing. As an essential source of product information, an anchor is
expected to be able to fully deliver product information to consumers
through explanation and interaction (Zhu et al., 2021). This finding is
consistent with prior studies that when consumers watch live broadcasts
of anchors with such characteristics as credibility, professionalism, and
attractiveness, they are likely to have strong impulses and make un­
planned purchases (Huang and Li, 2023). In addition, prior study has
confirmed that in traditional shopping scenarios, salespeople’s sales
pitches can stimulate consumers’ vulnerability (Shi et al., 2017). In this
study, we expand the research context to short-video live-streaming
e-commerce platforms, demonstrating that the characteristics of sales
personnel in live e-commerce, that is, anchor characteristics, likewise
directly stimulate consumer vulnerability.
Live stream activity is a novel characteristic of LE. It is found in the
current study that the more activity in the live stream, the easier for
viewers to make impulsive consumption. In a conventional e-commerce
setting, consumption is primarily an individual event, characterized by
limited consumer-to-consumer interaction. In contrast, within a live
stream room, where consumers collectively view live broadcasts, a
pronounced social influence prevails. Particularly when the live stream
garners a substantial viewership, individuals tend to be swayed by the
crowd, often emulating others’ purchasing decisions.
In addition, the activity level in the live stream is usually a reflection
of the anchor’s popularity (Kuan et al., 2014). In a highly active live
stream where the anchor is popular among consumers, consumers tend
to impulsively consume because they are fond of the anchor rather than
merely of product. Furthermore, this study first confirms that, as a
unique element of LE, live-stream activity can serve as an external
stimulus of consumer vulnerability (Kuan et al., 2014). High live stream
activity can trigger the herd effect, which makes consumers vulnerable
and likely to be influenced by group behaviors and characteristics (Yi
et al., 2020). Subsequently, consumers may follow the behaviors of
other consumers in the live stream to make decisions, resulting in
impulsive shopping (Yi et al., 2020).
Fig. 3. Moderation effects.
findings (see Table 8) reveal that the younger group (below 30 years)
displayed a significantly higher impulse buying intention (M = 4.15, SD
= 1.01) than the older group (above 30 years) (M = 3.77, SD = 1.23),
with a T-test result of t = 3.439, p = 0.001 (<0.05). This suggests that
younger consumers are more prone to impulse purchases on LESP, likely
due to their higher engagement with social media and greater adapt­
ability to new technologies.
6. Discussion
Short video live e-commerce has become a popular marketing model
nowadays. As a fast-growing and prospective new industry, its advan­
tages in attracting user traffic and changing it into cash have made
significant contributions to the development of digital economy.
Drawing on S–O-R, this study empirically examined the relationships
between anchor characteristics, time pressure, live stream activity,
Table 8
Independent-samples t-test.
IB
Younger group
Older group
Mean
Standard
deviation
Mean
Standard
deviation
4.15
1.01
3.77
1.23
Tstatistic
Pvalue
3.439
0.001
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Y. Zhang et al.
Journal of Retailing and Consumer Services 79 (2024) 103853
LESV impulsive purchasing. Although it has been proven that product
type can influence consumers’ purchase behavior in the fields of tradi­
tional retail shopping and e-commerce, there are only little studies
discussing whether and how product type influences consumers’ im­
pulse purchases in the LE context which find that product type did not
play a moderating role in understanding impulse purchases. This study
analyzes and empirically verifies that product type can serve as a
moderating variable among consumer vulnerability and impulse buying
in the LESV context. This finding expands the research on product type
of impulse buying in LE by validating its moderating effect.
Third, this study analyzes and verifies that live stream activity can
stimulate consumer vulnerability and prompt consumers to make im­
pulse purchases. Live stream activity is a unique social attribute of short
video live-streaming platforms. By confirming that live stream activity is
a source of external stimulus for impulse purchases, this study adds to
our understanding of impulsive purchasing, particularly in LE.
In line with the findings of earlier research (Dong et al., 2023), this
study found that time pressure is an important factor affecting con­
sumers’ impulse purchases. When consumers feel strong time pressure,
they will have a strong impulse to buy (Cui et al., 2022). LESV usually
demonstrates a product for a very short time, under which situation
consumers cannot frequently weigh all the options carefully and logi­
cally, which can easily result in impulsive shopping behavior. In addi­
tion, prior studies have concluded that time pressure in traditional
shopping environments can induce consumer vulnerability (Shi et al.,
2017). This conclusion holds in the current study as it confirms that time
pressure can also stimulate consumer vulnerability in LESV. The
real-time nature of live commerce signifies that consumers are con­
fronted with events occurring in real-time and time-limited offers. The
influence of time pressure adds to the urgency of the shopping experi­
ence and can be more effective in stimulating consumers’ vulnerability.
In traditional shopping contexts, consumer vulnerability has been
established to impact consumers’ impulsive buying intentions (Shi et al.,
2017). This study introduces the concept of consumer vulnerability into
the research of impulsive buying in live e-commerce. Through empirical
research, it has been confirmed that consumer vulnerability is activated
when consumers are stimulated by factors such as anchor characteris­
tics, time pressure, and live stream activity, thereby inducing impulsive
buying intentions. This vulnerability is manifested in consumers being
more susceptible to influence during the decision-making process, a
reduction in their capacity for independent thought and critical evalu­
ation, diminished resistance to shopping temptations, and an increased
likelihood of impulsive purchases.
The results suggest that product type moderates between consumer
vulnerability and impulse buying. In the context of LESV, compared with
experience products, consumers can gain obvious information or attri­
butes of search products before making purchases, which reduces the
uncertainty of the product as much as possible, increases trust in the
product, and makes it easier to inspire the vulnerability of the consumer
leads to make impulse purchases (Jin et al., 2020). In other words,
consumer vulnerability has a greater impact on impulsive buying
behavior towards search products than experience products. Current
research in the live commerce domain on whether and how product
types affect consumer impulsive buying is scant, with existing studies
finding that product types do not serve a moderating role in impulsive
purchasing (Hao and Huang, 2023). This study, through exploring
different mechanisms of action, validates the moderating role of product
types in live commerce. This discovery expands the research on the
moderating role of product types in impulsive buying within the live
commerce context.
7.2. Managerial implications
7. Conclusion
The research results can offer helpful recommendations for the
healthy development of LE by disciplining anchors’ behaviors, creating
a benign live broadcasting platform environment, and avoiding con­
sumers’ excessive impulsive consumption.
As public figures, anchors should improve their professional quality,
abide by industry norms, create an optimistic image, and guide con­
sumers to buy products actively and reasonably. When introducing
products, anchors should promote them within a reasonable range of
live room activities and should not put excessive time pressure on
viewers. The anchor should moderately and tactfully remind consumers
of rational consumption from time to time to reduce the likelihood of
consumer vulnerability.
The live-streaming platforms should regulate their contents and be­
haviors of the anchor to create a healthy and benign LE environment and
educate consumers about the consequences of impulsive buying. For
example, when the live stream is highly active, the platform should set
up pop-up tips to remind consumers of rational consumption. When
anchors conduct promotion activities such as sec-killing and flash sales,
the platform should allow a lag time between adding products to a
shopping cart and making payments before consumers complete the
purchase process.
Consumers should enhance their awareness that they can become
vulnerable beings when watching live-streaming because of anchor
characteristics, live room activity, and time pressure. Thus, they should
keep in mind of their actual needs and continuously improve their
ability to identify and choose suitable products, stay rational, and avoid
unnecessary impulsive consumption.
7.1. Theoretical implications
7.3. Limitations and future research
Three key theoretical distinctions are made as follows:
First, although previous research has verified that consumer
vulnerability can be used as an intrinsic mechanism of irrational con­
sumption behavior to predict some irrational decision-making behaviors
in the traditional shopping context (Shi et al., 2017), whether it is true in
LE and how to predict it has not yet been discussed. Meanwhile, while
most of the previous studies have examined impulse buying from the
perspective of merchants’ sales promotion (Luo et al., 2024; Zhang et al.,
2022), little study has examined impulse buying in LE from the view of
consumer protection. This study offers a unique view that consumers are
vulnerable agents to study impulsive buying in a live-streaming context.
By bringing consumer vulnerability to LE situation and examining the
role of consumer vulnerability in understanding consumers’ impulsive
buying behavior in LESV, this study fills the gap in research on impulsive
purchasing in LE from the view of consumer protection and broadens the
research field and depth of consumer vulnerability.
Second, the study verifies the moderating role of product type on
The study also has two limitations which worth further study. First,
the effects of some significant variables on consumers’ impulsive pur­
chases are under our control; however, consumer impulse buying is a
complex process, and there might be other possible factors besides the
factors considered in this study. Therefore, future studies can investigate
these additional influencing variables and examine them in future
research. Second, the data used to verify the research model in this
research were collected through online questionnaires following a
snowball sampling strategy. Future studies can adopt different forms of
questionnaires and sampling strategies to diversify the sample and in­
crease the sample size to improve the external validity of the survey
study.
Funding
This work was supported by the National Natural Science Foundation
of China [grant number 72025101].
8
Y. Zhang et al.
Journal of Retailing and Consumer Services 79 (2024) 103853
Submission declaration
Declaration of competing interest
The work reported in this manuscript is not under consideration for
publication elsewhere. Its publication is approved by all authors. If
accepted, it will not be published elsewhere in the same form.
The authors declare the following financial interests/personal re­
lationships which may be considered as potential competing interests:
Xiangbin Yan reports financial support was provided by National
Natural Science Foundation of China. If there are other authors, they
declare that they have no known competing financial interests or per­
sonal relationships that could have appeared to influence the work re­
ported in this paper.
CRediT authorship contribution statement
Yundi Zhang: Writing – original draft, Visualization, Validation,
Software, Investigation, Data curation. Tingting Zhang: Writing – re­
view & editing, Methodology, Formal analysis, Conceptualization.
Xiangbin Yan: Supervision, Resources, Project administration, Funding
acquisition.
Data availability
Data will be made available on request.
Appendix A. Measurement items
Construct
Item
Measurement
Anchor characteristics
AC1
AC2
AC3
AC4
AC5
AC6
AC7
AC8
AC9
AC10
AC11
AC12
LSA1
LSA2
LSA3
TP1
TP2
TP3
TP4
IB1
I trust the anchor from the live stream I watch
The anchor is reliable when recommending products
The content of the livestreaming is credible
The anchor has special skills expertise
The anchor has professional skills
The anchor has professional knowledge
The appearance of the anchor attracts me
The anchor is interesting
The anchor is fascinating
The anchor has good interaction with me
The live content of the anchor attracts me
The anchor keeps me well engaged
I was in a live stream with a large audience
There are many consumers who buy goods in the live stream
Consumers communicate very enthusiastically and frequently in the live stream
Because of the fear of missing the sec-killing, I felt compelled to act quickly when watching live
I feel too little time to engage in live purchasing.
I always feel rushed when I shop live.
I felt stressed due to the panic of the approaching purchase countdown.
Viewing the live stream often tempts me to purchase items, even those outside my intended shopping list, due to the host’s persuasive
recommendations.
The live stream frequently triggers spontaneous urges to shop.
The live stream evokes a desire in me to acquire products without thorough deliberation.
I felt in a state of powerlessness about purchasing live-streaming goods
I felt in a state of no choice about purchasing live-streaming goods
I feel as if I have no control over purchasing live-streaming goods
I seem to feel powerless about purchasing live-streaming goods
I felt helpless about purchasing the live-streaming goods
Purchasing the live goods was not from my willingness
Purchasing the live goods was not of my own volition
Purchasing the live goods did not reflect my true intentions
Purchasing the live goods seems to go against my original intention.
I recognize that it is my only choice at the moment to buy the live product
I realize that I am in a restricted situation when for deciding to purchase the live product
For the decision to buy the live product, I recognize that it was my choice for a helpless situation
For the decision to purchase the live product, I recognized that it was not my ideal choice
Live stream activity
Time pressure
Impulse buying
Consumer
vulnerability
IB2
IB3
CV1
CV2
CV3
CV4
CV5
CV6
CV7
CV8
CV9
CV10
CV11
CV12
CV13
(continued on next page)
9
Journal of Retailing and Consumer Services 79 (2024) 103853
Y. Zhang et al.
(continued )
Construct
Item
Measurement
Appendix B. Total variance explained
Ingredient
1
2
3
4
5
Initial eigenvalue
Extraction sums of squared loadings
Sum of squares of rotational load
total
Percentage variance
Accumulation %
Total
Percentage variance
Accumulation %
Total
Percentage variance
Accumulation %
13.947
4.712
2.957
1.619
1.134
39.848
13.464
8.449
4.625
3.241
39.848
53.312
61.761
66.386
69.627
13.947
4.712
2.957
1.619
1.134
39.848
13.464
8.449
4.625
3.241
39.848
53.312
61.761
66.386
69.627
8.725
7.977
3.126
2.394
2.148
24.928
22.791
8.931
6.839
6.138
24.928
47.720
56.650
63.489
69.627
Appendix C. Non-response bias test
Construct
early group (within 5 days) (n = 35)
late group (after 5 days) (n = 27)
t
p
AC
LSA
TP
IB
CV
3.32 ± 0.98
3.59 ± 0.99
3.38 ± 1.08
3.98 ± 1.10
3.34 ± 0.98
3.20 ± 1.04
3.73 ± 0.99
3.39 ± 1.09
3.95 ± 1.18
3.39 ± 0.97
1.202
− 1.391
− 0.082
0.320
− 0.531
0.23
0.165
0.935
0.749
0.596
Appendix D. Multi-group analysis of male and female sample
Paths
All
Male
Female
Path coefficient difference (Male-Female)
P-values
AC - > IB
AC - > CV
LSA - > IB
LSA - > CV
CV - > IB
TP - > IB
TP - > CV
0.144***
0.296***
0.310***
0.251***
0.141**
0.247***
0.144**
0.179**
0.310***
0.229**
0.310***
0.169*
0.231***
0.159*
0.121*
0.271***
0.391***
0.211**
0.129*
0.246***
0.138
− 0.058
− 0.040
0.161
− 0.099
− 0.041
0.014
− 0.02
0.470
0.667
0.094
0.353
0.668
0.864
0.851
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