Computers in Human Behavior 127 (2022) 107052
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Computers in Human Behavior
journal homepage: www.elsevier.com/locate/comphumbeh
How to retain customers: Understanding the role of trust in live streaming
commerce with a socio-technical perspective
Mingli Zhang 1, Yafei Liu 2, Yu Wang 3, *, Lu Zhao 4
School of Economics and Management in Beihang University, Beijing, China
A R T I C L E I N F O
A B S T R A C T
Keywords:
Live streaming commerce
Trust
Continuance intention
Social interactivity
IT affordance
Live streaming commerce has been the mainstream of e-commerce in recent years. Although live streaming
research has gained interest, a holistic model explaining why customers are willing to continuously use such a
new sale format remains absent. Drawing on socio-technical system theory, we propose a theoretical model to
explore the impact of social and technical enablers on trust and how trust affects users’ continuance intention in
the live streaming commerce scenario. Empirical results (N = 446) reveal that trust can be enhanced through live
interactivity (active control, two-way communication, synchronicity) and technical enablers (visibility,
personalization), consequently affects continuance intention. In addition, live streaming genres moderate the
impact of different types of trust on continuance intention. Research findings indicate that how trust is formed to
generate optimal consequences in live streaming commerce.
1. Introduction
With the development of information technology, live streaming, as
a new economic format that combines specific activities with videos, has
been widely recognized for its unique content presentation and highlevel interaction (Wang, 2019). Live streaming commerce, as an
important branch of live streaming, considerably extends traditional ecommerce through its highly social interaction realized by the virtual
face-to-face technology (Xu et al., 2020). It displays products to cus­
tomers in the form of real-time video live streaming, effectively nar­
rowing the distance between customers and products (Farman, 2019).
During the Double Eleven event in 2020 (an annual e-commerce activity
in China), akin to Black Friday in the U.S, more than 300 million people
watched TaoBao Live (Wu, 2020), reflecting the popularity of live
streaming commerce. Although live streaming commerce has made
significant progress in recent years, some critical issues remain unre­
solved, trust being one of them. Traditional e-commerce cannot interact
with sellers in real time to obtain dynamic product information, thus
increasing transaction risk and hindering trust-building. In contrast, the
real-time visual communication provided by live streaming commerce
can precisely solve the problems of information opacity in traditional
e-commerce. However, according to China Customers Association sur­
vey report, mistrust still exists in live streaming commerce, which is one
of the main reasons that customers are reluctant to use live streaming
commerce. Thus, as a prerequisite of users’ continuance intention (Tsai
& Hung, 2019; Shao et al., 2018; Talwar et al., 2020; Liang et al., 2018),
it is still unclear whether the antecedents of trust have changed in this
new context and how it affects subsequent behavior in live streaming
commerce.
According to the socio-technical system theory, the cooperation be­
tween the technical subsystem (technology) and social subsystem
(interpersonal) is critical to the optimization of output (Kong et al.,
2019). The real-time interactivity, visualization, and personalized ser­
vices fostered by live streaming commerce have become the unique
advantages that differentiates live streaming commerce from traditional
e-commerce (Hu & Chaudhry, 2020; Xue et al., 2020). From the
* Corresponding author.
E-mail address: wangyusem@buaa.edu.cn (Y. Wang).
1
His main research interest has been in the area of customer behavior in e-marketing, branding strategies, and customer value. His work has been published in
Internet Research, Computers in Human Behavior, International Journal of Information Management, Journal of Business Research, and other journals.
2
Her research interest comprises digital marketing, social interaction and value co-creation.Her work has been published in Tourism Management Perspectives.
3
Her research centers on brand community, business administration, and customer engagement. Her work has been published in International Journal of In­
formation Management, Behaviour & Information Technology, and Computers in Human Behavior, and other journals.
4
His research interest comprises social commerce and entrepreneurial management. His work has been published in Tourism Management Perspectives and
Journal of Business research.
https://doi.org/10.1016/j.chb.2021.107052
Received 13 June 2021; Received in revised form 4 September 2021; Accepted 8 October 2021
Available online 12 October 2021
0747-5632/© 2021 Elsevier Ltd. All rights reserved.
M. Zhang et al.
Computers in Human Behavior 127 (2022) 107052
2017), research on live streaming commerce is still at an embryonic
stage and researchers have so far paid insufficient attention to this new
phenomenon (Park & Lin., 2020). Previous studies on live streaming
have mainly focused on game or sport/e-sport (Chen & Lin, 2018; Hu
et al., 2017; Li et al., 2020; Lim et al., 2020; Xu et al., 2021; Zhao et al.,
2018), exploring streaming technology (Kim and Lee, 2017; Dong &
Wang, 2018; Liang et al., 2018; Barba-González et al., 2020), viewers’
motivations (Sjöblom & Hamari, 2016; Lu et al., 2018; Hilvert-Bruce
et al., 2018), personality traits (Todd & Melancon, 2017; Xu & Ye,
2020), and viewers’ behaviors like gift-sending (Yu, Zhang, Lin, & Wu,
2020; Wohn et al., 2018; Lee et al., 2005; Li & Guo, 2021).
Some recent studies have noticed the shift of traditional e-commerce
to the new economic format with live streaming as the medium and shed
fresh insight into live streaming commerce. Most existing studies have
focused on the following two perspectives: (1) From the perspective of
the customer, some scholars have explored customer value (e.g., utili­
tarian value, monetary value, social value, convenience value, symbolic
value), motivations (e.g., hedonic motivation, utilitarian motivation),
and perceived match-up and celebrity endorsement in live streaming
commerce (Wongkitrungrueng and Assarut, 2018; Singh et al., 2021;
Park & Lin, 2020; Xu et al., 2020). (2) From the perspective of the
platform, other researchers have studied the significant impact of IT
features and contextual cues on live streaming commerce (Cai et al.,
2019; Sun et al., 2019). Only a few studies have explored user behavior
in live streaming commerce from the perspective of relationships. For
example, Hu and Chaudhry (2020) focused on the significant impact of
relational bonds on customer engagement. Kang et al. (2021) investi­
gated that social tie strength between customers and streamers can be an
effective motivating factor for customer engagement behavior.
In fact, due to the nature of live streaming commerce is more dy­
namic and interactive than traditional e-commerce, it is particularly
important to explore customer behavior in live streaming commerce
from the perspective of interpersonal relationships. However, despite its
importance, how to use streaming technology to build trust relation­
ships, and how trust in different entities in live streaming commerce
affects user behaviors remains unclear.
technical subsystem perspective, the live streaming technology’s inter­
face enables customers to get more intuitive and customized information
(Sun et al., 2019), thus meeting the need for face-to-face communication
between customers and sellers (Sjöblom et al., 2019), which has been
proved to be a key to enhancing trust (Jiang et al., 2019). From the social
subsystem perspective, interaction, as a critical feature in live streaming
commerce, can promote the flow of information and emotion, effec­
tively reducing customers’ perceived risk, thus increasing customers’
trust (Bao et al., 2016). However, existing research lacks a joint exam­
ination of the social and technical enablers, two important features that
explain users’ behavior in this new type of e-commerce.
Drawing on existing research, this study aims to develop a model
investigating the determinant of customers’ trust in live streaming
commerce and its effect on customers’ continuance intention. The
distinct contribution of this study lies in the joint consideration of social
and technical factors, focusing on the effects of social interactivity and
technology features of the newer form of e-commerce on trust, which in
turn leads to users’ continuance intention. Additionally, we distinguish
trust in streamers from trust in products, two types of trust in live
streaming commerce, which may have different antecedents and
behavioral outcomes. Further, we consider the moderating effect of the
various live streaming genres in the influence mechanism of customers’
continuance intention. This research aims to help managers understand
how to enhance trust and thus retain them to create more value in live
streaming commerce.
2. Literature review and theoretical background
2.1. Literature review on live streaming commerce
As the integration of traditional e-commerce and streaming tech­
nology, live streaming commerce provides viewers with richer interac­
tive experiences in the virtual shopping scene, experiencing
unprecedented growth, especially during the coronavirus disease 2019
(COVID-19) pandemic period (McKnight et al., 2002). Compared with
traditional e-commerce, several unique advantages of live streaming
commerce can be seen. First, real-time interaction is a key feature in live
streaming commerce. It adds social attributes to e-commerce (a tradi­
tional product-oriented shopping environment) and shifts it into a more
customer-centered environment that focuses on customer relationship
maintenance (Busalim 2016; Cai et al., 2019; Xu et al., 2020). Real-time
interaction plays a key role in users’ experience because it facilitates the
flow of information and emotion (Lin et al., 2019), making it possible to
build solid and stable interpersonal relationships between sellers and
customers (Ou et al., 2014; Wongkitrungrueng & Asarut, 2018).
Different from traditional e-commerce in which customers can only
accept static information unilaterally and passively, customers are not
only the receivers of information in live streaming commerce, but also
participate in the design and delivery of services by interacting with
streamers or sharing information to other viewers directly (Sun et al.,
2019; Li et al., 2021). Second, streaming technology offers an oppor­
tunity to create a more authentic online transaction. The advent and
proliferation of new technologies influence existing human behaviors
(Tran &Strutton, 2019; Zhang, Wu, & Liu, 2019), providing sellers with
a virtual face-to-face communication with customers (Sjöblom, 2019).
Such communication approach effectively reduces customers’ perceived
risks of products and thus greatly improves the authenticity of online
shopping (Hu & Chaudhry, 2020). In addition, due to the existence of
automatic replies in traditional e-commerce, sellers can not capture
customers’ needs, which makes it difficult for customers to get person­
alized suggestions. The emergence of live streaming commerce makes it
easier for customers to get targeted responses. In other words, while the
deeper the personalized communication means the higher degree of
information transparency (Kang et al., 2021).
Albeit more and more practitioners are turning to use live streaming
as a new channel to interact with customers and sell products (Chen,
2.2. Literature review on customer trust
As a critical factor for the success of online enterprises, e-services, or
e-communications (Hansen et al., 2018), trust has been proven to have a
significant impact on customer behavioral intentions (Lee & Lee, 2005;
Pavlou, 2013; Tajvidi et al., 2018), even in the context of live streaming.
It refers to the belief that the other party in the social exchange will act
in a moral manner and will not take advantage of opportunistic behavior
(Gefen & Straub, 2003). During our literature review, we observe
several gaps in existing studies.
First, trust has been widely studied and recognized as a driving factor
in e-commerce or s-commerce setting (Alalwan et al., 2017), but still
relatively little attention has been paid to unveil how trust develops and
its role in live streaming commerce. The unpredictable environment due
to the lack of face-to-face communication is the main reason why pre­
vious e-commerce/s-commerce studies have explored the mechanism of
trust formation (Gefen & Straub, 2003; Jones & Leonard, 2008; Feath­
erman & Wells, 2010; Kim & Park, 2013). Factors such as social support,
information quality and social presence (Chen & Shen, 2015; El Amri &
Akrout, 2020; Zhao et al., 2020; Khan et al., 2020) have been proved to
be able to help sellers gain customers’ trust. Conversely, with the
introduction of live streaming, the real-time interaction enables cus­
tomers to visualize real products and sellers, thus avoiding the risks
caused by the opacity of transactions in traditional e-commerce. How­
ever, whether the antecedents of trust have changed in this new context
and how it affects subsequent behavior is still unknown in live streaming
commerce.
Second, existing studies tend to view trust as an aggregate construct
(e.g., Nath & Mukherjee, 2012; Krishnan et al., 2016; Wan et al., 2016;
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Computers in Human Behavior 127 (2022) 107052
Jiang et al., 2019). This whole concept is problematic because empirical
evidence shows that customer’s behavioral intention depends not only
on the expectation of the product but also on their attitudes towards the
people who provide service to them (Kim & Park, 2013). Different types
of trust have different effects on customer’s behavioral intentions
(Zaefarian et al., 2017). In live streaming commerce, the streamer is not
the spokesperson of the enterprise or product, while the live streaming
process is no longer the self-talk of the seller, which means that trust in
live streaming commerce is based on the co-function of streamer and
product information, thus further affecting customers’ behavioral
intention. However, there are scant studies on how trust in live
streaming commerce is transferred between different entities. Current
literature still lacks a deeper understanding of whether and how the
transferred trust affects customer behavior. Therefore, we distinguish
trust at the product level and the interpersonal level, namely, trust in
product and trust in streamer. The antecedents to these two kinds of
trust are both different and connected, which is exactly what our work is
trying to figure out and where the implication lies.
Third, previous studies have identified the important influence of
trust on customers’ behavioral intentions in live streaming commerce.
For example, Chen et al. (2019) demonstrated that building trust in live
streaming can positively affect viewers’ reward behavior. However, they
ignore the possibility that different genres of live streaming may vary in
the process of different types of trust affecting viewers’ behavioral in­
tentions. Specifically, fixed brand streamers usually employed by official
flagship stores and third-party streamers who sell multi-brand products,
as two different sources of information, may have apparent differences
in motivation, attitude, and behavior of viewers of these two genres of
live streaming. As Zhu et al. (2015) emphasized, different genres of
videos show radically different viewing patterns. Compared with official
streamers, customers who watch third-party streamers’ live streaming
are more likely attracted by their personal charm and thus may make
unplanned purchases without knowing about the products because they
trust in the streamer in such a special situation. However, there is no
research on the influence of live streaming genres on trust and customer
behavior in live streaming commerce.
capabilities of a system (Kong et al., 2020), which is reflected in the fact
that the live streaming commerce provides users with a highly visual
scenario, enabling them get quick feedback (Sun et al., 2019). Live
streaming technology facilitates a more transparent trading where
streamers can directly respond to viewers, while viewers can actively
participate in, and influence live streaming (Hilvert-Bruce et al., 2018),
which is critical for the forming trust (due to high uncertainties within
online shopping).
Compared with traditional e-commerce, the coexistence of social and
technical attributes is a key feature of live streaming commerce. As a
consequence, from a more integrated perspective, social enablers or
technical enablers in isolation are not sufficient to understand the user
behavioral in live streaming commerce. As socio-technical systems
theory emphasizes, these two subsystems need to work well together to
produce optimized outputs (Pasmore et al., 1982; Wan et al., 2016). Yet,
researchers mainly focus on the technical infrastructure since it is
considered the most salient factor for a successful system (Dong & Wang,
2018; Sjöblom et al., 2019). However, streaming services are about
more than just technology (Wang, 2019). Therefore, we adopt the
socio-technical systems perspectives as a theoretical lens to further un­
derstand trust and customers’ behavioral intention in live streaming
commerce.
2.4. Live interactions as social enabler
The concept of interaction was initially viewed as a psychological
state (Newhagen et al., 1995), regarded as the key factor for successful
communication. It refers to a communication that offers individuals
active control and exchange information reciprocally and synchronously
(Liu, 2003). As a unique characteristic of live streaming commerce, live
interaction can bring immersive feeling and engaging shopping experi­
ence, thus generating more intimate interpersonal relationships (Wohn
et al., 2018). Bao et al. (2016) found that better interaction provides a
channel for sellers to show their expertise to customers and increases
trust in the sellers. A higher degree of social interaction can alleviate
customers’ uncertainty and build their trust (Li et al., 2018) by
perceiving the seller as authentic. Therefore, social interactions are
selected to capture social attributes that emphasize real-time interaction
between multiple actors.
A two-dimensional construct was initially used to conceptualize
interactivity, which is the viewers’ psychological sense of efficacy and
the media system’s interactivity (Newhagen et al., 1995). Liu and Shrum
(2002) further elaborated that perceived interactivity is determined by
active control, two-way communication, and synchronicity. Active
control refers to the user’s autonomy and ability to engage in interac­
tion; two-way communication describes the two-way flow of informa­
tion between different entities; synchronicity emphasizes the speed of
multi-user interactivity. In live streaming commerce, the interaction
between customers and streamers is autonomous, bi-directional and
synchronizing. Live streaming provides customers with a live room to
get two-way interaction in real-time videos, where they can autono­
mously decide what to browse and how to get information, and they can
get promptly feedback from streamers by sending barrage (Wang and
Wu, 2019; Hu & Chaudhry, 2020). Hence, high-intensity interaction can
be regarded as the biggest difference between lice streaming commerce
and traditional e-commerce. Although live streaming commerce’s suc­
cess depends heavily on streamers and customers’ interaction (Kang
et al., 2020), we still unclear how this interaction affects customers’
trust. Therefore, we adopt a three-dimensional interaction concept,
including active control, two-way communication, and synchronization,
to explore the impact of social interaction on building user trust in live
streaming commerce.
2.3. Theoretical background: socio-technical systems theory
Proposed by Bostrom and Heinen (1977a, 1977b), socio-technical
systems theory posits that an information system consists of the tech­
nical and social subsystems. The social subsystem focuses on a more
human perspective, while the technical subsystem centers on its tech­
nical competencies (Leonardi, 2013). It is assumed that the two sub­
systems are interrelated and constitute a collective whole. Hence, the
system’s overall performance is primarily a function of both rather than
their individual properties (Sony & Naik, 2020). As a useful analytical
framework, the socio-technical systems approach has been applied to a
stream of social media, e-commerce and virtual communities research to
verify the crucial role of the combination of digital technology and social
activities in leading positive outcomes such as users’ participation,
engagement and continued intention (Hu, Zhang, & Luo, 2016; Kapoor
et al., 2021; Yu et al., 2016; Zhang, Wu, & Liu, 2019; Zhang et al.,
2019b).
In fact, as an extension of traditional e-commerce, live streaming
commerce is also a socio-technical information system (Lee & Lee, 2005t
socio-technical systems theory can be regarded as an effective approach
to explain customer behavioral in live streaming commerce. Specif­
ically, social attributes are reflected in the fact that live streaming
commerce provides opportunities to communicate and socialize among
viewers, streamers, and other co-viewers, facilitating the real-time
interpersonal interaction with each other (Hu et al., 2017; Kang et al.,
2021). These interactions in cyber contexts may lead to immersive,
engaging shopping experiences and a more interpersonal connection
(Haimson & Tang, 2017; Wohn et al., 2018) and further, develop trust in
various referents. The technical subsystem focuses on the technical
2.5. IT affordance as technical enabler
The concept of affordance was first proposed by ecological
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Computers in Human Behavior 127 (2022) 107052
psychology and then adopted to explain technology’s capacities in
specific situations (Argyris & Monu, 2015). It refers to the possibilities
for or the ease of taking certain actions (Leonardi, 2011) provided by
technology for goal-oriented individuals (Markus & Silver, 2008).
Therefore, individuals’ behavior is influenced by their interpretations of
information technology (Sundar, 2008, pp. 73–100). For instance, with
the same comment function on TaoBao live streaming, one can ask the
streamer to show products, give personalized recommendations, or
simply stay in the live streaming room without saying anything. While
the affordance lens encourages scholars to explore the combined impact
of IT’s capability and the actions taken in the specific context (Majchr­
zak et al., 2013), it has been rarely used in exploring user behavior (El
and Akrout, 2020).
Given the unique context of live streaming commerce, where
streaming technology can provide users and streamers with multiple
possibilities for action, we try to explore the impact of technology on
trust from the affordance lens. In live streaming commerce, streamers
can provide viewers with multi-angle product displays according to the
viewer’s barrage, so that they can obtain more intuitive and personal­
ized dynamic product information. From this perspective, the classifi­
cation proposed by Sun et al. (2019) is suitable for the live streaming
scenario, which includes visibility affordance, meta-voicing affordance,
and personalization affordance. Visibility affordance refers to visually
present the product to the customer, meta-voicing affordance enables
customers to communicate in a two-way interactive channel, and
personalization affordance represents the potential to help customers by
providing personalized services. Because the concept of two-way
communication in live streaming interaction overlaps with
meta-voicing, visibility affordance and personalization affordance are
chosen to depict the technical factors in live streaming commerce.
3.1. The effect of trust on continuous use intention
3.1.1. The effect of trust in streamers on continuous use intention
Trust in streamers refers to the belief that the streamer is trust­
worthy, provides good-quality services, and does not take advantage of
the customer (Wongkitrungrueng and Assarut, 2018). This interpersonal
trust involves both cognitive and affective components (Komiak &
Benbasat, 2004). Sufficient levels of cognitive trust can increase cus­
tomers’ confidence in the professionalism and competence of sellers
(Chen et al., 2021) and reduce customers’ suspicion of sellers’ oppor­
tunistic behavior (Hsiao & Chiou, 2012). Streamers act as opinion
leaders in the live room. Once users believe that the streamers are ex­
perts in certain fields or trust their personal taste, they will be more
willing to keep watching the live streaming and get help from them
(Chen & Lin, 2018). For example, many customers watch Li Jiaqi’s live
streaming (a streamer who holds the Guinness World Record for “most
lipstick applications in 30 s”) because they believe he is very profes­
sional in beauty makeup.
Meanwhile, the establishment of the emotional trust is based on
consumers’ emotional appraisal (Sun, 2010), which forms a close
emotional tie between customers and streamers, brings psychological
safety to customers (Schaubroeck et al., 2011), and stimulates them to
become active advocates (Sashi, 2012). Specifically, users who subscribe
to streamers’ channel to become their fans are more likely to be
immersed in live streaming than ordinary viewers. Li Jiaqi in China, for
example, has more than 48 million followers, many of whom follow and
trust him like a celebrity. Because of trust in him, these fans stayed in the
live room waiting for him, actively interacting with him like a friend (e.
g., asking him what kind of lipstick would suit them, sharing shopping
lists, etc.), sharing and buying the products he recommended. Therefore,
we propose that the higher the degree of viewer’s trust in streamers is,
the greater their continuance intentions will be.
H1a: Trust in streamers positively influences continuance intention.
3. Research model and hypotheses development
To analyze the antecedents and outcome of trust in live streaming
commerce, a comprehensive theoretical model is constructed (see
Fig. 1). This framework represents a progression from social interaction
(active control, synchronicity, and two-way communication) and tech­
nical enablers (visibility and personalization) to perceived trust and,
thus, to continuous use intention.
3.1.2. The effect of trust in products on continuous use intention
Trust in an object means believing that the object can meet expec­
tations (Komiak & Benbasat, 2004). This study adopts Wongkitrun­
grueng and Assarut’s (2018) viewpoint to define trust in products as the
belief that a product will meet their expectation and that it will look and
function as claimed. This indicates that trust in products may facilitate
customers to develop a positive attitude toward the product (Kiseol
et al., 2020; Zhao et al., 2019) and then increases product related
knowledge (Sang and Sang, 2005), which makes them more willing to
Fig. 1. Research model.
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Computers in Human Behavior 127 (2022) 107052
stay in the live room. When considering clothing purchases, for example,
consumers watching the live streaming may value the visual match
between her image and the product displayed by the streamer. In such
cases, the streamer’s try-on display provides an effective reference for
them. As a result, customers are more likely to stay when they hold
positively expectations of the actual results. Therefore, we propose that
customers are more willing to use live shopping streaming continuously
when they hold a high level of trust in the product.
H1b: Trust in products positively influences continuance intention.
shopping different from traditional e-commerce (Wu & Chang, 2005).
Real-time interaction provides customers with quick feedback, enabling
customers to obtain rich product information in time, thus effectively
alleviating their doubts and enhancing their confidence in the product
(Xue et al., 2020). Since the questions posed by viewers and the products
displayed by streamers are entirely random, problems like
over-beautification in pre-recorded videos can be avoided. This kind of
timely interaction enables customers to understand the actual situation
of the product and fosters the customer’s trust (Lee, 2005). Therefore,
we infer that synchronicity can increase trust in products.
H3b: Synchronicity positively influences trust in products.
3.2. The effect of live interactions on trust
3.2.5. The effect of two-way communication on trust
Two-way communication indicates that interaction allows a twoway flow of information (Hou et al., 2019). In live shopping, the inter­
action between streamers and customers is undoubtedly two-way. On
the one hand, the interaction between customer and streamer is the
central aspect of two-way communication. Customers can directly
comment in terms of products, services, and related activities. The
streamer’s response can increase customers’ product knowledge and
present the streamer’s responsibility and sincerity by presenting rich,
clear, and specific information (Xiao & Benbasat, 2007).
Meanwhile, from the viewer’s perspective, it is impossible to skip the
interaction between the streamer and other customers in a live
streaming, which is also one of the most important characteristics of live
streaming (Hou et al., 2019). This characteristic also means that other
customers’ questions provide the viewers with more comprehensive
product information. Unlike the one-to-one communication mode, the
one-to-many communication mode provides opportunities for viewers
to observe more streamers’ attitudes toward others and their familiarity
with the product (Hu & Chaudhry, 2020).
Additionally, customers can also interact with other viewers who are
watching the live streaming to get help from them regarding product
related knowledge and shopping experience (Chen & Shen, 2015; Taj­
vidi et al., 2018). It implies that other customers watching the same live
streaming provide external clues about shopping decisions (Cheung
et al., 2014). Peer customers’ referrals and purchase behaviors as a
strong signal about product quality, are important factors affecting trust
level (Sharma et al., 2017) (Amblee & Bui, 2011; Simpson et al., 2008;
Xu et al., 2017). Therefore, we infer that two-way communication can
increase trust in streamers and that in products.
H4a: Two-way communication positively influences trust in
streamers.
H4b: Two-way communication positively influences trust in
products.
3.2.1. The effect of active control on trust in streamers
Active control, as a core component of the interaction (Gao et al.,
2010) refers to the degree of control that users have over the information
exchanged (Hou et al., 2019). That is, users can control what they want
to see, how they see it, and in what order it is presented (Novak et al.,
2000;Mcmillan & Hwang, 2002). In live streaming commerce, cus­
tomers can autonomously decide which products need to be displayed
by streamers and how to interact with them. This implies that customers
have greater autonomy to make decisions during the whole live
streaming process. These increased sense of active control lead cus­
tomers to engage more with sellers, making them feel more involved
(Bao et al., 2016; Hu, Zhang, & Luo, 2016). Additionally, the profes­
sionalism of streamers in product explanation and their care for viewers
promote a harmonious relationship between them, which is conducive
to increasing customers’ trust in streamers (Tsai & Hung, 2019).
Therefore, we infer that active control can increase trust in streamers.
H2a: Active control positively influences trust in streamers.
3.2.2. The effect of active control on trust in products
According to environmental psychology, individuals with high levels
of active control tend to have more positive emotions and behaviors
(Proshansky et al., 1974). This means that greater autonomy motivates
customers to access information actively, thus effectively reducing
product uncertainty and increasing trust in products (Zhao et al., 2015).
While watching a live streaming, customers can independently decide
how to obtain product information (subscription, thumb-up, repost,
comment, purchase, etc.). As the information about detail and product
content is manipulated, customers feel more in control, which helps
increase the customer’s trust in the product (Cui et al., 2010; Lee, 2005).
Therefore, we infer that active control can increase trust in products.
H2b: Active control positively influences trust in products.
3.2.3. The effect of synchronicity on trust in streamers
Synchronicity emphasizes the speed of interaction, which refers to
the extent to which users can communicate synchronously (Hou et al.,
2019). The faster the response, the more interactivity there is (Lee,
2005). In live shopping, customers can ask questions directly to the
streamer without converting the page (Fang et al., 2018). Streamers give
quick feedback to show products’ unique selling points in a real-time
and multi-directional manner (Hu et al., 2017). The immediacy of
response allows customers to experience efficient communication
(Chiang & Hsiao, 2015) and thus enjoy the process of interaction (Lee
et al., 2015). Meanwhile, real-time interaction also helps form a rela­
tional bond between customer and streamer, thus generating a high
level of trust (Wongkitrungrueng and Assarut, 2018). The empathy
showed by streamers in the interaction and their concern for customers’
needs and interests improve customer service experience (Ou et al.,
2014) and shorten the psychological distance between them (Yoon,
Choi, & Sohn, 2010). Therefore, we infer that synchronicity can increase
trust in streamers.
H3a: Synchronicity positively influences trust in streamers.
3.3. The effect of IT affordance on trust
3.3.1. The effect of personalization affordance on trust in streamers
Personalization affordance describes the possibility of providing
personalized services to users (Sun et al., 2019). In the early days of
online communication, personalization addressed the receiver by their
name and offered personalized suggestions based on online data (Tam &
Ho, 2006). In live streaming commerce, personalization affordance has
been further enriched. Personalization affordance not only allow cus­
tomers promptly get satisfactory replies about products, service or
customized suggestions from streamers, but also shorten the distance
between customers and streamers (because the process of live streaming
is more like chatting between friends), thus creating trust in streamers
(Lee, 2005). Highly personalized replies can create a feeling of being
taken seriously (Liang et al., 2011). This makes customers believe that
the streamer is in their best interests, helps build a sense of belonging,
and eliminates alienation between customer and streamer (Edwards
et al., 2009; Zhang et al., 2014). Moreover, customized service is
conducive to the formation of immersion. This is because the person­
alized information provided by streamers can help customers develop
3.2.4. The effect of synchronicity on trust in products
As a crucial component of interactivity, synchronicity makes live
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M. Zhang et al.
Computers in Human Behavior 127 (2022) 107052
telepresence, making them feel like they are talking to a real person (Lim
& Ayyagari, 2018; Ou et al., 2014) and thus trusting the streamers more.
Therefore, we hypothesize:
H5a: Personalization affordance positively influences trust in
streamers.
experience (Yim & Yoo, 2020), thus increasing customers’ trust in the
product. Therefore, we infer that visibility affordance can increase trust
in products.
H6b: Visibility affordance positively influences trust in the products.
3.4. Trust transfer
3.3.2. The effect of personalization affordance on trust in products
The development of information technology makes it possible to
meet customers’ specific needs (Wattal et al., 2009). In live shopping,
streamers can understand customers’ needs through comments and then
provide personalized recommendations (Xiao & Benbasat, 2011). Such
personalized recommendation provided by streamers increases the
pertinence of products and makes customers realize that the products
recommended by streamers could meet their own needs. Meanwhile,
tailored reply can help the customer filter the overload information to
precisely match his/her personal preferences (Tam & Ho, 2006; Zhang
et al., 2014) and increase the customer’s perceived function value (Dong
& Wang, 2018). In addition, personalization provides opportunities for
customers to learn about products in several ways. In the process of live
streaming, customers can obtain product information according to their
preferences. They can choose to ask the streamer directly, or check the
product detail pages to get relevant information or ask for help from
customers who have purchased the product. This kind of information
obtained from multiple channels helps customers to form a compre­
hensive understanding of the product (Chen, 2011) and enhances their
trust in the product. Therefore, we hypothesize:
H5b: Personalization affordance positively influences trust in
products.
Based on trust transfer theory, trust in one entity can be transferred
to a relatively unknown person/entity through their association (Lim
et al., 2006; Shi et al., 2013). In the context of live shopping, trust to­
ward products can be generated via interpersonal trust because of their
associations for several reasons. First, trust in streamers means to trust in
their professional abilities. Streamer’s election ability, interpretation
ability, and professional knowledge can reduce customers’ uncertainty
about products and increase their trust in products. Second, trust in
streamers is also reflected in the belief that streamer is not opportunistic
and will not cheat them for benefits (Cui, Mou, Cohen, Liu, & Kurcz,
2020; Lu et al., 2010). In other words, it is believed that the streamer’s
description of the product’s function and experience is true. Product
information accessed from reliable channels is considered as more useful
and provides important clues to product quality (Chen & Shen, 2015).
Third, trust in streamers will make customers perceive a sense of identity
(Park & Lin, 2020). Customers tend to develop a positive stereotype of a
credible streamer, and the positive attitude toward the streamer will be
transferred to the endorsed product. Thus, once trust arises, the psy­
chological acceptance between user and service provider will be more
significant, and the products and services more preferred (Yuan et al.,
2020). Therefore, we infer that trust in streamers can increase trust in
products.
H7: Trust in streamers positively influences trust in products.
3.3.3. The effect of visibility affordance on trust in streamers
Visibility affordance refers to the possibility of visually presenting a
product to users, which is an important aspect for customers and
streamers to interact with each other through technology in live shop­
ping (Sun et al., 2019). In traditional e-commerce, sellers are invisible or
difficult to be seen by customers (Treem & Leonardi, 2013), which
hinders the acquisition of important clues for interpersonal communi­
cation and makes it difficult to establish trust relationships (Bai et al.,
2015). In live shopping, visual communication enables the customers to
observe the true reflection of streamers, thus effectively shortening the
perceived distance between customer and streamer (Lv et al., 2018),
generating a sense of psychological closeness and providing social
connection (O’Riordan et al., 2016). As customers invest more and more
energy and time in live streaming, the relationship between them will
become closer, and their trust in streamers will be higher and higher.
Additionally, visualization enables streamers to convey their charm and
emotions through the screen, increasing viewers’ sense of identity and
engagement, conducive to forming a trusting relationship. Therefore, we
infer that visibility affordance can increase trust in streamers.
H6a: Visibility affordance positively influences trust in the
streamers.
3.5. The moderating effect of live streaming genre
Several recent studies have discovered that different genres of in­
formation sources may trigger quite distinct user behaviors (Zhu et al.,
2015; Yang, 2012). In the context of live streaming commerce, fixed
brand streamers (i.e., streamers in official flagship stores of a certain
brand) and multi-brand streamers (i.e., Li Jiaqi and Wei Ya, etc.) can be
regarded as two types of information sources. Compared with streamers
who only sell fixed brand products, multi-brand streamers have more
cross-brand knowledge and can provide customers with a comparison
among different brands’ alternatives. In this sense, for multi-brand
streamers, once they win the customers’ trust, customers will be more
likely to keep following their live streaming and try the products rec­
ommended by streamers. This is because customers strongly identify
with sellers identification with the streamer and believe they have more
choice to buy products that meet their own personalized needs in
streamer’s live room). Therefore, compared with a fixed brand streamer,
customers are more willing to continuously use live streaming with
increased trust in a multi-brand streamer. Hence, we propose that:
H8a: The effect of trust in streamers on continuance intention is
stronger for customers who watch the live streaming of multi-brand
streamers.
In live streaming commerce, the influence of streamers and products
on customers will vary with the different genres of live streaming.
Customers who enter the virtual store to watch live streaming often have
a specific purpose or interest in a certain brand/product. Compared with
the product itself, streamers have less influence on customers’ decisions
because they already have certain beliefs about the product. Therefore,
the view counts and the huge followings of the official streamers are
probably due to the brand/product, so they rely more on the existing
brand/product knowledge in customers’ minds to gain customers’
attention. Since trust is generated through repeated positive experiences
(Molinillo et al., 2020), which means that trust in a product may be
based on prior experience. At this point, targeted highlighting the selling
points and deepening customer understanding of the product may be
3.3.4. The effect of visibility affordance on trust in products
Visualization is not only an important breakthrough for traditional ecommerce, but also an important way to enhance credibility. Since
traditional e-commerce can not provide customers with real-time and
dynamic product information, it increases the uncertainty of online
shopping and the risk of customers receiving inferior products (Dew
et al., 2017). Live streaming commerce enables products to be presented
to customers intuitively and thus increases the transparency of inter­
action, enhancing the trust in products (Eggert & Helm, 2003). The
description of products’ touch, smell, and function from the streamer
can help customers form vivid product imagination, make them closer to
the product (Farman, 2019; Yim et al., 2017), and solve their doubts
about the authenticity of the product (Chen et al., 2017; Zhou et al.,
2018). Such visualization facilitates customers to construct consumption
vision and conduct a mental simulation of the product or service
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M. Zhang et al.
Computers in Human Behavior 127 (2022) 107052
more in line with customer needs. In contrast, third-party streamers who
sell multiple brand products pay more attention to the emotional
connection with customers because they don’t have the spillover effect
of the product/brand. For example, many fans of Li Jiaqi will follow him
like a star and want to support him no matter what products he rec­
ommends. Hence, we propose that:
H8b: The effect of trust in products on continuance intention is
stronger for customers who watch the live streaming of fixed brand
streamers.
Table 1
Demographics information (N = 446).
Demographic profile
Categories
Frequency
Percentage
gender
Male
Female
Below 18
18–24
25–35
Above 35
Daily
Weekly
Monthly
Yes
No
207
239
8
240
162
36
71
222
153
329
117
46.41%
53.59%
1.79%
53.81%
36.32%
8.07%
15.92%
49.78%
34.30%
73.77%
26.23%
Age
Frequency
4. Method
Have you bought any products during the
live streaming
To validate the conceptual model constructed based on the theoret­
ical understandings, we conducted an online questionnaire survey to
collect empirical data. The measurement, data collection process, and
final sample analysis details are discussed next.
5.1. Measurement model
We initiated our analysis by assessing the validity and reliability of
the items used to measure the constructs using SPSS 21. As listed in
Table 2, each construct’s composite reliability (CR) ranged from 0.798
to 0.867, exceeding the 0.6 CR threshold value, meaning that all our
constructs demonstrate a sufficient level of reliability (Bagozzi, Davis, &
Warshaw, 1992; Fornell & Larcker, 1981). In addition, Cronbach’s alpha
coefficients of the latent constructs ranged from 0.793 to 0.879, which
are above 0.70, as recommended by Nunnally and Bernstein (1994),
showing high internal consistency.
In this study, we used factor loadings and the average variance
extracted (AVE) to assess the convergent validity. All factor loadings for
different constructs were significant and well above 0.50. Moreover,
AVEs for all the factors ranged from 0.572 to 0.669, exceeding the 0.5
AVE threshold value (Bagozzi, Davis, & Warshaw, 1992), which verified
the convergent validity is satisfied (Bagozzi et al., 1991). The AVE was
also used to assess the discriminant validity by comparing the AVEs
values with their associated pair of correlations. Table 3 lists the square
root of the AVE values that can be compared to the correlations. It shows
that for each pair of constructs, the square root of the AVE is far greater
than the correlation coefficients among latent variables, which provides
4.1. Measures
Construct measurement items were adapted based on previous
literature, with minor adaptations for the live streaming commerce
context. The back-translation is adopted to complete the EnglishChinese and Chinese-English translation, thus ensuring the validity of
the content. All items are assessed by six experts employed by e-com­
merce practitioners and universities, examining whether the statements
in the questionnaire reflect the constructs being measured (see Appendix
A).
The scale used to measure active control, two-way communication,
and synchronicity are adopted from Hou et al. (2019); The measurement
of visibility and personalization are developed based on Sun et al.
(2019); Trust in streamers, trust in products are adopted from Apiradee
and Nuttapol (2018); Items relating to continuance intention are
adapted from Dong et al. (2018). Moreover, we also measured a
moderator variable of live streaming commerce genre (e.g., whether the
streamer only sells fixed brand products), affecting trust and continuous
use intention.
4.2. Data collection and sample
Table 2
Results for reliability and convergent validity in CFA.
To confirm the appropriateness of the original instrument, we con­
ducted a pilot test. A group of 30 customers who had purchased products
on Taobao Live over the past six months were invited. We made minor
modifications in readability, length, and clarity based on the pretest
results.
The questionnaires were distributed through wjx. cn (a professional
online survey service platform), and participants were recruited from
Taobao Live Streaming (the most prominent e-commerce website) users.
We first asked the respondents whether they had any experience using
live shopping streaming, including watching, interacting, or shopping in
the live streaming room. If they have never used live shopping, the
questionnaire will be terminated then. Additionally, we gave a reward to
each participant to ensure the quantity and quality of questionnaires. In
total, 645 responses were received, and 446 were considered valid for
further analysis. Table 1 exhibits the demographic profile of the par­
ticipants. Of all the respondents, 53.59% were female and 46.41% were
male. 53.81% respondents were aged between 18 and 24, accounting for
the largest proportion among the total valid samples. Additionally, the
majority of respondents had bought products during the live streaming
(73.77%).
Constructs
Active control (AC)
Two-way
communication
(TC)
Synchronicity (SY)
Visibility (VI)
Personalization
(PE)
Trust in streamers
(TS)
5. Data analysis and results
Trust in products
(TP)
We performed the two-step approach for data analysis. That is, the
validity and reliability of the research model were examined first, fol­
lowed by confirmatory factor analysis (CFA) to evaluate the structural
model using Amos23.
Continuance
intention (CI)
7
Items
AC1
AC2
AC3
TC 1
TC 2
TC 3
TC 4
SY 1
SY 2
SY 3
VI 1
VI 2
VI 3
VI 4
PE 1
PE 2
PE 3
PE 4
TS 1
TS 2
TS 3
TS 4
TP 1
TP 2
TP 3
CUI 1
CUI 2
CUI 3
Factor
loading
Cronbach’s
Composite
reliability
AVE
α
0.644
0.751
0.858
0.841
0.813
0.777
0.672
0.808
0.754
0.708
0.837
0.784
0.753
0.741
0.762
0.804
0.688
0.808
0.806
0.778
0.757
0.805
0.675
0.892
0.870
0.749
0.694
0.876
0.793
0.798
0.572
0.867
0.859
0.601
0.801
0.801
0.574
0.860
0.861
0.608
0.850
0.851
0.588
0.879
0.867
0.619
0.865
0.857
0.669
0.828
0.819
0.603
M. Zhang et al.
Computers in Human Behavior 127 (2022) 107052
Table 3
Discriminant validity: Correlation matrices and the square root of AVE.
Constructs
1. AC
2. TC
3. SY
4. VI
5. PE
6. TS
7. TP
8. CI
1. Active control
2. Two-way Communication
3. Synchronicity
4. Visibility
5. Personalization
6. Trust in streamers
7. Trust in products
8. Continuance Intention
0.756
0.207
− 0.064
0.140
0.192
0.218
0.235
0.155
0.775
0.217
0.161
0.220
0.362
0.419
0.334
0.758
0.126
0.182
0.370
0.437
0.344
0.780
0.171
0.279
0.305
0.364
0.767
0.366
0.265
0.379
0.787
0.631
0.666
0.818
0.622
0.777
Note: Diagonal elements (bold figures) are the square root of Average Variance Extracted (AVE). Below-diagonal elements are the correlations among variables.
evidence for reasonable discriminant validity (Fornell & Larcker, 1981;
Hair et al., 2006).
Correlations between variables measured using the same methods
are inflated due to common method bias (CMB) (Spector, 2006). We
employed Harman’s single-factor test, one of the most widely used
techniques to check the extent of the method variance in the data
(Podsakoff et al., 2003). The results showed that the largest factor
accounted for 26.925%, well below 50%, indicating no obvious common
method bias in our dataset. Next, we performed the confirmatory factor
analytic to further test common methods bias. The result demonstrated
that all fit statistics of the simple factor model were unacceptable: χ2/df
= 10.732, NFI = 0.422, RFI = 0.375, IFI = 0.446, TLI = 0.399, CFI =
0.443, and RMSEA = 0.148. Thus, the common methods bias was not a
significant issue in this study.
employed multi-group structural equation models. The sample was split
into official and third-party groups. We built separate structural models
to estimate each of the moderating effects. An unconstrained model (the
paths are allowed to be freely estimated) was compared with a con­
strained model (the paths affected by the moderating variable are set to
equal). The χ2 difference test showed whether the moderator variables
significantly affected the hypothetic path, indicating whether there were
differences in subgroups.
The results revealed the moderating role of the live streaming genre
on the effects of trust in streamers, trust in products on continuance
intention. Compared with official streamers, customers’ trust in thirdparty streamers significantly impacted their continuance intention.
This indicates that customers who watch live streaming from a thirdparty streamer are more willing to continuously use live streaming
than those who watch the official streamer as their trust in streamers
increases. Similarly, if a customer is watching live streaming of an
official streamer, trust in products has a greater impact on the contin­
uance intention than watching live streaming with a third-party
streamer. This means that if customers watch the live streaming of
official streamers, they will be more willing to keep watching this live
streaming with their trust in the product increasing. In contrast, if they
watch the live streaming of third-party streamers, the continuance
intention will be lower.
5.2. Structural model
As mentioned above, after evaluating the measurement model, the
structural model is employed to examine our proposed model. As shown
in Table 4, according to Hu and Bentler (1999), the fit indices of our
structural model were satisfactorily: χ2/df = 1.559, NFI = 0.919, RFI =
0.909, IFI = 0.969, TLI = 0.965, CFI = 0.969, and RMSEA = 0.035.
The results represented in Fig. 2 and Table 5 provided evidence that
the model adequately fit to the data. The results of the proposed model
indicated that trust in streamers and trust in products were positively
affected by active control (β = 0.135, p < 0.05; β = 0.111, p < 0.05),
two-way communication (β = 0.246, p < 0.001; β = 0.202, p < 0.001),
synchronicity (β = 0.292, p < 0.01; β = 0.247, p < 0.001), and visibility
(β = 0.186, p < 0.001; β = 0.131, p < 0.01), supporting our hypotheses
H2a-H4a, H2b-H4b, H6a and H6b. However, personalization was posi­
tively associated with trust in streamers (β = 0.260, p < 0.001), but the
relationship between personalization and trust in products was found to
be statistically insignificant (β = 0.008, p > 0.05). Thus, H5a was sup­
ported, and H5b was not supported (see Table 6).
Regarding H7, the results suggested that trust in streamers had
positive effects on trust in products (β = 0.422, p < 0.001), providing
support for H7. For H1–H1b, trust in streamers and trust in products had
a significant influence on continuance intention (β = 0.455, p < 0.001; β
= 0.327, p < 0.001). As a result, H1a and H1b were supported.
As for the explained variance, the proposed model presented good
squared multiple correlations (R2) for the dependent variables (above
0.26, Cohen, 1988): trust in streamers (R2 = 0.27), trust in products (R2
= 0.45), continuance intention (R2 = 0.49).
5.2.2. Mediation effects
This study used the bootstrapping method to confirm whether trust
in products act as a mediator between trust in streamers and continu­
ance intention. We used 10,000 bootstrap samples with bias-corrected
percentile bootstrapping and percentile bootstrapping within 95%
confidence interval. Then, to test the significance of the indirect effects,
we further calculated the confidence interval of the lower and upper
bounds suggested by Preacher and Hayes (2008). As the results show in
Appendix B, the results showed that there was a positive complete
mediating effect for trust in streamers between personalization and trust
in products (0.110, p < 0.001), and positive partial mediating effect for
trust in streamers between active control, two-way communication,
synchronicity, visibility, and trust in products (0.057, p < 0.01; 0.104, p
< 0.001; 0.123, p < 0.001; 0.079, p < 0.01).
6. Discussion and implications
This study attempted to address the challenge of building trust for
the continued use of live streaming. For this purpose, we developed a
model that emphasizes the role of live interactivity and technical en­
ablers in trust formation, verifying how trust can be used to improve
continuance intention in the live streaming commerce context.
5.2.1. Moderation effects
To explore the moderating effects of the live streaming genre, we
Table 4
Model fit statistics.
Fit indices
χ2
χ2/df
NFI
RFI
IFI
TLI
CFI
RMSEA
Model value
Overall model fit
523.848
Yes
1.559
Yes
0.919
Yes
0.909
Yes
0.969
Yes
0.965
Yes
0.969
Yes
0.035
Yes
8
M. Zhang et al.
Computers in Human Behavior 127 (2022) 107052
Fig. 2. Research model with parameter estimates.
trust were found to be positively associated with continuance intention.
Additionally, our research showed that trust in streamers can also be
shifted to the products he/she recommends, thus influencing the cus­
tomers’ willingness to continuously use live streaming. The existence of
interpersonal trust provides an opportunity for the development of
product trust. These findings are consistent with prior studies in the
conclusions of Zhao et al. (2019), which indicates that trust is one of the
strongest drivers of behavioral intentions and can be transferred be­
tween different entities.
Second, our findings confirmed that social enablers (i.e., active
control, two-way communication, synchronicity) and technical enablers
(i.e., visibility, personalization) had a positive effect on customer
development of trust in live streaming commerce. These findings are
compatible with Kong et al. (2019), who highlights the importance of
cohesion between social and technology enablers to improve trust in
online settings. Live streaming technology provides opportunities for
two-way live interactivity between customers and sellers, promotes the
flow of information and emotions, and compensates for the fear of
sellers’ opportunism and the uncertainty of product quality caused by
the separation of sellers’ and customers’ time or space in traditional
e-commerce. Therefore, social interactivity and IT affordance are crucial
antecedents of trust in live streaming commerce.
Third, empirical findings confirmed that trust in streamers plays the
mediating role of between live interaction, IT affordance, and trust in
products. These findings indicated that when customers perceive a
higher degree of live social interactivity in a virtual face-to-face space, it
will enhance customers’ trust in streamers, affecting the products they
recommend. Interestingly, personalization only had an impact on trust
in streamers but fails to generated a direct impact on trust in products.
Its effect on trust in products was fully mediated by the viewer-streamer
relationship through the trust transfer process. This may be related to
the specific context studied in our research. In the shopping live
streaming scenario, streamers provide timely product information and
customized feedback to customers, thus effectively narrowing the dis­
tance between each other. It is precisely because the two parties have
established a trust relationship that customers will trust the products
recommended by streamers for their own personalized needs. Therefore,
personalization in this context does not have a direct impact on trust in
products.
The research findings further verified that the moderating effect of
the live streaming genre between trust and continuance intention is
Table 5
Path coefficients.
Path
Standardized
Coefficient
S.E.
C.R.
P
Conclusion
TS < — AC
TS < — TC
TS < — SY
TS < — VI
TS < — PE
TP < — AC
TP < — TC
TP < − SY
TP < —VI
TP < — PE
TP < − TS
CI < —TS
CI < —TP
0.135
0.246
0.292
0.186
0.260
0.111
0.202
0.247
0.131
0.008
0.422
0.455
0.327
0.048
0.082
0.058
0.052
0.044
0.033
0.058
0.043
0.036
0.030
0.045
0.071
0.092
2.578
4.521
5.350
3.626
4.915
2.335
4.003
4.657
2.801
0.161
7.039
7.382
5.416
*
***
***
***
***
*
***
***
**
NS
***
***
***
Supported
Supported
Supported
Supported
Supported
Supported
Supported
Supported
Supported
Not Supported
Supported
Supported
Supported
Note: *p < 0.05, **p < 0.01, ***p < 0.001, NS: Non-significant.
Table 6
Results of the moderated model.
Path
Live streaming genre
Official (N
= 204)
Third-party
(N = 242)
TS→CI
0.304***
0.689***
TP→CI
0.571***
0.214**
Chi-square
difference
Hypothesis
Support
Δχ2 (1) =
4.109, P <
0.05
Δχ2 (1) =
7.686, P <
0.01
H8a
Yes
H8b
Yes
Note: **p < 0.01, ***p < 0.001.
6.1. Key findings
The empirical study results validated most of our hypotheses,
demonstrating the model’s appropriateness in live streaming commerce
research. First, the results revealed that customers’ continuance inten­
tion is driven by their perceived trust. However, we did not measure
trust as a general concept as in existing researches (Chang et al., 2016;
Gao et al., 2015). Instead, we divided trust in live streaming commerce
into trust in products and trust in streamers. Specifically, two forms of
9
M. Zhang et al.
Computers in Human Behavior 127 (2022) 107052
positive and significant, a new phenomenon that differs from other
forms of live streaming. The possible reason is that the third-party
streamers who sell cross-brand products may have more contacts with
products and provide more diversified information of cross-brand
products to viewers than official streamers. Therefore, with the in­
crease of the viewer’s trust in the third-party streamer, they will be more
willing to sustain attention to this streamer’s live streaming. Meanwhile,
the customers who enter the official stores to watch the live streaming of
the official streamers tend to have relatively clear goals and specific
products’ expectations. Therefore, the more they trust the products, the
more actively they will continue to pay attention.
findings.
6.3. Managerial implication
This research also provides some actionable guidelines to marketers
and practitioners of live streaming commerce. First, social interaction
and IT affordance should be valued by streamers and managers to
enhance customers’ trust. Specifically, streamers should respond to
customers’ questions as soon as possible and make full use of visual
methods to display products for customers in all directions delivering
detailed product experiences. This can effectively reduce customers’
doubts about the products and encourage them to consider the streamer
honest and reliable. Simultaneously, streamers and managers should
seriously consider providing customers with advanced control options
(e.g., the ability to zoom in or zoom out the page, block the speech box,
etc.) to increase the users’ sense of control. Given that personalization
has an indirect effect by trusting the streamer, big data should be
properly used to provide customers with more accurate personalized
information, such as judging customers’ potential doubts by analyzing
the time of their stay in different areas of the webpage. Moreover,
streamers can also distribute exclusive coupons for regular customers.
On the one hand, it can create incentives for new users, and on the other
hand, it further increases user engagement and make them feel cared for
and warm.
Second, trust is the key to customers’ continuance intention. Trust­
—including trust in streamers and trust in products—is an essential
antecedent for determining whether customers are willing to subscribe,
watch, and place orders. Faced with the explosion of live streaming
commerce and the streamers number in blowout growth, it has become a
standard for enterprises to promote goods through live streaming.
Therefore, streamers and marketers should know how to accommodate
this new business model and improve performance in such a competitive
environment. Specifically, managers are suggested to provide systematic
training to streamer, which should not only improve live broadcast skills
in displaying commodities and interacting with customers, but also pay
attention to brand vision and professional knowledge related to prod­
ucts. In this way, streamers can be seen as professional and trustworthy
in the minds of customers. Additionally, since the trust in streamers will
be transferred directly to the products, managers are suggested to
evaluate co-streamers. Streamers with good WOM (e.g., fans with high
hierarchy account for a large proportion) should be valued by managers.
These streamers can provide customers with a good shopping experi­
ence, and the interpersonal trust that has been formed with customers
can be induced to further transfer to the product.
Third, the live streaming genre can significantly moderate the in­
fluence of trust on continuance intention. This finding provides new
knowledge for streamers and marketers to carry out marketing promo­
tional activities through live streaming. For streamers, precise posi­
tioning can effectively optimize marketing strategies to reach target
customers. Specifically, for streamers officially employed by enterprises,
trust in products can bring more positive feedback. Thus, these
streamers should focus more on improving their professional ability and
brand knowledge to convey more accurate product design concepts to
customers and enable them to have a deeper understanding of products.
For third-party streamers, trust in streamers is the key to use continu­
ously. Therefore, they should pay more attention to providing interproducts comparison information and sharing their own product expe­
riences. In this way, the psychological distance between the streamer
and the viewer can be narrowed. Viewers can feel that the information
delivered by the streamer is for their benefit, rather than any commer­
cial result. Also, the result may serve as a guide for business managers
regarding how to choose channels of live streaming to achieve expected
business goals. For example, for the promotion of new products, it may
be better to cooperate with third-party streamers because of their large
popularity, and the established trust relationship between streamers and
viewers will increase viewers’ understanding of the product, thus
6.2. Theoretical contribution
These findings contribute to the extant literature on live streaming
commerce in the following aspects. First, we add the knowledge of live
streaming commerce. Compared with the vigorous development in the
practice area, live streaming commerce is still in its infancy in the aca­
demic area and remains only partially understood. Previous researches
have focuses on examining the customer motivation, perceived value,
and psychological mechanism from customers’ perspective (e.g., Cai
et al., 2018, pp. 81–88; Hilvert-Bruce et al., 2018; Xu et al., 2020). In live
streaming commerce, trust-building is completely different from tradi­
tional e-commerce. Live streaming commerce solves the problem that
the sellers and the customers cannot interact in real time and improves
the transaction transparency. But even in such an environment, trust
remains a key barrier to continued use. In this study, a socio-technical
system view is adopted to emphasize the comprehensive consideration
of the mutual influence of the social subsystem and technical subsystem.
To our best knowledge, the current study is among the first to apply
socio-technical system theory to live streaming commerce to explore
customers’ continuance intention. We identify the previously unex­
plored mechanisms to explain the influence of different kinds of trust on
customers’ continuance intention in live streaming commerce, which
enriches the previous research on live streaming. This is also consistent
with studies indicating that trust can serve as forceful behavioral guid­
ance. (Fernandes & Oliveira, 2021; Kim & Park, 2013; Leung et al.,
2019; Luo et al., 2020; Shareef et al., 2021).
Second, we extend the literature on trust in the following aspects.
Whereas trust has been studied in the existing studies (e.g., Chong et al.,
2018; Shao et al., 2018; Komiak and Benbasat), in this research the
unique advantages of live streaming commerce that are various from
traditional e-commerce in forming trust are underscored. In addition,
although existing studies have found different trust entities (Rungsi­
thong & Meyer, 2020), most previous studies have focused mainly on a
certain dimension of trust (Chen et al., 2020; Meilatinova, 2021;
Bawack, Wamba, & Kevin Daniel AndréCarillo, 2021). To further refine
extant research on the trust concept, we distinguish between trust in
products and trust in streamers and find that both types of trust are
associated with customers’ continuous use intention. Further, we
explore the mediating effect of trust. Specifically, the findings suggest
that trust in streamers as a mediating variable which works on influence
of social interactivity and technology features on trust in products. Trust
in streamers would also transfer to the product they recommend.
Third, we also examine the moderating effect of different live
streaming genres, which fits the new situation of live streaming com­
merce and extends the scope of extant literature. Fixed brand streamers
in official flagship stores and third-party streamers selling multi-brand
products, as two different live streaming genres, can moderate the in­
fluence of trust (including trust in streamers and trust in products) on
customers’ continuance intention. However, little is known about how
different live streaming genres affect trust’s impact on behavioral
intention in live streaming commerce. Our finding indicates that cus­
tomers pay more attention to the products in the official live streaming
and more focus on the streamers of third-party live streaming, which
may inspire researchers to consider different scenarios for further
10
M. Zhang et al.
Computers in Human Behavior 127 (2022) 107052
generating their trust in the product.
streaming commerce. Our research showed that live interactivity and
technical enablers positively affect users’ trust, consequently affecting
their continuous use intention. Moreover, we identified the moderating
effect of the live streaming genre. This study enriches the socio-technical
systems theory and live streaming commerce literature and provides
practitioners with feasible strategies for improving customer loyalty.
6.4. Limitations and future research
This study also has the following limitations. First, we sampled from
a single live streaming commerce platform. Although Taobao is
considered the dominant e-commerce platform in China, it is not unique
in this respect. Future research can incorporate multiplatform samples
to compare the influence mechanism of trust further. Second, our
research did not consider trust in brands. Trust in brands is associated
with live streaming related to brand-specific genres, which could be
considered for future research. Third, this paper used cross-sectional
data, but users’ continuous use intention and trust evolve dynamically
over time. Thus, an interesting direction would be to track further the
dynamic changes of customer trust and related influence mechanisms
with panel data.
Credit author statement
The authors certify that they have participated sufficiently in the
work to take public responsibility for the appropriateness of model
design, and the collection, analysis, and the interpretation of the data.
No conflict of interest exits in the submission of this manuscript. All
authors have reviewed the final version of the manuscript that is
enclosed and approved it for publication. Credit statements as shown
further below.Mingli Zhang: Conceptualization, Resources, Writing –
original draft, Writing – review & editing, Visualization, Supervision,
Project administration, Funding acquisition, Yafei Liu, 1. Conceptuali­
zation, 2. Methodology, 3. Software, 4. Validation, 5. Formal analysis 6.
Data Curation, 7. Writing – Original Draft, 8. Writing – Review & Edit­
ing, Yu Wang, 1. Conceptualization, 2. Investigation, 3. Writing –
Original Draft, 4. Writing – Review & Editing, Lu Zhao, 1. Software, 2.
Investigation, 3. Data Curation
7. Conclusion
The explosive growth of live streaming commerce drives this
research to explore how to capture and retain customers. The issue of
trust is a potential hinder to users deciding whether to use live streaming
business, however, there is still no sufficient theoretical explanations on
how to solve this issue. Based on socio-technical systems theory, this
paper proposed a comprehensive model that underscores the integration
of both technical support and dynamic interaction to contribute trust in
live streaming commerce. Additionally, this study attempted to reveal
how trust in different entities affects customers’ continuance intention,
and further confirmed the moderating effect of different genres of live
Acknowledgment
This work was supported by Humanities and Social Science Fund of
Ministry of Education of the People’s Republic of China under Grant
[No.20YJA630091].
Appendix A. Variable definition and measurement items
Variable
Active Control
Hou et al. (2019)
Two-Way Communication
Hou et al. (2019)
AC1
AC2
AC3
TC1
TC2
TC3
TC4
Synchronicity
Hou et al. (2019)
Visibility
Sun et al. (2019)
SY1
SY2
SY3
VIS1
VIS2
VIS3
VIS4
Personalization (Sun et al.,
2019)
PL1
PL2
PL3
PL4
Trust in streamers
TS1
(Apiradee & Nuttapol, 2018)
TS2
TS3
TS4
Trust in products (Apiradee &
Nuttapol, 2018)
TP1
Items
Definition
I felt that I had a lot of control over my experience
I could choose freely what I wanted to see
My actions decided the kind of experiences I got
The shopping streamer was effective in gathering viewers’ feedback
The shopping streamer facilitated two-way communication between
herself/himself and viewers
The shopping streamer made me feel she/he wanted to listen to her/
his viewers
The shopping streamer gave viewers the opportunity to talk to her/
him
The shopping streamer responded to my questions very quickly
I was able to obtain the information I wanted without any delay
I felt I was getting instantaneous information
Live streaming shopping provides me with detailed pictures and
videos of the products
Live streaming shopping makes the product attributes visible to me
Live streaming shopping makes information about how to use
products visible to me
Live streaming shopping helps me to visualize products like in the
real world
Streamers on live streaming shopping can provide me with
information on all alternative products I intend to buy
Streamers on live streaming shopping can help me establish my
product needs
Streamers on live streaming shopping can help me identify which
product attributes best fit my needs
Streamers on live streaming shopping can provide me with personal
product customization based on my requirements
I believe in the information that the streamer provides through live
streaming shopping
I can trust the streamer on live streaming shopping
I believe that streamer on live streaming shopping is trustworthy
I do not think that streamer on live streaming shopping would take
advantage of me
I think the products I order from live streaming shopping will be as I
imagined
refers to the degree of control that participants have over the
information exchanged
the interaction allows two-way flow of information
the degree to which participants are able to communicate
synchronously
The possibility of visibly demonstrating the product to consumers
The potential to help customers make purchase decisions by
offering personalized services
the belief that the seller is trustworthy, provides good-quality
services, and does not take advantages of customers
the customer’s belief that a product will meet their expectation,
and that it will look and function as claimed
(continued on next page)
11
Computers in Human Behavior 127 (2022) 107052
M. Zhang et al.
(continued )
Variable
Items
TP2
TP3
Continuance intention
(Dong et al., 2008)
CI1
CI2
CI3
Definition
I believe that I will be able to use products like those demonstrated
on live streaming shopping
I trust that the products I receive will be the same as those shown on
live streaming shopping
Would you use live streaming shopping again if you had a choice
What is the likelihood that you will choose to use live streaming
shopping next time you need make a purchase
How likely would you be to use live streaming shopping in the future
a customer’s willingness to participate in service production and
delivery in the future
Appendix B. Standardized direct, indirect, and total effects
Point estimate
Standardized indirect effects
AC→TP
0.057
TC→TP
0.104
SY→TP
0.123
VI →TP
0.079
PE→ TP
0.110
AC→CI
0.116
TC→CI
0.212
SY→CI
0.254
VI →CI
0.153
PE →CI
0.157
TS →CI
0.138
Standardized direct effects
AC → TS
0.135
TC → TS
0.246
SY → TS
0.292
VI → TS
0.131
PE → TS
0.260
AC → TP
0.111
TC → TP
0.202
SY → TP
0.247
VI → TP
0.131
PE → TP
0.008
TS → TP
0.422
TS → CI
0.455
TP → CI
0.327
Standardized total effects
AC → TS
0.135
TC → TS
0.246
SY → TS
0.292
VI → TS
0.186
PE → TS
0.260
AC → TP
0.167
TC → TP
0.306
SY → TP
0.370
VI → TP
0.210
PE → TP
0.117
TS → TP
0.422
AC → CI
0.116
TC → CI
0.212
SY → CI
0.254
VI → CI
0.153
PE → CI
0.157
TS → CI
0.593
TP → CI
0.327
Product of coefficients
Bootstrapping
Bias-corrected Percentile 95% CI
Percentile 95% CI
SE
Z
Lower
Lower
Upper
0.026
0.030
0.032
0.027
0.033
0.041
0.041
0.042
0.042
0.040
0.034
2.192
3.467
3.844
2.926
3.333
2.829
5.171
6.048
3.643
3.925
4.059
0.017
0.062
0.077
0.039
0.063
0.046
0.143
0.183
0.086
0.093
0.089
0.101
0.163
0.182
0.130
0.174
0.183
0.278
0.323
0.222
0.223
0.204
0.015
0.057
0.074
0.036
0.059
0.048
0.144
0.183
0.087
0.093
0.081
0.099
0.156
0.177
0.125
0.168
0.184
0.279
0.324
0.223
0.224
0.194
0.061
0.063
0.059
0.057
0.059
0.055
0.060
0.056
0.057
0.054
0.070
0.067
0.072
2.213
3.905
4.949
2.298
4.407
2.018
3.367
4.411
2.298
0.148
6.029
6.791
4.542
0.031
0.141
0.193
0.094
0.167
0.020
0.105
0.153
0.039
− 0.082
0.302
0.343
0.205
0.231
0.350
0.386
0.282
0.357
0.198
0.302
0.337
0.227
0.100
0.531
0.565
0.441
0.036
0.142
0.194
0.092
0.164
0.024
0.104
0.154
0.038
− 0.082
0.302
0.348
0.203
0.235
0.351
0.388
0.281
0.355
0.203
0.301
0.338
0.226
0.100
0.531
0.569
0.440
0.034*
0.000***
0.000***
0.001**
0.000***
0.044*
0.000***
0.000***
0.020*
0.883NS
0.000***
0.000***
0.000***
0.061
0.063
0.059
0.057
0.059
0.059
0.058
0.058
0.061
0.056
0.070
0.041
0.041
0.042
0.042
0.040
0.051
0.072
2.213
3.905
4.949
3.263
4.407
2.831
5.276
6.379
3.443
2.089
6.029
2.829
5.171
6.048
3.643
3.925
11.627
4.542
0.031
0.141
0.193
0.094
0.167
0.068
0.209
0.268
0.109
0.024
0.302
0.046
0.143
0.183
0.086
0.093
0.504
0.205
0.231
0.350
0.386
0.282
0.357
0.262
0.400
0.462
0.311
0.209
0.531
0.183
0.278
0.323
0.222
0.223
0.670
0.441
0.036
0.142
0.194
0.092
0.164
0.071
0.209
0.270
0.109
0.026
0.302
0.048
0.144
0.183
0.087
0.093
0.507
0.203
0.235
0.351
0.388
0.281
0.355
0.265
0.400
0.463
0.310
0.210
0.531
0.184
0.279
0.324
0.223
0.224
0.673
0.440
0.034*
0.000***
0.000***
0.001**
0.000***
0.005**
0.000***
0.000***
0.001**
0.036*
0.000***
0.004**
0.000***
0.000***
0.000***
0.000***
0.000***
0.000***
Upper
Two-tailed significance
0.024**
0.000***
0.000***
0.001**
0.000***
0.004**
0.000***
0.000***
0.000***
0.000***
0.000***
Note: Standardized estimating of 10,000 bootstrap sample, *p < 0.05, **p < 0.01, ***p < 0.001, NS: Non-significant.
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