The impact of network social presence on live streaming viewers’ social support willingness: a moderated mediation model

Abstract

With the accelerating development of social networks and the popularization of intelligent personal communication devices, live streaming has provided fluid experiences in time and space for the Chinese people, especially during the COVID-19 pandemic. Live streaming has enabled the real-time communication and interaction between viewer and live host, and has created a range of live hosts and new forms of business models due to the affordance of virtual currencies and gift reward mechanisms featured on live streaming platforms. Based on a questionnaire survey of 515 live viewers, this study examines the impact of the viewers’ network social presence on social support willingness and analyzes the roles of parasocial interaction and emotional response. The study reveals that network social presence has a direct positive impact on emotional, instrumental, and economic support willingness. Additionally, parasocial interaction plays a mediating role in the impact of network social presence on emotional, instrumental, and economic support willingness. Furthermore, the higher the degree of emotional response, the stronger the mediating effect of parasocial interaction on the relationship between network social presence and instrumental support willingness. Findings shed light on the potential intermediate mechanism and the boundary conditions of the influence of network social presence on the social support willingness of viewers, providing new insights on promoting the relationships between live hosts and viewers on live broadcast platforms.

Full text

ARTICLE
The impact of network social presence on live
streaming viewers’social support willingness: a
moderated mediation model
Zhenwu You1,2, Meng Wang1,2 ✉& Yangjin Shamu1,2
With the accelerating development of social networks and the popularization of intelligent
personal communication devices, live streaming has provided fluid experiences in time and
space for the Chinese people, especially during the COVID-19 pandemic. Live streaming has
enabled the real-time communication and interaction between viewer and live host, and has
created a range of live hosts and new forms of business models due to the affordance of
virtual currencies and gift reward mechanisms featured on live streaming platforms. Based on
a questionnaire survey of 515 live viewers, this study examines the impact of the viewers’
network social presence on social support willingness and analyzes the roles of parasocial
interaction and emotional response. The study reveals that network social presence has a
direct positive impact on emotional, instrumental, and economic support willingness. Addi-
tionally, parasocial interaction plays a mediating role in the impact of network social presence
on emotional, instrumental, and economic support willingness. Furthermore, the higher the
degree of emotional response, the stronger the mediating effect of parasocial interaction on
the relationship between network social presence and instrumental support willingness.
Findings shed light on the potential intermediate mechanism and the boundary conditions of
the influence of network social presence on the social support willingness of viewers, pro-
viding new insights on promoting the relationships between live hosts and viewers on live
broadcast platforms.
https://doi.org/10.1057/s41599-023-01892-8 OPEN
1School of Journalism and Information Communication, Huazhong University of Science and Technology, Wuhan, Hubei Province, China.
2
These authors
contributed equally: Zhenwu You, Meng Wang, Yangjin Shamu. ✉email: yunaimeng@hust.edu.cn
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1234567890():,;
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Introduction
The rapid development of 5G mobile internet and personal
intelligent terminal equipment significantly supports the
adoption of live streaming platforms. Live streaming has
become an important component of China’s internet economy.
According to CNNIC’s 50th “Statistical Report”(2022), the
number of live streaming users in China reached 716 million by
June 2022, accounting for 68.1% of the total number of internet
users. Lu et al., (2018) demonstrate that live streaming in China
has significant differences in content, style, and format when
compared to live streaming in North America and Europe. In
contrast, live streaming in China is more widely used and inte-
grates entertainment short video, goods-selling, online social
networking, and knowledge dissemination, which goes deeply
into all aspects of social life and serves as the main channel for
obtaining and transmitting information. As digital platforms for
real-time recording and uploading audio and video, live stream-
ing platforms connect and construct viewers’virtual presence
experiences with the distant world, based on the characteristics of
hyper temporal and spatial attributes, para-authenticity, real-time
interactivity, and connectivity (Cunningham et al., 2019; Lim
et al., 2020), which significantly affects social media users’
interactions and willingness to convey information (Lin et al.,
2014). Live streaming transmits images and sounds in real time
through a variety of communication technologies, enabling the
viewer to interact in real time on the platform. In the live
streaming system, the live host and the viewer can obtain a sense
of participation through real-time interaction, providing a unique
immersive interactive experience that can trigger viewers’beha-
vioral intention. For example, the viewer can support their
favorite host through monthly subscriptions or gift-giving
(Wongkitrungrueng et al., 2020). This kind of physical and
situational experience can help to bridge the psychological gap
between the live host and viewer, so as to promote the estab-
lishment of a closer relationship between the viewer, the live host,
and the platform (Liu et al., 2020), and enhance viewers’social
support willingness. Therefore, as a new media for real-time
broadcast and interaction, the impact of the unique immersive
reality and real-time interactive social experience of webcasts on
viewer behavior requires further study.
A wealth of existing work has explored the characteristics of
live streaming from the perspective of regional, cultural, profes-
sional, and gender performance aspects, among others (Wohn,
Freeman (2020); Hsu et al., 2020). Studies also address live
viewers, largely discussing the factors influencing viewer partici-
pation, based on technology adoption, user attitudes (Xu and Ye,
2020), and user motivations (Hilvert-Bruce et al., 2018; Chen and
Lin, 2018). However, current studies ignore the new phenomenon
of live streaming, as characterized by “immersive”experiences,
and the real psychological state of the viewer in online interaction
(Wongkitrungrueng et al., 2020). In fact, the interaction between
the live host and viewer is a critical element of the live platform.
In this process, the viewers are regarded as “real people”who can
perceive the existence of others and thus experience individual
psychological feelings, such as intimacy and psychological parti-
cipation (Short et al., 1976), resulting in pseudo intimacy (Horton
and Whol, 1956), which affects the viewers’cognition and
behavior, thus increasing the willingness of social support for the
live platform (Hassanein and Head, 2007). Therefore, network
social presence is undoubtedly an appropriate perspective from
which to study the viewer’s social support willingness.
We have discussed that, on live platforms, the viewer’s will-
ingness to support the live host is affected by network social
presence. Furthermore, in the live broadcasting field, we need to
reveal the practices of and mechanisms behind all parties’actions,
which work together to build emotional connections. Parasocial
interaction is influenced by interaction experience and network
social presence; additionally, the viewer’s network social presence
is an important prerequisite for parasocial interaction (Xiong,
2016). The sense of belonging, immersion, and other aspects of
network social presence generated by online interaction when
watching live streaming reflects whether the viewer can have a
sense of intimacy or direct feeling in interpersonal interaction.
Therefore, network social presence, as an individual’s intention to
maintain relationships, can cultivate a large number of positive
and loyal users, and serves as an important factor for the con-
struction of parasocial interaction. Above all, this study explores
the impact of viewers’network social presence on parasocial
interaction and social support willingness based on the live
broadcast environment in China. The structure of the paper is as
follows: the following section presents a literature review and our
research hypotheses. In “Research Design and Methods”,we
propose a research model and further detail the research vari-
ables. In the “Data Analysis and Results”section, we report the
sample and verify the research hypothesis. The “Discussion and
Conclusion”section explains the contribution, inspiration, and
limitations of this work.
Literature review and research hypothesis
Social support willingness of live viewer. The existing literature
has defined and presented social support in numerous ways and
from different angles. Early studies interpreted social support
from a functional perspective and claimed that social support is
related to material, psychological, and spiritual support (Hoffman
et al., 1988), which can convey care and love to the recipient
(Shumaker and Brownell, 1984), and make the recipient realize
that they are part of an interpersonal network. Broadly, social
support includes tangible support and intangible support. Tan-
gible support, also known as physical support, refers to a type of
resource to enhance self-esteem and provide ways to meet
material needs, such as instrumental assistance, goods, and
property. Intangible support refers to emotional care and the
belief that support is available (Barrera, 1986). Furthermore,
social support can be divided into offline social support and
online social support. Online social support mainly focuses on the
potential and willingness to obtain information or emotional
support through interpersonal relationships (Williams et al.,
2006), which is also known as social capital (Ellison et al., 2014).
This study explores the social support willingness provided by live
viewers to live hosts in online communities. Therefore, the social
support willingness in this study refers to willingness to provide
rather than accept behavior, that is, one’s willingness to provide
support at the information, assistance, and emotional levels
(Introne et al., 2016; Barak et al., 2008).
Existing studies mostly focus on the willingness to provide
informational support (Introne et al., 2016), emotional support
(Barak et al., 2008), and tangible social support (Lu et al., 2018).
During live streaming, the viewer can interact with the live host
or other viewers through messages, or respond to the host’s
questions and requests (Haimson and Tang, 2017).In addition to
watching, the viewer on the live streaming platform can also
express their support and appreciation for the live host through
likes, comments, and gifts (Haimson and Tang, 2017; Yu et al.,
2018), and also provide immediate help at the live host’s request.
Based on the above research, the viewer’s willingness to provide
social support is reflected in three aspects: instrumental,
emotional and economic support willingness (Wohn et al.,
2018). Instrumental support willingness refers to the willingness
of the viewer to provide direct assistance or practical action to the
live host and help others by solving problems; emotional support
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willingness refers to the viewer’s emotional willingness to
comfort, encourage, or care for the live host; economic support
willingness refers to the willingness of the viewer to provide
rewards and other financial support for the live host. Therefore,
this study will conduct a more detailed investigation of the
viewer’s willingness to provide social support from these three
aspects: instrumental, emotional and economic social support
willingness.
Network social presence and social support willingness. Short
et al., 1976 first proposed the term “social presence”and defined it
as the saliency of objects in media communication and the sub-
sequent saliency of interpersonal relationships. However, there are
now different perspectives and dimensions for the definition of
social presence. Initially, scholars explored social presence within
the characteristics of media and revealed the communication effect
of different media through comparing the differences between
remote communication media and face-to-face communication
(Short et al., 1976). Some scholars began with a more psychological
perspective and claimed that users’perception of media is more
critical than the attributes of the media itself; these writers further
defined social presence as the feeling of being with others in the
media environment, including the degree of trust in the process of
interaction (Yeboah & Afrifa-Yamoah, 2023).
The development of networks and the ontological subversion
of virtual reality has promoted more in-depth and specific
discussions on the impact of social networks on individuals
(Zhou et al., 2019). These studies focus on the extent to which
individuals can perceive the existence of others in the process of
using social networks; the individuals’psychological feelings, such
as intimacy; and the individuals’psychological involvement,
forming the so-called “network social presence”. In other words,
network social presence is a sense of authenticity that individuals
achieve through the social network, which makes individuals feel
immersed in the the digital setting (Pettey et al., 2010) and even
enhances individuals’willingness to take action on social network
(Cheung et al., 2015). Some scholars have applied network social
presence to online interactions and marketing research.
E-commerce studies have found that online shopping behaviors
within social networks are highly similar to those within real-
world settings. Some shopping websites and brands stimulate
consumer behavior by creating a network social presence and
maintaining relationships with consumers (Algharabat, 2018).
Jiang et al. (2022)affirm that social presence affects the continued
use and purchase intentions of Chinese consumers. Therefore,
network social presence is an important factor driving individual
consumption behavior intention.
As previously acknowledged, network social presence triggers a
higher willingness to consume and promote the provision of
more social support. Previous studies have shown that, in virtual
networks, network social presence can increase the stability and
satisfaction of the relationship between the two parties, and then
increase the willingness of users to use, consume, and recommend
products and services in the future (Choi et al., 2016; Chen et al.,
2023), and improve the potential willingness of social support.
Therefore, if the viewer is closely related to the platform and has a
high sense of network social presence, the viewer is more willing
to provide more social support to the platform and hosts. Based
on the above analysis, network social presence plays an important
role in the willingness of social support. Consequently, the
hypotheses are developed as follows.
H1. Network social presence has a positive impact on the
viewer’s willingness to support the host (a: emotional support
willingness; b: instrumental support willingness; c: economic
support willingness).
Mediation: parasocial interaction. Parasocial interaction was
first proposed by Horton and Whol, 1956 who defined parasocial
interaction as a “simulacrum of conversational give and take.”
Horton and Strauss (1957) further indicated that parasocial
interaction is a solely one-sided experience of the audience; most
examples of this type of experience are based on the audience’s
own illusion. Rubin and McHugh (1987) echoed this finding,
describing parasocial interaction as a one-way interpersonal
relationship between media performers and their TV audiences.
Currently, parasocial interaction has been introduced into live
streaming contexts, emphasizing the “illusion”and unilateral
intimacy between the viewer and live host (Chen et al., 2021;
Sheng et al., 2022). Furthermore, parasocial interactions can
influence viewers’emotional responses, attitudes, and behaviors
(Chang and Kim, 2022; Sheng et al., 2022), the center of attention
in this study.
The development of the internet and social network has
promoted increasing numbers of scholars to explore the impact of
new media use on individual users from the perspective of
network social presence (Gao et al., 2017). In addition,
investigations of network social presence highlight that the
virtual space built through technology, similarly to real commu-
nication, can make media users perceive strong sociality,
authenticity, and intimacy, and produce a strong sense of
belonging, reflecting the degree to which individuals use media
to build interpersonal relationships. Therefore, network social
presence, as an important social psychological factor in the use of
individual new media, has a far-reaching impact on interpersonal
communication, which deserves more attention. The network
social presence can enhance the immersion and authenticity of
group communication, improve the interactive experience
between live hosts and strengthen the network density, which
can build a close connection between members, maintain the
rapid flow of information and resources, and then generate a
sense of identity with the group. In the live streaming, the viewer
and the host in the live room perceive each other’s existence,
cause emotional reactions, and gradually build a parasocial
relationship by continuously participating in online discussions.
Therefore, there is a positive relationship between network social
presence and parasocial interaction. In line with the above, the
following hypothesis was created.
H2. Network social presence has a positive effect on the
parasocial interaction between the viewer and live host.
Cohen (2004) believes that parasocial interaction is most
suitable for analyzing media figures who directly talk to the
audience, such as newscasters and hosts. Rubin and Step (2000)
found the parasocial interaction of radio hosts leads to changes in
audience attitudes and behaviors. In recent years, the vigorous
development of social networks, especially the widespread use of
live platforms, has narrowed the distance between media figures
and audiences, prompting new research on parasocial interaction.
Lee and Watkins (2016) highlights the potential of social
networks in establishing two-way communication and balancing
the relationship between media users and media properties.
Stever and Lawson (2013) claim that YouTube, TikTok, and other
social networking sites allow the audience to approach the
personal life of the media personality within the scope of the
media personality, so as to obtain more social support.
Furthermore, social media has expanded the phenomenon of
parasocial interaction from solely concerning the world of TV
characters to becoming a real tool for marketing brands to
consumers (Lee and Watkins, 2016). For example, Hsu et al.
(2020) found that vloggers can deepen viewers’identity and sense
of belonging and cultivate viewers’fluid experience by establish-
ing parasocial interaction, thereby urging viewers to purchase and
enabling addiction. Especially in the live streaming environment,
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parasocial interaction affects the social interaction between the
live host and the viewer (Hu et al., 2017; Lim et al., 2020), and can
promote the impulsive consumption of the audience (Xiang et al.,
2016).
In view of the above discussion, we believe that parasocial
interaction has a mediating effect between network social
presence and social support willingness. The existing studies
provide a logical basis for investigating this mechanism. It is
generally believed that the higher the viewer’s perception of
network social presence, the higher the frequency of interaction
on the network, so the viewer is more likely to feel that they are in
a“real”network society, which is also known as network society
presence. As for the impact of parasocial interaction on network
social presence and social support willingness, studies have found
that the degree of interactivity and vividness of online advertising
are regulated by the audience’s social presence, and influences
their attitudes and behavioral intentions (Lu et al., 2016). In
addition, according to the existing research, network social
presence can influence users’satisfaction and sense of belonging,
increase the possibility of contact, and strengthen the parasocial
interaction between the audience and the live host, so as to trigger
more social support (Lin et al., 2014). Therefore, the interpersonal
interaction in the live streaming environment should be included
in the influence of network social presence. According to the
different degrees of network social presence, viewers with high
network social presence are more suitable to perform tasks related
to interpersonal interaction. Above all, network social presence
may not only directly affect the social support willingness of
viewers but also indirectly affect the social support willingness by
enhancing the parasocial interaction. Therefore, we propose the
following hypothesis:
H3. Parasocial interaction plays a mediating role between
network social presence and social support willingness (a:
emotional support willingness; b: instrumental support will-
ingness; c: economic support willingness).
Moderation: emotional response. There are different opinions
regarding whether different emotional experiences produce dif-
ferent physiological reactions. Every emotion is multi-dimen-
sional; Mehrabian (1995) believed that an emotional response has
three aspects: pleasure, arousal, and dominance, namely, the PAD
emotional-state model. Pleasure refers to the positive or negative
performance of emotions such as happy, satisfied, and satisfied;
arousal refers to the level of individual physiological activation
and alertness, on a scale of drowsiness to excitement; dominance
is the state of individual control over situations or others. This
model is widely used in environmental psychology; although it is
intended to represent the dimensions of emotional response
rather than a complete typology of emotional responses (Eroglu
et al., 2003), its simple structure and widespread use make it an
appropriate choice in this context. Russell (1979) believed that
pleasure and arousal adequately capture the range of appropriate
emotional responses, and Eroglu et al., (2003) demonstrated that,
when studying emotions in the network, pleasure and arousal are
commonly used to present individual emotional responses.
Therefore, when exploring the emotional response of Chinese live
cast viewers, this study only draws on the pleasure and arousal
emotions in the PAD emotional state model, and the dominance
dimension is not included. As the core construct of emotional
response, the degree of pleasure and arousal is generated on the
basis of cognition, which in turn affects cognition. They are the
internal factors that regulate and control cognition, so as to
achieve different psychological dynamic response (Pan and
Huang, 2017). Multiple studies have explored how emotional
constructs interact with and influence user attitudes and
behaviors, confirming that individual behavior is regulated by
positive emotions (Gavriel-Fried and Ronen, 2016).
In the context of webcasts, the viewer’s social and psychological
state is an important determinant of how viewers choose a live
host to meet their needs; that is, the viewer can be aware of their
needs, consider various channels and content, evaluate the choice
of functionality, and choose the media that they believe can
provide the satisfaction they seek. In this framework, parasocial
interaction is considered to meet the emotional needs of the
viewer and can reduce anxiety (Suggs & Guthrie, 2017). If the live
host can provide positive emotions for the viewer and activate
individual energy, viewers will have high satisfaction with the live
host, which is the main reason for viewers to form parasocial
interaction and provide social support. Specifically, a parasocial
interaction relationship is formed between the live viewer and the
live host, which conveys valence and arousal to the viewer, thus
making the viewer more inclined to provide social support. At the
same time, the financial media environment enabled by new
technology promotes highly autonomous participation mechan-
isms, and the emotional perception of the webcast platform also
promotes the continuous use and recommendation willingness of
viewers (Han et al., 2015). Therefore, under the influence of
different degrees of valence and arousal, the influence of the
parasocial interaction perception on viewer’s social support
willingness is also accordingly different. Specifically, in the case
of high positive emotional response, the impact of parasocial
interaction on social support intention increases. However, in the
situation of low positive emotional response, the incremental
impact of parasocial interaction on social support decreases.
Based on the above analysis, we hypothesize the following:
H4. Emotional response amplifies parasocial interaction’s effect
on social support willingness (a: emotional support willingness; b:
instrumental support willingness; c: economic support willingness).
Due to the experiential communicability and flexibility (Dale
and Pymm, 2009) of live streaming, viewers feel a sense of
belonging and pleasure when watching the live content. In this
process, the relationship between viewer and the media has
become closer and has broken through the constraints of time
and space, which can guide the viewer’s online social presence
and trigger an obvious positive emotional response. Furthermore,
emotion, as one of the factors affecting user behavior (Gavriel-
Fried and Ronen, 2016), is guided by a positive relationship
between viewer and host that urges both parties to work together
to better meet each other’s needs. Previous studies have shown
that emotional response can not only affect audience satisfaction
but also adjust audience’s willingness to support (Cheikh-Ammar
and Barki, 2016). As a high level of network social presence can
arouse the viewer’s positive emotional response, the viewer may
therefore maintain a long-term good relationship with the host
and provide social support. Hence, we hypothesize the following:
H5. Emotional response has a moderation role in the process
of the effect of network social presence on social support
willingness (a: emotional support willingness; b: instrumental
support willingness; c: economic support willingness).
According to the above theoretical basis and research
assumptions, this study proposes the following research model,
as shown in Fig. 1.
Methodology
Sample and data collection. This study takes users who are over
18 years old and have watched the live as the research sample.
The data for the study was collected through a snowball sampling
procedure. Because of the unavailability of a valid sample frame
and the difficulty of conducting random sampling for all live
streaming viewers, this study used a non-probability sampling
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approach. The snowballing sampling is simple, inexpensive, and
usable, and it is also helpful in determining the relationships
between various events and situations (Sahu et al., 2021). Further,
due to the snowball effect of participant referrals, a higher
number of responses were obtained. Previous studies confirm that
the snowballing sampling method is effective and appropriate for
multivariate data processing and estimating the results (Almaq-
tari et al., 2023; Sahu et al., 2021; Chan, 2020; Noy, 2008; Wright
and Stein, 2004).
We designed the questionnaire on QuestionnaireStar (https://
www.wjx.cn/; a professional data collection website in China).
Respondents can access our questionnaire homepage through an
online link. The primary researcher used personal communica-
tion with “seeds”to assist the data collection process. The
questionnaire survey started on June 9, 2022 and ended on June
24, 2022. They sent the questionnaire to respondents via social
medias and asked respondents to send it to another potential
participant after completing the survey. Respondents’participa-
tion was completely consensual, anonymous, and voluntary.
Moreover, the questionnaire does not cover the highly sensitive
personal identity information such as the name, home address,
telephone number, ID number of the respondents, ensuring the
confidentiality and anonymity. The data obtained in this survey is
only for academic research purposes. In addition, all procedures
performed in studies involving human participants followed the
ethical standards of the institutional and/or national research
committee and with the 1964 Declaration of Helsinki and its later
amendments or comparable ethical standards.
To obtain valid samples, we created two screening questions:
“Are you over 18 years old”and “Have you watched live
streaming before?”If one of the respondent’sanswersis“no,”
the participant is directed to the end of the survey. After
excluding the samples who areundertheageof18,hadnot
watched the live streaming, and expressed an abnormal
response time, 515 valid samples remained, and the sample
pass rate was roughly 83.5%. Hair et al., (2006) mentioned that
thefactoranalysisrequiresaminimumsamplesizeofatleast
five times the number of measurement items in the study. This
present study has 25 measurement items adapted from previous
literature, thus, the appropriate number of sample size would be
at least 125 respondents. Thus, the current sample size of 515
participants was suitable for conducting the research. Therefore,
515 surveys were considered the final sample for the present
study. As the Kaiser-Meyer-Olkin measure of sampling
adequacy value is 0.954, it is greater than 0.7, this sample is
considered statistically adequate for estimating the results.
Furthermore, this test shows high significance at the 1% level
(p-value =0.000, <0.01), indicating the suitability and adequacy
of the sample.
Demographic information of the sample. To increase the gen-
eralizability of the findings, respondents with diverse backgrounds
(age, gender, education, residence, etc.) were selected (as per Mla-
denovićet al., 2020). As shown in Table 1, 227 respondents
(44.08%) were male. Most respondents were between 18 and 39
years old (78.84%). In terms of education level, the number of
respondents with a bachelor’s degree was the largest, 256 (49.71%);
followed by below a bachelor’s degree, 186 (36.12%); and then a
master’s degree or above, 73 (14.17%). Most of the respondents
watched live streaming platforms for an average of 2.6 days a week:
196 samples (38.06%) watched live streaming platforms for less
than one day, 199 samples (38.64%) for 1–3 days, and 120 samples
(23.3%) for more than three days. 390 samples (75.7%) spent an
average time per viewing of less than one hour, 94 samples (18.25%)
less than one hour, and 31 samples (6.02%) more than two hours.
According to the 2021 Research Report on the Development of
China’s Online Live Broadcasting Industry, in 2021, 74.4% of
China’s online live broadcasting users were 39 years old or younger:
47.1% of users were male, 52.9% were female users, 78.1% watched
each live broadcast for less than one hour, and 17.2% watched for
one to two hours (iiMediaResearch (2022)). Therefore, the basic
characteristics of the sample in this study are broadly consistent
with the current composition of China’s network live streaming
users, indicating that the sample is representative.
Fig. 1 Research model. The impact of network social presence on live streaming viewers’social support willingness: a moderated mediation model.
Table 1 Characteristics of respondents (n=515).
Variable Classification Respondents
N%
Gender Male 227 44.08
Female 288 55.92
Age
18–28 years old 237 46.02
29–39 years old 169 32.82
40–50 years old 94 18.25
>50 years old 15 2.91
Degree Below bachelor’s
degree
186 36.12
Bachelor’s degree 256 49.71
Master’s degree or
above
73 14.17
Number of days per week to watch
live cast
Less than one day 196 38.06
1–3 days 199 38.64
More than 3 days 120 23.30
Average time spent watching live
cast
Les than one hour 390 75.7
1–2 h 94 18.25
More than 2 h 31 6.02
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Variable measurement. To increase the validity and reliability of
the results, each construct in the model has multiple items
adapted from previous studies with minor changes to fit the
research context. All the questionnaire information was translated
from English to Chinese with manual testing to optimize wording
and grammar to ensure the linguistic accuracy and comprehen-
sibility of the questionnaire. This study used a seven-point Likert
scale to measure survey items, where one indicates strongly dis-
agree/inconsistent and seven indicates strongly agree/consistent.
Network social presence. The measurements of network social
presence were adopted from studies by Hassanein and Head
(2007), Lu et al., (2016), and Gao et al., (2017). Some scale items
were deleted per the CFA, leaving eight items. Respondents were
asked to rate how they felt about the following statements; a
seven-point Likert-type scale (one denoting “strongly disagree”
and seven denoting “strongly agree”) was utilized for the fol-
lowing eight statements: (1) “in the live studio, I will pay close
attention to the existence of others”; (2) “I felt someone
approaching me in the live studio”; (3) “in the live studio, I have a
sense of reality of face-to-face communication with others”; (4)
“in the live studio, I have a feeling of social interaction”; (5) “in
the live studio, I have a warm feeling”; (6) “in the live studio, I feel
close to others”; (7) “in the live studio, the knowledge shared by
the live host can benefitme”; (8) “in the live studio, I have a high
degree of recognition for the behavior and view of the live host.”
An overall network social presence composite measure was cre-
ated by averaging the seven items together, Cronbach’sα=0.888,
M=3.434, SD =1.035.
Parasocial interaction. Because the initial parasocial interaction
scale was created considering television news announcers (Rubin
et al., 1985), some measurement items are not suitable for online
live streaming (for example, “I miss seeing my favorite newscaster
when he or she is on vacation”) or present a specific program
format or content (for example, “When the newscasters joke
around with one another it makes the news easier to watch”);
these items were excluded. To measure the quasi-social interac-
tion of research objects, previous research adapted the initial 20
measurement items from Rubin, Perse, and Powell, (1985) and
reduced them to several measurement items, as found in studies
by Choi et al., (2019), Kim and Song, (2016). Reducing the
number of measurement items is suggested for addressing
response behavior and data quality problems (Cheah et al., 2018;
Drolet and Morrison, 2001), especially for participants from the
general public (Messer et al., 2012). This study selected five
current measurement items based on the characteristics of online
live streaming, and these five measurement items showed strong
reliability in this study (Cronbach’sα=0.913), indicating that the
internal consistency of these five measurement items is highly
suitable for measuring quasi-social interaction. The five items are
as follows: (1) “I look forward to watching the live broadcast on
her/his live channel”; (2) “Watching the live stream makes me feel
that the live host is accompanying me”; (3) “I think the live host is
like my friend”; (4) “I will pay attention to the news of my
favorite live host”; (5) “When I watch the live stream, I feel like I
am a member of their live team.”A seven-point Likert-type scale
(1 denoting “strongly disagree”and 7 denoting “strongly agree”)
was used for all items. To create a composite measure of para-
social interaction, the five items were averaged together,
M=3.529, SD =1.224.
Emotional response. The measurements of emotional response
were derived from studies by Mehrabian (1995) and Jin et al.,
(2020). The PAD emotional-state model is widely applied in
environmental psychology, although it is intended to represent
the dimensions of emotional response rather than a complete
typology of emotional responses (Eroglu et al., 2003). However,
its simple structure and widespread use make it a suitable choice
in this context. Initially, we designed three items to measure
pleasure and arousal, but some scale items were deleted per the
confirmatory factor analysis (for example, “Live streaming con-
tent makes me interested”,“Watching live streaming makes me
happy”, and “Watching live streaming makes me feel stimu-
lated”). A total of three measurement items remained, and these
three measurement items showed strong reliability in this study
(Cronbach’sα=0.813), indicating that the internal consistency of
these three measurement items is highly suitable for measuring
emotional response. The five items are as follows: (1) “I enjoy
myself in the live studio”; (2) “The live streaming content makes
me feel novel and fresh”; (3) “Watching the live stream can make
my life and work full of power.”A seven-point Likert-type scale
(one denoting “strongly inconsistent”and seven denoting
“strongly consistent”) was used for all items. In this study, three
items were summed and averaged to establish a comprehensive
index of emotional response, M=3.797, SD =1 .104.
Social support willingness. The measurements of emotional
support willingness, instrumental support willingness, and
economic support willingness were adopted from Wohn et al.,
2018 study. A seven-point Likert-type scale (one denoting
“strongly disagree”and seven denoting “strongly agree”)was
used for all items. Among them, emotional support willingness
includes three items: (1) “I am willing to send some encoura-
ging words on the bullet screen to show my support for the live
host”;(2)“I am willing to try to interact with the live host to
make them feel concerned”;(3)“I am willing to express my
support for the live host in some way.”This study sums the
three items and averages them to construct the index of emo-
tional support willingness (Cronbach’sα=0.898, M=3.790,
SD =1.360). Instrumental support willingness includes three
items: (1) “If the live host really needs it sometimes, I am
willing to try to help him/her”;(2)“If the live host needs to
complete a time-limited task, I am willing to help him/her”;(3)
“If there is a problem in the live studio, I am willing to help the
live host solve it.”This study sums the three items and averages
them to construct an indicator of instrumental support will-
ingness (Cronbach’sα=0.917, M=3.548, SD =1.328). Eco-
nomic support willingness includes three items: (1) “Iam
willing to reward the live host by giving virtual currency and
help him/her make a living”;(2)“I would like to reward the live
host to express my gratitude by giving virtual currency”;(3)“I
am willing to reward the live host and support his/her efforts by
giving virtual currency.”This study sums the three items and
averages them to construct the index of economic support
willingness (Cronbach’sα=0.931, M=2.862, SD =1.423).
Results
Analysis of variable correlation. Table 2shows the correlation
between the six variables. There is a positive correlation between
network social presence and emotional support willingness
(r=0.597, p< 0.01), instrumental support willingness (r=0.602,
p< 0.01), economic support willingness (r=0.518, p< 0.01), and
parasocial interaction (r=0.703, p< 0.01). Parasocial interaction
is also positively correlated with emotional support willingness
(r=0.703, p< 0.01), instrumental support willingness (r=0.700,
p< 0.01), and economic support willingness (r=0.577, p< 0.01).
These findings provide preliminary data support for the sub-
sequent hypothesis verification.
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Reliability, validity, and common method bias. In this study,
the software Mplus 8.3 was used for confirmatory factor analysis
to test the reliability and validity. According to Fornell, Larcker,
(1981) and Hair et al., (2019), the AVE value of all variables being
greater than 0.5 (Table 3) indicates that the six variables have
good aggregate validity. The factor load of the item corresponding
to each variable is greater than 0.5, and the square root of AVE is
greater than the correlation coefficient between all variables
(Table 2), indicating that the discriminant validity of each vari-
able is good. In addition, the combined reliability (CR) of all
variables and Cronbach’sαwere greater than 0.7, indicating that
the internal consistency of the questionnaire is high and each
variable has good reliability. Therefore, the variables measured in
this study are valid and credible.
This study used a single data source for six variables, so it had the
possibility of common method bias (CMB). To ensure the accuracy
of the research conclusion, we have taken several steps to solve the
potential CMB. First, we used anonymous questionnaires to
improve the objectivity and freedom of respondents to answer
questions. Second, we used different types of response scales (highly
agree–highly disagree; very consistent–very inconsistent). Third, we
used the test of multicollinearity through the variance inflation
factor (VIF) to check if CMB may be a threat (Kock, 2015). The
VIFs were lower than 3.3, indicating that common method variance
in the data was not detected as CBM (Kock, 2015).
Hypothesis testing
Direct effect test. H1 and H2 were verified by hierarchical
regression analysis in SPSS 26.0 software because linear models
usually require normal distribution of dependent variables.
However, the distribution of parasocial interaction (Kolmogorov-
Smirnov z=0.180, p< 0.001), emotional support willingness
(Kolmogorov-Smirnov z=0.185, p< 0.001), instrumental sup-
port willingness (Kolmogorov-Smirnov z=0.198, p< 0.001), and
economic support willingness (Kolmogorov-Smirnov z=0.169,
p< 0.001) significantly deviated from the normal distribution.
Therefore, this study adopts the bootstrapping method to per-
form regression analysis of 1000 samples under a 95% confidence
interval. Bootstrapping is a nonparametric statistical method. Its
basic principle is that when the assumption of normal distribu-
tion is not tenable, a certain number of samples are resampled
within the scope of the original sample data, and the parameters
obtained by averaging each sampling are taken as the final esti-
mation results.
The results of regression analysis (Table 4) show that network
social presence has a significant effect on emotional support
willingness (β=0.787, p< 0.001), instrumental support will-
ingness (β=0.769, p< 0.001), and economic support willingness
(β=0.708, p< 0.001). That is to say, the stronger the network
social presence, the more likely the viewer will have social support
for the live host. Therefore, H1a, H1b and H1c are supported.
The results of the regression analysis also show that the
network social presence can positively influence the parasocial
interaction (β=0.831, p< 0.001). This influence demonstrates
that the enhancement of network social presence can strength
Table 3 Reliability and validity test.
Constructs Items Loading CR AVE
Network social presence NSP1 0.592 0.890 0.506
NSP2 0.551
NSP3 0.746
NSP4 0.751
NSP5 0.834
NSP6 0.812
NSP7 0.668
NSP8 0.686
Parasocial interaction PIS1 0.809 0.914 0.681
PIS2 0.851
PIS3 0.865
PIS4 0.816
PIS5 0.783
Emotional response ER1 0.792 0.812 0.591
ER2 0.716
ER3 0.796
Emotional support willingness ESW1 0.855 0.900 0.751
ESW2 0.904
ESW3 0.839
Instrumental support willingness ISW1 0.857 0.918 0.790
ISW2 0.898
ISW3 0.910
Economic support willingness FSW1 0.876 0.933 0.824
FSW2 0.947
FSW3 0.898
Table 2 Correlation coefficient matrix and square root of each variable AVE.
No Variable 1 2 3 4 5 6
1 Network social presence 0.711
2 Parasocial interaction 0.703** 0.825
3 Emotional response 0.702** 0.744** 0.769
4 Emotional support willingness 0.597** 0.703** 0.642** 0.867
5 Instrumental support willingness 0.602** 0.700** 0.600** 0.773** 0.889
6 Economic support willingness 0.518** 0.577** 0.482** 0.595** 0.663** 0.908
**p< 0.01; The value bold at the diagonal is the square root of AVE.
Table 4 Hierarchical regression analysis.
Independent
variable
Parasocial
interaction
Emotional
support
willingness
Instrumental
support
willingness
Economic
support
willingness
First block
Gender 0.015
(0.110)
0.113
(0.122)
0.116
(0.119)
−0.121
(0.128)
Age 0.012
(0.009)
0.010
(0.010)
0.019
(0.010)
0.009
(0.011)
Education
level
0.106
(0.056)
0.102
(0.062)
0.135*
(0.061)
0.030
(0.065)
Marital status −0.107
(0.176)
−0.260
(0.195)
−0.203
(0.191)
−0.276
(0.205)
Monthly
income level
−0.011
(0.029)
−0.015
(0.032)
−0.023
(0.031)
0.035
(0.034)
△R20.011 0.016 0.018 0.012
Second block
Network
social
presence
0.831***
(0.037)
0.787***
(0.047)
0.769***
(0.045)
0.708***
(0.052)
△R20.498 0.370 0.373 0.274
Notes: ***p< 0.001;**p< 0.01; *p< 0.05; Standard error in parentheses.
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parasocial interaction between the live viewer and the live host.
Therefore, H2 is supported.
Mediating effect of parasocial interaction. SPSS PROCESS macro-
Model 4 was applied to analyze the mediating effect of parasocial
interaction on the relationship between network social presence
and emotional support willingness, instrumental support will-
ingness, and economic support willingness. The mediating effect
model results (Table 5) show that the parasocial interaction of live
viewers regarding the network social presence and emotional
support willingness (β=0.510, 95% CI [0.405, 0.612]), instru-
mental support willingness (β=0.487, 95% CI [0.388, 0.594]),
and economic support willingness (β=0.408, 95% CI [0.288,
0.531]) played a significant positive mediating role. Therefore,
H3a, H3b, H3c are supported.
Moderating effect of emotional response. Through the model 15 in
PROCESS V4.0 of SPSS 26.0, this study continues to test whether
the moderating effect of emotional response is tenable under the
mediation effect of parasocial interaction. We performed a
bootstrap test by taking the score of the average emotional
response with plus or minus one unit of standard deviation. The
results show that (Table 6) the parasocial interaction and emo-
tional response has a significant predictive effect on the instru-
mental support willingness (β=0.096, t=2.142, p< 0.05).
Therefore, emotional response plays a significant positive
moderating role in “network social presence-parasocial interac-
tion-instrumental support willingness”.
Table 7shows that the mediating effect of parasocial
interaction is significant at three levels of emotional response.
Specifically, when the level of emotional response is low (less than
1 standard deviation), the mediating effect of parasocial
interaction is supported (β=0.346, 95% CI [0.195, 0.503],
excluding 0). The mediating effect of parasocial interaction is
supported when the emotional response is averaged (β=0.434,
95% CI [0.315, 0.553], excluding 0) and higher than 1 standard
deviation (β=0.522, 95% CI [0.376, 0.653], excluding 0).
Moreover, the mediating effect of parasocial interaction increases
significantly with the improvement of emotional response. That is
to say, the mediating effect of parasocial interaction is the
strongest when emotional response is high (β=0.522). In
Table 5 Results of mediating effect.
Mediation path Effect Effect value SE 95%CI Mediation effect value
LL UL
X→M→Y
1
Total effect 0.787 0.047 0.696 0.878 --
Direct effect 0.277 0.057 0.166 0.389 35.2%
Indirect effect 0.510 0.054 0.405 0.612 64.8%
X→M→Y
2
Total effect 0.769 0.045 0.680 0.858 --
Direct effect 0.282 0.056 0.173 0.392 36.7%
Indirect effect 0.487 0.052 0.388 0.594 63.3%
X→M→Y
3
Total effect 0.708 0.052 0.606 0.811 --
Direct effect 0.300 0.069 0.165 0.435 42.4%
Indirect effect 0.408 0.063 0.288 0.531 57.6%
Xnetwork social presence, Mparasocial interaction, Y
1
emotional support willingness, Y
2
instrumental support willingness, Y
3
economic support willingness, S.E. standard error, CI confidence interval, LL
lower limit, UL upper limit.
Table 6 Results of moderated mediation effect.
Dependent variable Independent variable Fitting index Significant coefficient
R2FβS. E t-value
Emotional support willingness A 0.542 59.552 0.185** 0.061 3.040
B 0.494*** 0.055 9.049
C 0.264*** 0.062 4.239
A*C −0.045 0.050 −0.903
B * C 0.051 0.045 1.123
Instrumental support willingness A 0.528 56.455 0.246*** 0.060 4.080
B 0.523*** 0.054 9.674
C 0.133*0.062 2.154
A*C −0.080 0.050 −1.600
B * C 0.096*0.045 2.142
Economic support willingness A 0.374 30.051 0.291*** 0.074 3.914
B 0.470*** 0.067 7.049
C 0.071 0.076 0.932
A * C 0.079 0.062 1.291
B*C −0.001 0.055 −0.023
ANetwork social presence, BParasocial interaction, CEmotional response, ***p< 0.001, **p< 0.01, *p< 0.05.
Table 7 Mediation effect of parasocial interaction at
different levels of emotional response.
Emotional
response
Effect size S. E 95%CI
Ll UL
Parasocial
interaction
low 0.346 0.079 0.195 0.503
middle 0.434 0.060 0.315 0.553
high 0.522 0.071 0.376 0.653
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summary, the network social presence indirectly affects the
instrumental support willingness of the live viewers by influen-
cing the parasocial interaction. The strength of this indirect effect
depends on the level of emotional response. The higher the
emotional response, the stronger the mediation effect of
parasocial interaction. However, other moderating effects of
emotional response were not significant. Therefore, H5a is
verified and H5b and H5c are not supported.
Discussion
This paper examines the impact of network social presence,
parasocial interaction, and emotional response on livestream
viewers’social support willingness, as well as the interaction
mechanisms between these influencing factors. Through 515 valid
samples, we have supported some previous research conclusions
but also creatively proposed some research arguments, inspected
and tested them, and finally constructed a possible pathway that
affects the willingness of live viewers to provide social support to
the live host. The specific research conclusions are as follows.
This study found that network social presence can positively
promote the willingness of live viewers to support the host at
three levels of social support. These findings suggest that network
social presence is an important factor in explaining social support
willingness in live streaming environments. The findings show
that the network social presence can give the viewer a real
experience in the media intermediary environment, not only
allowing viewers to have a positive attitude towards the media,
improving the pleasure of media viewing, but also enhancing the
persuasive effect of media information (Westerman et al., 2015).
Previous research has also confirmed the importance of network
social presence in intermediary environments (Cummings and
Wertz, 2022). The findings support the proposition that network
social presence positively impacts the economic support will-
ingness of live viewers, and this is consistent with previous stu-
dies, which suggests that shaping viewers’network social presence
is an effective strategy for live streaming platforms to maintain
their cooperation with the viewers and subsequently trigger more
purchasing addiction (Huang et al., 2022; Algharabat, 2018).
However, apart from the economic support willingness, limited
research has explored the effect of network social presence and
other forms of support willingness. Therefore, this study further
investigated the relationship between network social presence and
emotional and instrumental support willingness, and found that
network social presence also had a positive predictive effect on
emotional and instrumental support willingness in the live
streaming environment. Compared to traditional media, live
streaming platforms have the advantage of being three-dimen-
sional, interactive, and real-time, allowing viewers to observe the
host’s facial expressions, body gestures, and their offices (or
homes) while watching live streaming, and to hear their voices in
real time. These rich sensory stimuli make online conversations
similar to face-to-face interactions (Zhang et al., (2022)), gen-
erating a sense of identity and companionship with the host, as
well as the sense of coexistence and connection, immersing
viewers in a virtual interaction. This sense of social presence in
the virtual space as a factor helping the development of close
social bonds (Maloney and Freeman, 2020) has a positive impact
on the viewer’s social support willingness. This research conclu-
sion expands upon and enriches the research and application
aspects of this relationship.
Moreover, network social presence was found to have a sig-
nificant and direct impact on parasocial interaction. This is
consistent with previous research results (Kim and Song, 2016;
Lee, 2013). These findings resonate well with the notion that
social presence can affect the formation of PSI (Lombard and
Ditton, 1997). As the media form evolves in the direction of
“humanization”proposed by Levinson, the necessity of “personal
participation”in social activities decreases with the evolution of
media (Meyrowitz, 1986). Live streaming not only enables the live
host to release information in the form of text and pictures but
also through voice and video that can convey richer social clues
over a variety of communication technologies. High network
social presence communication that uses humor, emojis, and
phatic communication to express interconnectedness with view-
ers can foster a sense of intimacy, which is part of the parasocial
interaction experience (Rubin, 2002). This humorous or warm
communication style encourages the viewer to believe that the
live host is friendly and warm, which helps to narrow the psy-
chological distance between them (Lu et al., 2016), thereby pro-
viding the viewers with an imaginary sense of intimacy and social
bond with the live host, even considered the live host to be a
friend and companion.
On the other hand, parasocial interaction plays a significant
positive mediating role in the relationship between network social
presence and emotional, instrumental, and economic support
willingness. That is to say, the network social presence can
indirectly affect the viewer’s social support willingness by influ-
encing their parasocial interaction. Specifically, during a live
stream, the live host not only provides the viewer with functional
benefits but also creates interactive relationships with viewers
which can help create emotional experiences such as setting off
the atmosphere, arising resonance, enhancing the authenticity
and intimacy of the communication, and improving the inter-
active experience between the viewer and host. The stable para-
social interaction as a spiritual relationship model can be
maintained to enhance the viewer’s willingness to provide sup-
port for the live host (Horton and Whol, 1956).
Significantly, this study also examines the emotional response
of the live viewer and investigates whether the mediating effect of
parasocial interaction on the relationship between network social
presence and social support willingness is moderated by emo-
tional response. Previous studies largely focus on the direct effect
of emotional response on individual behavior (Zhang et al., 2012;
Klein et al., 2009). There is a gap in the literature regarding
whether emotional response indirectly affects social support
willingness through parasocial interactions. This study offers new
insights that emotional response only moderates the relationship
between parasocial interaction and instrumental support will-
ingness. Specifically, emotional response strengthens the rela-
tionship between parasocial interaction and instrumental support
willingness. In the live streaming environment, the interaction
between the live host and the viewer makes the viewer feel as if
they are close friends in real life (Horton and Wohl, 1956),
arousing the viewer’s emotional pleasure, and thus enhancing the
willingness and motivation to provide instrumental support.
Nevertheless, emotional response has no moderating effect on the
relationship between parasocial interaction and emotional and
economic support willingness. The first reason for this lack of a
moderating effect may be that, for the sake of performance, the
live host is often oriented by task interaction; that is, the host
needs to interact with multiple viewers synchronously, utilizing
the limited live time to complete the explanation and promotion
of goods, ignoring the establishment of emotional social inter-
action with the viewer. This one-to-many asymmetric commu-
nication interaction mode weakens the real-time interaction
experience of some viewers and reduces the trust of the viewer
(Yu et al., 2018; Chen et al., 2017). Second, live streaming is
unlike other online community activities with reciprocal support
(Introne et al., 2016). The live host does not provide economic or
instrumental feedback to the viewer. Third, Chinese internet users
have been accustomed to a “free”online consumption mode for a
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long time, and many viewers instinctively have a resistance to the
reward and gift-giving mechanism in the live platform. The
interaction loss, one-way payment and free inertia make it diffi-
cult for emotional response to play a moderating role in the
process of parasocial interaction affecting emotional and eco-
nomic support willingness.
Research implications
Theoretical implications. This study has three theoretical con-
tributions. Firstly, social support willingness is a multi-
dimensional structure. Currently, academia has not reached
consensus on these dimensions, but research has consistently
shown that there is a difference between tangible support (such as
instrumental assistance, goods, services, money) and intangible
support (such as emotional care, information; Barrera, 1986;
Weiss, 1974). Around webcasting, our study has supported social
support willingness as a three-dimensional structure, including
emotional support willingness, instrumental support willingness,
and economic support willingness. One advantage of a multi-
dimensional conceptualization of social support intention is the
ability to better capture the different behavioral intentions of live
viewers, which overcomes the limitations of viewing social sup-
port intention as a one-dimensional structure. Moreover, pre-
vious research on the social support willingness of live viewers has
mainly focused on economic support, such as consumer purchase
willingness (Huang et al., 2022; Chen et al., 2018) and gift-giving
behavior (Zhou et al., 2019) in online live streaming, but little
progress has been made in studying the factors that affect the
instrumental and emotional support willingness of live viewers.
The findings of this study provide a deeper understanding of what
factors affect the three dimensions of social support willingness of
live viewers. Therefore, our research complements existing
research on the viewer’s social support willingness in the online
live streaming environment.
Secondly, this study provides a new path for the study of
network social presence. As an emerging type of social television,
live streaming is more attractive than other media such as video
games, online shopping, and online broadcasting because it
provides both entertainment and immersive experiences (Haim-
son and Tang, 2017). Past research has shown that network social
presence is an important factor affecting quasi-social interaction
(Kim and Song, 2016; Lee, 2013) and has also confirmed the
mediating role of parasocial interaction between social presence
and intention of financial supportive action offline (Shin et al.,
2019). However, the mediating role of parasocial interaction
between network social presence and online social support
willingness is not explored. In online consumption research,
network social presence is considered a powerful predictor of
consumer behavioral willingness (Huang et al., 2022; Algharabat,
2018), but few studies have explored the relationship between
network social presence and online nonmonetary support
willingness. Therefore, this study links network social presence
and parasocial interaction, confirming the positive relationship
between the two, echoing previous research, and also confirming
that parasocial interaction plays a mediating role between
network social presence and online social support willingness.
In addition, this study explores the monetary and nonmonetary
support driven by network social presence and finds that network
social presence can enhance the viewing experience in live
streaming situations. Network social presence impacts the
viewer’s willingness to support the host in terms of emotions,
tools, and economics, thereby echoing the role of network social
presence in controlling, engaging, and cognitively and emotion-
ally arousing the audience in an intermediary environment,
immersing the audience in it, and thus promoting audience
participation (Mollen and Wilson, 2010). This study combines
the discussion of network social presence in the field of media and
consumer behavior, expands the research and application level of
this concept, and constructs a complete pathway, providing a
theoretical reference for subsequent research on online live
streaming.
Thirdly, this study takes a step forward by empirically
investigating the regulatory role of emotional responses in these
relationships. In the past, most studies on emotional response
have focused on exploring its direct mechanism of action on
individual behavior (Zhang et al., 2012; Klein et al., 2009); few
studies have examined whether emotional response can exert an
indirect impact on social support willingness through parasocial
interaction. This study is the first attempt to provide empirical
evidence on the impact of emotional response on the relationship
between quasi-social interaction and the social support will-
ingness of live viewers in online live streaming settings, providing
guidance for future research in this field. The results indicate that
emotional response regulates the relationship between parasocial
interaction and instrumental support willingness. This finding
not only helps to answer the question of how parasocial
interaction enhances the instrumental support willingness of live
viewers but also helps to further enrich the theoretical implica-
tions and application fields of emotional response.
Practical implications. This study provides important practical
guidance for live host and live streaming platforms. The results
indicate that network social presence can effectively induce the
perception of parasocial interaction and emotional response of
live viewers, thereby enhancing their social support willingness.
However, improving the network social presence, parasocial
interaction, and emotional response in live streaming requires
multiple efforts.
From the perspective of live hosts, live hosts should adopt
effective, interactive, and collaborative strategies to enhance the
live viewer’s network social presence, parasocial interaction, and
emotional response (Rourke et al., 1999). Firstly, live hosts should
adopt emotional strategies. The live hosts can act as an
acquaintance and adopt emotional language, such as “My dear
family”,“Babies”, and other intimate terms, to quickly establish a
stronger and intimate relationship with the audience through this
kind of familial, friendly, and greeting language (Xie and Fang,
2021). In addition,the live host can also engage in daily care,
greetings, emotional sharing, and confiding intimate commu-
nication in a heart to heart mode through live room chats (Xie
and Fang, 2021), promoting emotional connection with the
audience, thereby increasing the viewer’s retention time and
social support willingness in the live room. Secondly, to increase
viewers’perception of online live streaming, live hosts should
communicate with viewers in an interaction-oriented manner.
Live content is not just a personal talk show for the live host, but
also shaped by the viewer’s responses and feedback (Hamilton
et al., 2014). Moreover, discussing familiar topics can also help
improve intimacy (Argyle, Cook (1975)), which is an important
factor affecting the viewer’s network social presence. Therefore,
the live host should increase real-time interaction with the viewer
during online live streaming, focusing on real-time feedback on
viewer behavior (such as entering live streaming, liking, following,
forwarding, commenting, and purchasing). For example, the live
host can view the comments of the viewers in real time, ask or
respond to targeted questions, understand the viewer’s surround-
ing environment and hobbies through viewer descriptions, and
appropriately adjust the live content to create attractive content
that meets viewer’s expectations, increasing their interest and
enhancing interactive effects, and further enhancing viewer’s
network social presence and parasocial interaction, thereby
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enhancing their social support willingness. In addition, the live
host can also play the role of “curators”by listening and
providing the viewer with multiple opportunities to exchange and
share their understanding and experience, and using various
incentive measures to enhance their sense of participation, in
order to fully stimulate and guide the viewer’s positive cognitive-
emotional experience. These real-time interactions can drive
viewers to better respond to live streaming content and services,
ensuring smooth interaction during the live streaming process.
The more interaction with the viewer, the greater the likelihood
that they will stay on the live streaming platform because they
believe they have a close relationship with the host and other
viewers, and experience a sense of immersion. Finally, the live
host should utilize a cohesive strategy, including phatic language
and online nicknames that enhance users’parasocial interaction
experience and convey a sense of connection (Labrecque, 2014),
this stable interaction can effectively make viewers feel like they
are part of the live streaming platform and immerse in it. To
further enhance the positive effects of social presence commu-
nication via social interaction and emotional response, live hosts
should improve their personal information such as their name,
gender, and avatar, effectively inducing the viewer’s perception of
establishing intimate and personal relationships with the live host.
Furthermore, live hosts should conduct responsive, reciprocal,
and back-and-forth conversations to maximize the viewer’s
emotional response.
From the perspective of live streaming platform operators, the
mass communication nature of online live streaming may not be
suitable for promoting direct interaction. Firstly, for live hosts
with relatively small numbers of viewers, direct interaction is still
feasible, but for live hosts with large viewers, it is physically
impossible for the live host to interact directly with a large viewer
simultaneously. To this end, live streaming platform operators
can use both robots and human hosts to help adjust the chat
function of the live host. In addition, live streaming platform
operators can also optimize the design and production of live
streaming platforms to help their live hosts interact more directly
with the viewers. For example, emotion monitoring tools can be
established such as facial expression analysis, audio analysis, and
text analysis with the help of artificial intelligence technology to
assist in dynamic interaction between live hosts and viewers by
displaying viewer emotions and viewer management suggestions
(Chen et al., 2023).
Secondly, live streaming, as a new environment that erodes the
boundaries of time and space, should give full play to its unique
advantages of social and life attributes, promote interaction
between viewers and increase their perception of network social
presence. Danmaku system is an effective tool for promoting
communication and interaction on a live streaming platform.
When watching the live broadcast, viewers can publish and read
Danmaku comments that update on the screen in real time,
which helps viewers create a shared viewing experience (Zhou
et al., 2019). The viewers can also trigger heated discussions
through Danmaku to improve their sense of social presence with
other viewers, create a higher level of immersion, and ignore the
existence of time, thereby affecting their level of arousal,
effectively promoting viewer online gift giving behavior. To do
this, engineers can highlight debate content or words related to
excitement in the bullet screen to enhance the viewer’s network
presence and emotional response (Zhou et al., 2019). Developing
real-time interactive voice functions, such as virtual conference
applications such as Zoom and VooV meeting, may bring better
co-awareness and positive emotional arousal.
Thirdly, the live streaming platform can reward the viewer with
points or create identity tags and identity symbols for the active
interactive viewer to encourage them to interact with other
viewers on the platform, so as to generate a stronger sense of
network social presence and social support willingness, which will
also enhance viewer’s continuous use of the live streaming
platform.
Finally, live streaming platform operators should improve
efforts to increase personalization, provide customized services,
and provide different types of live streaming content to meet the
social and psychological needs of different viewers, thereby
significantly enhancing the viewer’s immersive experience and
generating positive emotional resonance. For example, live
streaming platform operators can classify viewers by mapping
click streams to different types of visits, and provide personalized
information for different types of viewers based on their access
goals (Tam and Ho, 2006).
Limitation and future research directions. This study is subject
to certain limitations that require further investigation, which may
present opportunities for future research. Firstly, this study uses a
questionnaire survey method to conduct a preliminary under-
standing of the social support willingness of Chinese live viewers to
live hosts and determine the pathway of increasing the social sup-
port willingness of live viewers towards live hosts. However, the
cross-sectional nature of this study prevents us from reaching clear
conclusions about the causal relationship between the analyzed
variables. Although most previous studies have adopted a retro-
spective questionnaire survey method to explore the relationship
between network social presence and social support willingness
(such as Huang et al., 2022;Algharabat,2018), such retrospective
questionnaire surveys still cannot fully reflect the series of psy-
chological changes of respondents while watching live streaming.
The unique immediacy, dynamism, and interactivity of live casts are
more suitable parameters for testing the relationship between online
presence and social support willingness through real-time and
direct communication between media figures and viewers. There-
fore, future research can use experimental or observational methods
to further explore the causal relationship between network social
presence and social support willingness, also reducing commonly
used variables by collecting data from different time periods and
setting reference items.
Secondly, the AVE value of network social presence is only
0.506. In the future, the measurement and application of network
social presence can be further deepened. For example, measure-
ment can be divided into “co-existence,”“psychological partici-
pation,”and “intimacy”; further division could include
“emotional presence”and “cognitive presence”(Shen and Khalifa,
2008). Such multiple consideration can enhance the accuracy of
the research results and contribute to finding further mediators or
moderators that affect social support willingness, to find more
possible influence pathways of network social presence and social
support willingness.
Finally, this study did not make a more detailed classification
of live streaming, only measuring the influence mechanism of the
viewer’s willingness to support the live host in the general viewing
situation. However, the differences in the live content, the
characteristics of the live host and the live situation create
different degrees of influence on the viewer’s decision and
behavior (Zhou et al., 2019). Therefore, given that different types
of live streaming (such as travel live streaming, gaming live
streaming, shopping live streaming, and chat live streaming) meet
the different needs of viewers, future research can investigate
factors that can display the uniqueness of the live streaming
environment, to summarize and obtain more granular findings in
different live streaming environments. For example, product
category and gender may affect audience behavior in a live
streaming environment. Currently, taking Taobao as an example,
HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS | https://doi.org/10.1057/s41599-023-01892-8 ARTICLE
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Content courtesy of Springer Nature, terms of use apply. Rights reserved.

the most popular live streaming product categories are clothing,
shoes, accessories, jewelry, cosmetics, and household goods,
which attract more female viewers (Xu, Wu, and Li, 2020). Future
research on Taobao can be designed and studied for female
audiences to gain a more comprehensive understanding of the
social support willingness mechanism of female Taobao users.
Data availability
The datasets generated during and/or analyzed during the current
study are not publicly available due to ongoing research and
analysis, but are available from the corresponding author on
reasonable request.
Received: 12 November 2022; Accepted: 27 June 2023;
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Competing interests
The authors declare no competing interests.
Ethical approval
This study was designed in accordance with the regulations of all the authors’institutions
(Huazhong university of science and technology) and with the 1964 Helsinki Declaration
and its later amendments or comparable ethical standards. The study was not medical
research nor regarded human experimentation as stated in the Declaration of Helsinki.
Moreover, all the respondents were 18 years old and over and agreed to answer our
research questionnaire. The gathered information is strictly confidential and anonymous
and is only used for research purposes.
Informed consent
Participants were informed about the aim of the study, confidentiality of information,
voluntary participation, and ability to opt out of the study if needed. Participants were
informed through the question “Do you accept participation in this survey”.Iftheychoseto
“I accept to participate”, they could proceed the next page of the measures. All participants
gave their agreement to participate in the study and consented to processing of their data.
Additional information
Correspondence and requests for materials should be addressed to Meng Wang.
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