ArticlePDF Available

How Live Streaming Interactions and Their Visual Stimuli Affect Users’ Sustained Engagement Behaviour—A Comparative Experiment Using Live and Virtual Live Streaming

Authors:

Abstract and Figures

With the massive expansion in live streaming, enhancing the sustained engagement of users has become a key issue in ensuring its success. This study examines the relationship between real-time interaction, user perceptions, user intention to keep using live streaming, and whether this relationship differs between a live and a virtual live streaming environment. Using partial least squares (PLS) structural equation modelling (SEM), this paper analyses 240 valid questionnaire responses and finds that there is a link between real-time interactions, visual stimuli, and users’ sustained engagement. This shows that users’ active interactions while watching live streaming videos significantly affect their perceptions of social presence and trust, which in turn, affect their sustained engagement behaviour. These effects were found to vary with differences in the live streaming environment. The findings of this paper will play a positive role in understanding the differences between various live streaming environments, in optimizing the design of live streaming content and in improving the perceptions of emotional warmth by live streaming users.
Content may be subject to copyright.
Citation: Lv, J.; Cao, C.; Xu, Q.; Ni, L.;
Shao, X.; Shi, Y. How Live Streaming
Interactions and Their Visual Stimuli
Affect Users’ Sustained Engagement
Behaviour—A Comparative
Experiment Using Live and Virtual
Live Streaming. Sustainability 2022,
14, 8907. https://doi.org/10.3390/
su14148907
Academic Editors: Marc A. Rosen
and Francesco Caputo
Received: 8 May 2022
Accepted: 15 July 2022
Published: 20 July 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sustainability
Article
How Live Streaming Interactions and Their Visual Stimuli
Affect Users’ Sustained Engagement Behaviour—A
Comparative Experiment Using Live and Virtual Live Streaming
Jie Lv 1, Cong Cao 1, * , Qianwen Xu 1, Linyao Ni 1, Xiuyan Shao 2and Yangyan Shi 3
1School of Management, Zhejiang University of Technology, Hangzhou 310023, China;
2112104014@zjut.edu.cn (J.L.); 201906090121@zjut.edu.cn (Q.X.); 202001260102@zjut.edu.cn (L.N.)
2School of Economics and Management, Southeast University, Nanjing 211189, China;
xiuyan_shao@seu.edu.cn
3Department of Management, Macquarie University, Sydney, NSW 2109, Australia; peter.shi@mq.edu.au
*Correspondence: congcao@zjut.edu.cn
Abstract:
With the massive expansion in live streaming, enhancing the sustained engagement of
users has become a key issue in ensuring its success. This study examines the relationship between
real-time interaction, user perceptions, user intention to keep using live streaming, and whether
this relationship differs between a live and a virtual live streaming environment. Using partial least
squares (PLS) structural equation modelling (SEM), this paper analyses 240 valid questionnaire
responses and finds that there is a link between real-time interactions, visual stimuli, and users’
sustained engagement. This shows that users’ active interactions while watching live streaming
videos significantly affect their perceptions of social presence and trust, which in turn, affect their
sustained engagement behaviour. These effects were found to vary with differences in the live
streaming environment. The findings of this paper will play a positive role in understanding the
differences between various live streaming environments, in optimizing the design of live streaming
content and in improving the perceptions of emotional warmth by live streaming users.
Keywords:
interaction; emotional visual elements; social presence; perceived trust; sustained
engagement behaviour
1. Introduction
In recent years, internet users have been eager to share the joyful moments of their lives
through social media. Moving from text images to short videos and then on to real-time
streaming videos, the ways in which users share has been transformed. Among these, live
streaming has achieved explosive growth as a social media tool for recording and sharing in
real-time. During this period, at the end of 2019, the COVID-19 epidemic broke out around
the world, and the embargoes imposed for public safety reasons further boosted the live
streaming economy. According to the Global Live Streaming Market 2021 data published
by Research and Market, in 2021, the global live streaming market was $59.14 billion, and it
is expected to reach $223.98 billion by 2028.
Live streaming has expanded on a massive scale due to its various advantages, in-
cluding real-time interactivity, high levels of interactivity, a strong sense of consumer
engagement and the satisfaction of seeking novelty [
1
,
2
]. This growth means that live
streaming, based on social media, has an even greater influence on the viewing experi-
ence of internet users. High interactivity is the main feature that distinguishes real-time
streaming from the traditional media, and this has affected the consumption experiences of
internet users in an unprecedented way [
2
]. In traditional or social media, the separation in
time and space maintains the interaction between users and influencers at a certain physical
distance. However, the emergence of live streaming positions them at the same time, and
Sustainability 2022,14, 8907. https://doi.org/10.3390/su14148907 https://www.mdpi.com/journal/sustainability
Sustainability 2022,14, 8907 2 of 18
this real-time interaction closes the social distance between viewers and influencers, as
well as that with other viewers, thus achieving an immersive experience [3]. For example,
a study by Zhou et al. [
4
] points out that text-based real-time chat rooms, or interactive
mechanisms designed to express incentives through like-swiping gifts, attract internet users
to watch real-time videos in a more immersive manner. In addition, scholars have explored
the influence of factors such as live streaming interface design, system services, personal
attitudes, and perceived value on consumers’ willingness to view [
5
]. Existing studies have
discussed the influence of various factors on individual participation and interaction in live
streaming, but these results are often limited to the initial stages of specific behaviours, such
as consumer awareness and acceptance. Since the development of individual behaviours
often moves through different stages before reaching a more stable state, it is necessary
to explore which factors influence individuals’ willingness to sustain their engagement
and their possible internal mechanisms of action. Currently, these are being studied in a
limited way in both academia and industry. For example, a Lim et al. [
6
] study on users
who watch live streaming games points out that emotional involvement influences users’
sustained viewing behaviours through supersocial relationships. However, such studies
are limited to the unilateral intimacy of viewers to the anchor. In contrast, in live streaming,
due to the interactions, emotional connections are created not only between the anchors
and viewers but also between viewers and other viewers [
5
]. This paper goes further by ex-
ploring how the interactions between viewers and anchors and viewers with other viewers,
create emotional connections and thus, come to influence viewers’ sustained engagement
behaviours.
The anchor, who plays the central role in a live broadcast, is able to mobilize users’
emotions by communicating with them in real-time, thereby enhancing their immersive
experience and influencing their subsequent behaviours. In addition to sports anchors and
game anchors, who have been common since the early stages of the development of the live
economy, more recently, there has been large scale expansion in the roles created for anchors
in talent shows, reality shows and in the general sharing of life. Simultaneously, the nature
of the anchor has become diversified through the application of emerging technologies. For
example, virtual anchors, represented by cute, animated characters, have appeared, one
after another, in a wave of e-commerce development. These anchors are 2D or 3D animated
avatars that combine artificial intelligence with virtual simulation technology and are
capable of performing a range of tasks, such as media content production and distribution,
and are generally voiced-over by humans [
7
]. Since the debut of virtual anchor Kizuna
AI on YouTube in 2016, virtual anchors have started to develop and multiply rapidly, but
related research is limited. In recent years, some scholars have explored the use of virtual
anchors; for example, Xu [
8
] explored the possibility of applying artificial intelligence
technology to virtual anchors, while Lu, Shen, Li, Shen, and Wigdor [
7
] used qualitative
studies such as interviews, to explore the attractiveness of virtual anchors to internet
users. However, these studies were limited to the Otaku community, and the authors
did not clarify whether other users had similar perceptions. In general, current research
into the field of virtual hosting is still at the stage of initial acceptance and perceptions of
users, and it lacks any exploration of users’ sustained behaviours, or any in-depth analysis
of the mechanisms underlying their behaviour, whether in terms of the technology or
user-behaviour dimensions.
With the explosive growth in social media represented by live streaming, retaining
users has become an urgent issue for both major companies and individual bloggers;
however, this issue has been pursued in only a limited way by academia, and research
results are limited to the initial stages of specific behaviours, such as the awareness and
acceptance of live streaming users. In addition, the emergence of virtual anchors has
enriched the anchor format, yet this niche culture has not attracted sufficient attention
from academia, despite the fact that the corporate world is scrambling to launch its own
virtual anchors. The competition between virtual anchors and real ones should be an
issue worth exploring. Considering the questions raised above, the purpose of the current
Sustainability 2022,14, 8907 3 of 18
study is twofold: First, to discover how environmental factors (both visual and social)
can achieve long-term relationships with live streaming audiences by mobilizing their
emotional responses (through perceptions of presence and trust). Second, to illustrate the
variability in the results of the study described above in relation to different anchors.
In summary, this paper argues that it is necessary to further explore the factors in-
fluencing users’ willingness to sustain their engagement in a live streaming environment,
together with their mechanisms of action, and to explore the variability of these influencing
factors in both live and virtual streaming. Therefore, this paper poses the following research
questions:
1.
Can visual stimuli that convey emotions (e.g., emojis) influence the emotional connec-
tion between users of live streaming?
2.
Can the social engagement behaviour of live streaming users influence their intention
of continuous engagement?
3. Will the form of the anchor make a difference to the influencing factors identified?
Using social presence theory as its theoretical reference, this study developed a two-
stage model of the development of consumers’ willingness to sustain their participation
in the context of live streaming. The study uses partial least squares (PLS) structural
equation modelling (SEM) to analyse the data collected from 240 samples and to test the
research model and the hypotheses proposed in this paper. The study shows that interactive
texts containing emotion-rich, visual stimuli, such as love and gifts, in the live interactive
environment can enhance the emotional connections and trust relationship among users,
which in turn, affects their sustained engagement behaviours. In addition, this paper
contrasts and analyses the differences between different live streaming formats (virtual
vs. real life streaming) through group experiments. These results will help readers to gain
a comprehensive understanding of the formation and factors influencing the sustained
engagement intention of live streaming users. In this way, it will provide some theoretical
references for related future research and help platforms to optimize and adjust the interface
design of their live streaming to meet user needs better.
The remaining sections are organised as follows: Section 2provides a literature review
of research into social presence and sustained engagement behaviour. Section 3presents the
theoretical foundations and research hypotheses related to the study. Section 4elaborates on
the research methodology. Section 5derives the results of the analysis from the experimental
data. Section 6describes the implications of the study. This section also points out the
study’s limitations and makes recommendations for conducting future in-depth study.
Section 7draws conclusions.
2. Literature Review
In social media, user–company interactions are influenced by the characteristics of
the media sites concerned. Among these, images [
9
] and visual presentations [
10
] are
considered unique affective cues which prompt users’ cognitive and emotional responses
and thus, influence the user’s interaction behaviour with the site. Similarly, whether it is a
text chat or a virtual gift, the information is presented in real-time on the app interface and
is quickly seen by viewers. In general, the visual senses dominate our perceptions, which
in turn, profoundly influence our emotional responses and behaviours [
11
]. For example,
Fiore and Yu [12] conducted research on advertising which showed that when consumers
see imagination-stimulating content they may imagine the product being crafted, which
then evokes pleasant emotions. With the further development of internet technology and
the enrichment of web content, scholars have found that in internet marketing, the effect
of positive emotional expressions, and especially the use of virtual expressions, often
resonates more with consumers than mere textual expressions. Emoticons, as a surrogate
for conveying emotional tone and non-verbal gestures, such as facial expressions, add to
the richness of the message and promote a perception of fun among users [
13
]. It is likely
that the combined effect of text messaging and emoticon use creates media richness, which
Sustainability 2022,14, 8907 4 of 18
facilitates the perception of playfulness in the social interactions of mobile instant message
users, and in its turn, the perceived fun drives consumer engagement interactions [13].
Live streaming brings together liveness (as in live broadcasting) and the participatory
culture (social interactions) to an unprecedented level [
2
]. Engagement is defined as the
user’s investment of physical and psychological energy to fulfill certain psychological
needs [
14
]. For example, searching for entertainment information [
15
], making new friends,
and relieving social anxiety [
16
]. Existing research has explored the concept of engagement
from two main perspectives: cognitive involvement and affective involvement. Cognitive
involvement is associated with ‘rational thinking’ and is induced by utilitarian or cognitive
motives [
17
]. When users are exposed to environmental stimuli, such as novel technological
features (e.g., the ‘bullet screen’), the characteristics of broadcasters (e.g., a super-high
level of gameplay) or convenient interaction methods, the users’ cognitive engagement is
enhanced (i.e., their active participation in watching and learning useful game skills) [
8
].
Emotional engagement, on the other hand, denotes the emotional sharing behaviours of
viewers. For example, when watching sports events, viewers often express their happy or
frustrated feelings, and they also want to share their feelings with other viewers. This is
the basic practice of emotional engagement [
18
]. In the overall design of a live streaming
service, viewers can express their respective views through text chatting in the chat room,
or they can express their joy and share their emotions by ‘liking’ and giving virtual gifts.
During the real-time engagement of live interactions, consumers’ levels of emotional
connection change with the different stimuli and interactions [
7
,
19
]. In summary, both
cognitive and affective engagement are considered by scholars to be distinct psychological
experiences that enable the social presence of individuals [
18
]. This is due to the tendency
for emotional connections and perceptions of warmth between individuals to be enhanced
through the occurrence of actual interactions.
In a study on the impact of social media engagement on sports channel loyalty, Lim,
Hwang, Kim, and Biocca [
18
] found that the depth of viewers’ engagement gave rise to
enhanced channel loyalty by creating an emotional bond between the channel and the
viewer as well as between the viewer and other viewers. Hajli et al. [
20
] noted that real-
time interactions of engagement in social commerce enhance users’ perceptions of warmth,
effectively increasing their social skills and in turn, their long-term purchase intention. A
review of the literature reveals that the interactive engagement features of social media can
increase user loyalty by enhancing the emotional bond between users, and that loyalty is
considered an important factor in assessing the likelihood of subsequent user behaviour.
Loyalty was first defined in behavioural terms because behaviours (e.g., repeat purchases)
can be easily captured [
21
,
22
]; however, defining loyalty in terms of a single behavioural
dimension seems to be inadequate because it cannot distinguish between false loyalty
(high behavioural but low attitudinal loyalty) and true loyalty (high attitudinal with high
behavioural loyalty) [
23
]. Subsequent researchers measured customer loyalty in terms of
both the behavioural and the attitudinal dimensions and they have indicated that consumer
loyalty cannot be separated from positive attitudes and willingness to repeat purchases [
24
].
Later in-depth studies confirm that attitudinal loyalty positively influences behavioural
loyalty; for example, in an interview on branded toothpaste purchasers, Bandyopadhyay
and Martell [
25
] found that behavioural loyalty is influenced by attitudinal loyalty to the
brand. Both attitudinal and behavioural loyalty are considered key determinants of long-
term brand survival, and retaining existing customers and enhancing customer loyalty is
extremely important for service providers wishing to gain the competitive advantage [
26
].
In this study, in order to measure more accurately and to conduct comparative trials
effectively, we studied the behaviour of live streaming users from a behavioural loyalty
perspective and defined it as sustained engagement behaviour. Sustained engagement on
the internet means that the site makes a good impression on consumers and attracts them
to spend more time on it or to visit the site more frequently [27].
On 1 December 2016, the first generation of virtual VTuber Kizuna AI opened her
personal YouTube account and pitched her first video. Since then, virtual anchors like
Sustainability 2022,14, 8907 5 of 18
Kizuna AI, which use real-time animation capture and facial expression capture technology
to generate animated images, have been increasing rapidly. By June 2021, there were already
32,000 virtual idols and anchors on Bilibili, and in 2021, more than 60,000 companies related
to virtual characters. According to iiMedia Research (Ai Media Consulting), the virtual idol
industry has maintained a steady growth trend, and in 2021, for example, the driven and
core market size of virtual idols was expected to be 107.49 billion RMB and 6.22 billion RMB,
respectively. In China, these are expected to reach 186.61 billion RMB and 12.08 billion
RMB in 2022. Although the virtual host industry is growing rapidly, related studies are
limited. There is a noted lack of research on consumer willingness and behaviours in the
live streaming industry. In the past year, scholars have begun to explore the interaction
between consumers and virtual hosts; for example, Lu, Shen, Li, Shen and Wigdor [
7
]
explored viewers’ perceptions and interactions with virtual hosts through interviews with
members of the Otaku community.
3. Hypothesis Development
Scholars have defined social presence as ‘the extent to which a medium allows users to
experience others as being psychologically present’ [
28
]. More specifically, social presence
is the extent to which users perceive human warmth and social competence when partici-
pating in media activities. The act of engaging in an interaction in a live broadcast (e.g.,
text chats, liking, and swiping gifts) involves interactions between the user, the host, and
other users. Communication and interaction are considered to be the basis for generating
social presence [
29
]. The emergence of social media has made it possible for internet users
to interact in real-time from anywhere, and this interaction is similar to interpersonal
interaction in a real environment, which enhances the perception of social presence among
the participants [
30
]. The advent of live streaming has accelerated the speed and scope of
user participation in interactions, and as viewers engage in a fast-paced interactive chat
environment, they become aware of the presence of others [
31
] resulting in a perception of
immersion [
32
] and a subsequent emotional connection with others. This paper, therefore,
proposes the following hypothesis:
H1.
Users’ participation and interaction behaviours in a live streaming environment positively
influence their sense of social presence.
Trust can be defined as a sense of security that indicates a willingness to rely on
someone or something [
33
]. It is established through extensive and continuous interactions
between people [
34
]. Interpersonal interaction, whether face-to-face or virtual, is a prerequi-
site for trust [
34
], and the more frequent the interaction, the more conducive it is to building
trusting relationships [
35
]. In online communities, the strength of users’ interactions with
others has also been shown to be a key factor in fostering trust between them, and the more
active the communication and interaction between individuals, the deeper the trust that
develops [
36
]. For example, Wu and Chang [
37
] studied the interaction between members
and administrators in online travel communities, he found that the more members commu-
nicated with the administrators, the more they trusted them. Jiang, et al. [
38
], in a study of
social commerce, found that interactive communication between consumers, merchants,
and other consumers could reduce the sense of unreality caused by not being able to touch
the real product, thus enhancing the consumers’ perceptions of trust. Resulting from this,
we propose the following hypothesis:
H2. Users’ live participation and interaction behaviours positively affect their perceptions of trust.
Using social media to present emotional elements can enhance customers’ emotional
support for the company, which helps them to understand and improve their relationship
with the company [
39
]. Scholars have found that visually appealing emotional elements,
such as those conveying caring, understanding, and empathy, can significantly influence
customers’ perceived experience of emotional issues [
40
], while scholars such as Zhang
et al. [
41
] have shown that visual elements, such as images and videos, not only improve
Sustainability 2022,14, 8907 6 of 18
the overall appearance of a website but also elicit positive emotional responses from
users. Accordingly, this paper refers to the emotional influences presented by multimedia
technologies, which are based on images, emojis, etc., as emotional visual elements. When
these touch customers’ emotions and prompt experiences of warmth, they promote the
customers’ participation in virtual community interactions, which in turn, satisfies their
sense of belonging [
42
]. Users who are actively involved in social media are more likely to
experience social interactions and to feel happy [
43
]. For example, anchors and viewers in
live broadcasts use emojis, likes, and other forms of interaction to express their emotions,
thus creating a positive emotional connection for participating users [
6
]. Therefore, we
hypothesize that:
H3. Emotional visual elements positively influence the user’s social presence.
Perceptions of trust are derived from rational characteristics, such as reliability, compe-
tence, and responsibility, as demonstrated by a trusted person, as well as from perceptions
of factors such as emotional and social skills [
44
]. Because the virtual community is different
from the traditional marketplace environment, individuals tend to look to limited symbolic
cues to form impressions of others in a context where there is a lack of personalized cues.
At the same time, due to the lack of information, individuals tend to have stereotypical
perceptions of others’ images, and therefore, trust levels tend to be low [
45
]. However, our
visual senses dominate our perceptions; for example, Fogg [
46
] found that when online
articles contain photographs, it enhances their perceived trustworthiness. Marketing re-
search points out that advertising relies on the construction of friendly images to enhance
positive consumer attitudes [
47
]. Hassanein and Head [
48
] also notes in their study that
descriptive and graphic visual elements designed to evoke emotions have a positive impact
on consumer attitudes. Therefore, we propose the following hypothesis:
H4. Emotional visual elements positively influence perceptions of trust by users of live streaming.
Social presence has been shown to be an important influencing factor in increasing
users’ sustained engagement behaviours. For example, from a study on video pop-ups,
Fang et al. [
49
] concludes that social presence influences individuals’ intention to repeat
viewing due to their immersive experience and hedonic perceptions. Nadeem, et al. [
50
]
states, from the social commerce perspective, that social presence provides a human atmo-
sphere that allows consumers to experience fun and warmth in social interaction, which
leads to willingness to persist. Lim, Hwang, Kim, and Biocca [
18
] and others have studied
sports channel loyalty and conclude that social presence positively influences users’ will-
ingness to keep watching by enhancing their commitment to the channel. Scholars agree
that a high perception of social presence will influence individuals to join and to continue
using social networks [51]. In summary, we hypothesize that:
H5.
Social presence positively influences the sustained engagement behaviour of users of live
streaming.
Research by Hong and Cho [
52
] states that, in the field of relationship marketing, trust
is the cornerstone of building long-term relationships; it is a determinant of relationship
commitment and an important relationship marketing tool available to companies. Many
studies point out that trust directly affects the building of customer loyalty and has far-
reaching effects [
53
55
]. For example, in a study on mobile instant messages in China,
Deng, Lu, Wei, and Zhang [
54
] demonstrate that trust significantly influences consumers’
sustained engagement behaviour. In a virtual community environment, in which face-to-
face contact or physical evidence does not provide sufficient assurance, a perception of
trust can reduce the sense of risk in the transaction process. Consumers, therefore, tend to
be more willing to establish a long-term and stable trust relationship with a trustworthy
service provider to whom they will demonstrate loyalty [
56
], such as using the service
continuously (sustained engagement behaviour) or recommending the service to others.
As a new situation in e-commerce, live streaming still has inherent risks and uncertainties,
Sustainability 2022,14, 8907 7 of 18
and if internet users develop a trusting relationship, it will determine their readiness to
participate continuously. Therefore, we argue that:
H6.
Perceived trust positively influences the sustained engagement behaviour of the users of live
streaming.
This paper advocates for the following research model, as shown in Figure 1. In the
proposed model, we illustrate structural relationships between visual stimuli and inter-
actions, the affective responses of the live streaming users (presence perception and trust
perception), and the intention to keep watching. Visual stimuli and interactions are derived
from the live streaming environment and originate from observation and participation.
Presence perception and trust perception are both personal emotional responses affecting
the subsequent behaviour of the live streaming user. Sustained engagement, as one of the
behavioural manifestations of loyalty, is the final dependent variable.
Figure 1. Research model.
4. Research Method
4.1. Experimental Design
To test our research hypotheses, we designed a questionnaire (Appendix A) around a
scenario-based experiment. First, we measured the feasibility of the whole research model,
and next, we conducted group experiments for different live streaming formats (live vs.
virtual hosts). Live and virtual hosts that could understand the users’ requirements in a
timely and accurate manner and effectively fulfill their needs were used to interact with
the respondents in the respective groups. To ensure that the participants could relate to the
whole experimental scenario, we recruited subjects who met the following criteria: (1) older
than 18 years; (2) proficient in internet skills (with basic skills such as web browsing and
online shopping); (3) with at least one live-viewing experience in the past three months.
Due to COVID-19, this experiment was conducted in an online manner. Subjects were
randomly assigned to the two groups and then watched a pre-recorded live video (virtual
or real streaming) for 15 min. They were then asked to answer a questionnaire (the same
questionnaire was used for both groups).
To ensure the validity of the hypotheses testing, the design of the questionnaire was
based on established scales from relevant literature, with appropriate modifications and
adaptations according to the characteristics of the online live streaming environment and
human–computer interaction. When designing the study, we interviewed 15 users in
five regions and asked them to name the relevant factors that they thought affected their
continuous participation in live streaming. We then made adaptations to existing scales
based on their responses and combined them to arrive at our final questionnaire. The
questionnaire we have finalized consists of two main parts: the first part collects basic
information about the subjects, such as age, gender, and education level, and the second
part presents four questions relating to each of the variables, identified separately. All
questions, apart from the respondents’ demographic information, were measured using
Sustainability 2022,14, 8907 8 of 18
a 7-point Likert scale. Specifically, the answers to each question were to be given a value
of 1–7, with ‘1’ indicating strong disagreement and ‘7’ indicating strong agreement. Once
the initial questionnaire design was completed, we conducted a small-scale pretest. In
all, 30 questionnaires were completed to check whether the semantic and grammatical
expressions of the options were easy to understand and whether their reliability and validity
met the requirements. At this point, some statements on the questionnaire were modified
according to the respondents’ feedback, and this resulted in the final questionnaire.
4.2. Sample Selection
A total of 300 subjects were recruited and randomly divided into two groups (virtual
vs. live streaming). Of the 300 responses, 31 were removed as being invalid for the
following reasons: (1) the time taken to answer was less than three minutes: 7 samples;
(2) the question answers were too focused on a single option: 6 samples; (3) the choice of
options showed an obvious regularity: 3 samples; (4) the questionnaire was set up in such
a way that it detected a lack of validity in the subjects’ responses: 15 samples. The final
number of valid samples was thus 269 (of which, 142 were in the virtual live streaming
group and 127 in the live streaming group). Since our study is a group experiment to be
conducted in the context of live and virtual streaming, we expect the data to be equal for
each group, so we randomly selected 120 questionnaires from each group for analysis.
The demographic information is shown in Table 1. Female subjects accounted for
56.75% of the total number and male subjects accounted for 43.25%, so gender can be
considered evenly distributed. The subjects were mainly between 18 and 45 years of age
(88.33%), which is in line with current trends where middle-aged and young people are the
major online-consumer groups. Minors were excluded from the sample group because they
need the permission of their guardians to participate in an experiment, and their shopping
behaviours are limited by their lack of economic power and the influence of their guardians.
The respondents had generally received higher education, and 83.75% of them were either
studying at undergraduate level or had already received a bachelor’s degree. In relation to
the frequency with which respondents watched live streaming, most of them watched live
streaming 4–6 times per week, while about 25% of them watched live streaming more than
ten times per week. These figures are consistent with information about consumers’ live
streaming behaviours in the e-commerce environment. The data for this experiment were
not collected on the university campus, but the sample was selected through social media,
so the data is representative of the population as a whole.
The study used PLS-SEM to analyse 240 questionnaires and conduct a multiple group
analysis (MGA) to observe the heterogeneity between the virtual and real live streaming
groups. It has been noted that PLS-SEM is suitable for small sample studies when the
research model has complex relationships with many constructs and metrics and that
the model is flexible when dealing with non-normal data [
57
,
58
]. Also, the PLS-SEM
approach is the preferred method when the study aims to explore theoretical extensions
of established theories [
57
]. Therefore, in this study, 120 valid samples were randomly
selected from each of the two groups and the data were modelled by PLS-SEM using
SmartPLS 3.3.7 in two steps, which included measurement model analysis, to assess the
reliability and validity of the constructs. A structural model analysis was used to assess the
associations between the constructs and to check the propositions. In addition, considering
the heterogeneity between virtual and real live streaming, this paper conducted a multi-
group comparison to test whether users had different levels of persistent intention to use
the two streaming approaches. Henseler’s MGA nonparametric technique is easy to apply,
and it tests for potential group differences by using bootstrap results, and it does not require
any distributional assumptions. Our study therefore uses Henseler’s approach to evaluate
the MGA results using PLS-MGA [58].
Sustainability 2022,14, 8907 9 of 18
Table 1. Demographic description of the sample (n= 240).
Measure Category Total Group 1 Group 2
NPercent NPercent NPercent
Gender Male 139 57.92% 73 60.83% 66 55.00%
Female 101 42.08% 47 39.17% 54 45.00%
Age
18–25 77 32.08% 41 34.17% 36 30.00%
26–35 74 30.83% 35 29.17% 39 32.50%
36–45 61 25.42% 33 27.50% 28 23.33%
46–55 26 10.83% 9 7.50% 17 14.17%
56–65 2 0.83% 2 1.67% 0 0.00%
Over 65 0 0.00% 0 0.00% 0 0.00%
Education
College 74 30.83% 41 34.17% 33 27.50%
Bachelor’s Degree 127 52.92% 56 46.67% 71 59.17%
Post Graduate Degree 39 16.25% 23 19.17% 16 13.33%
Viewing Live streaming
Frequency (per week)
1–3 10 4.17% 7 5.83% 3 2.50%
4–6 119 49.58% 55 45.83% 64 53.33%
7–10 50 20.83% 31 25.83% 19 15.83%
> 10 61 25.42% 27 22.50% 34 28.33%
Notes: Group 1 = Virtual Life Streaming, Group 2 = Real Life Streaming.
5. Results
5.1. Measurement Models
The current study analysed measurement model methods to assess structural reliability,
composite reliability (CR), and average variance extracted (AVE). To measure reliability, we
used Cronbach alpha (CA) and composite reliability. The results for CA and CR are shown
in Table 2for sustained engagement behaviour (0.952, 0.952), social presence (0.934, 0.934),
perceived trust (0.936, 0.940), engagement interaction (0.893, 0.897), and affective visual
elements (0.968, 1.324), respectively. According to Hair et al. [
59
], a CR value above 0.7
indicates high reliability and a CA greater than 0.7 indicates good reliability of the indicator.
In this study, these were found to be within the acceptable range, thereby showing a high
degree of internal consistency.
Table 2. Descriptive statistics for the constructs.
Construct CA CR AVE
Sustained Engagement Behaviour (SEB) 0.952 0.965 0.874
Social Presence (SPR) 0.934 0.953 0.834
Perceived Trust (PTR) 0.936 0.955 0.840
Interaction (INT) 0.893 0.926 0.759
Emotional Visual Elements (EVE) 0.968 0.972 0.921
The structural validity of this study was evaluated by content validity, convergent
validity, and discriminant validity. All the scales in this study were selected from established
scales in the classical literature, so they had good content validity. Convergent validity is
usually evaluated by the average variance extraction (AVE) and the combined reliability
(CR) indicators. As shown in Tables 2and 3, the standardized external loadings of the
indicators in their structures, and the AVE of the different structures are greater than
0.85 [
60
,
61
]. Thus, all structures had good convergent validity. As shown in Table 4, the
square root of the AVE of any construct in the model was greater than the correlation value
corresponding to the other constructs. In addition, as shown in Table 3, the normalized
out-loadings of all the indicators in the constructs to which they belonged were greater
than their cross-loadings. This suggests that the measures of the different constructs in
this study have sufficient judgmental validity [
62
,
63
]. In summary, the overall quality of
Sustainability 2022,14, 8907 10 of 18
the measurement model in this study was relatively satisfactory according to the tests of
reliability and validity.
Table 3. Factor loadings and cross loadings.
SEB SPR PTR INT EVE
SEB.1 0.944 0.642 0.294 0.805 0.535
SEB.2 0.919 0.656 0.237 0.796 0.491
SEB.3 0.944 0.641 0.251 0.809 0.589
SEB.4 0.932 0.626 0.281 0.800 0.560
SPR.1 0.666 0.913 0.003 0.724 0.115
SPR.2 0.643 0.916 0.016 0.736 0.085
SPR.3 0.612 0.917 0.025 0.723 0.002
SPR.4 0.582 0.907 0.017 0.695 0.001
PTR.1 0.255 0.029 0.937 0.261 0.000
PTR.2 0.294 0.063 0.916 0.275 0.013
PTR.3 0.277 0.026 0.940 0.278 0.009
PTR.4 0.211 0.019 0.873 0.254 0.044
INT.1 0.822 0.738 0.257 0.916 0.337
INT.2 0.743 0.662 0.282 0.855 0.281
INT.3 0.767 0.691 0.264 0.893 0.303
INT.4 0.651 0.653 0.212 0.816 0.226
EVE.1 0.583 0.068 0.012 0.335 0.989
EVE.2 0.550 0.036 0.013 0.309 0.966
EVE.3 0.514 0.003 0.003 0.276 0.924
Note: Bold numbers indicate outer loading on the assigned constructs.
Table 4. Correlations among constructs and the square root of the AVE.
SEB SPR PTR INT EVE
SEB 0.935
SPR 0.686 0.913
PTR 0.285 0.001 0.917
INT 0.859 0.788 0.292 0.871
EVE 0.582 0.056 0.012 0.331 0.960
Note: Bold number represent the square roots of the AVEs.
5.2. Structural Model
In the second phase of the analysis, the study evaluated a structural model of con-
sumers’ ongoing engagement in live streaming behaviour. Specifically, this study examined
the statistical significance of the t-values of the path coefficients in the study model by
bootstrapping in SmartPLS 3.3.7. The original sample size was 240. We used SmartPLS
software to evaluate the structured equation model using 5000 bootstrapping procedures,
and the results of the path coefficients and statistical significance tests are shown in Table 5.
The results of the structural model evaluation showed that all six hypotheses proposed in
this paper passed the significance test. Specifically, the relationship between engagement
interaction and social presence showed a
β
value of 0.865 and a p-value of 0.000, while the
relationship with perceived trust showed a
β
value of 0.332 and a p-value of 0.000, which
proved to be significantly positive, meaning that H5 and H6 were verified. The relationship
between affective visual elements and social presence showed a
β
value of 0.230 and a
p-value of 0.000, and the relationship with perceived trust showed a
β
value of 0.12 and
p-value of 0.005, so H1 and H3 were verified. Finally, both social presence and perceived
trust showed a positive relationship with consumers’ continuous participation behaviour,
so we can assume that both social presence and perceived trust, as perceived by consumers
during the live broadcast, can positively drive their participation behaviour, thus, H2 and
H4 also passed the significance test. The complete model is shown in Figure 2.
Sustainability 2022,14, 8907 11 of 18
Table 5. Path coefficients.
Path Relationship Original Sample T Statistics pValues
SPR -> SEB 0.686 15.900 0.000
PTR -> SEB 0.286 9.795 0.000
INT -> SPR 0.865 36.175 0.000
INT -> PTR 0.332 4.307 0.000
EVE -> SPR 0.230 4.307 0.000
EVE -> PTR 0.122 2.786 0.005
Figure 2. Statistical significance test results using the research model.
The coefficient of determination (R
2
) is the most commonly used coefficient when
evaluating structural models and is used to evaluate the predictive power of the model [
63
].
The R
2
value is between 1 and 0. The higher its value, the greater the predictive power.
Generally, when the R
2
value is between 0.5 and 0.75, the explanatory power is moderate.
In this study, an R
2
value of 0.552 was reached for the continuous participation behaviour of
consumers, indicating that the model proposed in this study has good explanatory power.
5.3. Multi-Group Analysis
Hair, Risher, Sarstedt, and Ringle [
57
] suggest using MGA for categorical moderators
that affect all independent and dependent variable relationships at the same time. Based on
this, in order to test the effect of different anchors on consumers’ sustainable engagement
behaviour, this study relied on PLS-MGA to test the different effects of virtual and real an-
chors on consumers. However, measurement invariance should be confirmed before using
SmartPLS for MGA [
58
]. This is because the accuracy of the results cannot be confirmed
unless the researcher is certain of their measurement invariance, and variations in the
structural relationships between potential variables may be due to different interpretations
or understandings of the phenomena by different groups, rather than to differences in
the structural relationships. In this study, configurable invariance, component invariance,
and equality of composite means and variances were determined through a multi-group
validation factor analysis.
Further, this study conducted a comparison of the two different live streaming formats
(virtual vs. real life streaming) based on the PLS-MGA parameterization method proposed
by Sarstedt et al. [
64
] to further test the research hypotheses. The following Equation (1) and
the t-test for independent samples were used to determine whether there was a significant
difference between the different treatment groups. Where the parameters are significantly
different and when the p-value is less than 0.05, the comparison results are shown in Table 6.
t=Pathsample_1 Pathsample_2
"r(m1)2
(m+n2)S.E2
sample1 +(m1)2
(m+n2)S.E2
sample2][q1
m+1
n#(1)
Sustainability 2022,14, 8907 12 of 18
Table 6. Parametric significance test.
Path
Coefficients-Diff
(1–2)
p-Value Original
1-Tailed (1 vs. 2)
p-Value New
(1 vs. 2)
SPR -> SEB 0.233 0.000 0.001
PTR -> SEB 0.180 0.986 0.027
INT -> SPR 0.028 0.618 0.764
INT -> PTR 0.584 1.000 0.001
EVE -> SPR 0.474 0.000 0.000
EVE -> PTR 0.275 0.055 0.110
Notes: Group 1 = Virtual Life Streaming, Group 2 = Real Life Streaming.
By looking at Table 6, in the two pairs of relationships for sustained engagement
behaviour, the p-values for social presence and perceived trust in relation to sustained
engagement behaviour are 0.001 and 0.027, respectively, meaning that the differences
are significant. In the two pairs of relationships for social presence, the p-value between
engagement interaction and social presence is 0.764, which therefore does not pass the
parametric significance test, while the emotional visual element (p= 0.000) does pass. In
addition, in the two pairs for perceived trust, the p-value for engagement interaction is 0.001,
which means that real and virtual anchors do create different engagement interactions
and significantly affect consumers’ perceived trust. However, the p-value for the effect
of emotional visual elements on perceived trust is 0.110, which therefore does not pass
the parametric significance test. Thus, the different types of anchor can give different
perceptions to users and eventually influence their continuous engagement behaviour. The
specific structural model is shown in Figure 3.
Figure 3. Path coefficient of two groups using the research model.
6. Discussion
The following findings emerged from this study: first, the interactive information
displayed on the live streaming interface affects viewers’ perceptions of social presence and
trust. Specifically, we found that interactions between live streaming users and hosts, and
user–user interactions, enhanced users’ perception of human warmth. This is consistent
with the findings of Kruikemeier, van Noort, Vliegenthart, and de Vreese [
30
], Lim, Hwang,
Kim, and Biocca [
18
] and others that individuals are able to perceive the presence of oth-
ers in a rapid real-time interactive environment, which in turn enhances their emotional
connection. This finding did not differ between the virtual and real live streaming environ-
ments. Furthermore, consistent with the findings of previous research, interaction enhances
users’ perceptions of trust, and it is a prerequisite for generating trusting relationships [
33
].
However, in the virtual live environment, trust perceptions are less significant than in the
real live environment, one possible reason for this being that in the virtual environment,
Sustainability 2022,14, 8907 13 of 18
the virtual nature of the host tends to keep the users calm and rational, thus making it more
difficult to establish trust. This suggests that it is more difficult to build trust in a virtual
environment [65].
Second, emotional visual elements have been shown to significantly influence users’
perceptions of both social presence and trust. Visual stimuli expressing emotions such as
emojis, hearts, and gifts can trigger positive emotional responses, including perceptions
of warmth and trust. This is due to the fact that hearts and gifts often represent the
users’ praise and support for the host [
66
], or their appreciation and recognition of shared
content [
67
], with the perception of trust gradually increasing with recognition and support
for others. At the same time, emotional visual elements were once again shown to influence
users’ perceptions of social presence [
68
]. However, in the group comparison, we found
that visual stimuli in the virtual live streaming environment had a greater impact on users’
perception of social presence, which may be determined by the image characteristics of the
virtual hosts being 2D or 3D anime characters. Users watching virtual live streaming are
often anime enthusiasts, and when groups with similar interests watch live streaming at
the same time, the sense of community between groups is enhanced [
69
], and in turn, this
enhances the emotional connection and perceptions of social presence among users.
Finally, as we suspected, both social presence and perceived trust significantly influ-
enced users’ sustained engagement behaviour. Perceived social presence reflects the user’s
perception of human warmth in the environment [
50
], and when a user experiences social
warmth in live streaming, he or she is more likely to engage in it over time. Trust was once
again shown to be the cornerstone of long-term relationships [
52
], and users’ perceptions
of trust increased their time and frequency of live-viewing [
70
]. In addition, in the group
comparisons, we found that the social presence factor in virtual live streaming influenced
users’ sustained engagement behaviour to a greater extent than in real live streaming, while
the perceived trust factor did the opposite. A reasonable guess is that users of virtual live
streaming watch videos more for enjoyment, they do not need to consider whether the
anchor is trustworthy, whereas in real live streaming, where live shopping is the hottest
live category, a perception of trust tends to reduce the risks of the transaction process and
therefore builds long-term, stable relationships.
6.1. Theoretical and Practical Implications
As the scope of the live streaming industry continues to expand, research results on
live streaming are becoming increasingly abundant. The research reported in this paper
will also contribute to the development of this field. First, we demonstrated that interactive
text, love, and gifts, which convey personal emotions on a live streaming interface, can
bring a warm social experience to live streaming users and thus influence their willingness
to continue watching. The group experiments show that the difference in the live streaming
format (virtual vs. live host) does not affect this result. This result enriches the research into
social presence theory in the field of virtual live streaming. Second, we found that perceived
problems of trust in the live streaming environment tend to focus on social commerce, but
as the foundation of long-term relationships, we believe it is necessary to explore their
impact on users’ long-term viewing intention in any live streaming environment, and we
confirmed our conjecture through quantitative research. These findings further enrich
the research into the sustained engagement behaviour of live users and are applicable in
both real and virtual environments. Finally, the strictly-controlled experimental setting
and the resulting relatively high internal effects provide us with favourable conditions for
exploring persistent behaviour in live streaming users.
In live streaming, both virtual and real anchors should aim to improve users’ sense
of socialization and trust through various means, so as to retain users and enhance user
stickiness. Specifically, the frequent interactive messages that pop up in the live broad-
cast can significantly affect the emotional connection between users, so anchors should
fully mobilize the interactive atmosphere by throwing out topics and actively answering
questions. In addition, participation in real-time interaction is not only between users and
Sustainability 2022,14, 8907 14 of 18
anchors, but also between users, and both interactions are found to be driven by needs such
as self-presentation and interpersonal communication. Anchors should therefore give the
live stage to users at the right time and give them opportunities to show themselves (such
as liking and giving gifts) to encourage their continuous participation in the interaction
and to enhance social perception and perception of trust.
We compared the group experiments and concluded that the sense of social presence in
virtual live streaming has a greater impact on users’ sustained engagement behaviour than
in real live streaming. We believe that this difference mainly comes from user differences,
as most of the users watching virtual live streaming have similar anime hobbies, so they
care more about the emotional connection and sense of belonging in their live streaming,
while, in live streaming, the viewers’ preferences may be less concentrated. We therefore
suggest that virtual hosts can talk appropriately about anime-related topics to stimulate
the viewers’ interest and motivate them to participate in the live streaming interactions.
6.2. Limitation and Future Work
Although this study provides a favourable discussion of the sustained engagement
behaviour of live users, it has inherent limitations. Further improvements are therefore
needed in future studies. First, the sample size investigated in this study was 240. This
meets the minimum sample size criterion required by PLS-SEM, but a larger sample size
would effectively improve the accuracy and evaluation of the model. In addition, during the
study, although we conducted group experiments to explore the similarities and differences
in user engagement behaviours in the context of live and virtual live streaming, the study
did not consider whether the differences in live content would have different effects, and
some existing cutting-edge studies and surveys point out that live streaming heterogeneity,
such as different live streaming platforms and content, may result in different perceptions
of presence due to different user concerns. In addition, we also found that most of the users
who watch virtual live streaming are concentrated in the circle of anime lovers (Otaku),
and we must consider whether anime lovers and non-anime lovers would have different
perceptions of presence and trust in the virtual live streaming environment. Therefore, in
our future research, we will continue to deepen our work in the field of live streaming, for
example, by exploring the impact of differences in live content on user engagement.
7. Conclusions
By observing the live streaming industry and searching through a large amount of
literature, this paper identifies a viewer behaviour that is currently lacking in research:
continuous participation behaviour. By exploring the many antecedents of continuous
participation, we found that live streaming environmental stimuli (both interactions and
visual stimuli) can influence the emotional cognition (sense of presence and trust) of live
streaming viewers and, thus, influence their continuous participation behaviour. This study,
therefore, constructed a structural equation model and demonstrated, through responses to
the questionnaire, that the interactive information and visual stimuli displayed on a live
streaming interface positively influence the emotional perceptions (presence and trust) of
live streaming users and, thus, their continuous engagement behaviour. By comparing
groups, the study argues, for the first time, that these influences differ between live and
virtual streaming. The analysis of the results suggests that companies should stimulate
interaction between live viewers, anchors, and other viewers in various ways, as interaction
is a decisive factor in promoting viewers’ emotional perceptions in both live and virtual
streaming. Regarding visual stimulation, more may be needed from a virtual live interface,
as the anchor and the interface itself are virtual animated images, so people will demand
more, such as visual aesthetics, from the live streaming, and this poses challenges for the
company. Finally, we demonstrated that perceived trust and social presence positively
influence consumer loyalty.
Sustainability 2022,14, 8907 15 of 18
Author Contributions:
Conceptualization, J.L. and C.C.; data curation, Y.S.; formal analysis, X.S.;
investigation, Q.X. and L.N.; methodology, J.L. and C.C.; resources, C.C., X.S. and Y.S.; writing—
original draft, J.L., Q.X. and L.N.; writing—review and editing, C.C. All authors have read and agreed
to the published version of the manuscript.
Funding:
This research was funded by the Zhejiang University of Technology Subject Reform Project,
grant number SKY-ZX-20210175; the National Natural Science Foundation of China, grant number
72001040 and the Social Science Foundation of Jiangsu Province, grant number 21GLC013.
Institutional Review Board Statement:
Ethical review and approval was not required for the study
on human participants in accordance with the local legislation and institutional requirements.
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the
study. Written informed consent from the participants was not required to participate in this study in
accordance with the national legislation and the institutional requirements.
Data Availability Statement:
The data presented in this study are available on request from the
corresponding author. The data are not publicly available due to the privacy restrictions.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A Measurement Items
Table A1. Questionnaire Items.
Construct Item References
INT
When watching a live-stream, I will exchange and share opinions
with the streamer or other audiences.
[5,18]
When watching a live-stream, I interacted with other viewers using
the hashtags related to the live streaming.
When watching a live-stream, I posted my feelings in real-time
online conversation.
When watching a live-stream, I will answer questions from the
anchor and other viewers.
EVE
The interactive content in the live interface is visually appealing.
[41]
The interactive content in the live interface is visually pleasing.
The interactive content in the live interface is visually cheerful.
The interactive content in the live interface is visually interesting.
PTR
Promises made by this live streaming are likely to be reliable.
[20,48]
I do not doubt the honesty of this live streaming.
Based on my experience with this live streaming, I know it is
honest.
I feel that this live streaming is trustworthy.
SPR
When I participate in a live-streaming chat, I feel emotionally
connected with users I am chatting with.
[6,48]
There is a sense of human contact on this live streaming.
There is a sense of sociability on this live streaming.
There is a sense of human warmth on this live streaming.
SEB
I feel more attached to my favorite live-streaming channels than
other channels.
[6]
I will continue to watch my favorite live-streaming channel.
I will increase the amount of time I spend watching my favorite
live-streaming channel.
I consider myself to be a committed fan of my favorite
live-streaming channel.
References
1.
Ma, L.; Gao, S.; Zhang, X. How to Use Live Streaming to Improve Consumer Purchase Intentions: Evidence from China.
Sustainability 2022,14, 1045. [CrossRef]
Sustainability 2022,14, 8907 16 of 18
2.
Yu, E.; Jung, C.; Kim, H.; Jung, J. Impact of viewer engagement on gift-giving in live video streaming. Telemat. Inform.
2018
,35,
1450–1460. [CrossRef]
3.
Lee, J.; Lee, H. The computer-mediated communication network: Exploring the linkage between the online community and social
capital. New Media Soc. 2010,12, 711–727. [CrossRef]
4.
Zhou, F.; Chen, L.Y.; Su, Q.L. Understanding the impact of social distance on users’ broadcasting intention on live streaming
platforms: A lens of the challenge hindrance stress perspective. Telemat. Inform. 2019,41, 46–54. [CrossRef]
5. Chen, C.-C.; Lin, Y.-C. What drives live-stream usage intention? The perspectives of flow, entertainment, social interaction, and
endorsement. Telemat. Inform. 2018,35, 293–303. [CrossRef]
6.
Lim, J.S.; Choe, M.-J.; Zhang, J.; Noh, G.-Y. The role of wishful identification, emotional engagement, and parasocial relationships
in repeated viewing of live-streaming games: A social cognitive theory perspective. Comput. Hum. Behav.
2020
,108, 106327.
[CrossRef]
7.
Lu, Z.; Shen, C.; Li, J.; Shen, H.; Wigdor, D. More Kawaii than a Real-Person Live Streamer: Understanding How the Otaku
Community Engages with and Perceives Virtual YouTubers. In Proceedings of the 2021 CHI Conference on Human Factors in
Computing Systems, Yokohama, Japan, 7 May 2021; p. 137.
8.
Xu, S.H. The Research on Applying Artificial Intelligence Technology to Virtual YouTuber. In Proceedings of the 2021 IEEE
International Conference on Robotics, Automation and Artificial Intelligence (RAAI), Hong Kong, China, 21–23 April 2021;
pp. 10–14.
9.
Chowdhury, R.M.M.I.; Olsen, G.D.; Pracejus, J.W. Affective Responses to Images in Print Advertising: Affect Integration in a
Simultaneous Presentation Context. J. Advert. 2008,37, 7–18. [CrossRef]
10.
Park, J.; Stoel, L.; Lennon, S.J. Cognitive, affective and conative responses to visual simulation: The effects of rotation in online
product presentation. J. Consum. Behav. 2008,7, 72–87. [CrossRef]
11.
Schirmer, A.; Adolphs, R. Emotion Perception from Face, Voice, and Touch: Comparisons and Convergence. Trends Cogn. Sci.
2017,21, 216–228. [CrossRef]
12.
Fiore, A.M.; Yu, H. Effects of imagery copy and product samples on responses toward the product. J. Interact. Mark.
2001
,15,
36–46. [CrossRef]
13.
Hsieh, S.H.; Tseng, T.H. Playfulness in mobile instant messaging: Examining the influence of emoticons and text messaging on
social interaction. Comput. Hum. Behav. 2017,69, 405–414. [CrossRef]
14. Ruggiero, T.E. Uses and Gratifications Theory in the 21st Century. Mass Commun. Soc. 2000,3, 3–37. [CrossRef]
15.
Cheung, G.; Huang, J. Starcraft from the stands: Understanding the game spectator. In Proceedings of the SIGCHI Conference on
Human Factors in Computing Systems, Vancouver, BC, Canada, 7–11 May 2011; pp. 763–772.
16.
Hilvert-Bruce, Z.; Neill, J.T.; Sjöblom, M.; Hamari, J. Social motivations of live-streaming viewer engagement on Twitch. Comput.
Hum. Behav. 2018,84, 58–67. [CrossRef]
17.
Park, C.W.; Young, S.M. Consumer Response to Television Commercials: The Impact of Involvement and Background Music on
Brand Attitude Formation. J. Mark. Res. 1986,23, 11–24. [CrossRef]
18.
Lim, J.S.; Hwang, Y.; Kim, S.; Biocca, F.A. How social media engagement leads to sports channel loyalty: Mediating roles of social
presence and channel commitment. Comput. Hum. Behav. 2015,46, 158–167. [CrossRef]
19.
Ma, S.C.; Byon, K.K.; Jang, W.; Ma, S.M.; Huang, T.N. Esports Spectating Motives and Streaming Consumption: Moderating
Effect of Game Genres and Live-Streaming Types. Sustainability 2021,13, 4164. [CrossRef]
20.
Hajli, N.; Sims, J.; Zadeh, A.H.; Richard, M.-O. A social commerce investigation of the role of trust in a social networking site on
purchase intentions. J. Bus. Res. 2017,71, 133–141. [CrossRef]
21.
Blattberg, R.C.; Sen, S.K. Market Segmentation Using Models of Multidimensional Purchasing Behavior: A new segmentation
strategy designed to provide better information to the marketing decision maker. J. Mark. 1974,38, 17–28. [CrossRef]
22.
Srinivasan, S.S.; Anderson, R.; Ponnavolu, K. Customer loyalty in e-commerce: An exploration of its antecedents and consequences.
J. Retail. 2002,78, 41–50. [CrossRef]
23.
Day, G.S. A Two-Dimensional Concept of Brand Loyalty. In Mathematical Models in Marketing: A Collection of Abstracts; Funke,
U.H., Ed.; Springer: Berlin/Heidelberg, Germany, 1976; p. 89.
24.
Dick, A.S.; Basu, K. Customer Loyalty: Toward an Integrated Conceptual Framework. J. Acad. Mark. Sci.
1994
,22, 99–113.
[CrossRef]
25.
Bandyopadhyay, S.; Martell, M. Does attitudinal loyalty influence behavioral loyalty? A theoretical and empirical study. J. Retail.
Consum. Serv. 2007,14, 35–44. [CrossRef]
26.
Krishnamurthi, L.; Raj, S.P. An Empirical Analysis of the Relationship between Brand Loyalty and Consumer Price Elasticity.
Mark. Sci. 1991,10, 172–183. [CrossRef]
27.
Xu, F.; Qi, Y.; Li, X. What affects the user stickiness of the mainstream media websites in China? Electron. Commer. Res. Appl.
2018
,
29, 124–132. [CrossRef]
28.
Fulk, J.; Steinfield, C.W.; Schmitz, J.; Power, J.G. A Social Information Processing Model of Media Use in Organizations. Commun.
Res. 1987,14, 529–552. [CrossRef]
29.
Piller, F.T.; Walcher, D. Toolkits for idea competitions: A novel method to integrate users in new product development. R&D
Manag. 2006,36, 307–318. [CrossRef]
Sustainability 2022,14, 8907 17 of 18
30.
Kruikemeier, S.; van Noort, G.; Vliegenthart, R.; de Vreese, C.H. Getting closer: The effects of personalized and interactive online
political communication. Eur. J. Commun. 2013,28, 53–66. [CrossRef]
31.
Brockmyer, J.H.; Fox, C.M.; Curtiss, K.A.; McBroom, E.; Burkhart, K.M.; Pidruzny, J.N. The development of the Game Engagement
Questionnaire: A measure of engagement in video game-playing. J. Exp. Soc. Psychol. 2009,45, 624–634. [CrossRef]
32.
Shin, D.-H. Do Users Experience Real Sociability through Social TV? Analyzing Parasocial Behavior in Relation to Social TV. J.
Broadcast. Electron. Media 2016,60, 140–159. [CrossRef]
33.
Chung, N.; Kwon, S.J. The Effects of Customers’ Mobile Experience and Technical Support on the Intention to Use Mobile Banking.
Cyberpsychol. Behav. 2009,12, 539–543. [CrossRef]
34. Gefen, D. E-commerce: The role of familiarity and trust. Omega 2000,28, 725–737. [CrossRef]
35.
Gefen, D.; Straub, D.W. Consumer trust in B2C e-Commerce and the importance of social presence: Experiments in e-Products
and e-Services. Omega 2004,32, 407–424. [CrossRef]
36.
Hsu, M.-H.; Ju, T.L.; Yen, C.-H.; Chang, C.-M. Knowledge sharing behavior in virtual communities: The relationship between
trust, self-efficacy, and outcome expectations. Int. J. Hum. Comput. 2007,65, 153–169. [CrossRef]
37.
Wu, J.J.; Chang, Y.S. Towards understanding members’ interactivity, trust, and flow in online travel community. Ind. Manag. Data
Syst. 2005,105, 937–954. [CrossRef]
38.
Jiang, C.; Rashid, R.M.; Wang, J. Investigating the role of social presence dimensions and information support on consumers’
trust and shopping intentions. J. Retail. Consum. Serv. 2019,51, 263–270. [CrossRef]
39. Schau, H.J.; Muñiz, A.M.; Arnould, E.J. How Brand Community Practices Create Value. J. Mark. 2009,73, 30–51. [CrossRef]
40.
Liang, T.-P.; Turban, E. Introduction to the Special Issue Social Commerce: A Research Framework for Social Commerce. Int. J.
Electron. Commer. 2011,16, 5–14. [CrossRef]
41.
Zhang, H.; Lu, Y.; Wang, B.; Wu, S. The impacts of technological environments and co-creation experiences on customer
participation. Inf. Manag. 2015,52, 468–482. [CrossRef]
42.
Ren, Y.; Harper, F.M.; Drenner, S.; Terveen, L.; Kiesler, S.; Riedl, J.; Kraut, R.E. Building Member Attachment in Online
Communities: Applying Theories of Group Identity and Interpersonal Bonds. MIS Q. 2012,36, 841–864. [CrossRef]
43.
Valkenburg, P.M.; Peter, J.; Schouten, A.P. Friend networking sites and their relationship to adolescents’ well-being and social
self-esteem. Cyberpsychol. Behav. 2006,9, 584–590. [CrossRef]
44.
Yeh, Y.-H.; Choi, S.M. MINI-lovers, maxi-mouths: An investigation of antecedents to eWOM intention among brand community
members. J. Mark. Commun. 2011,17, 145–162. [CrossRef]
45.
Lea, M.; Spears, R. Paralanguage and social perception in computer-mediated communication. J. Organ. Comput.
1992
,2, 321–341.
[CrossRef]
46. Fogg, B.J. Persuasive technology: Using computers to change what we think and do. Ubiquity 2002,2002, 5. [CrossRef]
47.
Riegelsberger, J.; Sasse, M.A.; McCarthy, J.D. Shiny happy people building trust? Photos on e-commerce websites and consumer
trust. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Fort Lauderdale, FL, USA, 5–10 April
2003; pp. 121–128.
48.
Hassanein, K.; Head, M. Manipulating perceived social presence through the web interface and its impact on attitude towards
online shopping. Int. J. Hum. Comput. 2007,65, 689–708. [CrossRef]
49. Fang, J.; Chen, L.; Wen, C.; Prybutok, V.R. Co-viewing Experience in Video Websites: The Effect of Social Presence on E-Loyalty.
Int. J. Electron. Commer. 2018,22, 446–476. [CrossRef]
50.
Nadeem, W.; Khani, A.H.; Schultz, C.D.; Adam, N.A.; Attar, R.W.; Hajli, N. How social presence drives commitment and loyalty
with online brand communities? The role of social commerce trust. J. Retail. Consum. Serv. 2020,55, 102136. [CrossRef]
51.
Cheung, C.M.K.; Chiu, P.-Y.; Lee, M.K.O. Online social networks: Why do students use facebook? Comput. Hum. Behav.
2011
,27,
1337–1343. [CrossRef]
52.
Hong, I.B.; Cho, H. The impact of consumer trust on attitudinal loyalty and purchase intentions in B2C e-marketplaces:
Intermediary trust vs. seller trust. Int. J. Inf. Manag. 2011,31, 469–479. [CrossRef]
53.
Assaker, G.; O’Connor, P.; El-Haddad, R. Examining an integrated model of green image, perceived quality, satisfaction, trust,
and loyalty in upscale hotels. J. Hosp. Mark. Manag. 2020,29, 934–955. [CrossRef]
54.
Deng, Z.; Lu, Y.; Wei, K.K.; Zhang, J. Understanding customer satisfaction and loyalty: An empirical study of mobile instant
messages in China. Int. J. Inf. Manag. 2010,30, 289–300. [CrossRef]
55.
Ribbink, D.; van Riel, A.C.R.; Liljander, V.; Streukens, S. Comfort your online customer: Quality, trust and loyalty on the internet.
Manag. Serv. Qual. 2004,14, 446–456. [CrossRef]
56. Morgan, R.M.; Hunt, S.D. The Commitment-Trust Theory of Relationship Marketing. J. Mark. 1994,58, 20–38. [CrossRef]
57.
Hair, J.F.; Risher, J.J.; Sarstedt, M.; Ringle, C.M. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev.
2019
,31,
2–24. [CrossRef]
58.
Henseler, J.; Ringle, C.M.; Sinkovics, R.R. The use of partial least squares path modeling in international marketing. In New
Challenges to International Marketing; Advances in International Marketing; Sinkovics, R.R., Ghauri, P.N., Eds.; Emerald Group
Publishing Limited: Bingley, UK, 2009; Volume 20, pp. 277–319.
59.
Hair, J.F.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M.; Thiele, K.O. Mirror, mirror on the wall: A comparative evaluation of
composite-based structural equation modeling methods. J. Acad. Mark. Sci. 2017,45, 616–632. [CrossRef]
60. Bagozzi, R.P.; Yi, Y. On the evaluation of structural equation models. J. Acad. Mark. Sci. 1988,16, 74–94. [CrossRef]
Sustainability 2022,14, 8907 18 of 18
61.
Li, M.Y.; Wang, J.J.; Zhao, P.J.; Chen, K.; Wu, L.B. Factors affecting the willingness of agricultural green production from the
perspective of farmers’ perceptions. Sci. Total Environ. 2020,738, 140289. [CrossRef] [PubMed]
62.
Fornell, C.; Larcker, D.F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark.
Res. 1981,18, 39–50. [CrossRef]
63.
Hair, J.F.; Sarstedt, M.; Ringle, C.M.; Mena, J.A. An assessment of the use of partial least squares structural equation modeling in
marketing research. J. Acad. Mark. Sci. 2012,40, 414–433. [CrossRef]
64.
Sarstedt, M.; Henseler, J.; Ringle, C.M. Multigroup Analysis in Partial Least Squares (PLS) Path Modeling: Alternative Methods
and Empirical Results. Advances in International Marketing. In Measurement and Research Methods in International Marketing;
Sarstedt, M., Schwaiger, M., Taylor, C.R., Eds.; Emerald Group Publishing Limited: Bingley, UK, 2011; Volume 22, pp. 195–218.
65.
Bosch-Sijtsema, P.M.; Haapamäki, J. Perceived enablers of 3D virtual environments for virtual team learning and innovation.
Comput. Hum. Behav. 2014,37, 395–401. [CrossRef]
66.
Zhang, G.; Hjorth, L. Live-streaming, games and politics of gender performance: The case of Nüzhubo in China. Convergence
2019,25, 807–825. [CrossRef]
67.
Oh, S.-K.; Choi, H.-J. Broadcasting upon a shooting star: Investigating the success of Afreeca TV’s livestream personal broadcast
model. Int. J. Web Based Communities 2017,13, 193–212. [CrossRef]
68.
Lombard, M.; Ditton, T. At the Heart of It All: The Concept of Presence. J. Comput.-Mediat. Commun.
1997
,3, JCMC321. [CrossRef]
69.
Obst, P.; Zinkiewicz, L.; Smith, S.G. Sense of community in science fiction fandom, Part 2: Comparing neighborhood and interest
group sense of community. J. Community Psychol. 2002,30, 105–117. [CrossRef]
70.
Heo, J.; Kim, Y.; Yan, J.Z. Sustainability of Live Video Streamer’s Strategies: Live Streaming Video Platform and Audience’s Social
Capital in South Korea. Sustainability 2020,12, 1969. [CrossRef]
... It is intended for consumption by the public and often accessed via social media (e.g., YouTube Live, Facebook Live; Rogers, 2023). Mechanisms of interaction within live streaming is under researched (Wang and Li, 2020), although there is some research to suggest that interaction between presenter and audience is key to engagement (Lv et al., 2022). Live streaming in higher education (HE) institutions is well-documented with implementation in surgical teaching (Williams et al., 2011;Brandt, 2020;Fang et al., 2022), dental teaching (Iwaki et al., 2013;Wang et al., 2021), development of English-speaking skills (Shen et al., 2008;ChanLin, 2020), film studies (Robert and Lenz, 2009), and nutrition education programs (Adedokun et al., 2020). ...
... However, before further developing these live fieldwork broadcasts, future research should focus on methods to increase viewer numbers and engagement. Due to the novelty of live broadcasting in fieldwork, this may require looking toward other applications of live streaming/broadcasting and live interactive polling for inspiration on appropriate approaches (Wang and Li, 2020;Lv et al., 2022) and investigating the suitability of these methods within the live fieldwork broadcast content. This would help to better understand the viewer experience and identify ways to improve the live broadcasts. ...
Article
Full-text available
Most live broadcast work in education operates with an expert to novice delivery mode, and in indoor settings such as surgical teaching environments. Those few examples of live broadcasts from outdoor locations have heavy resource requirements, limiting their uptake within Higher Education. Working with undergraduates in a students as partners approach, this research aims to test the feasibility of a low-cost and low-tech solution to co-produce a live fieldwork broadcast within the biosciences. The co-production partnership successfully produced a live broadcast from conception to delivery in 2022–2023 with three placement students and in 2023–2024 with two placement students and three mentors. The students were involved in all aspects of design, development, and delivery of the live fieldwork broadcast. A pocket wireless modem creates an outdoor wireless network with a mobile device and wireless microphones used to deliver the broadcast. Semi-structured interviews, student self-assessments, and a reflective researcher diary explored the impact of this approach to co-produce a live fieldwork broadcast. Enjoyable aspects of the placement identified were the opportunity for new experiences and a sense of achievement. The live fieldwork broadcast placement enabled the placement students to develop 28 skills, with 73% of skills identified by at least two of the placement students. Most skills developed were transferable (54% of student identified skills), including teamwork and project planning. The simple and low-cost technology used provides a solution to address the barriers of technology integration within fieldwork and offers insight into the experience of working in partnership during a live fieldwork broadcast.
... In the context of live streaming, visual effects as important attributes contained in the platform such as images and videos displayed by broadcasters in real-time, can create social interaction and provide a sense of warmth between users through attractive visualizations (Tong et al. 2022). This is also supported by research conducted by Lv et al. (2022) which shows that by watching live streaming with visual elements as stimuli such as live chat, giving love and gifts can have a significant effect on users' emotional responses, namely the perception of social presence so as to create long-term relationships between users of the live streaming platform. This research shows that emotional visual elements have a significant effect on social presence. ...
... Social interactions that occur such as giving gifts caused by live speech or the broadcaster's ability to create social presence through social interaction-oriented content. In research conducted by Lv et al. (2022) also stated that there is a positive effect of interaction that occurs when displayed through platforms between users on social presence. This is because the social interactions that occur can be created by broadcasters through their socializing ability to attract consumers. ...
Article
Full-text available
Background: The use of live streaming on a social commerce platform is now an innovative step for business actors in marketing their products in real-time that can reach a wider market through digitalization. Purpose: The purpose of this study is to analyze the effect of livestream attributes on social presence and behavioral intention also aims to determine the effect of motivational mindset as a moderator.Design/Methodology/Approach: This research uses a quantitative method by distributing questionnaires online using a purposive sampling method to 200 sample respondents who have used and made transactions via live streaming TikTok Shop in Surabaya. The analysis technique used in this research is descriptive statistical analysis using the SEM-PLS and the data is processed using Smart PLS. Findings/Result: The results of the study stated that sociability has a positive significant effect on social presence, while information task fit and visual effect do not have a significant effect on social presence. Social presence has a significant effect on behavioral intention. Promotion focus does not moderate while prevention focus moderates the effect of social presence on the behavioral intention. Conclusion: Broadcasters need to focus on sociability such as friendliness to create a social presence for potential customers to make purchases.Originality/value (State of the art): The difference is the social commerce used, namely TikTok Shop, and previous studies using QQ and WeChat. This also complements previous research that suggests testing in each country with different societies, such as economic levels, political backgrounds, and cultural backgrounds, especially in Surabaya, Indonesia. Keywords: live stream attributes; social presence; behavioral intention; online live streaming shopping; motivational mindset
... Since the first generation virtual Vtuber Kizuna AI opened her YouTube account and released her first video on December 1, 2016, utilizing advanced real-time animation capture and facial expression capture technologies, the market size of virtual anchors has exceeded $50 billion worldwide, with the user count surpassing 5 million. Recent studies have begun to focus on the interaction between virtual anchors and users, finding that compared to real live streaming, virtual anchors show greater potential in enhancing users' sense of social presence and continuous engagement behavior (Jie,2022) [1]. ...
... In the realm of user experience research, prior studies have identified several factors that directly or indirectly affect user satisfaction, including but not limited to perceived usefulness (Davis,1989) [5], perceived ease of use (Davis,1989) [5], user satisfaction itself (Bhattacherjee, 2001) [6], and perceived playfulness (Moon,2001) [14].Despite this, discussions on the impact of perceived trust on user experience satisfaction are relatively scarce in the academic field. Recent research by Jie(2022) [1]has revealed the positive role of perceived trust in promoting continuous user engagement, highlighting its critical importance in maintaining and enhancing user experience. Perceived trust is often understood as the likelihood of users experiencing the joy of social interaction and happiness when actively participating in social media activities (Valkenburg,2006) [7]. ...
... Current research about virtual live streaming mainly focuses on virtual anchors, however, Lv et al. [35] argue that both at the technical level and user behavioral level, current research in this field is still at the stage of initial user acceptance and cognition and lacks any in-depth exploration of users' ongoing behaviors and mechanisms behind. Xu [54] mentions that the future will focus on the impact of color and light variation on the audience's sense of experience under this background. ...
... Plenty of visual information has been identified as an effective tool for attracting customers and enhancing immersion in content [44]. The anchor and interface are virtual animated images, so virtual live streaming interfaces will have higher requirements for visual stimulation and aesthetics [35]. Li [30] found that the construction of live-streaming virtualization scenes includes virtual role-playing, spatiotemporal experiences where the world is as close as neighbors, and interactive technology promoting sensory fusion, bringing a sense of participation and immersion to the audience through synchronicity. ...
... Secondly, this study classified customer engagement into cognitive, emotional, and behavioral (Chan et al., 2010;Hollebeek et al., 2016) to illustrate the effectiveness of webcare. Previous researchers have found that webcare plays a vital role in increasing customer engagement (Wongkitrungrueng & Assarut, 2020;Lv et al., 2022). This study demonstrates that proactive webcare and reactive webcare had a significant effect on customer engagement under the live streaming platform, further confirming that webcare improves customer engagement. ...
... The main managerial implication of this study is that we provide new insights into managing NeWOM on live-streaming platforms. Increasing customer brand loyalty on live-streaming platforms is a great challenge for companies (Lv et al., 2022). This study found that webcare can be an essential tool for managing customers' NeWOM and increasing brand loyalty. ...
Article
Full-text available
Based on the negative electronic word of mouth (NeWOM) under the live streaming platform, the paper aims to explore the mechanism of proactive and reactive webcare on customer brand loyalty. We constructed three separate experiments with no webcare, proactive webcare, and reactive webcare in response to NeWOM during the live streaming platform. In this study, 210 valid questionnaires were collected for statistical analysis, including the Mann-Whitney U test and Pearson correlation analysis. Our findings reveal that proactive and reactive webcare positively influence cognitive, emotional, and behavioral engagement, with proactive webcare demonstrating greater effectiveness. We confirm a positive correlation between these forms of engagement and brand loyalty, highlighting the strong connection between behavioral engagement and brand loyalty. At the same time, this study provides crucial inspiration for the live-streaming platform managers to focus on providing proactive webcare and develop behavioral activities in live-steaming platforms to enhance customers’ brand loyalty.
... The selection of cluster heads in sensor networks can be based on different criteria. According to [12], the two criteria of node energy and node location were simultaneously considered to select the optimal cluster. In the proposed method, normal nodes request to join by sending join messages and sending their remaining energy to the temporary cluster head. ...
Article
Full-text available
The application of machine learning in wireless sensor networks (WSN) has attracted much attention. Since references in WSNs are pre-defined, determining how to optimize the utilization of resources and achieve efficient load balancing has become a critical problem in WSNs. The goal of conventional green routing algorithms is to reduce energy consumption and increase network life cycles by improving routing schemes in wireless networks. However, sometimes problems arise, such as poor flexibility, focusing on a single operative, and relying on precise algebraic models. Machine learning techniques can adapt to environmental changes and employ multiple agents to make informed decisions, providing new ideas for energy-saving and intelligent routing algorithms in wireless networks. In this piece, we examine the suggestion of fictitious artificial intelligence. Developing a mathematical framework is an effective approach to formulating an ideal green routing strategy that addresses the shortcomings of conventional green networking techniques. This research summarizes past, present, and future advancements in environmentally friendly routing algorithms within wireless communication networks. The information in this article will be interesting for individuals interested in applications of machine learning in WSNs.
... Verified accounts of idols on platforms provide fans with a sense of direct connection and authenticity, ensuring that the information they receive is accurate and trustworthy. Features like live chats or personalized responses from idols-whether through comments, mentions, or fan-exclusive content-further enhance this TR, fostering a sense of closeness and engagement (Yang et al., 2024b;Jie et al., 2022). ...
Article
Full-text available
Trust (TR) significantly influences users’ Behavioural Intentions (BI) on Social Media (SM), particularly in highly engaged communities like K-pop fandoms. This study focuses on a single SM platform, X (formerly known as Twitter), examining TR as a multidimensional construct encompassing platform TR, idol TR, and community TR, and exploring its role in driving K-pop fans’ BI to engage in fandom activities. Using a qualitative approach, the study conducted non-participatory observations of 30 K-pop fans’ X accounts over a one-month period. Observations focused on behaviors such as content sharing, engagement with verified idol accounts, and participation in collaborative campaigns. Thematic analysis identified patterns linking TR dimensions with fans’ use of the platform. The findings reveal that platform TR establishes a secure foundation for engagement, as fans value X’s reliability and tools for fandom-specific activities. Idol TR, fostered by verified accounts and authentic interactions, amplifies content sharing and loyalty. Community TR strengthens collaborative efforts, with fans using X to organize hashtag campaigns and coordinate streaming projects. Together, these TR dimensions significantly influence fans’ BI to engage with the platform. The study contributes to technology acceptance literature by emphasizing TR’s multidimensional role within the unique context of K-pop fandoms on X. Practical implications suggest enhancing platform security, promoting authenticity in idol communication, and supporting community-driven initiatives. However, the study’s focus on a single platform limits its generalizability. Future research should examine multiple platforms, incorporate mixed methods, and explore TR dynamics across different fan communities and cultural contexts. Kepercayaan (TR) mempengaruhi Niat Tingkah Laku (BI) pengguna di Media Sosial (SM), khususnya dalam komuniti peminat K-pop. Kajian ini memfokuskan pada platform X (sebelumnya Twitter) dan meneliti tiga dimensi TR: kepercayaan terhadap platform, idola, dan komuniti. Dengan pemerhatian kualitatif terhadap 30 akaun peminat K-pop selama sebulan, kajian mendapati bahawa kepercayaan terhadap platform membina asas penglibatan melalui kebolehpercayaan dan alat khusus fandom. Kepercayaan terhadap idola, melalui akaun sah dan interaksi autentik, meningkatkan kesetiaan dan perkongsian kandungan. Kepercayaan terhadap komuniti menyokong usaha kolaboratif seperti kempen tanda pagar dan projek penstriman. Dimensi TR ini memacu penglibatan peminat di platform X. Kajian ini menyarankan peningkatan keselamatan platform, keaslian komunikasi idola, dan sokongan terhadap inisiatif komuniti. Penyelidikan masa depan perlu melibatkan pelbagai platform, kaedah campuran, dan konteks budaya yang berbeza.
... Broadcasts with an on-screen presenter often include interactive elements such as polls, games, and real-time reactions to comments, making the viewing experience more dynamic and engaging, helping to keep viewers' attention. A study by J. Lv et al. (2022) confirmed that having a presenter in the frame allows for emotional support, comfort and greater engagement with the content, which is relevant for creating long-term viewer commitment to a channel or platform. It also revealed that virtual hosts have a greater impact on perceptions of social presence compared to real hosts, which is related to viewers' interest in anime and virtual characters. ...
Article
Full-text available
The aim of this study was to create a new, comprehensive methodology for assessing the quality and performance of video broadcasts using virtual human technologies. The research methodology included analysing existing methodologies and adapting them to the specifics of virtual hosts. New evaluation tools were developed, considering parameters such as technical quality, emotional support, interactivity, social presence, streamer attractiveness and intention to continue watching. The main results of the study showed that technological aspects of video streaming have a significant impact on viewers’ perception of such videos. High video and audio quality, and broadcast stability increase audience satisfaction and engagement. In addition, the emotional interaction between the virtual host and the audience promotes a deeper understanding and increases trust. The interactivity and social presence of the virtual host create a sense of community and engagement, which positively affects the overall perception of the broadcast. Viewers’ self-efficacy, information overload and cognitive dissonance factors were also examined, which helps to better understand the psychological state of viewers. The findings suggest that in order to achieve a high level of authenticity and trust in virtual influencers, it is necessary to consider technological aspects, aesthetic aspects, the level of trust in the host, parameters of its audience (their motivations, cultural and personal characteristics that can affect the specifics of assessing the quality and effectiveness of the broadcast), parameters of the host itself (realism, emotional expressiveness, interactivity, presence, and absence of humour, and so on). The proposed methodology allows for a comprehensive assessment of all these parameters, contributing to the improvement of the quality and effectiveness of live broadcasts with virtual hosts
... To create an engaging environment, streamers should use visually appealing backdrops, music, and timely responses to viewer questions and comments. Lv et al. (2022) emphasize the importance of social presence in building trust, which can be cultivated through interactive features like polls and Q&A sessions that foster personal connections with viewers. ...
Thesis
Full-text available
Aim: This study investigated the impact of live-streaming e-commerce on customer impulsive buying behavior. Methodology: This study used the Stimulus-Organism-Response model. It surveyed 385 participants in Toledo City, where live streaming is increasingly utilized for product promotion. Findings: The study reveals that all examined factors exhibit significant positive correlations with one another, though varying in strength. These correlations range from moderate to very strong, indicating that an increase in one variable will likely coincide with increases in others. The findings suggest a positive relationship between tiktok live-streaming, perceived enjoyment, arousal, pleasure, and impulsive buying behavior. Individuals who experience heightened levels of enjoyment, arousal, and pleasure in response to TikTok Live-Streaming stimuli are more inclined to engage in impulsive purchasing behaviors. Conclusion: The interrelationships observed among the independent variables of perceived enjoyment, arousal, and pleasure offer valuable insights into the drivers of impulsive buying behavior. In today's highly competitive global market, where success or failure hinges on differentiation, gaining a strategic edge is essential. Continuous improvement is a cornerstone of business evolution. A deep understanding of customer buying behavior provides a critical advantage in a rapidly changing marketplace, where staying ahead of competitors is key to sustained success.
... Additionally, the anchor, who plays the central role in ELS, can mobilize users' emotions by communicating with them in real time, thereby enhancing their immersive experience and influencing their subsequent behaviors (Cao et al., 2022). The engagement between the host and the consumer can alleviate the fatigue resulting from extended periods of ELS. ...
Article
With the popularity and improvement of short video platforms, short video e-commerce live streaming (ELS) has emerged as a prevalent trend for promoting produce. Despite the widespread adoption of short video platforms and ELS, there remains a notable gap regarding the content of short video ELS, with many researches mainly emphasizing the path of ELS general development. Therefore, this study aims to optimize agricultural short-video ELS content, by identifying factors contributing to purchase intention and leveraging the advantages of short-video platforms. This study employs questionnaires, interviews, and the stability coefficient method to explore pathways for innovating agricultural ELS content. The results show that ELS scenes and anchor identity significantly affect purchase intention. Furthermore, an assessment of the hierarchical order of content elements has been established. Additionally, the interview findings indicate the significance of recognizing synergies between the benefits inherent in short video platforms and the content of agricultural ELS. This study can serve as a reference for ELS content optimization and have implications for agricultural ELS influence improvement.
Article
Full-text available
As a new business model, live-streaming commerce has great commercial value. This study used the stimulus–organism–response framework to explore the psychological mechanisms of how live peculiarities impact consumer behavioral responses as well as the effects of gender and platform differences, and to make clear how to choose the two dependent variables of engagement and purchase intentions. Using 454 valid questionnaires from consumers who had made purchases during live streaming, the authors employed partial least squares structural equation modeling to analysis the research model. The results suggest that interactivity, visualization, entertainment, and professionalization play considerable roles in consumer behavioral responses and that their psychological mechanisms are different. Male respondents are more satisfied with interactivity than females. E-commerce platforms are more interactive, visible and professional than social media platforms, and the trust mechanism of social media platforms is immature. If we use engagement to describe consumer behavioral responses of interactivity and purchase intentions to describe consumer behavioral responses of visualization, entertainment, and professionalization, this provides a basis for selecting the two dependent variables in live-streaming commerce. This study extends existing theoretical research on live-streaming commerce and provides some managerial implications for platforms, stores, and streamers.
Article
Full-text available
Previous studies have paid little attention to spectators' consumption behaviors and motives for watching different types of esports live-streaming and game genres. This study, therefore, investigates spectator motives and consumption behaviors based on the interaction effects of live-streaming types and game genres. Convenience sampling was conducted to collect 312 responses from Taiwanese individuals via the Professional Technology Temple. The measurement tools include the motivation scale for sport consumption, esports streaming consumption behaviors, and two moderators (i.e., game genres and live-streaming types). The moderating effects were examined using the PROCESS macro. The results showed that esports spectating motives and consumption behaviors are determined by different types of live-streaming and game genres. A matrix of esports spectator segments was developed to illustrate the findings and managerial implications. The study's findings broaden our understanding of esports consumption behaviors and can contribute to the fast-growing esports marketing literature. In addition, the results are expected to help practitioners better segment their consumer groups to develop more tailored marketing programs.
Article
Full-text available
Live streamers’ power and attraction influence consumer behavior. This study focuses on streamer-central formed social capital and the relationship between streamers and audiences on live streaming video platforms (LSVP). First, we explored the impact of trust, norm of reciprocity, and network on social capital formation. Second, we investigated the effect of social capital on streamers’ attributes (attractiveness, expertise, and trustworthiness) and on the audience’s social capital formation. The main findings show that trust and network positively affect social capital. Social capital increases the level of streamers’ attractiveness, expertise, and trustworthiness perceived by the audience, which facilitates sustainable development of the LSVP and the streamer. Perceived streamers’ attractiveness negatively affects social capital formation, while perceived expertise positively affects it. To promote social capital development, streamers and operators of LSVPs should continuously emphasize social capital formation. Moreover, LSVPs should provide audiences with novel and interesting content to enable active networking. For sustainable development of LSVPs, when providing live streaming video services, streamers should deliver content that the audience perceives as based on their expertise rather than on their physical attractiveness.
Article
In the context of increasingly tight resource and environmental constraints, understanding why and how farmers were willing to engage in agricultural green production (AGP) had become a major practical issue that needed to be answered to promote sustainable development of agricultural economy. This study collected first-hand data of 645 grain growers and used structural equation model (SEM) to explore the impact of farmers' perceptions on AGP willingness focusing on perceived value and its antecedent factors, namely perceived benefits and perceived risks. Results showed that perceived value and perceived benefits had significantly positive impacts on AGP willingness, while the impacts of perceived risks was significantly negative. Furthermore, it was found that although the direct effect from perceived value (0.364) on farmers' AGP willingness was greater than perceived benefits and perceived risks, the decisive factor that ultimately played a key role in AGP willingness was farmers' perceived benefits (0.501). This was because perceived value played an actively mediating role of 23.1% in the path from perceived benefits to green production willingness. Besides, the multi-group analysis (MGA) found that the variable ‘whether to join a cooperative or not’ had a positively moderating impact on the relationship between farmers' AGP perceived value and willingness. Meanwhile, the variable ‘joining cooperative group’ (0.552) had a greater impact on the perceived value-green production willingness path of farmers' AGP than the variable ‘non-joining cooperative group’ (0.251), which indicated that farmers' AGP willingness could be enhanced by joining a cooperative. This study provides some enlightenment and reference for policy makers and practitioners to design or adjust programs related to reducing rural environmental pollution and implementing AGP.
Article
Increasing interest in social commerce has been accompanied by concerns about creating high-quality customer relationships. Brands are particularly interested in how they may foster the creation of commitment and loyalty regarding their online social commerce communities. The present study contributes to this question by examining the relationship between social presence and customer relationship quality by means of customer commitment and loyalty. More specifically, this study clarifies the role of social commerce trust in this relationship. Based on 189 questionnaires from social commerce users, the direct relationships between social presence and commitment as well as loyalty are not supported, in contrast to prior findings. The results show that social commerce trust fully mediates the relationship between social presence and commitment as well as loyalty in social commerce online brand communities.
Article
This study considered a comprehensive model for understanding how green image, perceived quality (PQ), guest satisfaction, and trust simultaneously influence guests’ loyalty/behavioral intention. The relationships between the proposed constructs/variables were tested using partial least squares structural equation modeling (PLS-SEM) based on data collected from 200 UK travelers who stayed at upscale hotels in Europe. Results show that green image has an indirect positive effect on guests’ loyalty mediated through perceived quality (PQ) and trust, with PQ in turn influencing loyalty indirectly through satisfaction and trust. These results help to demystify the causal relationships among the investigated constructs/variables, suggesting in particular that a hotel’s green/eco-friendly image positively influences guests’ perception of a hotel’s products and services as well as establishing the role of PQ, satisfaction, and trust as mediators of green image as a determinant of guest loyalty.
Article
Grounded in Bandura’s (2001) social cognitive theory of mass communication and Giles’ (2002) model of parasocial relationship (PSR) development, the current research examines how a viewer’s wishful identification with an online video game streaming personality and emotional engagement with other viewers lead to behavioral loyalty through PSR with their favorite live-streamer. To test the proposed mediation model, the researchers conducted a survey using a representative sample drawn from a national panel of a professional survey firm in South Korea. Results of a mediation analysis employing structural equation modeling reveal that both wishful identification and emotional engagement have indirect effects on behavioral loyalty through PSR. Put another way, a viewer’s likeliness to continue viewing a live-streaming game increase as the viewer develops stronger PSR. The current research also demonstrates that wishful identification and engagement with others/streamers develop into PSR, as suggested by Giles’ PSR development model.
Article
Social presence of consumers is increasingly important in social commerce and online merchants are looking for ways to enhance that for boosting consumers? trust and product sales. The prior studies have recognized the influence of social presence on trust, while it is not explicit which dimensions of social presence will enhance consumers? trust. Moreover, as a key factor how information support affects the relationship between social presence and trust is unclear. This study aims to examine how does information support moderates the relationship between different social presence dimensions and trust in social commerce. Further, the influence of consumers' trust in online merchants is examined on shopping intentions. Based on information support and social presence theory, we construct a theoretical model integrating the above relationships. The quantitative data is collected from different cities of China (n = 389) and analyzed through Partial Least Squares-Structural Equation Modeling (PLS-SEM). The findings demonstrate the validity of three dimensions of social presence: interactions between consumers, interactions between consumers and merchants, and interactions between consumers and commodities. Particularly, information support is not positively moderating the relationship between social presence through consumers? interactions and consumers? trust in online merchants, while information support moderates the effect of the other two dimensions on trust. Finally, consumers? trust in online merchants has a significant effect on shopping intentions. The findings have some theoretical enlightenment and practical implications.