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INFORMATION & TECHNOLOGY MANAGEMENT | RESEARCH ARTICLE
COGENT BUSINESS & MANAGEMENT
2025, VOL. 12, NO. 1, 2479178
User continuance intention to use social commerce livestreaming
shopping based on stimulus-organism-response theory
Kamila Alia Imanuddin and Putu Wuri Handayani
Department of Computer Science, Universitas Indonesia, Depok, Indonesia
ABSTRACT
This study was conducted to identify the factors that drive the intention to continue
using livestream shopping on social commerce. The research model was built by
adopting the stimulus-organism-response theory. Using mixed methods, quantitative
data from 572 respondents were collected through questionnaires and processed using
covariance-based structural equation modelling. We also interviewed 16 respondents to
in-depth understand the questionnaire result and analyzed the data using the content
analysis technique. We collected the data from the 20 February until 5 March 2024. The
findings reveal that all constructs namely personalization, visibility, susceptibility to
informational influence, co-creation behaviour, trust in products, trust in streamers, and
perceived value can influence continuance intention to use livestream shopping on
social commerce, but only trust in products and perceived value can have a direct
impact to continuance intention to use livestream shopping on social commerce. The
results of this study are used to provide recommendations for service providers to help
retain their customers by implementing some features, such as virtual streamer, virtual
try-on, and non-anthropomorphized chatbot.
Introduction
Nowadays, the popularity of live streaming is increasing along with the development of mobile technol-
ogy and social media (Hu et al., 2017). Live streaming is not only used for entertainment (Singh et al.,
2021), but for other purposes such as education (Chen et al., 2021) and marketing (Gilbert, 2019). Live
streaming used for marketing/buying and selling is called livestream shopping. The phenomenon of
livestream shopping that is currently trending is driven by social commerce. The development of social
media and the adoption of Web 2.0 have triggered e-commerce to evolve into social commerce (Huang
& Benyoucef, 2013). This revolution is intended to increase customer participation and achieve greater
economic value (Huang & Benyoucef, 2013).
Indonesia has the potential to implement successful social commerce because it has a large number
of social media users and has the largest e-commerce market in Southeast Asia (Meilatinova, 2021). The
total number of social media users in Indonesia in 2023 is reach 73.7% of the population (Panggabean,
2024). With a large number of social media users, Indonesia ranks 5th with the largest number of lives-
tream commerce customers in the world (Statista, 2023). Indonesia is also the largest contributor to
e-commerce Gross Merchandise Value in Southeast Asia in 2022 (Annur, 2023).
However, many customers prefer to use e-commerce rather than social commerce because the lives-
tream shopping feature is more complete, easier, with cheaper product prices, and more and bigger
discounts (Purwanti, 2023). This is proven by the data, which shows that 83.4% of Indonesians use
Shopee Live (which is a feature of e-commerce), while only 42.2% use TikTok Live (which is a feature of
© 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
CONTACT Putu Wuri Handayani putu.wuri@cs.ui.ac.id Department of Computer Science, Universitas Indonesia, Depok, Indonesia
https://doi.org/10.1080/23311975.2025.2479178
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been
published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
ARTICLE HISTORY
Received 29 August 2024
Revised 5 February 2025
Accepted 9 March 2025
KEYWORDS
Livestream shopping;
social commerce;
continuous intention;
Stimulus-Organism-
Response (S-O-R) model;
Indonesia
SUBJECTS
Information &
Communication
Technology (ICT); Internet
& Multimedia -
Computing & IT; Legal,
Ethical & Social Aspects
of IT
2 K. A. IMANUDDIN AND P. W. HANDAYANI
social commerce) (Annur, 2022). There are also customers who feel uncomfortable making purchases on
social media, do not trust the product or the seller, and various other reasons (Korte, 2024). From the
seller’s perspective, 80% of them most often use Shopee to sell online, while 10% of sellers most often
use Tokopedia (ANTARA, 2023). Only 6% of sellers most often use Lazada and Tiktok Shop (ANTARA,
2023). This indicates that social commerce is still less popular than e-commerce. To compete with
e-commerce, livestream shopping service providers on social commerce need to know what factors can
influence continued usage intentions. Therefore, social commerce can retain their customers so they
don’t switch to other platforms.
Previous studies focus on purchase intentions in social media live streaming (Chan & Asni, 2023;
Fransiska & Oka, 2023; Silaban et al., 2023). To the best of our understanding, there has not examined
the continued usage intentions of livestream shopping on social commerce in Indonesia. Similar studies
have been conducted in China (Chong et al., 2023; Zhang et al., 2022) and in Taiwan (Yu et al., 2024).
Yu et al. (2024) found that friendship factors play an important role in social commerce continuance
intention in Taiwan. In Saudi Arabia, streamers’ influence amplifies the perceived benefits of livestream
shopping by enhancing the mediating impact of customers’ positive attitude attitudes, which turns these
benefits into greater purchase intention (Mutambik, 2024). Due to impulsive purchases, livestream shop-
ping often results in regretting and returning the product as customers in India later realize their deci-
sions were influenced by immersion rather than genuine product value or utility (Kumar & Taneja, 2024).
Meanwhile, Picot-Coupey et al. (2023) found that customers’ decision to choose livestream shopping is
motivated by their interest to learn about trends and new products in France. However, the results in
the Indonesian context could be different due to demographics, circumstances, and culture in Indonesia.
In addition, research in Indonesia related to the phenomenon of livestream shopping on social com-
merce does not study post-adoption behavior (i.e. Saffanah et al., 2023). Based on this background, our
research question was formulated: What are the factors that influence the intention to continue using
livestream shopping on social commerce? Thus, this study could provide guidance to social commerce
service providers to do a better livestream shopping experience.
This article consists of six sections namely literature review, methodology, results, discussion, implica-
tions and conclusion. We described our conceptual model in the literature review section and explained
the research design in the methodology section. We interpreted our results in the results and discussion
sections. Then, we described the theoretical and practical contributions of this study in the implication
section. Through theoretical contribution, this study helps fill the gaps in the existing literature on cus-
tomers’ continuance intention to use social commerce livestream shopping in Indonesia and provides a
reference for further analysis of its influencing factors. This study extends existing theories by integrating
technological and social dimensions into a cohesive framework for understanding continuous user
behaviour. This study also highlights how technological factors influence continuance intention differ-
ently. It challenges the dominance of early-stage adoption studies by emphasizing post-adoption
behaviour, offering a richer understanding of user retention in the social commerce domain. Finally, we
summarized the result of our study in the conclusion section.
Literature review
Social commerce is a subset of e-commerce that uses social media to combine user-generated content
and social interactions in the shopping experience (Mikalef et al., 2021; Rahman et al., 2020). Livestream
shopping is one of the social commerce features where audiences can watch and interact with streamers
to increase engagement and build a customer base (Merritt & Zhao, 2022). Livestream shopping can
drive strong purchase intentions (Ang et al., 2018; Lu et al., 2018), encourage impulse buying (Bray,
2024), and overcome online shopping risks related to trust (Sun etal., 2020). Chen (2020) stated that the
culture of livestream shopping outside China is still developing; thus, this makes research related to
livestream shopping in Indonesia urged to be done. The research model shown in Figure 1 is designed
with eight variables and eleven hypotheses to study the culture of livestream shopping in Indonesia.
Our research model is built on the basis of the Stimulus-Organism-Response (S-O-R) theory (Mehrabian
& Russell, 1974) and social exchange theory (Chou & Hsu, 2016) by using perceived value and continu-
ance intention to use live streaming on social commerce variables. Thus, this model extends the S-O-R
COGENT BUSINESS & MANAGEMENT 3
theory according to the Indonesian and social commerce context. The S-O-R theory explains that envi-
ronmental stimuli, whether tangible or intangible (Guo & Li, 2021), influence an individual’s emotions and
internal states, which eventually trigger behavioral responses. In the context of online shopping, stimuli
can include website quality or product features, organisms represent changes in consumer emotions or
behavior, and responses encompass purchase intentions or customer satisfaction (Guo & Li, 2021; Zhang
et al., 2014). The S-O-R theory has been widely applied in research across various fields, including infor-
mation systems, retail, tourism, and e-commerce (Asyraff etal., 2023; Busalim etal., 2024; Friedrich et al.,
2019; Qu et al., 2023). Recent studies also highlight its application in livestream shopping (Ma, 2023; Ma
etal., 2022; Ming et al., 2021), analyzing factors such as personalization and co-creation behavior on trust
and perceived value, which affect the sustained intention to use livestream shopping.
According to Sun etal. (2020), personalization means providing services to customers individually. Qu
et al. (2023) demonstrated that personalization influences cognitive and hedonic shopping values, which
subsequently determine customer satisfaction and platform engagement. In the context of livestream
shopping, personalization aids customers in making purchase decisions through tailored services, enhanc-
ing their sense of being valued (Zhang et al., 2022), increasing purchase intentions (Liu etal., 2021), and
customer engagement (Busalim et al., 2024). Trust in products is the belief that a product meets its
expectations, including its quality and functionality as claimed (Wongkitrungrueng & Assarut, 2020).
According to Wongkitrungrueng et al. (2020), streamers focusing on product details reduce uncertainty
and enhance trust, positively affecting sales (Chen etal., 2023). Zhang et al. (2022) stated that personal-
ization increases product relevance, making customers aware that the product can fulfil their needs.
Personalization helps customers filter information (Zhang etal., 2014) and learn about products in depth,
thereby increasing trust in products. Thus, we propose the following hypotheses.
H1: Personalization (PL) inuences trust in products (TP) oered in livestream shopping.
Trust in streamers is the belief that streamers provide good service (Wongkitrungrueng & Assarut,
2020) and are not opportunistic (Hsiao & Chiou, 2012). Customer trust in streamers develops through
social interaction (Lu & Chen, 2021). High levels of trust influence customer perceptions of the stream-
er’s professionalism and competence, positioning streamers as opinion leaders whose views are
Figure 1. Research model.
4 K. A. IMANUDDIN AND P. W. HANDAYANI
respected and followed (Chen etal., 2021; Guo et al., 2021; Ma, 2023). Trusting customers believe that
streamers will recommend high-quality products, making them more likely to purchase (Lu & Chen,
2021). Personalization allows customers to get fast responses and suggestions tailored to customer
conditions (Zhang et al., 2022). Personalization makes customers believe that the streamer cares
about their interests (Zhang etal., 2014). Furthermore, personal information shared by streamers cre-
ates an immersive experience (Lim & Ayyagari, 2018; Ou et al., 2014). It creates the impression of
chatting with friends, creates a sense of ownership, eliminates feelings of disaffection, and builds
trust in the streamer (Edwards et al., 2009; Zhang et al., 2022). Thus, we propose the following
hypotheses.
H2: PL inuences trust in streamers (TS) of livestream shopping.
Visibility is a technical aspect of live streaming that allows streamers to visually present products
to customers (Sun etal., 2020). It provides explicit and relevant information about the product (Tandon
et al., 2021) and enhances customer understanding through real-time interaction (Sun et al., 2020).
Previous studies have found a positive relationship between visibility and adoption, intentions, and
actual customer behaviour in e-commerce (Sawang et al., 2014; Shaikh & Karjaluoto, 2015). Visibility in
livestream shopping reduces uncertainty by providing real-time product information, which traditional
e-commerce cannot deliver (Zhang et al., 2022). Sun et al. (2019) show that visibility can significantly
influence customer behavior in livestream shopping by increasing customer engagement and pur-
chase intentions. Livestream shopping allows intuitive product presentations that enhance transpar-
ency (Eggert & Helm, 2003). Descriptions of the touch, smell, and function of the product help
customers form product imaginations (Yim et al., 2017) and reduce doubts (Zhou et al., 2018). This
process builds customer trust in the product (Zhang et al., 2022), helping them form a more vivid
consumer perspective and enriched shopping experience (Yim & Yoo, 2020). Thus, we suggest the
following hypotheses.
H3: Visibility (VS) inuences TP oered in livestream shopping.
In traditional e-commerce, sellers are not easily visible to customers, hindering information acquisition
and trust-building (Bai etal., 2015; Treem & Leonardi, 2013). In contrast, social commerce with livestream
shopping features offers higher visibility. Higher visibility not only influences purchase intention through
trust in social media platforms but also trust in sellers (Tuncer, 2021). Through livestream shopping,
real-time visibility allows customers to have a more targeted understanding of product information (Sun
et al., 2020) and increases the sense of audience involvement, which is beneficial for forming trust rela-
tionships (Zhang et al., 2022). Visual communication allows customers to observe the streamer directly,
strengthen relationships, and foster stronger social connections (Lv et al., 2018; O’Riordan et al., 2016).
This psychological closeness is further strengthened as customers spend more time in livestream shop-
ping, deepening their relationship with streamers and increasing their trust (Zhang etal., 2022). Thus, we
suggest the following hypotheses.
H4: VS inuences TS of livestream shopping.
Susceptibility to informational influence (SII) is an individual’s tendency to accept information from
others as factual evidence in a decision-making (Chen et al., 2016; Huang et al., 2012). People with high
susceptibility are more likely to have their attitudes, beliefs, and behaviours influenced by others, while
those with low susceptibility seek multiple information sources and are less reliant on electronic word of
mouth (eWOM) (Chen et al., 2016). Perceived value (PV) is a customer’s assessment of the usefulness of
a product based on its benefits and drawbacks (Doha etal., 2019). It is a critical factor in adopting tech-
nology or services, as success depends on the value customers perceive or desire (Singh etal., 2021). PV
increases trust in the product and seller, as well as customer engagement (Wongkitrungrueng & Assarut,
2020). SII is expected to shape customers’ PV (Sharma & Klein, 2020). Influenced customers tend to follow
the recommendations of others and eWOM to gain a sense of security (Chen et al., 2016; Park & Lin,
2020) so that livestream shopping appeals to them by providing comprehensive information sources
(Park & Lin, 2020), fulfilling their informational needs effectively (Ma, 2021). Therefore, we define the
following hypothesis.
COGENT BUSINESS & MANAGEMENT 5
H5: Susceptibility to informational inuence (SII) inuences the PV of livestream shopping.
According to Zhu etal. (2022), co-creation is a participatory and interactive customer experience that
challenges the traditional notion of companies as sole value creators (Prahalad & Ramaswamy, 2004).
Co-creation involves active collaboration between companies or service/product providers and customers
to create shared experiences and value (Oyner & Korelina, 2016; Zhang et al., 2018). In the context of
live streaming, the real-time viewing experience and the opportunity to communicate and socialize with
streamers and other viewers are considered efficient elements in attracting and retaining audiences (Lu
& Chen, 2021). Chan et al. (2022) found that information sharing and interpersonal communication have
a positive effect on PV. This is in line with the findings of Zhu et al. (2022) who stated that customer
participation and employee/company support as cooperative behaviours have a positive impact on cus-
tomer PV. Co-creation, therefore, fosters mutual value creation through active engagement and interac-
tion. Thus, we identify the following hypothesis.
H6: Co-creation behavior (CB) between customers and between customers and streamers inuences the PV
of livestream shopping.
Based on the trust transfer theory, trust can be transferred from one entity to another (Shi et al.,
2013). Zhao et al. (2019) identify two processes of trust transfer: cognitive and communicative. The cog-
nitive process occurs when there is a positive association or connection between a trusted entity and a
third party. In contrast, the communicative process arises through interaction between the trusted entity
and the third party. Wongkitrungrueng and Assarut (2020) stated that trust in a product can be trans-
ferred to a seller to increase customer engagement. Zhang etal. (2022) explained that trust in livestream
shopping is formed from the collaboration between the streamer and product information. The stream-
er’s credibility is crucial, as their professional knowledge and skills can reduce customer doubts about
the product (Zhang et al., 2022). This indicates that trust in livestream shopping depends not only on
the streamer’s credibility but also on how effectively the streamer communicates product information.
Trustworthy streamers provide useful product information, which serves as a quality indicator (Chen &
Shen, 2015). Trust in the streamer increases positive attitudes and a sense of identity (Park & Lin, 2020),
which are then transferred to the product (Zhang etal., 2022). Consequently, when customers trust and
accept a streamer, they are more likely to favor the products the streamer recommends (Yuan et al.,
2020). Thus, we propose the following hypothesis.
H7: TS inuences TP oered in livestream shopping.
Hu et al. (2023) found a positive relationship between trust and perceived value, but the direction of
this relationship remains a subject of debate. Chae et al. (2020) argued that perceived value influences
trust, while Aw et al. (2019) argued that trust shapes perceived value and can mediate the effect of
perceived value on customers’ behavioural intentions. Similarly, trust in internet platforms positively
impacted perceived social commerce value (Rouibah etal., 2021). Furthermore, Silva etal. (2020) explained
that trust reduces perceived risk in online shopping, thereby enhancing perceived value. In livestream
shopping, streamers are considered opinion leaders and are followed by customers (Guo et al., 2021).
Trust is formed because customers perceive streamers as authentic and capable of providing information
(Chong et al., 2023). Uncertainty in online shopping makes customers rely on additional cues for pur-
chasing decisions (Chong et al., 2023). Trust in streamers reduces uncertainty because the products rec-
ommended by the streamer are believed to be of good quality and according to customers’ needs (Lu
& Chen, 2021). Thus, we propose the following hypothesis.
H8: TS inuences the PV of livestream shopping.
Continuance intention refers to a customer’s intention to continue using a system in the future
(Liébana-Cabanillas et al., 2019). When customers believe that a system meets their expectations and
needs, they are more likely to keep using it to gain its benefits (Bhattacherjee, 2001). Measuring contin-
uance intention helps assess the long-term sustainability of a system (Tumaku et al., 2023). Previous
studies have highlighted the significant impact of trust on customers’ intentions to continue using a
product or service and to avoid switching to alternatives (Acikgoz etal., 2024; Zhang et al., 2022; Zhou
6 K. A. IMANUDDIN AND P. W. HANDAYANI
et al., 2018). Different types of trust affect behavioural intentions in varied ways. For instance, trust in
livestream shopping can positively influence customer behaviours, such as providing rewards to stream-
ers (Chen etal., 2020). Chong et al. (2023) study showed that trust in streamers strengthens the relation-
ship between PV and the intention to continue using livestream shopping. The higher the customer’s
trust in the streamer, the greater their intention to continue using it (Zhang et al., 2022). This trust is
based on emotional assessment, which forms a close emotional bond and a sense of security (Schaubroeck
et al., 2011), and stimulates active support (Sashi, 2012). Customers who strongly trust streamers will
become loyal followers, actively interact, share, and purchase recommended products (Zhang et al.,
2022). Thus, we suggest the following hypothesis.
H9: TS inuences the intention to continue using livestream shopping (CI).
Zhou and Fan (2021) highlighted that customer trust in a product stems from technical trust in prod-
uct quality and institutional trust in after-sales services. When customers feel that the seller can provide
high-quality products and services, they will eliminate the sense of uncertainty in online shopping and
increase trust in the product (Wu & Huang, 2023). High trust in a product also influences customers’
willingness to continuously engage in livestream shopping by facilitating the development of a positive
attitude and enhances product knowledge (Yang etal., 2020; Zhang et al., 2022). Consequently, custom-
ers are more inclined to spend extended periods watching live streams (Yang et al., 2020). For instance,
when purchasing apparel, customers might compare visuals of the product shown during livestreams.
Streamers trying on the clothing serve as effective references, helping customers assess the product
more confidently (Zhang etal., 2022). As a result, customers tend to stay longer in livestreams when they
hold positive expectations about the actual product (Zhang et al., 2022). Thus, we define the following
hypothesis.
H10: TP inuences the CI.
According to social exchange theory, customers’ perceived value can trigger intrinsic motivation to
maintain a relationship with a supplier, thereby motivating them to continue using a service in the
future (Xu & Du, 2018; Zhu et al., 2022). Previous studies have acknowledged that customer behavioural
intentions are significantly influenced by perceived value (Chong etal., 2023). Alalwan etal. (2018) found
that perceived value influences trust, loyalty, and customer satisfaction, ultimately shaping their intention
to continue using a product or service. Chou and Hsu (2016) applied social exchange theory to examine
how users’ evaluations of online services affect their intention to repurchase. Chou and Hsu (2016) iden-
tified two key evaluations namely emotional (satisfaction with outcome and process quality) and rational
(trust and learning). Zhu et al. (2022) also described that customers not only focus on the product val-
ues, but also on intangible values, such as satisfaction and pleasure in the consumption process. Higher
perceived value in these areas leads to increased customer satisfaction and a stronger intention to con-
tinue using the service (Chou & Hsu, 2016). Similarly, PV has a positive effect on customers’ willingness
to continue using mobile applications and livestream shopping services (Shang & Wu, 2017; Singh et al.,
2021). Overall, PV plays a pivotal role in fostering long-term relationships with customers (Chong et al.,
2023; Singh et al., 2021), including loyalty, continuation intention, and purchase intention (Aw, 2019;
Singh et al., 2021). Thus, we propose the following hypothesis.
H11: Customers’ PV inuences the CI.
Methodology
This study has been approved by the Faculty of Computer Science Universitas Indonesia (reference num-
ber S-16/UN2.F11.D1.5/PPM.00.00/2024). All participants involved in this study approved to contribute to
this study by agreeing the written informed consent. We used a mixed methods approach using ques-
tionnaires and interviews to collect the data. All participants involved in this study were social commerce
users in Indonesia who had used the livestream shopping feature (purposive sampling). Purposive sam-
pling is used for the selection of respondents who are relevant to the context of this study that are
social commerce users in Indonesia. The questionnaire contains indicators related to the research
COGENT BUSINESS & MANAGEMENT 7
variables (Appendix A), which are answered on a five-point Likert scale. Before widely distributing the
questionnaire, we conducted a readability test with five participants. This step was important to ensure
that the research instruments are understandable and conform to the context. Some necessary changes
were made due to unclear word selection. After that, a pilot study was conducted with 50 respondents
to measure the feasibility and reliability of the research instrument. The Cronbach’s Alpha obtained
through the pilot study was 0.943, which means the questionnaire has been deemed feasible and reli-
able. The questionnaire was created and distributed through online platforms. Data collection was car-
ried out within one week, from 27 February 2024 to 5 March 2024. The data was collected from 572
respondents, with a total of 548 respondents who filled out the questionnaire completely. Detailed
respondents’ demographic results are shown in Table 1.
Furthermore, data processing and analysis were carried out using the covariance-based structural
equation modelling (CB-SEM) method using the AMOS version 26; thus, all results are referred to AMOS
output. CB-SEM was applied because this study aims to test and confirm existing theories (Dash & Paul,
2021). We followed CB-SEM guidelines based on Hair et al. (2014) started by conducting measurement
and structural model testing and hypothesis testing. To find out the reasons for accepting or rejecting a
hypothesis, semi-structured interviews with open-ended questions (Appendix B) were conducted with 16
respondents. Table 2 illustrates the demographics of the respondents.
The interviews are transcribed and analyzed using the content analysis technique to understand the
hypothesis testing results. Treadwell and Davis (2019) define content analysis as a technique to analyze
recorded communications (e.g. texts, media, visual content) quantitatively by systematically sampling,
coding, and counting to uncover patterns and insights. The analysis was performed as shown in Figure 2.
In the coding step, we conducted open and axial coding. Open coding aims to systematically analyze
data by breaking it down into smaller parts. Interview transcripts will be grouped into themes or con-
cepts based on relevant theories or frameworks, by providing meaningful labels or categories in text
form (Corbin & Strauss, 2015). Meanwhile, axial coding aims to refine, align, and categorize themes
(Williams & Moser, 2019). We look for relationships between labels or categories identified in the open
coding step. Previously analyzed data will be collected together by connecting all existing categories and
Table 1. Questionnaire respondents’ demographics.
Variable Number of Respondents Percentage
Gender Male 449 81.9%
Female 99 18.1%
Age <17 years old 1 0.2%
17–25 years old 205 37.4%
26–35 years old 285 52%
36–45 years old 49 8.9%
46–55 years old 7 1.3%
> 55 years old 1 0.2%
Domicile Jakarta 122 22.3%
Bogor 31 5.7%
Depok 30 5.5%
Tangerang 35 6.4%
Bekasi 33 6%
Outside Greater Jakarta 235 42.9%
Outside Java 62 11.3%
Occupation Student 67 12.2%
Civil servant 9 1.6%
Employee 114 20.8%
Employee in state-owned enterprise 3 0.5%
Self-employed 111 20.3%
Housewife 192 35%
Unemployed 20 3.6%
Others 32 6%
Frequency of livestream shopping
usage (per month)
0–1 times 77 14.1%
2–3 times 204 37.2%
4–5 times 119 21.7%
6–7 times 48 8.8%
> 7 times 100 18.2%
Livestream shopping usage period < 3 months 106 19.3%
3–6 months 123 22.4%
6–12 months 134 24.5%
> 12 months 185 33.8%
8 K. A. IMANUDDIN AND P. W. HANDAYANI
subcategories (Corbin & Strauss, 2015). The results of this step are used as considerations to determine
the research implications and recommendations.
Results
Measurement and structural model testing
First of all, a measurement model test was conducted, starting with a convergent validity test. The value
of factor loading of each indicator must be above 0.7 (Hair etal., 2014). Indicators with a factor loading
value below 0.7 need to be removed or added with error variance (Hair etal., 2014). Then, the conver-
gent validity test was continued by calculating the average variance extracted (AVE). The recommended
AVE is above 0.5 (Hair et al., 2014). A reliability test was also conducted by calculating the Composite
Reliability (CR) and Cronbach’s Alpha (CA). The recommended CR and CA values are above 0.7 (Hair etal.,
2014). Table 3 presents the test results.
Next, a discriminant validity test was conducted to ensure the indicators examine the correspond-
ing latent variables. The test was conducted by calculating the correlation value of each indicator
and ensuring that the correlation value for the latent variable being measured was the largest com-
pared to other latent variables (Hair et al., 2014). Then, a goodness of fit (GoF) test was conducted
to see how well the model fit the research data. Initial testing showed that the measurement model
did not meet the good fit criteria. Therefore, 13 iterations of modification indices were conducted by
Table 2. Interview respondents’ demographics.
Occupation Gender Age
INT-01 Employee Male 46–55 years old
INT-02 Employee Male 17–25 years old
INT-03 Employee Male 25–34 years old
INT-04 Self-employed Male 35–45 years old
INT-05 Lecturer Female 46–55 years old
INT-06 Housewife Female 46–55 years old
INT-07 Housewife Female 35–45 years old
INT-08 Employee Male 17–25 years old
INT-09 Employee Male 17–25 years old
INT-10 Employee Female 17–25 years old
INT-11 College student Female 17–25 years old
INT-12 College student Female 17–25 years old
INT-13 College student Female 17–25 years old
INT-14 Employee Female 17–25 years old
INT-15 Employee Female 17–25 years old
INT-16 Civil servant Female 25–34 years old
Figure 2. Content analysis process (Treadwell & Davis, 2019).
COGENT BUSINESS & MANAGEMENT 9
adding covariance. The GoF test was also conducted on the structural model. Based on the results
of the initial test, one metric was found to be a poor fit so four iterations of modification indices
were conducted to achieve a good fit. Table 4 presents the results of the last GoF test on the struc-
tural model.
Hypothesis testing
Finally, a two-tailed hypothesis test is conducted. If the p-value < 0.05 then the hypothesis is accepted,
otherwise if p value > 0.05 then the hypothesis is rejected (Hair et al., 2014). The results of the hypoth-
esis test can be seen in Table 5. Among the eleven hypotheses, three hypotheses were rejected. The path
coefficient (β) represents how strong the direct influence between variables (Kline, 2016), which is divided
into weak (β < 0.2), moderate (0.2 < β < 0.5), and strong (β > 0.5).
The relationships between variables can be further viewed by calculating the Squared Multiple
Correlation (R2) value. A strong correlation between variables is indicated by a value of R2 > 0.5 (Hair
Table 3. Validity and reliability test results.
CA CR AVE
PL 0.786 0.786 0.550
VS 0.829 0.832 0.553
SII 0.813 0.813 0.684
CB 0.711 0.992 0.985
TP 0.881 0.882 0.653
TS 0.893 0.897 0.744
PV 0.752 0.991 0.982
CI 0.853 0.857 0.667
Table 4. Final goodness of t test results.
Goodness of Fit Index Requirement Result Description
Nilai minimum C discrepancy / degree of freedom (CMIN/df) < 2.0 1.264 Good t
Root Mean Square Residual (RMR) Close to 0 0.035 Good t
Goodness of Fit Index (GFI) Close to 0.9 0.932 Good t
Adjusted Goodness of Fit Index (AGFI) ≥ 0.9 0.906 Good t
Normed Fit Index (NFI) ≥ 0.9 0.935 Good t
Tucker Lewis Index (TLI) ≥ 0.9 0.982 Good t
Comparative Fit Index (CFI) ≥ 0.9 0.986 Good t
Parsimony Ratio (PRATIO) 0–1 0.791 Good t
Parsimony Adjustment to the NFI (PNFI) 0–1 0.739 Good t
Parsimony Adjustment to the CFI (PCFI) 0–1 0.779 Good t
Root Mean Square Error of Approximation (RMSEA) < 0.07 0.030 Good t
Table 5. Hypothesis test results.
Hypothesis Parameter Eect pResult
H1 TP <-- PL 0.140 Weak 0.195 Rejected
H2 TS <-- PL 0.358 Moderate 0.006 Accepted
H3 TP <-- VS 0.247 Moderate 0.028 Accepted
H4 TS <-- VS 0.321 Moderate 0.050 Rejected
H5 PV <-- SII 0.139 Weak 0.045 Accepted
H6 PV <-- CB 0.094 Weak 0.039 Accepted
H7 TP <-- TS 0.472 Moderate 0.004 Accepted
H8 PV <-- TS 0.422 Moderate 0.004 Accepted
H9 CI <-- TS 0.086 Weak 0.394 Rejected
H10 CI <-- TP 0.417 Moderate 0.004 Accepted
H11 CI <-- PV 0.277 Moderate 0.004 Accepted
Table 6. Squared multiple correlation value.
Variable R2
TS 0.405
PV 0.314
TP 0.574
CI 0.430
10 K. A. IMANUDDIN AND P. W. HANDAYANI
et al., 2014). Table 6 presents the R2 values obtained in this study. TS, PV, and CI have a value of R2 <
0.5, meaning: (i) PL and VS as antecedents are not sufficient to explain TS; (ii) SII, CB, and TS as anteced-
ents are not sufficient to explain PV; and (iii) TP, TS, and PV as antecedents are not sufficient to explain
CI. On the other hand, TP has a strong correlation because the value of R2 > 0.5, which means PL, VS,
and TS can sufficiently explain TP.
Content analysis
We conducted and transcribed the interview. We did some reading and re-reading the transcript to
become familiar with the data. Then, we started to organize the data systematically in a meaningful
Figure 3. Example of open coding.
Table 7. Axial coding result.
Category Code Hypothesis
Frequency of
Appearance
Livestream shopping behavior Product evaluation H1 9
Already have a product in mind or already know what to seek H1, H3 8
Not interested in other products other than the desired one H1 4
To get entertainment H8, H9 9
Not always intended to purchase H8, H9 6
Preferred streamer characteristics Seller/streamers credibility H1-H4 4
Showing care and appreciation to the audience H2, H7 2
Give emotional support H6 2
Interactivity H6, H7, H11 5
Engaging H8, H9 5
A public gure H9 3
Disliked streamer characteristics Unclear/insucient response H1, H2 6
Not giving recommendations properly H1, H2 4
Do not show empathy H5 3
Not attractive H7, H8 3
Take audience for granted H2 2
Concern about the product Product does not match personal needs H2, H4 6
Product does not match the one shown on the live streaming H1, H3 4
Unclear quality standards H3, H4 2
Customer needs while online
shopping
Validation from others H5 3
Information from another customers H5 5
Livestream shopping advantages Sale, lower price H11 9
Eortless shopping experience H8, H11 8
Avoid spending more money H11 5
Plenty of choices H11 5
Decision support H3 7
Avoid risk of online shopping H3 4
Re-assurance H3 4
Livestream shopping demotivation Cognitive bias H5 3
Bad reviews/testimonials H5 7
Other shopping alternatives H9 3
Downside of traditional e-commerce Have to search for information personally H2, H8 2
Slow response H8 6
COGENT BUSINESS & MANAGEMENT 11
manner. We broke the text into small segments and assigned a code to each segment. Figure 3 shows
an example of open coding.
After we coded all data, we reviewed the existing codes to identify any connections or relationships
among them. Related codes are grouped into broader categories based on their similarities or shared
themes. Lastly, we reflected on how these categories might relate to one another. Table 7 presents the
result of axial coding.
Discussion
The relationship between personalization (PL) and trust in products (TP)
This study found that PL does not directly affect customers’ TP. In contrast, Komiak and Benbasat (2006)
found that PL significantly increases consumers’ cognitive and emotional trust. Another study provides a
different perspective, stating that PL plays a positive role in product interaction (Liu et al., 2021). Product
interaction aimed at obtaining or sharing relevant information based on product knowledge, such as
customers commenting during a livestream shopping session. Personalization helps customers access
information about products they care about, which can motivate them to engage by commenting on
topics of shared interest (Erdoğmuş & Tatar, 2015). However, Zhang etal. (2022) stated that the effect of
PL on TP is mediated by trust in streamers (TS). Streamers are considered opinion leaders whose opin-
ions are trusted by consumers (Guo etal., 2021) because they consider streamers to be able to provide
factual information (Chong etal., 2023). Customers’ TS will go through a process of trust transfer so that
consumers who trust streamers will also trust the products recommended by the streamer (Chen etal.,
2022; Zhang et al., 2022).
The interviews we conducted revealed the reason for rejecting this hypothesis. In terms of livestream
shopping, customers prioritize products they have already considered, rather than those recommended
by streamers. They tend to ignore the products recommended or showcasted by streamers. ‘When I buy
something, it’s because I really wanted it beforehand. So, I already know what product I want, then I join live
shopping to learn the product further, see it directly. Usually, I am not interested in other products’. (INT-01).
Long et al. (2024) found that consumers’ attitudes toward livestream shopping are significantly influ-
enced by their pre-existing intentions and perceptions, suggesting that prior interest plays a crucial role
in their purchasing decisions. Moreover, customers are reluctant to trust streamers’ recommendations if
the explanation is neither detailed nor convincing enough. ‘If the streamer’s answer was hurried, I’m wor-
ried that he gave nonrational recommendations’. (INT-04). In addition, customers mentioned the impor-
tance of streamers’ product knowledge to avoid inappropriate product recommendations. ‘The lack of
streamers’ product knowledge could lead to misleading recommendations. It makes me unsure about the
product’. (INT-15). These show that the PL alone is insufficient to build consumers’ trust in products.
The relationship between personalization (PL) and trust in streamers (TS)
On the other hand, PL affects customers’ TS. According to 54.4% of surveyed respondents, PL is a crucial
factor in livestream shopping, as they are expecting product recommendations that are more in line with
personal preferences. TS can be interpreted as customers’ confidence in the professionalism and compe-
tence of the streamer (Chen etal., 2021), which can be seen from his ability to answer consumers’ ques-
tions and provide appropriate recommendations. ‘If the streamer is able to provide appropriate
recommendations, we know that he really understands the products he is selling.’ (INT-11). Lu and Chen
(2021) and Zhang etal. (2022) also support this hypothesis. Zhang etal. (2022) mentioned that PL allows
customers to get quick suggestions that are in accordance with the customer’s conditions. Our respon-
dent agrees with this statement ‘when the streamer can provide information that suits my personal needs
and preferences, the shopping experience becomes more enjoyable and efficient’. (INT-06). PL also makes cus-
tomers feel the presence of the streamer as if talking in front of them (Lim & Ayyagari, 2018) and feel
that their interests are being considered (Zhang et al., 2014). Our respondent said, ‘I feel appreciated as
a customer. I feel like they (the streamers) really care about us, not just wanting to meet sales targets’. (INT-09).
12 K. A. IMANUDDIN AND P. W. HANDAYANI
The relationship between visibility (VS) and trust in products (TP)
The results of this study show that VS affects customers’ TP. Fang et al. (2020) and Zhang et al. (2022)
support this finding, stating that VS increases transparency and reduces product uncertainty, thereby
increasing trust. VS can increase customers’ TP by reducing the uncertainty that arises when consumers
lack information about a product while reducing the risk of consumers receiving a product that does not
meet expectations (Zhang et al., 2022). Referring back to the survey, it is known that 13.9% of respon-
dents have experienced disappointment in livestream shopping because the product received did not
meet expectations. This means that the lack of VS in livestream shopping can reduce consumers’ TP.
According to Sun etal. (2019), customers need more information about the product before making a
purchase decision, which makes them focus more on the visual aspects of the product. This is in line
with the survey, which found that 54.9% of respondents used livestream shopping to see products
directly and in detail. Interviews also indicated that customers are more confident in purchasing products
after seeing them directly. With livestream shopping ‘I can compare one product with another, so I don’t
choose the wrong one to buy and I don’t regret it later.’ (INT-09). Another respondent added, ‘when the
streamer displays the product, I can judge the product clearly and more realistically’. (INT-02). However,
detailed descriptions of the product presented by the streamer should not only be about the product’s
visuals, but also the taste, aroma, texture, and other aspects that can help customers imagine the prod-
uct, and then reduce doubts about the product (Yim et al., 2017; Yim & Yoo, 2020; Zhou et al., 2018).
The relationship between visibility (VS) and trust in streamers (TS)
In contrast with the previous hypothesis, this study found that VS did not directly affect customers’ TS.
As mentioned by Lv et al. (2018), Sun et al. (2020), Tuncer (2021), and Zhang et al. (2022) VS can influ-
ence customers’ TS through high levels of real-time interaction, targeted information delivery, and the
physical appearance of the streamer’s and emotions on the screen. These aspects foster a sense of psy-
chological closeness and social relationships (O’Riordan et al., 2016). Zhang et al. (2022) added that the
fear of streamers’ opportunism due to the separation of space and time between sellers and customers
can be avoided with VS.
The interview data revealed a complex interplay between consumer TP and TS. While live streaming
offers a visual experience of products, consumers still prioritize physical inspection to assess quality. As
one participant noted, ‘Even if the streamer says the condition/quality of the item is good, I can’t verify it
myself. I still need to check its condition’. (INT-13). This highlights the need for initial trust in the product
before extending trust to the streamer. Once consumers trust the product, that trust is then extended
to the streamer promoting it. This aligns with the trust transfer theory, as stated by Wongkitrungrueng
and Assarut (2020), trust in a product can be transferred to the seller to achieve customer engagement.
Hsu and Hu (2024) further emphasize the pivotal role of product trust in building streamer trust, stating
that trust in the product is the key to developing trust in the streamer, which is even more influential
than the credibility of the streamer itself. In addition, negative shopping experiences make customers
more sceptical of streamers, even if they have shown the product in detail. ‘Previously, I bought a skirt
through live streaming because it looked pretty worn by the streamer. But when it arrived at my house, it
didn’t meet my expectations. The skirt was too thin, making it see-through. So now I’m hesitant to shop again’.
(INT-06). Respondents also realize that even though the product is well-visualized, certain techniques are
used by streamers to enhance the product’s appeal. ‘Lighting plays a significant role. This often makes it
tricky, especially regarding the product’s colour. So, I usually check user reviews first rather than just relying on
what the streamer shows’. (INT-13). These findings show that while visibility can enhance product appeal
and foster trust transfer, customers remain cautious, searching for additional validation due to past neg-
ative experiences and promotional tactics.
The relationship between susceptibility to informational inuence (SII) and perceived value (PV)
This study shows that customers’ SII influences their PV of livestream shopping. Chong et al. (2023) and
Sharma and Klein (2020) support this hypothesis. Customers who are susceptible to social influence are
more likely to be influenced by eWOM and reviews or recommendations from other customers (Chen
COGENT BUSINESS & MANAGEMENT 13
et al., 2016; Park & Lin, 2020), and this susceptibility increases the PV (Do & Vo, 2021). Yang (2022) also
revealed that WOM in social commerce has a significant influence on the value perceived by customers.
Eagly and Wood (1985) stated that women are more easily influenced by information than men. The
survey shows that the majority of livestream shopping users are women (81.9%). This means that lives-
tream shopping users are very likely to be influenced by information.
Interviews revealed that customers who lack knowledge or experience with a product are more likely
to rely on information from others. ‘Sometimes when I feel like I wasn’t sure about a product, I look for
validation from others who might know more than me’. (INT-02). The interviews also revealed that custom-
ers with high SII will easily experience cognitive bias (Acciarini etal., 2021), which in turn will affect how
they evaluate a product. Cognitive bias is a condition where the human brain uses shortcuts to process
information quickly, but often results in inaccurate or illogical conclusions (Cherry, 2024). ‘Usually, if we
believe an information, we will ignore information from other people that contradicts our previous beliefs.
Well, if it is connected to the context of online shopping, I tend to only pay attention to information from
people I already trust. For example, if someone else says product X is good, but someone I trust says product
X is bad, I will think the product is bad’. (INT-01). A few negative opinions from customers regarding a
product makes them doubt the value of the product. ‘Sometimes one bad review makes me doubt the
product, even though there are more positive reviews’. (INT-14).
The relationship between co-creation behaviour (CB) and perceived value (PV)
CB has a positive effect on customers’ PV. Based on the survey, 47.4% of respondents found it easy to
obtain information through livestream shopping. The ease of obtaining this information is one of the
benefits of livestream shopping that makes customers willing to use this feature. Chan et al. (2022) and
Zhu et al. (2022) showed that cooperative behaviour between customers and employees increases the
perception of product value. In live streaming, interactions between streamers and audiences through
messages and questions effectively attract and retain audiences (Lu & Chen, 2021).
Co-creation enables voluntary collaboration, providing benefits to both parties (Payne etal., 2008) and
creating a better live streaming environment and experience. Audiences can gain information and knowl-
edge by listening to streamers’ explanations and reading other audiences’ comments (Liu et al., 2024).
Meanwhile, streamers will feel appreciated, recognized, and confirmed for their role when the audience
acknowledges their professionalism through real-time messages (Liu et al., 2024). Through co-creation
behaviour, social interaction and active collaboration are established, thereby increasing consumer
engagement and loyalty (Alhumud & Elshaer, 2024). 47.4% of respondents found it easy to obtain infor-
mation through livestream shopping. The ease of obtaining this information is the reason why they are
willing to use livestream shopping. Consumers who actively interact with streamers and other viewers
feel more involved and connected. This creates a higher sense of ownership and increases their trust in
the benefits of livestream shopping (Wang et al., 2024). Selain itu. diketahui dari hasil survei,
The relationship between trust in streamers (TS) and trust in products (TP)
Customers’ TS is found to influence TP. Align with the trust transfer theory (Shi et al., 2013), customers
trust products recommended by streamers who are considered honest (Cui et al., 2020). Credible stream-
ers reduce doubts about a product, which then increases customers’ positive perceptions of the product
(Park & Lin, 2020; Zhu et al., 2020). This statement is supported by Yuan et al. (2020), who proved that
consumer trust and acceptance of streamers make the products offered more preferred by consumers.
In social commerce, sellers are not only from companies, but ordinary people can also act as sellers. In
this case, usually the products sold are unbranded or do not yet have a strong reputation, making it
difficult for sellers to gain trust (Zhao et al., 2019). Therefore, sellers need to provide emotional support
and information so that consumer trust in sellers will increase effectively and the products they sell can
also be trusted (Zhao etal., 2019). From the respondent’s perspective, emotional support is agreed as an
important aspect that needs to exist between consumers and streamers. A respondent said ‘if I already
like a streamer, we often chat and feel close enough to each other, I will come back to the store. Then when-
ever I get an offer of a new product from the streamer, I believe that the product is good like the previous
14 K. A. IMANUDDIN AND P. W. HANDAYANI
products I have bought’. (INT-05). Thus, emotional support from streamers is very important to build trust,
especially when the products sold are unbranded or do not have a strong reputation.
The relationship between trust in streamers (TS) and perceived value (PV)
Customers’ TS also influence the PV of livestream shopping. Previous research supports that trust reduces
perceived risk, thereby increasing the PV (Aw etal., 2019; Silva et al., 2020). Surveys show that less inter-
active streamers reduce trust and benefits (28.6%). On the other hand, Wu and Huang (2023) provide a
different perspective by explaining that PV has an impact on customers’ TS. Several studies also argue
that PV influences trust (Chae et al., 2020; Hsieh & Lin, 2022; Shuhaiber et al., 2025). This difference of
opinion occurs because there is no definite consensus regarding the direction of the relationship between
trust and PV (Hu et al., 2023).
The theory adopted in this study is in line with Aw et al. (2019) study, which states that trust has a
significant impact on the formation of PV. This is proven through a survey, where 28.6% of respondents
stated that the lack of interactivity or responsiveness of streamers was an obstacle when watching lives-
tream shopping. It was also stated in the interview that TS can reduce the perception of risk associated
with purchases through livestream shopping. ‘I feel more confident that I will not be deceived or get a bad
product if it is recommended by a streamer I trust’. (INT-16). In addition, TS makes consumers feel more
comfortable asking questions and receiving answers from the streamer so that they can obtain informa-
tion quickly and effectively. ‘If we ask the streamer directly, we don’t have to bother looking for information
ourselves. If we ask the admin/seller via chat, usually the answer is late, but if we ask the streamer, we can
get information instantly.’ (INT-07). Streamers can also be the main actors in creating a more enjoyable
and entertaining live-streaming experience. ‘Sometimes watching live shopping doesn’t always end in a pur-
chase. If the streamer is fun and the content/products are interesting, I just watch it so I don’t get bored.’
(INT-12). This proves that the trust built by streamers not only influences consumers’ views of the prod-
ucts being sold, but also increases the value/benefits felt from the livestream shopping experience.
The relationship between trust in streamers (TS) and continuance intention (CI)
Customers’ TS has no direct effect on CI to use livestream shopping. This is contrary to previous studies
that show trust affects customers’ CI to use a service (Moriuchi & Takahashi, 2022; Zhou et al., 2018).
According to Chong etal. (2023) and Zhang et al. (2022), TS is a crucial factor in determining the CI to
use livestream shopping. This statement is supported by Ma (2023), who explained that TS affects cus-
tomers’ perceptions and behaviours. However, Joo and Yang (2023) found that TS did not affect custom-
ers’ intention to shop through live streaming. In the United States, TS does not significantly impact
consumers’ utilitarian or hedonic attitudes, nor does utilitarian attitude influence the intention to watch
or purchase via live streaming (Ni & Ueichi, 2024). TS also does not significantly affect consumer enjoy-
ment, which in turn does not significantly influence purchase intention through live streaming (Zhou &
Lou, 2024). Meanwhile, Chen et al. (2022) revealed that TS does not have an influence on purchase
intentions, but trust in the product partially mediates the relationship between them. In another context,
there is no significant effect of trust on the intention to continue using mobile banking services (Acikgoz
et al., 2024).
Interviews with consumers revealed that trust in streamers, while important, is not the primary
factor driving CI to use livestream shopping. Instead, respondents emphasized the importance of inter-
active and entertaining streamers who create an enjoyable viewing experience. A respondent said, ‘I’m
more likely to watch for a long time if the streamer is fun and exciting, just for entertainment’. (INT-16). The
role of emotions in this dynamic is particularly important. Chen et al. (2023) supports this statement
by proving that happy streamers will spread their happiness to their audiences, thereby increasing
their engagement. Another key factor influencing consumer behavior is the abundance of choices
available to them. Respondents expressed that they are willing to switch to other platforms or stream-
ers if their expectations are not met. ‘We have access to various stores and platforms, so if the experience
with a streamer is not as expected, I still have many other options’. (INT-02). Another respondent pointed
out, ‘if the streamer is boring, I usually skip it. I’m not interested in watching live shopping again’. (INT-10).
COGENT BUSINESS & MANAGEMENT 15
With access to various stores and platforms, customers feel no obligation to remain with a streamer
who does not deliver a satisfactory experience. This competitive environment raises the stakes for
streamers, who must continuously meet or exceed audience expectations to maintain their audiences.
A study conducted by Chen et al. (2023) supports this phenomenon by proving that happy streamers
will transmit their happiness to their audience so that they can increase their engagement.
Furthermore, respondents mentioned that streamers from public figures, influencers, or key opinion
leaders (KOLs) can increase trust and viewing habits. ‘I would prefer if the host were someone famous with
a good image, like a celebrity or influencer who actually has the power to influence people to buy a product’.
(INT-11). Other respondents even develop a habit of watching livestream shopping in their daily lives if
they like a particular streamer. ‘If the streamer is someone I like, usually someone I often find on social
media, I will build a habit of watching the live first before buying a product. To some extent, even when I don’t
want to buy any product, I will still watch it live because it has become a habit’. (INT-12). Recently, more
celebrities have been invited to live streaming platforms to serve as streamers and help promote prod-
ucts, attracting millions of fans and boosting sales (Chen & Yang, 2023; Q. Zhang et al., 2024). To maxi-
mize the impact of livestream shopping, businesses should partner with influential figures who resonate
with their target audience. For optimal results, consider collaborating with public figures active in the
same industry or those whose image aligns with the store’s brand identity (Chen & Yang, 2023).
The relationship between trust in products (TP) and continuance intention (CI)
This study found that customers’ TP influence CI to use livestream shopping. TP triggers purchase inten-
tion (Chen et al., 2022), engagement (Shih et al., 2024), and viewing intention (Chandrruangphen et al.,
2022) in livestream shopping. Customers who trust the product quality will not worry about uncertainty
in online shopping (Wu & Huang, 2023). TP can also increase their knowledge about the product (Yang
et al., 2020) and make them willing to watch livestream shopping longer (Zhang etal., 2022). Zhou and
Fan (2021) stated that TP comes from customers’ technical trust in the quality of the product. Therefore,
streamers must choose and control the quality of the product themselves to reduce the possibility of
promoting unqualified products and to increase consumer TP (Chen et al., 2022). Zhou and Fan (2021)
added that trust in a product also comes from institutional trust in after-sales service guarantees. This
statement is supported by the survey regarding expectations of livestream shopping services, as 57.7%
of respondents want a return and refund guarantee if the products received are damaged, not as
expected, and so on. In addition, 38% of respondents also expect a complaint service that is
solution-oriented and accessible. With the improvement of after-sales service, they will feel safer and
more comfortable in making purchases through livestream shopping, which ultimately increases con-
sumer loyalty and retention (Nasir, 2023).
The relationship between perceived value (PV) and continuance intention (CI)
The results of the study show that PV influences CI to use livestream shopping. In developing countries,
consumers tend to compare the benefits of various products/services (Singh et al., 2020) as an indication
of their intention to continue using them (Xu & Du, 2018). This finding is supported by Pham etal. (2018),
which show that PV affects the intention to continue using in an online shopping environment. Alalwan
et al. (2018) shows a different perspective, in which PV affects trust, loyalty, and satisfaction, and eventu-
ally affects continued usage intention. In the context of livestream shopping, Chong et al. (2023) and
Singh et al. (2021) also showed that PV significantly influences customers’ CI to use livestream shopping.
This finding is supported by social exchange theory, which states that PV can generate intrinsic motiva-
tion to reconnect with sellers (Zhu et al., 2022). Dastane et al. (2024) highlighted the hedonic dimension
of perceived value which has a positive and significant effect on the stickiness of livestream shopping users.
Through the interview, participants mentioned good deals can only be obtained during the livestream
shopping session. ‘They often hold flash sales, where discounts can only be obtained during the live session.
After the live shopping is over, the price becomes more expensive’. (INT-08). Another respondent addressed
shopping convenience made customers continue using livestream shopping. ‘With livestream shopping,
you don’t have to drive, spend time on the road, not to mention paying for parking, and if you go to the mall,
you definitely buy snacks, it’s impossible not to buy snacks. While in live (streaming), you can also see the
16 K. A. IMANUDDIN AND P. W. HANDAYANI
items. What’s even better is that you can ask the host to explain. The price is also much cheaper, there are
more choices’. (INT-03). These values are the reasons why consumers continue using livestream shopping.
Implications
This study enriches previous studies on livestream shopping in Indonesia (Saffanah et al., 2023), as well
as other studies that mostly focus on user behaviour in the early stages of adoption, such as viewing
intention and purchase intention, compared to post-adoption behavior (Chong et al., 2023). Using the
S-O-R theory, this study explains how user behaviour is influenced by external stimuli (personalization,
visibility, susceptibility to informational influence, and co-creation behaviour) and emotional states (trust
in products, trust in streamers, and perceived value). Thus, S-O-R theory can be used to explain how
external circumstances influence an individual’s internal state, which in turn influences individual
behaviour, in this context post-adoption behaviour of continued use intention of livestream shopping on
social commerce. This study also confirms the theory of trust transfer, showing that user trust can be
passed from streamer to product.
This study also provides practical implications for social commerce platforms and livestream shopping
service providers. To enhance personalization, social commerce platforms can use artificial intelligence,
such as chatbots, to answer customer questions quickly and accurately. However, to avoid negative
effects on perceived value, the use of non-anthropomorphized chatbots should be minimized. Instead,
virtual communities and chat rooms can be formed to increase customer trust. Social commerce plat-
forms can also integrate data analytics from various social media for more accurate personalization and
conduct sentiment analysis to understand customer preferences. To increase visibility, social commerce
platforms can implement augmented reality and virtual try-on features; thus, allowing customers to try
products virtually, reducing uncertainty and increasing trust.
Data analytics allow sellers and livestream shopping service providers to build customer profiles and
develop content according to their profiles. Intensive training for streamers on product knowledge and
sales techniques is also important. In addition, the use of artificial intelligence to enhance co-creation
behavior and voice chat features can increase emotional engagement between streamers and audiences.
Trust in streamers can be improved by professional training or using virtual streamers who can handle
large volumes of interactions, maintain consistent brand image, and avoid human error. To increase the
influence of trust in product and perceived value, stores need to provide quality products and optimize
livestream content with interactive features and after-sales service. Implementing interactive features
such as gamification and real-time reactions can enhance the shopping experience. Meanwhile, the
after-sales service must support product return, refund, and solution-oriented complaint handling.
Conclusion
This study showed user continuance intention in Indonesia to use social commerce livestreaming shop-
ping based on stimulus-organism-response theory. This study found that external stimuli (personalization,
visibility, susceptibility to informational influence, co-creation behaviour), and emotional states (trust in
products, trust in streamers, and perceived value) can influence the intention to continue using lives-
tream shopping. However, only trust in products and perceived value have a direct influence, while per-
sonalization, visibility, informational influence, co-creation behaviour, and trust in streamers indirectly
influence continued intention using livestream shopping. This study also found that personalization does
not directly affect trust in products, visibility does not directly affect trust in streamers, and trust in
streamers does not directly affect continued intention using livestream shopping.
We acknowledge that our study has limitations where the respondents are still dominated by women,
in a productive age, earning 1 to 5 million per month. Future studies need to make demographic factors
such as gender and age as a moderating factor in order to examine differences in customer behaviour.
Next, we could also compare the result of this study with other cultural contexts particularly in regions
with different economic context and different levels of technological adoption. Emerging technologies
such as artificial intelligence-driven personalization, augmented reality, and blockchain in enhancing trust
and perceived value could be explored for future studies. In addition, future work could focus more on
COGENT BUSINESS & MANAGEMENT 17
platform-specific context. Then, other trust factors, such as trust in the platform or brand can also be
explored in order to explore the entire entity of the livestream shopping platform. Since R2 values for
variables trust in streamers, perceived value, and continuance intention is still in weak category; thus,
future studies can implicate other factors, like active control, synchronicity, convenience value, monetary
value, customer engagement, and satisfaction. Moreover, future work could also analyze in depth for
trust in trust in streamers such as credibility, authenticity, and expertise as well as trust in products.
Finally, future work could explore the impact of negative experiences, such as product misrepresentation
or poor customer service, on user retention.
Acknowledgement
We want to convey our gratitude to the Faculty of Computer Science University of Indonesia for the internal grant
year 2025.
Authors’ contributions
Kamila Alia Imanuddin and Putu Wuri Handayani were responsible for conceptualization and designed the study.
Kamila Alia Imanuddin was responsible for data collecting and analysis. Putu Wuri Handayani wrote the original
draft, supervised and validated the data analysis results. Finally, Kamila Alia Imanuddin and Putu Wuri Handayani
were reviewed and approved the nal manuscript.
Disclosure statement
The authors declare that they have no potential conicts of interest.
Funding
We want to convey our gratitude to the Faculty of Computer Science University of Indonesia for the internal grant
year 2025.
About the authors
Kamila Alia Imanuddin is a Lecturer at the Faculty of Computer Science Universitas Indonesia. She received her
master degree from Faculty of Computer Science University of Indonesia. Her research interest is related to e-com-
merce and health information system.
Putu Wuri Handayani is a Professor at the Faculty of Computer Science University of Indonesia. She received her
Doctoral degree from Faculty of Computer Science University of Indonesia. Her research interest related to informa-
tion system specically in health information system and e-commerce.
ORCID
Putu Wuri Handayani http://orcid.org/0000-0001-5341-3800
Data availability statement
The corresponding author can provide the anonymized questionnaire data upon request due to respondents’ data
privacy.
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Appendix A. Questionnaire instrument
Indicators Source
PL1 The streamer can provide me with information about all alternative products I want to purchase. (Zhang et al., 2022)
PL2 The streamer can help me determine my product needs.
PL3 The streamer can help me identify which product features best meet my needs.
PL4 The streamer can customize products based on my needs.
VS1 Livestream shopping displays detailed images and videos of products. (Zhang et al., 2022)
VS2 Livestream shopping makes product features visible to me.
VS3 Livestream shopping makes information about how to use the product visible to me.
VS4 Livestream shopping helps me visualize products as if in real life.
SII1 To buy the right product, I often observe what others buy and use. (Chong et al., 2023)
SII2 I often ask others about a product when I have little experience with it.
SII3 I often gather information from others about a product before deciding to buy it.
SII4 I often consult with others to help choose the best available option from a product category.
CB1 I often reect on my product usage experience on social commerce platforms. (Zhu et al., 2022)
CB2 I think social commerce platforms are good for connecting people in the community.
CB3 When I encounter problems, I negotiate with the Streamer to resolve them.
CB4 I interact directly with the Streamer when I want to order the products they sell.
TS1 I trust the information provided by the streamer. (Zhang et al., 2022)
TS2 I can trust the streamer.
TS3 I believe that the streamer is trustworthy.
TS4 I feel the streamer will not take advantage of me.
TP1 I feel that the products I order from livestream shopping will match my expectations. (Zhang et al., 2022)
TP2 I am condent that I will be able to use the products as demonstrated on livestream shopping.
TP3 I believe that the products I receive will be the same as those shown on livestream shopping.
TP4 The products oered on livestream shopping are likely to be reliable. (Guo et al., 2021)
PV1 I feel that engaging in livestream shopping can save money and time. (Zhu etal., 2022)
PV2 I feel that the prices of products oered on livestream shopping are aordable.
PV3 I feel that the emergence of livestream shopping helps me solve many problems.
PV4 Overall, using the livestream shopping feature provides good value to me. (Singh et al., 2021)
CI1 I intend to continue using the livestream shopping feature rather than stopping. (Chong et al., 2023)
CI2 I will continue to use the livestream shopping feature as often as I do now.
CI3 I intend to continue using the livestream shopping feature rather than alternatives.
CI4 I intend to increase my usage of the livestream shopping feature in the future.
24 K. A. IMANUDDIN AND P. W. HANDAYANI
Appendix B. Interview instrument
Hypothesis Question
1. H1 Why do you think personalization doesn’t inuence your trust in the products being oered in livestream
shopping?
2. How can personalization inuence your trust in the products?
3. H2 Why do you think personalization inuence your trust in the streamers?
4. H3 Why do you think visibility inuence your trust in the products?
5. H4 Why do you think visibility doesn’t inuence your trust in the streamers?
6. How can visibility inuence your trust in the streamers?
7. Do you think there are other factors that can inuence your trust in the streamers?
8. H5 Why do you think ‘susceptibility to informational inuence’ inuence your perceived value of livestream
shopping?
9. H6 Why do you think co-creation behavior inuence your perceived value of livestream shopping?
10. H7 Why do you think trust in the streamers inuence trust in the products?
11. Are there any other factors that might inuence your trust in the products?
12. H8 Why do you think trust in the streamers inuence your perceived value of livestream shopping?
13. Are there any other factors that might inuence your perception of the product’s benets?
14. H9 Why do you think trust in the streamers doesn’t inuence your intention to continue using livestream
shopping on social commerce in the future?
15. How can trust in the streamers inuence your intention to continue using livestream shopping?
16. H10 Why do you think trust in the products inuence your intention to continue using livestream shopping?
17. H11 Why do you think perceived value of livestream shopping inuence your intention to continue using livestream
shopping?
18 Are there any other factors that may inuence your intention to continue using livestream shopping?
