INTRODUCTION:

The rise of livestream shopping has revolutionized the retail landscape, offering a dynamic and interactive platform for brands to connect with consumers in real-time. This innovative approach transcends the limitations of traditional online shopping by fostering a sense of community, immediacy, and entertainment. This research delves into the intriguing interplay between user-generated content (UGC) and purchase intention in livestream shopping, exploring its potential role as a mediator shaping consumer perceptions and purchase decisions.

Unlike traditional marketing messages controlled by brands, UGC offers authentic, unfiltered perspectives from fellow viewers and trusted influencers. This authenticity resonates with consumers, fostering a sense of trust and credibility that can significantly impact their brand perception. Positive reviews, insightful comments, and enthusiastic reactions to products showcased during the livestream serve as social proof, validating the brand's claims and encouraging purchase decisions. Moreover, UGC can address concerns and questions in real-time, providing valuable insights and fostering trust in the brand's transparency and responsiveness. This research proposes that UGC acts as a mediator in the relationship between livestream shopping experiences and purchase intention. UGC serves as a bridge, facilitating communication and trust between brands and consumers. By providing a platform for authentic and unfiltered feedback, UGC allows consumers to engage in a two-way dialogue with the brand, shaping their perceptions and influencing their emotional attachment. Additionally, UGC can act as a valuable source of information for potential customers, addressing concerns and providing real-world insights that can influence their decision-making process. By delving into the mediating role of UGC in livestream shopping, this research aims to provide a deeper understanding of consumer behavior within this dynamic environment. This research project delves into the complexities of livestream shopping, focusing on the potential of user-generated content (UGC) to act as a mediating force between consumers' experiences and purchase intention. While the immediate allure and entertainment of livestreams are undeniable, concerns linger about the inauthenticity of marketing tactics, the lack of transparency in product demonstrations, and the difficulty in gauging genuine customer sentiment. This research aims to bridge this gap by investigating how UGC, in its various forms (comments, reviews, social media discussions), shapes consumer perceptions and ultimately influences purchase intention of livestream shopping.

LITERATURE REVIEW:

Livestreaming features can reduce uncertainty and encourage purchases based on the Uncertainty Reduction Theory¹. Livestreams build loyalty by fostering engagement and value creation, offering valuable insights for platform development and management². There is an importance of "presence" in livestream shopping and it influences buying behavior in different ways³. Customers engaging in livestream shopping, focuses on the features like real-time interaction and perceived authenticity. It create a sense of social support⁴. Building strong online communities could enhance support for live hosts, offering valuable insights for platform development⁵. Product visibility, interaction, and buying guidance enhance user immersion and interaction, leading to actual purchases⁶. Customers who get less engaged with the influencer benefits more from the immersive metaverse experience⁷. TikTok live shopping (TTL) influences customer’s continued purchase intentions⁸. Livestreaming in unique ways, pushes its boundaries as an interactive tool⁹. The social presence in livestream e-commerce enhances consumer identification and also positively moderate self-improvement goals for customers¹⁰. Businesses seeking to integrate livestream sales into their strategies¹¹. Livestreaming empowers new forms of interaction between sellers and viewers in e-commerce¹¹. It builds a hybrid model for selecting live-streamers for shopping websites¹². Customer behavior analytics and engagement tools boost engagement in virtual environments¹³. The rising popularity of livestreaming affects retail platforms and merchants¹⁴. Understanding presence can help design more engaging social media experiences¹⁵.

User Generated Contents:

There is an impact of user-generated content (UGC) on online sales, particularly for "risky" products like cosmetics^16.User-Generated Content (UGC) on Facebook impacts Vietnamese consumers' purchase decisions for vitamins and supplements during COVID-19¹⁷. AI holds immense potential for growth and innovation in media, but ethical considerations must be addressed to ensure trust and credibility in this evolving landscape¹⁸. Strong CSR communication on social media can lead to positive customer engagement and brand loyalty¹⁹. Better use of social media to attract tourists by creating relatable content and encouraging them to share their own experiences²⁰. User reviews and social media posts (UGC) influence people²¹. UGC as a mediator in today's marketing landscape, offering valuable insights for managers to optimize their communication strategies²². Personal factors like age and family situation also influence UGC usage, while others like income don't seem to matter²³. Online reviews on an Indian food delivery platform (Zomato) affects delivery ratings²⁴. Impact of User-Generated Content (UGC) and financial factors on hotel sales²⁵. Different types of social media communication, offering valuable insights for researchers and marketers²⁶. Social media marketing, particularly in monitoring brand sentiment²⁷. Reviews from different platforms work together to boost sales¹⁶ (Jia et al., 2023). UGC and social media knowledge and offers tips for creators to be more influential online²⁸. Seven potential factors, including influencer notes, note quantity/quality, network interaction, and network trust, hypothesizing their impact on purchase intention²⁹. Brand-generated content generally has a stronger impact than user-generated content³⁰. Real-life experience and transparency boosted purchase intent, especially for demonstration videos³¹. There is a positive relation between brand experience, brand love, and Brand behaviour outcomes like brand loyalty, positive word-of-mouth, purchase intention, active engagement³². A study on brand perception in a public sports service, found that credibility of the brand has significant impact on trust and attitude, but does not influence congruence whereas trust has significant impact on congruence and attitudes. It further found that congruence has significant impact on brand loyalty and WOM (word of mouth) ³³. Juice producers should prioritize building brand loyalty, awareness, and association to influence consumer behavior³⁴. Brands to build and maintain loyal customers by focusing on positive experiences³⁵. Building emotional connections and trust alongside ethical and social responsibility initiatives for luxury brands targeting this young Indian demographic ³⁶. Online and offline loyalty interact and how brand trust and attachment influence them, across cultures³⁷. Digital advertising can be a powerful tool for building brand preference and loyalty, encouraging marketers to leverage it effectively utilise the virtual platform and generate real customers^38.Word-of-mouth is the key for purchase decisions, especially for trend-sensitive consumers³⁹. Brand loyalty and brand association were identified as important factors influencing WOM through these relationships⁴⁰. Positive brand experiences and emotional connections for smartphone brands in China⁴¹. Digital marketing (e.g., online recommendations) and territory management (e.g., brand position and reputation ⁴². Understanding and improving these factors can be crucial for marketers to build customer loyalty and gain market share⁴³. A study among 250 motorcycle brands found that service quality and brand image influences brand satisfaction and in turn brand satisfaction improves brand loyalty⁴⁴Combined influence of CSR, credibility, and identification on loyalty in banking, offering valuable insights for banks to strengthen customer relationships⁴⁵. Three important branding ideas: brand value (equity), customer loyalty, and brand image lead to purchase intention⁴⁶. The model (Technology Acceptance Model 3) to understand what drives people to buy on these platforms that is, livestream shopping⁴⁷. For a brand it is very important to choose from the different types of influencer marketer’s viz., mega, macro, micro and nano influencers. It is found that brands must not focus on the number of followers of influencers, rather it need to focus on influencer scale and reliability towards customer engagement⁴⁸.

RESEARCH METHODOLOGY:

The impact of livestream shopping on consumer behavior is not fully understood. One crucial factor that likely plays a significant role is user-generated content (UGC). UGC encompasses comments, reviews, and ratings shared by viewers during or after the livestream. This real-time feedback loop creates a sense of community and social proof, influencing how viewers perceive the products and the overall shopping experience. This research project delves into the intricate relationship between livestream shopping, purchase intention, and the mediating role of UGC. By examining these factors, we aim to develop a comprehensive theoretical framework that explains how livestreams influence consumers' buying decisions. Figure 1 shows the theoretical framework which serve as a foundation for further research and provide valuable insights for businesses seeking to optimize their livestreaming strategies.

(Source: Author)

Fig. 1 Theoretical Framework

Livestream shopping platforms integrate with e-commerce allowing viewers to seamlessly browse products, ask questions to brand representative, and make purchases from the brand itself within the livestream environment. The sub variables of livestream shopping include interaction, enjoyment, presence, and perceived proximity can lead to purchase intention⁴⁹. Viewers can engage with the host through comments, questions, and polls, while hosts can use humor, storytelling, and relatable personalities to create a positive and enjoyable atmosphere⁴. High-quality visuals, live demonstrations, and close-up shots of products can immerse viewers in the experience⁵⁰. This perceived closeness can make viewers comfortable ultimately influencing their purchase decisions^1.Thus the first hypothesis.

H1: Livestream shopping has a significant impact on purchase intention.

User-Generated Content (UGC), in the context of livestream shopping, refers to any content created by viewers during or after a livestream broadcast. It can be comments, questions, or contributions from viewers, expressing their reviews and ratings on the product's quality, performance, and value.

This in turn can build trust by offering social proof and a sense of shared experience with the product. Live chat comments and discussions during a livestream provide a platform for viewers to ask questions, share their thoughts, and offer real-time feedback. Engaging comments that address concerns, highlight product benefits, and showcase genuine user experiences can foster trust by creating a sense of transparency and open communication. Thus the second hypothesis that

H2: Livestream shopping has a significant impact on user generated content(UGC).

UGC content in Livestream gives an opportunity to view the product in action from a non-commercial perspective. This allows viewers to assess its functionality, aesthetics, and suitability for their needs. Positive reviews, informative comments, and user-generated visuals collectively offer a more authentic perspective on the product compared to traditional marketing messages which may affect the purchase intentions. Thus the third hypothesis

H3: User generated content (UGC) has a significant impact on purchase intention.

A proper understanding on the mediating effect of UGC on livestream shopping and purchase intention is a crucial metric that reflects the effectiveness of livestream shopping in driving viewers towards making a purchase. High engagement, of user generated content in livestream shopping such as a steady stream of comments, questions, and positive reactions, creates a sense of community and excitement. Thus the businesses can leverage UGC to achieve their marketing and sales objectives of the organization. Thus the fourth hypothesis

H4: User generated content (UGC) has a significant mediating effect on livestream shopping and purchase intention.

RESEARCH DESIGN:

This research will utilize a purposive sampling approach. In purposive sampling, participants are chosen for their specific characteristics that align with the research goals. For this study, the target population encompasses all consumers who have some familiarity with livestream shopping. Unlike random selection methods, purposive sampling allows to identify individuals who are most likely to provide valuable data due to their relevant experiences or behaviors. A sample size of 210 was selected for the study which follows the ten times number of items⁵¹.For more accuracy 260 responses were taken.

INSTRUMENT PREPARATION AND DATA COLLECTION:

Questionnaires allow researchers to efficiently collect information on various aspects of interest, such as attitudes, beliefs, behaviors, and demographics. For this study, a 24 item questionnaire was used. Three questions assessed demographic data, like age, gender, and education level. Next 21 questions analysed various items used for the study. The participants responded on a 5-point Likert scale ranging from "Strongly Disagree" to "Strongly Agree." Utilising the statistical software SPSS, data is analysed in a systematic manner. For the examination of sample data and assessment of model fit, this study employs CFA and SEM. Confirmatory Factor Analysis (CFA) or the measurement model assess the validity of observed variables and their underlying latent constructs. Structural Equation Modeling (SEM) asses the model complex relationships between multiple variables, both observed and latent. SEM is facilitated through the application of Analysis of Moment Structure (AMOS). Mediating Analysis is performed to explore the mediating impact of variables in the study using bootstrap, a feature in the application of AMOS.

ANALYSIS AND DISCUSSION:

ASSESSMENT OF THE MEASUREMENT MODEL:

A Confirmatory Factor Analysis (CFA) was utilized to evaluate the validity, reliability, and fitness of the measures, as well as to assess the proposed theoretical model. IBM SPSS AMOS software was employed for CFA to confirm the convergent validity and reliability of the proposed model. Additionally, CFA scrutinize the model fit prior to proceeding with structural equation modelling. The factor loading, reliability and convergent validity of each factor is shown in Table 1. For assessing the reliability of the scale, Cronbach's alpha coefficient and composite (CR) reliability is checked. From table 1 it is found that the Cronbatch’s alpha coefficient of all variable is ≥ 0.6, and composite reliability is ≥ .80 which indicates a good reliability. It is found that the factor loading for each item for latent variable is above 0.7 which is acceptable.

AVE serves as an indicator of convergent validity, showing how well a set of indicators aligns with and measures the same underlying construct. It represents the average variance of the items relative to the total variance of the construct. When the value is greater than 0.5, it suggest a stronger convergent validity, suggesting close interrelation among the indicators and the underlying construct. In terms of convergent validity, the construct reliability (CR) should exceed 0.7, and CR should consistently exceed the average variance explained (AVE). However, acceptable measurement quality can still be achieved if AVE is below 0.5 but the CR is high (>0.7). Table 1 shows that the AVE value for all the variables are greater than 0.5 which suggest a good convergent validity.

Table 1: Reliability and Convergent validity analysis

Variable name	Factor Loading	Cronbach's Alpha	Composite reliability	AVE
Interaction		0.849	0.8125	0.596667
IN1	0.82
IN2	0.71
IN3	0.76
Enjoyment		0.877	0.847	0.581625
EN1	0.77
EN2	0.76
EN3	0.74
EN4	0.78
Presence		0.851	0.823	0.6086
PR1	0.79
PR2	0.76
PR3	0.79
Perceived Proximity		0.851	0.826	0.614233
PP1	0.79
PP2	0.75
PP3	0.81
User Generated Content		0.909	0.918	0.69292
UGC1	0.87
UGC2	0.84
UGC3	0.82
UGC4	0.79
UGC5	0.84
Purchase Intention		0.905	0.908	0.768967
PI1	0.9
PI2	0.88
PI3	0.85

Discriminant Validity

Regarding Discriminant Validity, it's essential for the square root of average variance Extracted (AVE) of latent variables must be higher than the correlation towards other variables⁵² and AVE should surpass the Maximum Shared Variance (MSV).

Table 2 gives the discriminant validity of the constructs. It is found that the square root of AVE of each construct is greater than the correlation with other latent construct. This suggest that each latent variables are distinct from each other and established discriminant validity.

MODEL FIT OF MEASUREMENT MODEL ANALYSIS:

Table 3: Model fit of Measurement model analysis

RMSEA	GFI	NFI	CFI	CMIN/DF
0.078	0.91	0.927	0.904	4.138

(Source: Author)

Table 3 gives the model fit values. The default model's RMSEA rating of 0.078 which is within the acceptable fit range, often defined as values less than 0.08. CFI value of 0.90 for the default model indicates that it captures approximately 90% of the variance in the observed variables, relative to the independence model^51,53As per the analysis result the CMIN/DF value for the proposed model is 1.667 which is less than 5. Goodness of fit (GFI) value of the model is 0.910 which is in the acceptable range^{51, 53}. A Goodness of Fit Index (GFI) value above 0.90 indicates that the observed data is well-fitted by the suggested structural equation model. The Normed Fit Index (NFI) evaluates the relative fit of the proposed model compared to a null model. NFI values above 0.90 are often considered acceptable, while values surpassing 0.95 indicate a good fit⁵⁴. The default model's NFI rating of 0.927 indicates that it fits the data rather well. The fit indices for the model presented in Table 3 met these criteria: CMIN/df = 4.138, GFI = 0.910, CFI = 0.904, and RMSEA = 0.078, with NFI = 0.927.

STRUCTURAL EQUATION MODELLING:

Structural Equation Modelling (SEM) path analysis was undertaken to explore the connections between the constructs. IBM SPSS AMOS was utilized for estimation, employing the maximum likelihood estimate, a commonly utilized method for estimating model parameters. Path analysis, a valuable technique within SEM, allows for the examination of both direct and indirect effects among variables in a hypothesized model. It aids in identifying the most impactful factors in the relationship under investigation. Through this approach, the study aimed to enhance comprehension of the intricate relationships among variables and to assess the theoretical model. The structural model is depicted in the following fig. 2.

(Source: Author)

Fig. 2: Structural equation modelling

Table 4: Structural Equation Modelling

Relationship	Estimate	S.E.	C.R.	P	Label
UGC <--- LS	0.848	0.050	16.845	***	Supported
PI <--- UGC	0.655	0.226	15.312	***	Supported
PI <--- LS	0.632	0.183	15.250	***	Supported

(Source: Author)

From the Table 4, this study indicates H1, H2 and H3 is accepted because p value is showing less than 0.05 and it is significant. Thus study shows that livestream shopping has a significant impact on purchase intention as well as user generated content(UGC). It also reveals that user generated content (UGC) has a significant impact on purchase intention.

MEDIATION ANALYSIS:

The next step of the study is to analyse the mediating role of User Generated Content (UGC) on Consumer Livestream Shopping (LS) and Purchase Intention (PI). Table 4 shows the mediating effect of UGC on livestream shopping and purchase intention. The results shows a significant indirect effect of Livestream Shopping (LS) on Purchase Intention (PI) with a positive significance (β = 0.065, p = .000), supporting H1. Furthermore, the direct effect of the mediator was also found significant (β = 0.233). Hence, User Generated Content (UGC) partially mediated the relationship between Livestream Shopping (LS) and Purchase Intention (PI).

Table 4: Mediation analysis of UGC on Livestream shopping and purchase intention

Relationship

Direct Effect

pvalue

Indirect effect

value

Decision

LS<----UGC<----PI

0.632

***

0.065

***

Partial Mediation

(Source: Author)

From the above table, this study indicates that H4 is accepted because p value is showing less than 0.05 and it is significant.

The structural model presented in the data has demonstrated a good fit with the data. All the four hypotheses have been supported, indicating that Livestream Shopping (LS), Purchase Intention (PI), User Generated Content (UGC), and their associations, have been confirmed.

FINDINGS AND DISCUSSION:

The study was intended for assessing the effect of user generated content (UGC) on the livestream videos and purchase Intention. Majority of the respondents are male which is 50.4%, majority of the respondents are from the age group of 19 – 24 which is 64.2% and 197 respondents are post graduates which is 75.8%. All constructs demonstrated composite reliability exceeding 0.70, indicating internal consistency within the scales used to measure them. Cronbach's Alpha values for all constructs were greater than 0.6, further supporting the reliability of the measurement scales. All constructs displayed AVE exceeding 0.50, suggesting they capture enough variance compared to measurement error. This indicates good convergent validity. The positive square root of the AVE for each latent variables shows greater value than the highest correlation with any other latent variable supported the fact that the variables are discriminant. . The fit indices for the model presented in Table are CMIN/df = 4.138, GFI = 0.910, CFI = 0.904, and RMSEA = 0.078, with NFI = 0.927. Through structural equation modelling (SEM) analysis, it is found that all research hypotheses have been accepted. The outcome shows a positive relationship among the three constructs: Livestream Shopping, User Generated Content and Purchase Intention. The analysis revealed an indirect effect of livestream shopping on purchase intention mediated by UGC, estimated at 0.065. A portion (6.5%) of the positive influence of livestream shopping on purchase intention can be attributed to the presence and influence of user-generated content within the livestream environment. User generated content was found to partially mediate the relationship between livestream shopping and purchase intention. Livestream shopping has a significant positive direct effect on purchase intention (β = 0.632). This suggests that livestreams can be a powerful tool for influencing consumers' purchase decisions, even in the absence of user-generated content. The findings suggest that livestream shopping is a powerful strategy for influencing purchase intention. The presence of positive UGC within the livestream can further strengthen this effect. The study reveals partial mediation by UGC in the relationship between livestream shopping and purchase intention.

CONCLUSION:

This study examines the impact of livestream shopping on consumers' purchase intention. The research shows that livestreaming positively influences purchase intention, acting as a powerful tool for businesses. However, user-generated content (UGC) like comments and reviews during the livestream partially mediates this effect. This means that while livestreams directly influence decisions, UGC plays an additional role in shaping viewers' perceptions. Positive UGC can further increase product appeal and nudge viewers towards buying. The study highlights opportunities for future research to explore the specific types of UGC that most influence purchase decisions and how different livestreaming platforms can affect viewers' experiences. Overall, this research underlines the effectiveness of livestream shopping with UGC as a mediating factor. By understanding these dynamics, businesses can improve their livestreaming strategies and leverage the power of social influence for better e-commerce results.

CONFLICT OF INTEREST:

The authors have no conflicts of interest regarding this investigation.

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Received on 15.10.2025 Revised on 13.01.2026

Accepted on 28.02.2026 Published on 20.07.2026

Available online from July 30, 2026

Asian Journal of Management. 2026;17(3):229-236.

DOI: 10.52711/2321-5763.2026.00036

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.

Variables	AVE	IN	EN	PR	PP	TT	PI
Interaction	0.596667	0.772
Enjoyment	0.581625	0.742**	0.763
Presence	0.6086	0.771**	0.755**	0.78
Perceived Proximity	0.614233	0.757**	751**	740**	0.784**
UGC	0.69292	0.755**	0.741**	0.753**	0.716**	0.832
Purchase Intention	0.768967	0.735**	0.756**	0.751**	0.706**	0.825**	0.877