Actualizing Social Affordances in Live Streaming: 
The Role of Real-Time Interaction in Building Community Commitment

Xinjie Bong; Aleksandra Margushina

doi:doi:10.11648/j.ijefm.20261402.15

Research Article |

| Peer-Reviewed

Actualizing Social Affordances in Live Streaming: The Role of Real-Time Interaction in Building Community Commitment

Xinjie Bong

, Aleksandra Margushina^*

Published in International Journal of Economics, Finance and Management Sciences (Volume 14, Issue 2)

Received: 23 March 2026 Accepted: 8 April 2026 Published: 21 April 2026

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Abstract

This study investigates how real-time interaction in live streaming environments drives users’ community commitment from a social affordance perspective. Drawing on Social Exchange Theory and the affordance perspective, a structural model is developed to explain how real-time interactivity facilitates parasocial interaction, promotes vicarious learning and knowledge adoption, and generates perceived benefits that ultimately lead to community commitment. Using survey data collected from 451 users of the Douyin platform and analyzed through structural equation modeling, the results provide empirical support for the proposed relationships. The findings indicate that real-time interactivity significantly enhances parasocial interaction between users and streamers, which in turn promotes both vicarious learning and knowledge adoption. These cognitive processes further generate perceived benefits, specifically hedonic benefit and self-esteem benefit, both of which positively influence users’ community commitment. Notably, self-esteem benefit shows a relatively stronger effect than hedonic benefit, indicating its greater importance in predicting sustained user engagement in live streaming communities. This study contributes to the literature by clarifying the mechanism through which social affordances are actualized in live streaming contexts and by integrating emotional and cognitive pathways to explain user commitment. It also extends prior research by positioning parasocial interaction as a key mediating mechanism linking interactive features and user outcomes. From a practical perspective, the findings provide insights for platform designers and content creators on how to enhance interactive features and develop engagement strategies that foster long-term community participation.

Published in	International Journal of Economics, Finance and Management Sciences (Volume 14, Issue 2)
DOI	10.11648/j.ijefm.20261402.15
Page(s)	161-172
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Live Streaming, Social Affordances, Real-time Interaction, Parasocial Interaction, Community Commitment

1. Introduction

In recent years, live streaming has emerged as a prominent form of digital interaction and content dissemination on social media platforms. With the rapid development of mobile internet technologies and social commerce, live streaming platforms such as Douyin, Taobao Live, and Kuaishou have attracted large numbers of users worldwide. Unlike traditional online media, which primarily rely on one-way information delivery, live streaming provides a highly interactive environment where streamers and viewers can communicate in real time. Through features such as live chat, virtual gifts, emoji reactions, and fan communities, users can actively participate in the broadcasting process rather than remain passive content consumers. These interaction mechanisms create a dynamic digital environment where social relationships and community engagement can gradually emerge.

The interactive nature of live streaming platforms can be understood through the concept of social affordances, which refers to the action possibilities that technological features provide for social interaction. Within live streaming environments, technological features such as comment systems, real-time feedback, and virtual gifting offer user opportunities to express opinions, interact with streamers, and communicate with other viewers. Through these affordances, users may gradually develop emotional and cognitive connections with the streamer and the broader audience community. Previous studies have shown that these interactions can foster parasocial relationships, facilitate learning behaviors, and generate various perceived benefits for users, ultimately influencing their engagement and participation within online communities.

Despite the growing body of research on live streaming and social media interaction, few studies have systematically examined the mechanisms by which real-time interaction contributes to users’ commitment to a live streaming community. In particular, it remains unclear how interactive experiences in live streaming environments translate into cognitive outcomes, emotional benefits, and long-term community attachment. Understanding this mechanism is important for both academic research and platform management, as user commitment plays a crucial role in sustaining active communities and enhancing platform competitiveness.

To address this research gap, this study investigates how real-time interaction on live streaming platforms influences users’ community commitment from a social affordance’s perspective. Using a questionnaire survey of live-streaming users, this research constructs and empirically tests a structural model linking real-time interaction, parasocial interaction, learning processes, perceived benefits, and community commitment. By examining these relationships, the study aims to provide a clearer understanding of how social affordances are actualized in live streaming environments and how interactive experiences shape users’ psychological and behavioral engagement with online communities.

2. Literature Review

2.1. Live Streaming as an Interactive Social Environment

Live streaming has emerged as a transformative online service that integrates real-time audio and video transmission with interactive social features, fundamentally altering how content is consumed and how online communities are formed

[1, 2]

. Unlike traditional one-way video streaming, social live-streaming platforms facilitate a dynamic, bidirectional communication environment where users are not merely passive recipients of content but active participants

[1, 3]

. This core characteristic of real-time interaction allows for immediate engagement between streamers and viewers, creating a new type of digital social space

[4]

The environment of a live-streaming room is rich with mechanisms designed to foster interactivity. Viewers can engage with streamers and other audience members through danmaku—real-time scrolling comments that create a shared viewing experience—as well as through liking, sharing, and sending virtual gifts

[1, 2, 5]

. These interactive features enable users to shape the social atmosphere, express support for streamers, and gain social recognition within the virtual space

[1, 6]

. The nature of these interactions can be further categorized into entertainment-type interactions, which activate the room’s atmosphere, and information-type interactions, focused on exchanging knowledge and evaluating products

[2]

This active participation transforms users from bystanders into co-creators of the live streaming event. Through chatting, praising, and sending virtual gifts—digital tokens purchased with real currency—users contribute to a sense of community and shared experience

[3, 5]

. The integration of e-commerce has further enriched this social environment, with live-streaming commerce becoming a significant economic force in which social interaction and commercial transactions increasingly converge

[1, 2]

. Platforms like Douyin exemplify this hybrid model, serving as spaces where entertainment, social networking, and commerce coexist and attract massive user engagement.

Empirical research confirms that these interactive features generate measurable psychological outcomes. Studies have found that social interaction in live streaming significantly predicts user immersion and satisfaction, while also fostering parasocial interaction—the illusion of intimate, friend-like relationships with streamers

[3, 6]

. These findings underscore that the interactive environment of live streaming is functionally essential for user retention and platform success. Therefore, real-time interaction can be understood as a core social affordance in live streaming environments, providing the foundation for its actualization and subsequent user engagement.

2.2. Social Affordances and Their Actualization in Digital Platforms

The concept of affordances was originally introduced by psychologist James Gibson to explain how individuals perceive possibilities for action in their environment

[7]

. In simple terms, an affordance is what an environment “offers” an individual—what it allows them to do. A key insight from Gibson’s theory is that affordances are not fixed properties of objects; rather, they emerge from the relationship between the individual and the environment

[8]

This foundational idea was later adopted in information systems research to understand how people interact with technology. In this context, IT affordances refer to the action possibilities that technological systems enable for users, depending on how users perceive and use system features to achieve their goals

[9]

. When applied to social media, researchers have developed the concept of social media affordances, defined as the goal-oriented behavioral possibilities that social media platforms offer to their users

[10]

. Various typologies have been proposed, including Treem and Leonardi’s four key affordances—visibility, editability, persistence, and association—and Majchrzak et al.’s affordances such as metavoicing and triggered attending in online knowledge sharing

[11, 12]

In the specific context of live streaming, researchers have identified affordances that are particularly salient. Jia et al. highlight four key affordances on platforms like TikTok and Bilibili: metavoicing (commenting, liking, sending virtual gifts), communication (real-time interaction via danmaku or video chat), browsing others’ content (observing and learning from others’ demonstrations), and relationship formation (connecting with others through fan clubs or chat rooms)

[10]

The process by which users realize these potential actions is known as actualization—turning what is possible into what is done

[13]

. In live streaming, actualization occurs when users actively engage with platform features: sending a comment actualizes the communication affordance; joining a fan club actualizes relationship formation; scrolling through personalized recommendations actualizes recommendation accuracy and effortlessness

[8, 10]

. For content creators, actualization can take strategic forms. For instance, multi-channel networks (MCNs) actualize visibility and metavoicing affordances to enhance customer engagement and drive business performance in B2B live streaming contexts

[13]

Importantly, the actualization of social affordances in live streaming is what transforms passive viewers into active participants. Through actualization, users do not simply consume content; they interact, learn vicariously by observing others, form parasocial relationships with streamers, and ultimately build commitment to the community

[10]

. This study adopts this theoretical lens to examine how real-time interactivity, as a core social affordance, is actualized by users on platforms like Douyin, thereby triggering the cognitive and emotional processes that lead to community commitment.

2.3. Parasocial Interaction and Social Learning in Live Streaming

Horton and Wohl introduced the concept of parasocial interaction (PSI) to describe the illusory, one-sided relationships that viewers develop with media personalities

[5, 14]

. In traditional media like television, audiences often feel a sense of intimacy with performers despite the absence of real interaction. Jiang et al. characterize PSI by three features: it is illusory (a psychological experience), self-established (formed unilaterally by the viewer), and involves weak bonds (unlike strong interpersonal relationships)

[1]

In live streaming, the nature of PSI has evolved significantly. Unlike traditional one-way media, live streaming platforms enable real-time, bidirectional communication through danmaku, virtual gifts, and likes, with streamers responding immediately to viewers

[1, 15]

. This interactivity creates a powerful illusion of mutual awareness and reciprocity, strengthening viewers’ emotional connection with streamers

[6, 10]

. Research demonstrates that PSI in live streaming enhances trust and satisfaction, increases purchase intention, and drives gifting behavior through perceived benefits in relationship development and image building

[10, 15, 16]

. This enhanced parasocial interaction not only strengthens emotional bonds but also facilitates social learning processes, as users are more likely to observe and imitate behaviors from streamers they feel connected to.

Beyond consumption behaviors, PSI plays a crucial role in facilitating social learning. According to Social Learning Theory, individuals learn by observing others’ behaviors and their consequences

[17, 18]

. In live streaming, viewers engage in two forms of learning: observational learning (watching streamers’ demonstrations and others’ comments) and reinforcement learning (replicating behaviors that yield positive outcomes)

[17]

Hao and Chen suggest that streamers and viewers serve as distinct learning sources: learning from streamers increases certainty about product quality, demand, and streamer quality, while learning from viewers boosts product-related certainty but may reduce streamer quality certainty due to inconsistent opinions

[17]

In summary, PSI in live streaming has evolved into a dynamic phenomenon shaped by real-time interactivity. This enhanced PSI serves as a foundation for social learning, motivating viewers to observe and learn from both streamers and co-viewers. The interplay between PSI and social learning ultimately influences outcomes like impulsive buying, knowledge adoption, and gifting intentions

[10, 15, 16]

2.4. Perceived Benefits and Community Commitment from a Social Exchange Perspective

Social Exchange Theory (SET) provides a foundational lens for understanding why individuals engage in and sustain relationships within online communities

[1]

. Originally developed by Homans

[19]

and Blau

[20]

, SET posits that human interactions are driven by subjective cost-benefit analysis and the expectation of reciprocity. Unlike economic exchanges, social exchanges involve intangible resources like emotional support and recognition

[17]

In live streaming, streamers invest effort and personal qualities to create engaging content, while viewers reciprocate with attention and engagement

[21]

. This exchange is deeply social and emotional, as viewers seek to fulfill psychological needs and receive perceived benefits—the subjective value derived from participation

[1]

. Prior studies suggest that perceived benefits can be categorized into social, hedonic, and self-esteem dimensions

[22, 23]

. Parasocial interaction (PSI) is a key antecedent of these benefits. Jiang et al. found that PSI positively influences all three benefits, with the strongest effect on the self-esteem benefit

[1]

These benefits, in turn, lead to community commitment—users’ desire to maintain a long-term relationship with the streamer and community

[22]

. Jiang et al. found that hedonic and self-esteem benefits significantly influence community commitment, whereas social benefits did not

[1]

. Jiang et al. show that community commitment significantly influences advocacy, helping others, and feedback, demonstrating how committed members actively contribute to value co-creation

[1]

In summary, perceived benefits mediate the link between community experiences and commitment, which in turn generates reciprocal behaviors that sustain the community. This study adopts this SET lens to examine how real-time interactivity on Douyin triggers perceived benefits that lead to community commitment.

3. Research Model and Hypothesis Development

3.1. Research Model

The research model of this study is shown in Figure 1. Drawing on Social Exchange Theory and the affordance perspective, the model treats real-time interactivity as a core characteristic of live-streaming social affordances and explores its influence on users’ community commitment. Parasocial interaction is conceptualized as a mediating mechanism that facilitates social learning, including vicarious learning and knowledge adoption. These cognitive outcomes further generate two perceived benefits – hedonic benefit and self-esteem benefit, which jointly contribute to users’ behavioral outcome: commitment to the streaming community. This model explains how technological and social affordances on platforms like Douyin trigger emotional and cognitive responses that drive users’ commitment. By incorporating both emotional (parasocial ties and perceived benefits) and behavioral (interactive actions and community participation) elements, the model provides a comprehensive understanding of how live streaming platforms actualize social affordances and foster sustained user involvement.

Download: Download full-size image

Figure 1. The research model.

3.2. Hypothesis Development

Real-time interactivity (RTI) is a key characteristic of live streaming that enables users to engage with streamers in real time. This includes actions such as sending comments, asking questions, reacting to content with emojis, or sending virtual gifts. Such interactivity creates the impression that users are in direct contact with the streamer, even though the interaction remains one-sided. This sense of closeness and engagement forms the basis for parasocial interaction, in which users begin to perceive the streamer as a close acquaintance or even a friend

[14]

Parasocial interaction (PSI) is a psychological phenomenon in which viewers develop the illusion of a personal relationship with a media personality

[24]

. Real-time interactivity strengthens this connection, as users feel their actions (e.g., comments or gifts) directly impact the streamer and the live stream. This sense of connection has been emphasized in previous studies on social live-streaming, where interactive behaviors on platforms like TikTok have been shown to enhance emotional bonds

[1]

. Thus, we hypothesize as follows:

H1: Real-time interactivity positively affects parasocial interaction between users and streamers.

Parasocial interaction enhances trust and emotional bonds between users and streamers. When users feel closer to the streamer, they are more likely to learn by observing their actions and behaviors. This is particularly relevant in live streaming contexts, where streamers often demonstrate specific skills, share knowledge, or showcase product usage. Users who experience parasocial interaction are more inclined toward vicarious learning, as they perceive the streamer as a credible source of information

[4]

Knowledge adoption (KA) is the process by which users absorb and apply new knowledge from external sources. The emotional connection and trust that result from parasocial interaction encourage users to be more receptive to the streamer's knowledge. When users feel closer to a streamer, they are more likely to perceive them as an authoritative and trustworthy source of information. Thus, we hypothesize as follows:

H2: Parasocial interaction positively affects vicarious learning.

H3: Parasocial interaction positively affects knowledge adoption.

Vicarious learning (VL), based on observing the streamer’s actions, allows users to acquire knowledge and skills without direct participation. In the context of live streaming, this is particularly effective, as users can observe processes in real time and adopt them. For instance, if a streamer demonstrates how to use a particular product, users can easily absorb this information and apply it in their own lives, as observing others is a key way to acquire new information. Thus, we hypothesize as follows:

H4: Vicarious learning positively affects knowledge adoption.

Hedonic benefit (HB) refers to the enjoyment and positive emotions users derive from interacting with a product or service. In the context of live streaming, this can include both the learning process itself and the stream's entertainment aspects. That is, when users acquire new knowledge or skills through live streaming, they experience pleasure and satisfaction from the learning process. This creates a hedonic benefit associated with positive emotions and entertainment

[22]

Self-esteem benefit (SE) refers to the sense of self-worth and confidence users gain from engaging with a product or service. Acquiring new knowledge and skills through live streaming enhances users’ self-esteem, making them feel more competent and confident in their abilities

[22]

. This, in turn, strengthens their attachment to the platform and the streamer. For example, if a user learns something new through a stream, they may feel pride in their achievement. Thus, we hypothesize as follows:

H5: Knowledge adoption positively affects hedonic benefit.

H6: Knowledge adoption positively affects self-esteem benefit.

Community commitment (CC) refers to the extent to which users feel part of a community and are willing to support it. The positive emotions and enjoyment users gain from participating in live streams (i.e., hedonic benefit) strengthen their attachment to the community. As a result, they are more likely to engage in community activities, increasing their loyalty and involvement

[22]

. For example, if users derive enjoyment from live streams, they are more likely to return to the platform and interact with other participants.

Self-esteem benefit enhances community commitment because users feel more confident and valued within the group. The boost in self-esteem and self-assurance that users gain from acquiring new knowledge encourages them to participate more actively in the community. They feel a sense of belonging and strive to maintain connections with other members. For instance, if a user gains confidence through learning in a live stream, they are more likely to share their knowledge with others in the community

[1]

. Thus, we hypothesize as follows:

H7: Hedonic benefit positively affects community commitment.

H8: Self-esteem benefit positively affects community commitment.

4. Data Collection

The questionnaire items used in this study were adapted from prior literature and refined to fit the live-streaming context. The measurement items for real-time interactivity were adapted from Dong et al.

[25]

; parasocial interaction, vicarious learning, and knowledge adoption were adapted from Jia et al.

[10]

; hedonic benefit, self-esteem benefit, and community commitment were adapted from Jiang et al.

[1]

. A five-point Likert scale was used to measure respondents’ perceptions, where 1 indicated strongly disagree, and 5 indicated strongly agree.

The questionnaire was primarily distributed to users in Mainland China, as this group represents a major user base of the Douyin platform and provides a relevant context for this study.

5. Data Analysis

This section presents the results of the quantitative analysis, including sample characteristics, reliability and validity tests, correlation analysis, and structural equation modeling.

5.1. Sample Characteristics

The survey was conducted online via the Wenjuanxing platform, targeting Douyin users across China. A total of 500 questionnaires were distributed, and 466 responses were received. After data screening, 15 responses were removed due to incompleteness or inconsistency, leaving 451 valid responses for analysis. The final sample includes users with diverse demographic characteristics and usage patterns, providing a representative basis for this study. The demographic details of the respondents are presented in Table 1.

Table 1. Demographics of survey respondents.

	Item	Frequency	Percent (%)
Gender	Female	221	49.00%
Gender	Male	230	51.00%
Age	Below 18	61	13.53%
	18-25	152	33.70%
	26-35	168	37.25%
	Above 36	70	15.52%
Highest education level	High school	69	15.30%
	Diploma	171	37.92%
	Bachelor	136	30.16%
	Master and above	75	16.63%
Occupation	Individual	25	5.54%
	Enterprise personnel	264	58.54%
	Civil servant	41	9.09%
	Student	99	21.95%
	Unemployed	22	4.88%
Monthly income	Below 2000	125	27.72%
	2001-4000	43	9.53%
	4001-6000	72	15.97%
	6001-8000	112	24.83%
	Above 8000	99	21.95%

5.2. Reliability and Construct Validity

Reliability and validity were assessed to examine the measurement quality. As shown in Table 2, the Cronbach’s α and composite reliability (CR) values for all constructs exceed the recommended thresholds, while the average variance extracted (AVE) values indicate adequate convergent validity.

Table 2. Reliability and validity analysis.

Construct	Item	$α$	CR	AVE
RTI	RTI1	0.870	0.858	0.602
	RTI2
	RTI3
	RTI4
PI	PI1	0.882	0.859	0.549
	PI2
	PI3
	PI4
	PI5
VL	VL1	0.918	0.896	0.550
	VL2
	VL3
	VL4
	VL5
	VL6
	VL7
KA	KA1	0.872	0.841	0.569
	KA2
	KA3
	KA4
HB	HB1	0.846	0.859	0.670
	HB2
	HB3
SE	SE1	0.856	0.849	0.584
	SE2
	SE3
	SE4
CC	CC1	0.873	0.866	0.617
	CC2
	CC3
	CC4

The suitability of the data for factor analysis was assessed using the KMO and Bartlett’s tests. As shown in Table 3, the KMO value is 0.941, exceeding the recommended threshold, and Bartlett’s test of sphericity is significant (p < 0.05), indicating that the data are appropriate for factor analysis.

Table 3. KMO & Bartlett’s test.

KMO		0.941
Bartlett’s test of Sphericity	Approx. Chi-Square	8205.866
	df	465
	p	0

Factor analysis was conducted using SPSS 27.0 with principal component analysis and varimax rotation. The rotated factor loadings are presented in Table 4. All items load strongly on their respective factors, with factor loadings exceeding 0.4, indicating a clear factor structure. In addition, the item communality estimates are generally above 0.65, suggesting that the extracted factors explain a substantial proportion of the variance.

Table 4. Rotated factor loadings.

Item ID	1	2	3	4	5	6	7	R²
RTI1	0.13	0.166	0.091	0.766	0.158	0.142	0.09	0.694
RTI2	0.189	0.147	0.133	0.792	0.169	0.101	0.09	0.749
RTI3	0.182	0.151	0.114	0.787	0.101	0.123	0.091	0.721
RTI4	0.214	0.161	0.183	0.758	0.072	0.142	0.129	0.722
PI1	0.244	0.747	0.168	0.056	0.156	0.175	0.058	0.707
PI2	0.14	0.715	0.122	0.252	0.115	0.139	0.124	0.657
PI3	0.167	0.759	0.084	0.163	0.214	0.143	0.115	0.717
PI4	0.25	0.75	0.077	0.112	0.069	0.168	0.13	0.694
PI5	0.193	0.733	0.178	0.152	0.152	0.103	0.126	0.680
VL1	0.743	0.15	0.117	0.084	0.135	0.193	0.117	0.665
VL2	0.754	0.201	0.104	0.155	0.098	0.129	0.116	0.684
VL3	0.72	0.124	0.131	0.175	0.181	0.172	0.162	0.671
VL4	0.744	0.156	0.113	0.147	0.141	0.169	0.107	0.673
VL5	0.734	0.133	0.16	0.111	0.169	0.191	0.046	0.662
VL6	0.743	0.18	0.2	0.15	0.129	0.13	0.068	0.685
VL7	0.755	0.191	0.102	0.129	0.168	0.096	0.125	0.687
KA1	0.211	0.181	0.168	0.134	0.198	0.766	0.056	0.752
KA2	0.2	0.155	0.16	0.149	0.103	0.765	0.146	0.730
KA3	0.239	0.182	0.137	0.147	0.102	0.743	0.152	0.716
KA4	0.243	0.171	0.136	0.12	0.149	0.744	0.092	0.705
HB1	0.14	0.195	0.06	0.147	0.075	0.114	0.816	0.767
HB2	0.149	0.088	0.16	0.09	0.1	0.084	0.839	0.785
HB3	0.198	0.145	0.069	0.113	0.106	0.164	0.801	0.757
SE1	0.185	0.157	0.138	0.073	0.765	0.124	0.113	0.697
SE2	0.199	0.14	0.137	0.136	0.755	0.152	0.048	0.692
SE3	0.197	0.176	0.071	0.155	0.769	0.114	0.044	0.706
SE4	0.178	0.126	0.171	0.132	0.767	0.108	0.115	0.707
CC1	0.162	0.158	0.786	0.119	0.106	0.125	0.137	0.729
CC2	0.135	0.161	0.778	0.167	0.143	0.165	0.065	0.729
CC3	0.197	0.155	0.796	0.084	0.085	0.119	0.062	0.729
CC4	0.184	0.06	0.782	0.14	0.186	0.138	0.067	0.727

5.3. Correlation Analysis

As shown in Table 5, all variables are significantly positively correlated (p < 0.01), with most correlation coefficients ranging from 0.3 to 0.5, indicating moderate relationships among the constructs.

Table 5. Correlation analysis.

	Mean	Std. Dev.	RTI	PI	VL	KA	HB	SE	CC
RTI	3.368	1.101	1
PI	3.417	1.07	0.469**	1
VL	3.442	1.047	0.473**	0.533**	1
KA	3.451	1.13	0.440**	0.503**	0.547**	1
HB	3.292	1.175	0.356**	0.399**	0.406**	0.384**	1
SE	3.378	1.078	0.409**	0.461**	0.494**	0.441**	0.315**	1
CC	3.381	1.112	0.408**	0.424**	0.456**	0.451**	0.314**	0.412**	1

^* p<0.05, ** p<0.01.

5.4. Structural Equation Modeling

Structural equation modeling was conducted to test the proposed hypotheses, as shown in Figure 2.

Download: Download full-size image

Figure 2. Structural model and hypothesis testing results.

The model fit was evaluated using multiple indices, including χ²/df, RMSEA, GFI, CFI, NFI, TLI, and IFI. As shown in Table 6, all indices meet the recommended thresholds, indicating an acceptable model fit.

Table 6. Model fit indices.

Fit indexes	Recommendation	Structural model	Fit measures
χ²/df	<3	1.633	Acceptable
RMSEA	<0.10	0.038	Acceptable
CFI	>0.85	0.966	Acceptable
NFI	>0.85	0.917	Acceptable
GFI	>0.85	0.912	Acceptable
TLI	>0.90	0.963	Acceptable
IFI	>0.90	0.966	Acceptable

As shown in Table 7, real-time interactivity significantly positively affects parasocial interaction (path coefficient = 0.547, p < 0.001), indicating that users’ real-time interactive behaviors during live streaming significantly enhance their virtual social experience with the streamer. Therefore, hypothesis H1 is supported. Parasocial interaction further significantly positively affects vicarious learning (path coefficient = 0.622, p < 0.001), suggesting that parasocial interaction between users and streamers promotes vicarious learning, in which users acquire knowledge or skills by observing others’ behavior. Thus, hypothesis H2 is supported. At the same time, parasocial interaction also significantly positively influences knowledge adoption (path coefficient = 0.404, p < 0.001), indicating that the interaction between users and streamers not only facilitates vicarious learning but also directly affects users’ acceptance and adoption of knowledge. Hence, hypothesis H3 is supported. Vicarious learning significantly positively affects knowledge adoption (path coefficient = 0.444, p < 0.001), demonstrating that observing and imitating others’ behaviors significantly promote users’ acceptance and adoption of new knowledge. Therefore, hypothesis H4 is supported.

Knowledge adoption further significantly positively influences hedonic benefit (path coefficient = 0.496, p < 0.001), suggesting that after users accept and adopt new knowledge during live streaming, their hedonic experience is significantly enhanced. Thus, hypothesis H5 is supported. Knowledge adoption also significantly positively influences self-esteem benefit (path coefficient = 0.535, p < 0.001), indicating that after users accept and adopt new knowledge during live streaming, their self-esteem benefit improves. Therefore, hypothesis H6 is supported. Hedonic benefit significantly positively influences community commitment (path coefficient = 0.224, p < 0.001), suggesting that the pleasurable experience gained from live streaming significantly enhances users’ sense of community commitment. Hence, hypothesis H7 is supported. Self-esteem benefit also significantly positively influences community commitment (path coefficient = 0.424, p < 0.001), indicating that the higher users’ self-esteem benefit, the stronger their sense of belonging and commitment to the live streaming community. Therefore, hypothesis H8 is supported.

Table 7. Hypothesis testing results.

Hypothesis	Relationship	Est.	S.E.	C.R.	P	Conclusion
H1	RTI→PSI	0.547	0.053	10.299	<0.001	Support
H2	PSI→VL	0.622	0.055	11.333	<0.001	Support
H3	VL→KA	0.444	0.061	7.328	<0.001	Support
H4	PSI→KA	0.404	0.062	6.533	<0.001	Support
H5	KA→HB	0.496	0.054	9.164	<0.001	Support
H6	KA→SE	0.535	0.051	10.529	<0.001	Support
H7	SE→CC	0.424	0.056	7.56	<0.001	Support
H8	HB→CC	0.224	0.05	4.522	<0.001	Support

6. Conclusions

This study investigates how real-time interaction in live streaming environments is translated into users’ community commitment from a social affordance perspective. Based on survey data from Douyin users and structural equation modeling, the findings provide empirical support for the proposed mechanism linking interaction, cognition, and behavioral outcomes.

The results demonstrate that real-time interactivity is a key affordance that facilitates parasocial interaction, which, in turn, promotes vicarious learning and knowledge adoption. These cognitive processes further generate perceived benefits – specifically, hedonic benefit and self-esteem benefit, which in turn drive users’ commitment to the community. Notably, self-esteem benefit shows a stronger effect on community commitment than hedonic benefit, indicating its relatively greater importance in predicting users’ sustained engagement.

This study makes several theoretical contributions. First, it extends the affordance perspective by conceptualizing real-time interactivity as a core social affordance and empirically demonstrating its actualization in live-streaming contexts. Second, it enriches the literature on parasocial interaction by positioning it as a key mediating mechanism that bridges interaction and social learning. Third, by integrating Social Exchange Theory with affordance theory, this study develops a comprehensive framework that explains how interactive experiences are transformed into perceived benefits and, ultimately, community commitment.

From a practical standpoint, the findings suggest that live streaming platforms should prioritize the design of interactive features that enhance immediacy and responsiveness, thereby strengthening users’ perceived connection with streamers. In addition, promoting content that facilitates learning and enhances users’ self-perception may further increase perceived benefits and foster sustained community engagement.

Despite these contributions, several limitations should be acknowledged. The data are limited to a single platform and a cross-sectional design, which may restrict generalizability and causal inference. Future research could adopt longitudinal or experimental designs and examine different platform contexts to further validate and extend the proposed model.

Abbreviations

RTI	Real-Time Interactivity
PSI	Parasocial Interaction
KA	Knowledge Adoption
VL	Vicarious Learning
HB	Hedonic Benefit
SE	Self-Esteem Benefit
CC	Community Commitment
CR	Composite Reliability
AVE	Average Variance Extracted

Author Contributions

Xinjie Bong: Conceptualization, Data curation, Formal Analysis, Methodology, Project administration, Resources, Validation, Writing – original draft

Aleksandra Margushina: Conceptualization, Resources, Supervision, Visualization, Writing – review & editing

Funding

This work is not supported by any external funding.

Data Availability Statement

The data is available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Bong, X., Margushina, A. (2026). Actualizing Social Affordances in Live Streaming: The Role of Real-Time Interaction in Building Community Commitment. International Journal of Economics, Finance and Management Sciences, 14(2), 161-172. https://doi.org/10.11648/j.ijefm.20261402.15

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ACS Style

Bong, X.; Margushina, A. Actualizing Social Affordances in Live Streaming: The Role of Real-Time Interaction in Building Community Commitment. Int. J. Econ. Finance Manag. Sci. 2026, 14(2), 161-172. doi: 10.11648/j.ijefm.20261402.15

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AMA Style

Bong X, Margushina A. Actualizing Social Affordances in Live Streaming: The Role of Real-Time Interaction in Building Community Commitment. Int J Econ Finance Manag Sci. 2026;14(2):161-172. doi: 10.11648/j.ijefm.20261402.15

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@article{10.11648/j.ijefm.20261402.15,
  author = {Xinjie Bong and Aleksandra Margushina},
  title = {Actualizing Social Affordances in Live Streaming: 
The Role of Real-Time Interaction in Building Community Commitment},
  journal = {International Journal of Economics, Finance and Management Sciences},
  volume = {14},
  number = {2},
  pages = {161-172},
  doi = {10.11648/j.ijefm.20261402.15},
  url = {https://doi.org/10.11648/j.ijefm.20261402.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20261402.15},
  abstract = {This study investigates how real-time interaction in live streaming environments drives users’ community commitment from a social affordance perspective. Drawing on Social Exchange Theory and the affordance perspective, a structural model is developed to explain how real-time interactivity facilitates parasocial interaction, promotes vicarious learning and knowledge adoption, and generates perceived benefits that ultimately lead to community commitment. Using survey data collected from 451 users of the Douyin platform and analyzed through structural equation modeling, the results provide empirical support for the proposed relationships. The findings indicate that real-time interactivity significantly enhances parasocial interaction between users and streamers, which in turn promotes both vicarious learning and knowledge adoption. These cognitive processes further generate perceived benefits, specifically hedonic benefit and self-esteem benefit, both of which positively influence users’ community commitment. Notably, self-esteem benefit shows a relatively stronger effect than hedonic benefit, indicating its greater importance in predicting sustained user engagement in live streaming communities. This study contributes to the literature by clarifying the mechanism through which social affordances are actualized in live streaming contexts and by integrating emotional and cognitive pathways to explain user commitment. It also extends prior research by positioning parasocial interaction as a key mediating mechanism linking interactive features and user outcomes. From a practical perspective, the findings provide insights for platform designers and content creators on how to enhance interactive features and develop engagement strategies that foster long-term community participation.},
 year = {2026}
}

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TY  - JOUR
T1  - Actualizing Social Affordances in Live Streaming: 
The Role of Real-Time Interaction in Building Community Commitment
AU  - Xinjie Bong
AU  - Aleksandra Margushina
Y1  - 2026/04/21
PY  - 2026
N1  - https://doi.org/10.11648/j.ijefm.20261402.15
DO  - 10.11648/j.ijefm.20261402.15
T2  - International Journal of Economics, Finance and Management Sciences
JF  - International Journal of Economics, Finance and Management Sciences
JO  - International Journal of Economics, Finance and Management Sciences
SP  - 161
EP  - 172
PB  - Science Publishing Group
SN  - 2326-9561
UR  - https://doi.org/10.11648/j.ijefm.20261402.15
AB  - This study investigates how real-time interaction in live streaming environments drives users’ community commitment from a social affordance perspective. Drawing on Social Exchange Theory and the affordance perspective, a structural model is developed to explain how real-time interactivity facilitates parasocial interaction, promotes vicarious learning and knowledge adoption, and generates perceived benefits that ultimately lead to community commitment. Using survey data collected from 451 users of the Douyin platform and analyzed through structural equation modeling, the results provide empirical support for the proposed relationships. The findings indicate that real-time interactivity significantly enhances parasocial interaction between users and streamers, which in turn promotes both vicarious learning and knowledge adoption. These cognitive processes further generate perceived benefits, specifically hedonic benefit and self-esteem benefit, both of which positively influence users’ community commitment. Notably, self-esteem benefit shows a relatively stronger effect than hedonic benefit, indicating its greater importance in predicting sustained user engagement in live streaming communities. This study contributes to the literature by clarifying the mechanism through which social affordances are actualized in live streaming contexts and by integrating emotional and cognitive pathways to explain user commitment. It also extends prior research by positioning parasocial interaction as a key mediating mechanism linking interactive features and user outcomes. From a practical perspective, the findings provide insights for platform designers and content creators on how to enhance interactive features and develop engagement strategies that foster long-term community participation.
VL  - 14
IS  - 2
ER  -

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Author Information

Xinjie Bong

School of Management and E-business, Zhejiang Gongshang University, Hangzhou, China

Contact Email

http://orcid.org/0009-0005-5987-1008
Aleksandra Margushina

School of Management and E-business, Zhejiang Gongshang University, Hangzhou, China

Contact Email

http://orcid.org/0009-0006-7188-5653

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Bong, X., Margushina, A. (2026). Actualizing Social Affordances in Live Streaming: The Role of Real-Time Interaction in Building Community Commitment. International Journal of Economics, Finance and Management Sciences, 14(2), 161-172. https://doi.org/10.11648/j.ijefm.20261402.15

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ACS Style

Bong, X.; Margushina, A. Actualizing Social Affordances in Live Streaming: The Role of Real-Time Interaction in Building Community Commitment. Int. J. Econ. Finance Manag. Sci. 2026, 14(2), 161-172. doi: 10.11648/j.ijefm.20261402.15

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AMA Style

Bong X, Margushina A. Actualizing Social Affordances in Live Streaming: The Role of Real-Time Interaction in Building Community Commitment. Int J Econ Finance Manag Sci. 2026;14(2):161-172. doi: 10.11648/j.ijefm.20261402.15

Copy | Download

@article{10.11648/j.ijefm.20261402.15,
  author = {Xinjie Bong and Aleksandra Margushina},
  title = {Actualizing Social Affordances in Live Streaming: 
The Role of Real-Time Interaction in Building Community Commitment},
  journal = {International Journal of Economics, Finance and Management Sciences},
  volume = {14},
  number = {2},
  pages = {161-172},
  doi = {10.11648/j.ijefm.20261402.15},
  url = {https://doi.org/10.11648/j.ijefm.20261402.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20261402.15},
  abstract = {This study investigates how real-time interaction in live streaming environments drives users’ community commitment from a social affordance perspective. Drawing on Social Exchange Theory and the affordance perspective, a structural model is developed to explain how real-time interactivity facilitates parasocial interaction, promotes vicarious learning and knowledge adoption, and generates perceived benefits that ultimately lead to community commitment. Using survey data collected from 451 users of the Douyin platform and analyzed through structural equation modeling, the results provide empirical support for the proposed relationships. The findings indicate that real-time interactivity significantly enhances parasocial interaction between users and streamers, which in turn promotes both vicarious learning and knowledge adoption. These cognitive processes further generate perceived benefits, specifically hedonic benefit and self-esteem benefit, both of which positively influence users’ community commitment. Notably, self-esteem benefit shows a relatively stronger effect than hedonic benefit, indicating its greater importance in predicting sustained user engagement in live streaming communities. This study contributes to the literature by clarifying the mechanism through which social affordances are actualized in live streaming contexts and by integrating emotional and cognitive pathways to explain user commitment. It also extends prior research by positioning parasocial interaction as a key mediating mechanism linking interactive features and user outcomes. From a practical perspective, the findings provide insights for platform designers and content creators on how to enhance interactive features and develop engagement strategies that foster long-term community participation.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Actualizing Social Affordances in Live Streaming: 
The Role of Real-Time Interaction in Building Community Commitment
AU  - Xinjie Bong
AU  - Aleksandra Margushina
Y1  - 2026/04/21
PY  - 2026
N1  - https://doi.org/10.11648/j.ijefm.20261402.15
DO  - 10.11648/j.ijefm.20261402.15
T2  - International Journal of Economics, Finance and Management Sciences
JF  - International Journal of Economics, Finance and Management Sciences
JO  - International Journal of Economics, Finance and Management Sciences
SP  - 161
EP  - 172
PB  - Science Publishing Group
SN  - 2326-9561
UR  - https://doi.org/10.11648/j.ijefm.20261402.15
AB  - This study investigates how real-time interaction in live streaming environments drives users’ community commitment from a social affordance perspective. Drawing on Social Exchange Theory and the affordance perspective, a structural model is developed to explain how real-time interactivity facilitates parasocial interaction, promotes vicarious learning and knowledge adoption, and generates perceived benefits that ultimately lead to community commitment. Using survey data collected from 451 users of the Douyin platform and analyzed through structural equation modeling, the results provide empirical support for the proposed relationships. The findings indicate that real-time interactivity significantly enhances parasocial interaction between users and streamers, which in turn promotes both vicarious learning and knowledge adoption. These cognitive processes further generate perceived benefits, specifically hedonic benefit and self-esteem benefit, both of which positively influence users’ community commitment. Notably, self-esteem benefit shows a relatively stronger effect than hedonic benefit, indicating its greater importance in predicting sustained user engagement in live streaming communities. This study contributes to the literature by clarifying the mechanism through which social affordances are actualized in live streaming contexts and by integrating emotional and cognitive pathways to explain user commitment. It also extends prior research by positioning parasocial interaction as a key mediating mechanism linking interactive features and user outcomes. From a practical perspective, the findings provide insights for platform designers and content creators on how to enhance interactive features and develop engagement strategies that foster long-term community participation.
VL  - 14
IS  - 2
ER  -

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