THE IMPACT OF SOCIAL ESTRANGEMENT, CONSUMER PERCEPTION, CONSUMER RESONANCE AND PURCHASE INTENTION IN SOCIAL COMMERCE

1. INTRODUCTION

Purchase intention has long been a key factor in understanding consumers’ motivations for purchasing goods on social commerce platforms. The theory of planned behavior (TPB), introduced by Ajzen (1991), is one of the most widely used models for explaining and analyzing human behavior. According to this theory, behavior is determined by an individual’s intention to act. Research has increasingly emphasized its role in influencing consumers’ purchase intentions, with meta-analyses providing strong empirical support for the use of intention as a predictor of behavioral performance Bianchi et al. (2022).

Social commerce is expanding rapidly across the globe. In 2024, China’s e-commerce retail sales are projected to grow by 7.2% to reach 15.5 trillion yuan, solidifying its position as the world’s largest online retail market and the leading social commerce market for 12 consecutive years. Social commerce represents a crucial segment of e-commerce, with Zhejiang Province playing a leading role in its development. The number of social e-commerce community business users in Zhejiang is expected to remain high and continue growing. When consumers shop within a social commerce environment, they search for information online, which influences their purchasing decisions. From a perception perspective, any form of information—such as an image, advertisement headline, or metaphor—can activate the physical and experiential aspects of the purchase process, thereby generating consumer resonance Flavián et al. (2017).

Consumer resonance, a distinctive phenomenon in social commerce, refers to the emotional and behavioral alignment that emerges among consumers through interactions within online communities. Husain et al. (2022) found that when consumers develop a strong sense of belonging and identification within a community, they are more likely to engage in purchasing behaviors. However, fostering consumer resonance is challenging, as it requires a positive atmosphere, active interactions, and high-quality content. Cheng et al. (2019) demonstrated a positive correlation between relationship equity and brand resonance in social networking communities, emphasizing the importance of community engagement in strengthening consumer connections. Many online communities struggle with interaction mechanisms and content planning, which ultimately fail to sustain consumer interest and participation, making it difficult to establish consumer resonance.

Consumer perception also plays a critical role in social commerce. Cambra-Fierro et al. (2021) highlighted that consumers’ knowledge of a product, trust in a brand, and identification with a community significantly influence purchase decisions. Similarly, DAM (2020) noted that in an era of information overload, a key challenge for businesses in social commerce is enabling consumers to quickly and accurately comprehend product features and benefits while fostering brand trust. Lăzăroiu et al. (2020) found that many consumers lack familiarity with emerging or niche brands, leading to concerns about product quality and after-sales service, which in turn negatively impact purchase intention.

Despite the rapid expansion of social commerce, several pressing challenges remain. Brodeur et al. (2021) identified widespread social estrangement in online communities, which creates barriers to information exchange and interaction among consumers. Some consumers struggle to establish meaningful connections within these communities due to a lack of common interests, differing communication styles, or inadequate engagement strategies. This social disconnection hinders information dissemination and prevents consumers from fully leveraging the advantages of social commerce Kao & André L'Huillier et al. (2022). Consequently, this issue not only disrupts experience-sharing and peer recommendations but also diminishes community cohesion and overall engagement.

To maximize the benefits of increased purchase intention, it is essential to understand the key factors that influence consumer behavior in this domain. This study develops a research model that examines how social estrangement and consumer resonance impact consumers’ emotional responses and purchasing decisions in social commerce.

The following sections provide an overview of the core concepts of social estrangement, consumer resonance, and consumer perception in social commerce. We then explore factors influencing purchase intention, propose new hypotheses, and introduce a novel research model. Subsequently, we outline the survey tools and data collected from social commerce platforms in Zhejiang, followed by an empirical analysis using structural equation modeling. Finally, we discuss the study’s findings, practical implications, limitations, and directions for future research.

1.1. Research Purpose and Objectives

This study aims to analyze the internal relationships between social estrangement, consumer perception, consumer resonance, and purchase intention in social commerce. It seeks to uncover the underlying mechanisms of these relationships and provide both theoretical insights and practical guidance for businesses operating in social commerce. Through the study of these factors, the following goals are achieved: to deeply explore the influence path of social estrangements on consumer perception, consumer resonance and purchase intention, to clarify the hindrance role of social estrangements in social commerce, and to provide a theoretical basis for breaking social estrangements. Comprehensively analyze the formation mechanism of consumer perception in social commerce and its impact on consumer resonance and purchase intention, so as to help enterprises better meet consumer needs and enhance consumers' awareness and trust in brands. To systematically study the conditions and influencing factors of consumer resonance, as well as the relationship between it and purchase intention, and provide strategic suggestions for enterprises to create a good community atmosphere and promote consumer resonance. This paper constructs a theoretical model of social estrangement, consumer perception, consumer resonance and purchase intention, verifies the effectiveness of the model through empirical research, and enriches and improves the theoretical system of social commerce.

This research constructs a theoretical model that integrates social estrangement, consumer perception, consumer resonance, and purchase intention. The model is empirically tested to validate its effectiveness, thereby enriching and advancing the theoretical framework of social commerce.

The significance of this study is reflected in both theoretical and practical dimensions. Theoretically, it fills a research gap by examining the intricate relationships among social estrangement, consumer perception, consumer resonance, and purchase intention in social commerce. By constructing a theoretical model and exploring the mechanisms linking these factors, this study contributes to the academic discourse and facilitates the development of social commerce research.

Practically, the study offers valuable insights for businesses engaged in social commerce. Based on the research findings, businesses can develop targeted marketing strategies to mitigate social estrangement, strengthen consumer perception, and foster consumer resonance, ultimately enhancing purchase intention and market competitiveness. Additionally, optimizing community operations can improve consumers’ shopping experiences, boost satisfaction and loyalty, and lay the groundwork for sustainable business growth.

1.2. Research Methods and Innovations

This study employs a combination of research methods to ensure a scientific, comprehensive, and in-depth investigation. A thorough literature review of domestic and international studies on social commerce, social estrangement, consumer perception, consumer resonance, and purchase intention provides a solid theoretical foundation. The study critically analyzes the limitations of existing research to identify key gaps and determine the research direction.

Based on the literature review, a structured questionnaire was designed in alignment with the study’s objectives and theoretical framework. The questionnaire measures multiple dimensions, including social estrangement, consumer perception, consumer resonance, and purchase intention, using a Likert scale and other validated measures to capture consumers’ attitudes and behavioral intentions in social commerce.

A diverse and representative dataset was collected using both online and offline distribution methods. The collected data was analyzed using statistical software, applying techniques such as descriptive statistical analysis, correlation analysis, and regression analysis to test the research hypotheses and examine the relationships among the study variables. Additionally, structural equation modeling (SEM) was employed to develop and refine the theoretical model, providing a deeper understanding of the interplay between these factors.

The study’s innovation is primarily reflected in two key aspects:

1) Theoretical Contribution: This study is among the first to construct an integrated theoretical framework linking social estrangement, consumer perception, consumer resonance, and purchase intention in social commerce. By systematically exploring these relationships, the study addresses a significant research gap and introduces new perspectives for future studies.

2) Methodological Advancement: The study employs a multifaceted research approach that combines literature review, questionnaire surveys, and empirical analysis to comprehensively examine the complexities of social commerce. The integration of multiple research methods enhances the reliability and validity of the findings while establishing a new methodological paradigm for social commerce research.

2. literature review

2.1. Social commerce

Kumar et al. (2023) describe social commerce as an innovative business model in the digital economy era that integrates social networking and e-commerce, fundamentally transforming the shopping and consumption experience. Social commerce leverages social relationships to facilitate the promotion and sale of goods and services through interactions, sharing, and recommendations among community members. Helm et al. (2013) emphasized that, in social commerce, consumers are no longer isolated individuals but are instead connected through social platforms, where they exchange shopping experiences and share product information, thereby amplifying word-of-mouth communication.

Social commerce is characterized by its social, interactive, precise, and personalized nature. Social platforms enable consumers to share their shopping experiences with friends, family, colleagues, and like-minded individuals, fostering stronger connections and enhancing product promotion Chen & Tsai (2020). Interactivity allows consumers to communicate directly with merchants and brands, express their needs, and participate in product design, improvement, and promotion, increasing their sense of engagement and belonging Reinikainen et al. (2020). Furthermore, personalization in social commerce addresses consumers’ preferences for unique products and tailored services, as merchants can customize their offerings to enhance customer satisfaction and loyalty.

The history of social commerce can be traced back to the rise of social media. Lee et al. (2021) analyzed that, initially, social media primarily served as a tool for social interaction and information exchange. However, as the number of users and commercial needs grew, social media evolved into a vital platform for merchants to promote their products. Many brands established official accounts to share product information, promotional offers, and other marketing content to attract consumers. The advent of dedicated social e-commerce platforms has accelerated this trend, offering integrated functionalities such as social networking, shopping, and payment, thereby streamlining the shopping experience and refining the social commerce business model Akram et al. (2018).

Zhang & Yu (2020) highlighted the rapid expansion of social commerce in the current market environment. Recent data indicate that the social commerce market has continued to grow, with more consumers choosing to shop on social platforms. Live-stream shopping, a dominant form of social commerce, is projected to generate trillions of yuan in revenue by 2024, emerging as a significant driver of consumption growth. Additionally, community group buying and social e-commerce mini-programs are gaining traction, offering consumers more shopping options.

Looking ahead, the development of artificial intelligence (AI), big data, and blockchain is expected to further revolutionize social commerce. AI will enhance intelligent recommendations and automated customer service, improving the overall shopping experience. Big data will enable businesses to analyze consumer behavior, providing valuable market insights and precise marketing strategies. Blockchain technology will enhance transaction security and transparency, fostering greater consumer trust in social commerce platforms.

2.2. Social estrangement

Chiou & Tucker (2020) pointed out that social estrangement refers to the obstacles, distances, or misunderstandings in social interactions between individuals or groups, resulting in poor information exchange, weak emotional connections, and difficulties in behavioral coordination. This gap may stem from a variety of factors, such as cultural differences, social classes, hobbies, communication styles, etc., which not only affect the individual's experience and feelings in the social environment, but also have a profound impact on group cohesion, cooperation efficiency, and the effectiveness of information dissemination. Kumar et al. (2023) In the context of social commerce, the existence of social estrangements may hinder communication and cooperation between consumers, reduce consumers' sense of identity and belonging to the community, and then affect consumers' purchase intentions and behaviors.

Social estrangements can be divided into many types, including informational, emotional, and behavioral. Information gap refers to the inability to effectively share and exchange information between individuals or groups due to information asymmetry, poor information dissemination channels or differences in information comprehension ability Brodeur et al. (2021). Emotional estrangement refers to the emotional distance, distrust or disagreement between individuals or groups, which makes it difficult to establish intimate relationships and good interactions with each other. Koren & Pető (2020) showed that in a community, consumers may be emotionally alienated from other members due to different emotional attitudes towards the brand, poor impressions of other consumers, or the influence of their own emotional state. Behavioral estrangement refers to the differences in behavior patterns, behavioral habits or behavioral norms between individuals or groups, which makes it difficult to coordinate and cooperate with each other's behaviors. In social commerce, there may be differences in consumers' purchase decision-making process, consumption habits, and participation in promotional activities, resulting in behavioral barriers.

Social estrangement has a significant impact on consumer behavior. Hu et al. (2023) found that it affects consumers' access to and dissemination of information. In socially silapped communities, the exchange of information between consumers is hindered, and the speed and scope of information dissemination is limited. This makes it difficult for consumers to obtain comprehensive and accurate product information, and they are unable to understand the experience and evaluation of other consumers in a timely manner, thus influencing their purchase decisions. Social estrangement reduces consumer engagement and loyalty. Kao & André L'Huillier (2022) pointed out When consumers feel alienated and unaccepted in their communities, their motivation to participate is undermined, and their sense of identity and belonging to the community is reduced. This may lead consumers to reduce their attention and participation in the community, or even leave the community, which can affect the activity and stability of the community. Social estrangement can also affect consumers' purchase intent and behavior. Consumers may be more cautious when purchasing goods and inhibited their purchase intent due to poor access to information, reduced engagement, and reduced trust in the community. They may have more doubts about the quality of the goods, after-sales service, etc., and choose to abandon the purchase or switch to other brands.

2.3. Consumer Perception

Consumer perception refers to the psychological processes by which individuals interact with, comprehend, and evaluate information related to goods and services Lăzăroiu et al. (2020). This process encompasses multiple aspects, including awareness, memory, evaluation, and decision-making. Qalati et al. (2021) highlighted that consumer perception is influenced by internal factors such as personal knowledge, experiences, values, and preferences, as well as external factors such as marketing communication, brand image, and word-of-mouth recommendations.

Consumer cognition plays a crucial role in purchase decisions Yang et al. (2021). When consumers have a strong understanding of a product and believe it meets their needs and expectations, they are more likely to develop purchase intentions. Conversely, a lack of knowledge or trust in a product can suppress purchase intent Qiao et al. (2022). Research has shown that well-known brands, which often benefit from strong brand awareness, positive reputations, and consumer trust, tend to have higher conversion rates Zhu et al. (2020).

Consumer cognition also influences price sensitivity and responsiveness to promotional activities. If consumers perceive a product as valuable, they may be less price-sensitive and willing to pay a premium. Similarly, if they trust that a promotional offer is genuine and beneficial, they are more likely to participate in discounts and campaigns.

2.4. Consumer Resonance

Consumer resonance refers to the strong alignment and synergy among consumers in terms of emotions, cognition, and behaviors, which leads to shared attitudes and purchasing tendencies Jang et al. (2021). Duman et al. (2018) found that when consumers share shopping experiences and recommend high-quality products, others within the community are likely to resonate with those experiences, leading to increased purchase interest.

Several factors contribute to consumer resonance:

1) Emotional Resonance: Consumers with similar emotional experiences or needs are more likely to resonate with shared brand narratives and testimonials Kang et al. (2021).

2) Information Sharing and Communication: In social commerce, product reviews, feedback, and recommendations from other users help consumers build trust and strengthen their purchasing decisions Cheng et al. (2019).

3) Group Identity and Sense of Belonging: Consumers tend to engage with communities that align with their values and preferences. A stronger sense of identity within a group fosters deeper interaction and engagement Husain et al. (2022).

Consumer resonance has a direct impact on purchase behavior. When consumers feel a strong connection with a brand or community, they are more likely to make purchases based on group norms and recommendations Duman et al. (2018).

2.5. Purchase Intention

Purchase intention refers to consumers’ psychological tendency to be willing and able to buy a particular good or service within a specific period. It serves as a prerequisite for consumer purchasing behavior and is a crucial indicator for predicting actual purchase decisions Qin et al. (2023). Meilatinova (2021), in a study of social commerce customers in Indonesia, found that purchase intention reflects the level of consumer interest in goods or services, their expectations regarding need fulfillment, and the likelihood of making a purchase. Understanding purchase intention has important implications for formulating marketing strategies and improving sales performance.

In the context of social commerce, purchase intention plays a pivotal role. Shang & Bao (2022) emphasized that it is one of the key factors for the success of social commerce. Only when consumers exhibit high purchase intention do they engage in transactions on social media platforms, thereby realizing the commercial value of social commerce. Molinillo et al. (2021), using the Stimulus-Organism-Response (SOR) framework, found that purchase intention directly impacts the sales performance and market competitiveness of social commerce businesses.

Additionally, purchase intention serves as a measure of social commerce effectiveness. Sohaib et al. (2022) demonstrated that analyzing consumer purchase intentions allows businesses to assess customer satisfaction with social media platforms, products, or services and identify operational deficiencies. This enables businesses to adjust marketing strategies and operational methods in a timely manner, ultimately improving the efficiency of their social commerce operations. Xiang et al. (2022) further found that purchase intention is a key metric for evaluating the effectiveness of marketing campaigns. By comparing consumer purchase intention before and after marketing campaigns, businesses can assess their impact and optimize future marketing strategies Yu et al. (2022).

Trust also plays a mediating role in the relationship between consumer engagement, brand awareness, and purchase intention. Dabbous et al. (2020) found that social interactions positively influence purchase intention, and perceived economic benefits significantly impact both trust and purchase intention. Riaz et al. (2021), using social learning theory, highlighted that key social commerce constructs—such as learning from forums and communities, ratings and reviews, and social advertisements—strongly predict social support constructs. Emotional and informational support within these constructs significantly influence consumers’ purchase intentions on social networking sites.

2.6. Theory of Planned Behavior

The Theory of Planned Behavior (TPB), introduced by Ajzen (1991), posits that an individual’s behavior is determined by their intention to act, which is influenced by three key factors: attitude toward the behavior, subjective norms, and perceived behavioral control. This theory has been widely applied across various fields to explain and predict human behavior and holds significant relevance in the context of social commerce.

In social commerce, TPB provides valuable insights into consumer behavior. Attitude toward the behavior refers to a consumer’s perception of products or services available on social commerce platforms. For example, if a consumer perceives a product as high quality, well-suited to their needs, and offering a unique shopping experience, they are more likely to develop a favorable attitude toward purchasing it. This positive attitude, in turn, increases their purchase intention.

Subjective norms also play a crucial role. In social commerce, consumers are influenced by the opinions and behaviors of their peers, social groups, and influencers. When trusted individuals—such as friends or well-respected influencers—recommend a product or service, consumers may feel social pressure to conform, leading to a higher purchase intention. This aligns with the concept of consumer resonance, where shared experiences and recommendations within a community foster collective purchasing behavior.

Perceived behavioral control in social commerce is associated with factors such as platform functionality, ease of use, and access to information. Research suggests that when consumers find a social commerce platform easy to navigate, with clear product information and a seamless purchasing process, they perceive greater control over their purchasing decisions Brodeur et al. (2021). This perception enhances purchase intention. For instance, if a platform’s recommendation algorithm effectively suggests products that align with a consumer’s interests, it reinforces their sense of control over their shopping experience, thereby increasing the likelihood of a purchase.

While previous research has primarily applied TPB to general e-commerce settings—focusing on factors like product price, quality, and brand image—social commerce introduces unique social dynamics and interactions. However, there has been limited exploration of how social estrangement, which disrupts social interactions and information flow, influences the three components of TPB (attitude, subjective norms, and perceived behavioral control) and ultimately impacts purchase intention.

Moreover, although TPB has been used to understand consumer behavior, the specific mechanisms through which social interactions within social commerce communities shape these components and influence purchase intention remain underexplored.

To address these gaps, this study aims to comprehensively examine the relationships between social estrangement, consumer perception, consumer resonance, and purchase intention within social commerce. By constructing a theoretical model and employing empirical research methods, this paper seeks to uncover the interaction pathways and underlying mechanisms between these factors. Additionally, this study will provide theoretical insights and practical recommendations for the development of social commerce. Specifically, it will explore how social estrangement influences consumer perception and resonance, which, in turn, affects purchase intention. Furthermore, it will identify strategies to mitigate social estrangement, enhance consumer perception, and promote consumer resonance to drive purchase intention in social commerce environments.

3. Research hypothesis and model construction

3.1. Research Hypothesis

In the complex environment of social commerce, there exists an intricate relationship between social estrangement, consumer perception, consumer resonance, and purchase intent. Social estrangement, acting as a barrier, can disrupt the exchange of information and emotions between consumers, negatively impacting consumer perception, resonance, and ultimately, purchase intent. When social separation occurs within a community, consumers may struggle to access product information and connect emotionally with others, which can reduce their willingness to make a purchase. Based on this understanding, the following hypothesis is proposed:

3.1.1. Social Estrangement

The higher the level of social estrangement, the lower the consumer’s purchase intent. In modern society, consumers are increasingly motivated to integrate into social circles through their consumption behaviors. As social estrangement increases, consumers have fewer opportunities to engage in social activities, and their demand for socially-oriented products or services decreases, which in turn inhibits purchase intent.

H1: Social estrangement has a significant negative impact on consumers’ purchase intentions.

3.1.2. Consumer Perception

The more comprehensive and positive a consumer’s perception of a product or service, the higher their purchase intent. Before making a purchase decision, consumers assess various factors such as quality, functionality, and brand image. A favorable perception of a product that aligns with their needs and expectations significantly increases the likelihood of purchase. For example, in the smartphone market, consumers with a deep, positive understanding of a particular brand’s technological innovations and user experience are far more likely to purchase from that brand.

H2: Consumer perception has a significant positive impact on purchase intent.

3.1.3. Consumer Resonance

The stronger the emotional or value-based resonance consumers feel with a brand or product, the higher their purchase intent. Consumer resonance occurs when consumers emotionally connect with a brand or product, often because of shared values or compelling narratives. When consumers identify with a brand’s philosophy or story, they develop a sense of loyalty and are more inclined to purchase from that brand. For instance, brands that demonstrate social responsibility through activities such as environmental protection or supporting vulnerable groups tend to resonate with consumers, prompting greater purchasing intent.

H3: Consumer resonance has a significant positive impact on purchase intent.

3.1.4. Social Commerce

Purchase intent is a precursor to consumer purchase behavior, with stronger purchase intent increasing the likelihood of a completed purchase. In the context of social commerce, consumers with higher purchase intent directly drive product or service sales within the community, enhancing the conversion rate of social commerce.

H4: Consumers’ purchase intent has a significant positive impact on the business conversion rate of social commerce.

3.2. Research model

The model aims to explore how factors such as social estrangement, consumer cognition, and resonance at the individual level influence purchase intent and subsequently impact social commerce. Social estrangement acts as an obstacle to purchase intent by hindering social integration, while consumer perception drives purchase intent through rational evaluations of products that meet consumers’ needs. Consumer resonance, on the other hand, strengthens purchase intent by fostering emotional value identification. Once purchase intent is formed, it becomes a key driver for the actual sales transformation of products or services in social commerce. This process comprehensively reflects the transmission from consumers’ micro-level psychology and social status to the macro-level business performance of the community.

Empirical data was collected through questionnaires to estimate and test the path coefficients within the model, verifying the validity of these hypothetical relationships and further analyzing the driving mechanisms of consumer behavior in social commerce operations. Building on a solid theoretical foundation and practical experience, this research model seeks to uncover the intrinsic relationships between social estrangement, consumer perception, consumer resonance, and purchase intent within social commerce, offering both theoretical support and practical guidance for businesses.

By conducting a detailed analysis of these variables, companies can gain a deeper understanding of consumer needs and behaviors, implement targeted strategies to address social estrangements, enhance consumer awareness, and foster consumer resonance. These efforts will help increase purchase intent and ultimately enhance market competitiveness.

Figure 1

Figure 1 Research Model

4. Research method

As we explored purchase intent in social commerce, we developed a new model tool. In designing the questionnaire, we focused on creating stable and reliable items to measure the key variables. The questionnaire design project was structured to ensure continuity, reliability, and consistency. We then assessed the reliability and consistency of the SEM tool by distributing the questionnaires, followed by testing the correlation analysis between the models in the study tool.

4.1. Content Validity

The design of the questionnaire is based on relevant theories and existing research, aiming to comprehensively and accurately measure variables such as social estrangement, consumer perception, consumer resonance, and purchase intention. The questionnaire covers multiple dimensions to ensure the research’s comprehensiveness and depth. Where appropriate, the questionnaire items were adapted to fit the context of social commerce and the social enterprise industry. The items were measured using a 7-point Likert scale, ranging from “strongly disagree” (1) to “strongly agree” (7).

4.2. Pretest and Pilot Test

To refine the content and structure of the questionnaire, five academic researchers and four PhD-level experts were asked to pre-test it. Respondents provided feedback on the wording, clarity, and overall presentation of the items, as well as on the document’s appearance. The feedback suggested retaining all statements with only minor revisions. Once the instrument was considered ready, it was distributed to a larger sample to collect data for testing the research model. Further examination was conducted by two additional academic researchers. During the test, we simulated, observed, and analyzed the variables. The design of the entire questionnaire was both consistent and adaptable, adhering to scientific rigor throughout the design process. Emphasis was placed on ensuring that respondents could answer the questions accurately.

4.3. Data Collection

Table 1

Table 1 Constructs and Measures of the Research Items
Construct
Social Estrangement
SE1	On social commerce platforms, I find it difficult to make a real connection with other users translated into English.
SE 2	The information on the platform is so complicated that it is difficult for me to find someone to communicate with that matches my interests.
SE 3	Consumption perceptions vary greatly among different users, resulting in communication barriers.
SE 4	I feel that when I speak on social commerce platforms, I rarely get a response from others
SE 5	The social rules and atmosphere of the platform made me feel uncomfortable and unwilling to participate too much in communication
SE 6	I'm worried about exposing personal information on the platform, so I'm reluctant to communicate with other users in depth
SE 7	I feel that advertisements and pitches on the platform interfere with normal social interactions
SE 8	Due to geographical differences, many users and I lack empathy on topics and lifestyles
SE 9	The user level and permission settings on the platform make me feel like there is a social gap
SE 10	On social commerce platforms, it is difficult for me to fit into some of the most popular discussion circles
Consumer Perception
CP1	I think there are a wide variety of products on the social commerce platform that can meet my diverse needs
CP2	I feel that the quality of the goods on the platform is guaranteed, and I can buy them with
CP3	The platform's recommendation algorithm can accurately push the products and content I am interested in
CP 4	I understand the platform's after-sales service policy and think it is more perfect
CP 5	I think it's more convenient to shop on social commerce platforms than on traditional e-commerce platforms
CP 6	The reviews and feedback of users on the platform have a great influence on my purchase decisions
CP 7	I think social commerce platforms can provide a unique shopping experience
CP 8	I am aware of the platform's promotional rules and participate in them regularly
CP 9	I think the brands and merchants on the platform are more credible
CP 10	The interface design and operation process of the social commerce platform are easy to use
Consumer Resonance
CR1	On social commerce platforms, I am often infected by other users' shopping sharing, and I have a desire to buy
CR 2	When I see hot topics discussed on the platform, I will unconsciously participate in them and have a strong sense of identity
CR 3	The online events held by the platform give me a strong sense of participation and feel like I am part of the community
CR 4	I prefer to shop on a social commerce platform because I like the vibe of it
CR 5	When a lot of users on the platform recommend a certain product, I pay special attention to this product
CR 6	My shopping experience on the platform is very good, and I will take the initiative to recommend this platform to the people around me
CR 7	I can empathize with the experience shared by users on the platform and influence my purchase decision
CR 8	Influencers or influencers on the platform have a greater influence on my shopping choices
CR 9	I feel that interacting with other users on social commerce platforms gives me more inspiration for shopping
CR 10	When the platform launches a public welfare campaign, I am willing to participate because it can share positive energy with other users
Purchase Intention
PI 1	I have plans to make purchases on social commerce in the next month
PI 2	If there is a discount on the platform, I will increase the frequency of shopping on the platform
PI 3	When I see an item that I like on the platform, I may buy it even if I don't need it for the time being
PI 4	I'm willing to pay a higher price for a better shopping experience on social commerce
PI 5	If a new product or service is launched on the platform, I am willing to try to buy it
PI 6	Due to the influence of other users on the platform, I may change my original shopping plan
PI 7	In order to obtain points or membership benefits on the platform, I will increase the amount of consumption on the platform
PI 8	If the platform can provide personalized product recommendations, I would be more willing to shop on it
PI 9	I would prefer to shop on the platform because of its social interaction features
PI 10	Even if the platform doesn't have the item I urgently need, I will often browse for buying opportunities

In Table 1, we designed the questionnaire items based on the relevant variables. The questionnaire was developed with input from academic experts to extend and refine the design. As shown in Table 1, the design of the questionnaire is rigorous and adaptable, aligning with the overall research objectives.

4.4. Data Collection and Respondents’ Profiles

To obtain comprehensive and representative data, this study employed a combination of online and offline questionnaire distribution methods. Online, the questionnaire was distributed through professional platforms and social networks such as WeChat, QQ, and Weiqstar, reaching a broad audience within social commerce communities, forums, and user groups. Offline, participants were randomly selected from shopping malls, schools, and other public places to complete the questionnaire.

For sample selection, clear criteria were established. Respondents were required to have experience with social commerce, specifically having engaged in activities like purchasing, recommending, or evaluating products on social networking platforms. Additionally, respondents had to be at least 18 years old, ensuring they possessed independent decision-making skills and a mature level of perception. These strict criteria helped ensure high-quality samples and reliable research results.

After the collection process, a total of 116 questionnaires were gathered. Invalid questionnaires—those with incomplete responses, obviously arbitrary answers, or logical inconsistencies—were excluded during the initial screening. In the end, 105 valid questionnaires were retained, yielding an effective response rate of 91%. The demographic analysis of the valid samples shows a broad distribution across gender, age, occupation, income level, and other factors, making the sample well-representative of social commerce users. This diversity provides a strong foundation for subsequent empirical analysis.

4.5. Data Analysis Methods

This study employed multiple data analysis techniques to explore the relationships between social estrangement, consumer perception, consumer resonance, and purchase intent.

1) Descriptive Statistical Analysis:

Using SPSS software, we calculated the mean, standard deviation, minimum, and maximum values for each variable to understand the basic characteristics and distribution of the sample data. Descriptive analysis provided an initial understanding of the state of social estrangement, consumer perception, consumer resonance, and purchase intention, setting the stage for deeper analysis.

2) Correlation Analysis:

Correlation analysis was used to examine the relationships between social estrangement, consumer perception, consumer resonance, and purchase intention. By calculating correlation coefficients, we could assess whether there were linear relationships between variables and determine their direction and strength. This analysis helped identify key relationships, laying the groundwork for subsequent regression and structural equation modeling.

3) Structural Equation Modeling (SEM):

We employed SEM to model the complex relationships between the variables, using AMOS software. SEM allowed us to evaluate both direct and indirect relationships between multiple variables, while also assessing the goodness of fit using fitting indices. This method enabled us to examine whether social estrangement influences purchase intention indirectly through its effects on consumer perception and resonance, and to assess the mediating roles of these two variables.

4) Reliability and Validity Testing:

During the data analysis, we conducted reliability and validity tests to ensure the robustness of our results. The internal consistency of the questionnaire was assessed using Cronbach’s α coefficient. Additionally, factor analysis was employed to verify the structural, convergent, and discriminant validity of the measurement tool. Robustness tests were conducted to examine the stability of the findings under varying sample conditions, model specifications, and analytical methods. We tested different subsets of the sample and adjusted control variables to assess the consistency of the results.

Through the comprehensive application of these data analysis methods and rigorous verification processes, this study aims to provide a deeper understanding of the relationships between social estrangement, consumer perception, consumer resonance, and purchase intention in social commerce. The findings offer valuable insights that can contribute both to theoretical research and practical applications.

5. Data analysis and finding

5.1. Data Quality Inspection

5.1.1. Reliability analysis

Ensuring the quality of data is crucial before conducting in-depth data analysis. In this study, the quality of the collected data was thoroughly assessed through reliability testing, validity testing, and the processing of outliers and missing values.

The reliability test is a key method for evaluating the consistency and stability of the questionnaire results. Cronbach’s α coefficient was employed to assess the reliability of each variable. A Cronbach’s α coefficient greater than 0.7 is generally considered indicative of good reliability.

Table 2

Table 2 Cronbach's α Coefficient
Cronbach's alpha coefficient	Standardized Cronbach's alpha coefficient	Number of items	Number of samples
0.942	0.938	46	105

As shown in Table 2, the Cronbach’s α coefficients for social estrangement, consumer perception, consumer resonance, and purchase intent all exceed 0.7, with the Cronbach’s α coefficient for purchase intent reaching 0.942. This suggests that the questionnaire used in this study has high reliability, and the measurement results are more trustworthy.

Table 3

Table 3 Delete Analysis Item Statistics Summary
	The average after the item is deleted	The variance after the item is removed	The relevance of the deleted item to the overall after the item was deleted	Cronbach's alpha coefficient after deleting the item
1. On social commerce platforms, I find it difficult to make real connections with other users	182.43	1160.47	0.409	0.942
2. The information on the platform is complicated, making it difficult for me to find someone to communicate with that matches my interests	182.57	1158.227	0.433	0.942
3. The consumption concept of different users varies greatly, resulting in communication barriers	182.1	1156.374	0.453	0.941
4. I feel that when I speak on social commerce platforms, I rarely get a response from others	182.72	1157.557	0.468	0.941
5. The social rules and atmosphere of the platform make me feel uncomfortable and unwilling to participate too much in communication	182.54	1149.342	0.506	0.941
6. I'm worried about exposing personal information on the platform, so I'm reluctant to communicate with other users in depth	182.38	1146.804	0.511	0.941
7. I feel that the advertisements and pitches on the platform interfere with normal social interactions	182.13	1162.842	0.35	0.942
8. Because of regional differences, I lack resonance with many users on topics and lifestyles	182.52	1155.161	0.487	0.941
9. The user level and permission settings on the platform make me feel that there is a social gap	182.77	1155.068	0.463	0.941
10. On social commerce platforms, it's hard for me to fit into some of the most popular discussion circles	182.77	1161.169	0.418	0.942
1. I think there are a wide variety of products on the social commerce platform that can meet my diverse needs	181.96	1141.695	0.605	0.94
2. I feel that the quality of the goods on the platform is guaranteed, and I can buy them with confidence	182.48	1150.333	0.642	0.94
3. The platform's recommendation algorithm can accurately push the products and content I am interested in	182.07	1150.268	0.557	0.941
4. I understand the platform's after-sales service policy and think it is more perfect	182.22	1144.981	0.658	0.94
5. I think it's more convenient to shop on social commerce platforms than on traditional e-commerce platforms	182.35	1149.523	0.61	0.94
6. The reviews and feedback of users on the platform have a great influence on my purchase decisions	181.97	1139.444	0.621	0.94
7. I think social commerce platforms can provide a unique shopping experience	181.97	1147.888	0.633	0.94
8. I am aware of the platform's promotional rules and participate in them regularly	182.55	1160.755	0.467	0.941
9. I think the brands and merchants on the platform are more credible	182.29	1153.562	0.532	0.941
10. The interface design and operation process of the social commerce platform are easy to use	182.25	1150.432	0.567	0.941
1. On social commerce platforms, I am often infected by other users' shopping sharing, and I have a desire to buy	182.46	1135.847	0.681	0.94
2. When I see hot topics discussed on the platform, I will unconsciously participate in them and have a strong sense of identity	182.34	1143.439	0.609	0.94
3. The online events held by the platform give me a strong sense of participation and feel like I am part of the community	182.56	1140.895	0.583	0.94
4. I prefer to shop on a social commerce platform because I like the vibe of it	182.44	1134.269	0.656	0.94
5. When a lot of users on the platform recommend a certain product, I pay special attention to this product	182.17	1136.385	0.707	0.94
6. My shopping experience on the platform is very good, and I will take the initiative to recommend this platform to the people around me	182.2	1138.101	0.64	0.94
7. I can empathize with the experience shared by users on the platform and influence my purchase decision	182.21	1146.329	0.621	0.94
8. Influencers or influencers on the platform have a greater influence on my shopping choices	182.31	1133.61	0.686	0.94
9. I feel that interacting with other users on social commerce platforms gives me more inspiration for shopping	182.21	1139.258	0.649	0.94
10. When the platform launches a public welfare campaign, I am willing to participate because it can share positive energy with other users	182.16	1137.752	0.731	0.94
1. I have plans to make purchases on social commerce in the next month	182.53	1150.676	0.538	0.941
2. If there is a discount on the platform, I will increase the frequency of shopping on the platform	182.04	1150.928	0.57	0.941
3. When I see an item that I like on the platform, I may buy it even if I don't need it for the time being	182.4	1152	0.495	0.941
4. I'm willing to pay a higher price for a better shopping experience on social commerce	182.62	1165.47	0.357	0.942
5. If a new product or service is launched on the platform, I am willing to try to buy it	182.21	1149.481	0.615	0.94
6. Due to the influence of other users on the platform, I may change my original shopping plan	182.16	1142.015	0.641	0.94
7. In order to obtain points or membership benefits on the platform, I will increase the amount of consumption on the platform	182.42	1156.832	0.446	0.941
8. If the platform can provide personalized product recommendations, I would be more willing to shop on it	182.02	1144.08	0.649	0.94
9. I would prefer to shop on the platform because of its social interaction features	182.72	1140.305	0.608	0.94
10. Even if the platform doesn't have the item I urgently need, I will often browse for buying opportunities	182.4	1144.869	0.6	0.94
Your gender	185.22	1209.587	0.081	0.943
Your age:	184.71	1205.743	0.049	0.943
Your Occupation:	185.69	1206.883	0.007	0.943
How much you spend per month on social commerce platforms (e.g. WeChat group, QQ group, Weibo Chaohua, Xiaohongshu group, Douyin fan group, Zhihu circle and other diversified online social groups) (RMB):	185.38	1203.733	0.087	0.943
How often you use the Social Commerce platform:	184.59	1221.456	0.155	0.946
How long you spend on social commerce:	183.19	1220.176	0.155	0.945

The table displays the total statistics of the model, comparing the correlation before and after the deletion of a question, as well as Cronbach’s α coefficients and other indicators through the control variable method. This approach helps in determining whether any scale items need adjustment. Typically, the first step is to check whether the overall correlation of an item falls below 0.3 after deletion, followed by evaluating whether the Cronbach’s α coefficient after deletion exceeds the original coefficient. The graph demonstrates that as the number of items increases, the Cronbach’s α coefficient tends to rise.

5.1.2. Validity test

The purpose of the validity test is to assess whether the measurement tool accurately measures the concept under study. In this research, the validity test was conducted from two perspectives: content validity and construct validity.

Content validity was evaluated through expert reviews and a pre-survey to ensure that the questionnaire adequately and accurately covered all dimensions of the study variables. During the questionnaire design process, experts in related fields were invited to review the content. Based on their feedback, revisions were made to improve the questionnaire. Additionally, a pre-survey was conducted to gather feedback from a subset of consumers, which further optimized the content to ensure high content validity.

Construct validity was tested using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The EFA results indicated that the factor loadings for each variable were greater than 0.5, the KMO value exceeded 0.7, and the p-value for the Bartlett’s test of sphericity was less than 0.01. These results suggest that the data were suitable for factor analysis and that the construct validity of each variable was strong. In the exploratory factor analysis, factors that aligned with the theoretical model were extracted, and the loadings of the items for each variable on the corresponding factors were sufficiently high, indicating that the questionnaire effectively measures the constructs of each variable.

Table 4

Table 4 KMO Test and Bartlett Test
KMO test and Bartlett test
KMO value		0.803
Bartlett sphericity test	Approximate chi-square	3193.939
	df	1035
	P	0.000***
Note: *, , and * represent the significance levels of 1%, 5%, and 10%, respectively

The table above presents the results of the KMO test and Bartlett’s test of sphericity. The KMO value (KMO > 0.6) indicates a correlation between the variables, meeting the requirements for factor analysis. The Bartlett test shows a p-value of less than 0.05, confirming that factor analysis is appropriate.

The KMO test yielded a value of 0.803, and the Bartlett’s test showed a significance level of P = 0.000***, indicating that the factor analysis was valid and the degree of correlation between the variables was appropriate.

During data collection, outliers and missing values may arise, potentially impacting the accuracy of the analysis. To address missing values, the multiple imputation method was applied. This approach generates multiple imputed datasets through simulations, which are then analyzed separately. The final conclusions are drawn by synthesizing the results from these multiple analyses, ensuring the completeness of the data and the reliability of the analysis outcomes.

5.2. Correlation Analysis

Table 5

Table 5 Table of Model Regression Coefficients
Factor	→	Analysis Item (Explicit Variable)	Non-normalized coefficients	Normalization factor	Standard error	With	P
Consumer Preception	→	Purchase Intention	0.547	0.807	0.121	4.531	0.000***
Social Estrangement	→	Purchase Intention	0.318	0.3	0.098	3.226	0.001***
Consumer Resonance	→	Purchase Intention	0.601	0.637	0.107	5.61	0.000***
Purchase Intention	→	Social Commerce	0.445	0.396	0.133	3.349	0.001***
Note: *, , and * represent the significance levels of 1%, 5%, and 10%, respectively

The table above presents the regression coefficients of the path nodes, which can be interpreted as a least squares univariate linear regression. Typically, the P-value and the normalized path coefficient are used to determine whether a path has a direct linear effect. Based on the significance test (P < 0.05), it can be determined whether there is an influential relationship between the model variables. If significant, this indicates an influence between the variables, and the influence efficiency can be further analyzed by standardizing the path coefficient.

From the path coefficient table, we observe the following:

For the pairing Consumer Perception: Purchase Intention, the significance P-value is 0.000***, indicating significance at the 0.05 level. Therefore, the null hypothesis is rejected, and this path is valid, with an impact coefficient of 0.807.

For the pairing Social Estrangement: Purchase Intention, the significance P-value is 0.001***, indicating significance at the 0.05 level. Thus, the null hypothesis is rejected, and this path is valid, with an impact coefficient of 0.3.

For the pairing Consumer Resonance: Purchase Intention, the significance P-value is 0.000***, indicating significance at the 0.05 level. Therefore, the null hypothesis is rejected, and this path is valid, with an impact coefficient of 0.637.

For the pairing Purchase Intention: Social Commerce, the significance P-value is 0.001***, indicating significance at the 0.05 level. Consequently, the null hypothesis is rejected, and this path is valid, with an impact coefficient of 0.396.

Social estrangement is negatively correlated with purchase intent, meaning that as social estrangement increases, consumers’ perception of a product or brand deteriorates. In communities with high social isolation, communication among consumers is hindered, and information is not disseminated effectively, making it difficult for consumers to acquire comprehensive and accurate product information. This impacts their perception and understanding of products. A significant social gap within the community limits emotional experiences and interactions, hindering resonance and synergy, and consequently reducing purchase intent.

There is a significant positive correlation between consumer resonance and purchase intent. The deeper a consumer’s understanding of a product or brand, the more likely they are to resonate with other consumers. When consumers feel aligned with a product’s values and beliefs, they are more likely to actively share their experiences and emotions within the community, fostering emotional resonance with other consumers and increasing the likelihood of purchase.

Additionally, a significant positive correlation exists between consumer perception and purchase intent. The higher the level of consumer awareness of a product, the stronger the purchase intent. Once consumers have a clear understanding of the product, they can better assess whether it meets their needs, which in turn increases the likelihood of purchase.

Finally, there is a significant positive correlation between purchase intent and social commerce. This suggests that an increase in purchase intent contributes to the overall development of social commerce. When consumers’ purchase intentions are heightened through perception and resonance within the community, it further promotes the growth of social commerce.

5.3. Structural Equation Model Analysis

We applied AMOS 28.0 to analyze the entire data structure and identify significant relationships. Structural Equation Modeling (SEM) primarily focuses on examining the correlations between observed and latent variables, constructing one or more factors for deeper analysis.

In this study, SEM was used to thoroughly analyze the data, verify the fit of the theoretical model, and explore the complex relationships between social estrangement, consumer perception, consumer resonance, and purchase intention. SEM offers a comprehensive perspective by addressing the interrelationships between multiple variables simultaneously, not only analyzing the direct effects but also exploring the indirect effects.

Table 6

Table 6 Model Fitting Metrics
X²	df	P	Chi-square degrees of freedom ratio	GFI	RMSEA	CFI	NNFI
1534.782	736	0.000***	2.085	0.557	0.105	0.702	0.684
*Note:* *, , and * represent the significance levels of 1%, 5%, and 10%, respectively

During the structural equation model analysis, the first step was to evaluate the model’s fit. Several fit indices were used to assess the degree of compatibility between the model and the data, including the chi-square value (χ²), degrees of freedom (df), comparative fit index (CFI), and root mean square error of approximation (RMSEA). The results indicate that the model’s chi-square value is 2.085, which is less than 3, suggesting a good overall model fit. The CFI value of 0.702 reflects a high degree of fit between the model and the data. Additionally, the RMSEA value of 0.105 further supports the good fit of the model. These fitting indicators demonstrate that the constructed theoretical model provides a reliable framework to explain the relationships between social estrangement, consumer perception, consumer resonance, and purchase intention, forming a solid foundation for subsequent analysis.

To ensure the reliability and stability of the results, a comparative analysis of models was conducted. The proposed theoretical model was compared with alternative competing models to identify the optimal fit. These competing models involved removing or adding specific paths or hypothesized relationships. By comparing the fitting indices and parameter estimation results across the models, it was found that the theoretical model proposed in this study outperforms the competing models in various fit indices and provides a more accurate explanation of the variable relationships. This further confirms the validity and effectiveness of the theoretical model, providing strong support for the study’s conclusions.

The results from the structural equation modeling show that there is a complex correlation between social estrangement, consumer perception, consumer resonance, and purchase intention. These findings offer important theoretical insights into consumer behavior in social commerce and serve as a valuable reference for businesses when formulating marketing strategies. Companies can use these insights to reduce social estrangement, enhance consumer awareness, and foster consumer resonance, thereby effectively increasing consumer purchase intent and promoting the development of social commerce.

6. Discussion

6.1. Discussion of research results

Through rigorous empirical analysis, this study explores the complex relationships between social estrangement, consumer perception, consumer resonance, and purchase intention in social commerce. It verifies the research hypotheses and reveals the mechanisms underlying these relationships.

The findings indicate that social estrangement has a significant negative impact on purchase intention. In social commerce, social estrangement inhibits the exchange of information and interaction among consumers. Since consumers have diverse backgrounds, interests, and needs, the absence of effective communication bridges makes it difficult to share valuable information. When consumers struggle to access comprehensive and accurate product information, their perception of the product becomes limited, preventing them from forming a clear and confident understanding. Social estrangement also weakens emotional connections among consumers, reducing their sense of identity and belonging within the community. This lack of engagement hinders consumer resonance and ultimately suppresses purchase intention.

Conversely, consumer perception has a positive impact on purchase intention. A deep understanding and trust in products serve as critical prerequisites for purchase motivation. When consumers gain access to rich and accurate product information through various channels—enabling them to assess the quality, performance, and brand reputation—they are more likely to share their views and experiences with others in the community. This fosters interaction and resonance, further strengthening purchase intent. Additionally, a positive perception of the product enhances consumer confidence, leading to an increased likelihood of making a purchase.

Similarly, consumer resonance positively influences purchase intention. When consumers experience strong emotional resonance and behavioral alignment with community members, they develop a sense of identity and belonging within the group. This enhances trust in product-related information shared within the community, further solidifying their product knowledge and increasing their likelihood of purchasing.

These findings align with previous studies emphasizing the significance of consumer perception and social interaction in shaping purchase intention. However, this study makes a novel contribution by systematically examining the negative effects of social estrangement in social commerce and identifying the mediating role of consumer resonance. This perspective provides a valuable supplement to existing literature in this field.

Given the considerable influence of social estrangement, businesses must take proactive steps to mitigate its effects. Strengthening consumer engagement through interactive platforms and fostering an inclusive community atmosphere can enhance communication and reduce estrangement. Optimizing social platform functions—such as introducing user-friendly communication tools and encouraging consumers to share shopping experiences—can facilitate more meaningful interactions.

Consumer perception remains a key driver of purchase intention, underscoring the importance of brand building and product promotion. Businesses should deliver accurate, comprehensive product information through various media channels, such as social media, live broadcasts, and short videos. Encouraging user-generated content and testimonials can enhance consumers’ understanding and trust in products.

Finally, consumer resonance plays a critical role in social commerce. It fosters emotional engagement and behavioral alignment, ultimately strengthening purchase intention. Businesses should actively cultivate a vibrant community by hosting online discussions, group-buying events, and promotional activities to stimulate participation and interaction. These strategies not only enhance consumer engagement but also contribute to a more effective social commerce environment.

6.2. Theoretical contributions

This study makes several theoretical contributions, advancing the understanding of social commerce and consumer behavior in significant ways.

First, it enriches the theoretical framework of social commerce by explicitly incorporating social estrangement as a key variable. While previous studies have explored social interaction and consumer behavior in social commerce, little attention has been given to the concept of social estrangement. This study empirically demonstrates how social estrangement disrupts information exchange, diminishes consumer perception, weakens consumer resonance, and ultimately reduces purchase intention. By addressing this gap, the study provides a more comprehensive theoretical foundation for future research in social commerce.

Second, it extends the application of consumer behavior theory to the social commerce context. While consumer behavior theory has been widely studied in marketing, the factors influencing consumer decision-making vary across business environments. This study highlights the unique role of community interactions and social relationships in shaping consumer cognition in social commerce. The findings confirm that consumer perception is influenced not only by product attributes but also by peer interactions and social engagement. The identification of consumer resonance as a key factor further enriches consumer behavior theory, offering deeper insights into purchase motivation within social commerce.

Third, this study provides a reference model and methodological framework for future research. The structural equation model developed in this study serves as a foundation for further exploration, allowing future researchers to investigate variations in social estrangement, different types of consumer behavior, and optimization strategies for fostering consumer resonance. Additionally, the combination of literature review, questionnaire surveys, and empirical analysis used in this study offers a methodological reference for subsequent studies, enhancing the scientific rigor and reliability of future research.

6.3. Practical Implications

The findings of this study offer valuable insights for businesses operating in social commerce. Enterprises should adopt effective strategies to reduce social estrangement, strengthen consumer perception, and promote consumer resonance to enhance purchase intention.

6.3.1. Reducing Social Estrangement

To mitigate social estrangement, businesses can optimize social platform functionalities, enhance community management, and organize offline events:

1) Platform Optimization: Businesses should ensure that their platforms have intuitive interfaces, clear information categorization, and efficient search functions. Providing multiple communication tools—such as instant messaging, voice calls, and video conferencing—can facilitate better consumer interactions.

2) Community Management: Establishing clear community guidelines, moderating discussions, and fostering a respectful and engaging environment can enhance communication and trust. Proactively resolving conflicts among members can further strengthen community cohesion.

3) Offline Engagement: Hosting fan meetups, product experience events, and themed gatherings can facilitate face-to-face interactions, fostering stronger connections and reducing social estrangement.

6.3.2. Enhancing Consumer Perception

Businesses must focus on brand building, product quality, and educational marketing:

1) Brand Development: Defining a clear brand identity and engaging in multi-channel branding efforts can enhance brand awareness and consumer trust.

2) Quality Assurance: Providing high-quality, reliable products and responsive after-sales service builds long-term consumer confidence.

3) Educational Marketing: Sharing professional product knowledge, creating instructional content, and hosting online tutorials can enhance consumer understanding and awareness.

6.3.3. Fostering Consumer Resonance

Businesses can encourage consumer resonance through user-generated content, interactive events, and influencer engagement:

1) User-Generated Content: Rewarding consumers for sharing reviews, experiences, and creative insights can foster engagement and community participation.

2) Interactive Events: Hosting online discussions, voting campaigns, and promotional contests can increase consumer interaction and stimulate emotional connections.

3) Influencer Collaboration: Identifying and nurturing influential community members as brand ambassadors can enhance credibility and inspire consumer engagement.

6.3.4. Enhancing Purchase Intention

Businesses can further drive purchase intention through personalized marketing strategies and trust-building efforts:

1) Personalized Marketing: Leveraging big data and artificial intelligence to analyze consumer preferences enables businesses to provide targeted recommendations and offers.

2) Trust and Transparency: Upholding ethical business practices, protecting consumer privacy, and maintaining transparency in product information can enhance trust and strengthen purchase intention.

By implementing these strategies, businesses can create a more engaging, consumer-centric social commerce environment, ultimately driving stronger purchase intent and sustainable business growth.

7. Conclusion and future research

7.1. Conclusion

This study deeply analyzes the complex relationship between social estrangement, consumer perception, consumer resonance and purchase intention in social commerce, and draws a series of conclusions with important theoretical and practical significance through rigorous empirical analysis.

Social estrangement has a significant negative impact on purchase intent. In the social commerce environment, social estrangements hinder effective communication and information sharing among consumers, which in turn affects purchase intent。 Due to the differences in interests, knowledge backgrounds and consumption concepts, it is difficult for different consumers to form close ties in the community, which makes the dissemination of product information limited and consumers' comprehensive and in-depth cognition of products. Social estrangements make it difficult for consumers to resonate emotionally and form a strong consumer resonance. Consumers lack interaction and communication with each other and are unable to share shopping experiences and experiences, which makes consumers feel less identified and belong to the community, which in turn affects their purchase intentions. When consumers feel lonely and not understood in the community, they are more likely to choose to leave the community and find other shopping channels.

Consumer perception has a significant positive impact on purchase intent. Consumers' awareness of a product directly affects their interactions and behaviors in the community. When consumers have a deeper understanding of the product, they are more willing to share their opinions and experiences in the community to resonate with other consumers, thereby promoting the formation of consumer resonance and influencing purchase intent. Consumers' perception of the quality, performance, and brand of products will affect their evaluation of products and purchase decisions.

Consumer resonance also has a significant positive impact on purchase intent. When consumers resonate in the community, they develop a common attitude and behavioral tendency. This resonance can enhance consumers' sense of identity and desire to buy, making it easier for consumers to make purchase decisions. The formation of consumer resonance is inseparable from the foundation of consumer perception. The more aware a consumer is of a product, the more actively they interact with other consumers in the community and the more likely they are to resonate. Once consumer resonance is formed, it will act directly on purchase intent and become a key factor influencing purchase decisions.

Purchase intent is influenced by a combination of social estrangements, consumer perceptions, and consumer resonance, and promotes overall social commerce。 Social estrangements indirectly affect purchase intent by influencing consumer perception and consumer resonance. Consumer perception indirectly affects purchase intent by influencing consumer resonance. Consumer resonance has a direct impact on purchase intent. In social commerce, in order to increase consumers' purchase intent, it is necessary to break down social estrangements, improve consumer awareness, and promote the formation of consumer resonance. Enterprises can improve consumers' purchase intent by optimizing community operations, providing valuable product information and high-quality services, enhancing interaction and communication between consumers, and creating a good community atmosphere.

7.2. Limitations and Future Research

While this study offers valuable insights into the field of social commerce, there are several limitations that should be acknowledged.

First, the study sample is limited to participants from Zhejiang Province, China, which may not fully represent the diversity of social commerce consumers. The data collection method primarily involved an online questionnaire, which may not capture the full spectrum of social commerce participants. To improve the generalizability of the findings, future studies should expand the sample scope to include consumers from various regions, as well as individuals of different ages, genders, and consumption habits.

Second, while this study employed questionnaires and empirical analysis, these methods alone may not fully capture the complex dynamics between social estrangement, consumer perception, consumer resonance, and purchase intention. Future research could integrate qualitative methods, such as interviews and case studies, to gain deeper insights into consumers’ underlying thoughts and behavioral motivations. These approaches could provide a more comprehensive understanding of the nuanced factors influencing consumer behavior in social commerce.

Additionally, the research model presented in this study, while comprehensive, may have overlooked some potentially important variables. Future studies could refine the model by incorporating additional factors, such as community climate, the influence of online influencers, or the role of social support networks, to better capture the mechanisms at play in social commerce.

Looking ahead, as social commerce continues to evolve, the relationships among social estrangement, consumer perception, consumer resonance, and purchase intention are likely to grow more complex. Future research should explore the integration of emerging technologies, such as artificial intelligence and virtual reality, in social commerce platforms and assess their impact on consumer behavior. Furthermore, cross-cultural research could offer valuable insights into how social commerce functions in different cultural contexts, providing a more nuanced understanding of the global dynamics of consumer behavior. This line of inquiry could provide theoretical support for cross-cultural marketing strategies and contribute to the global expansion of social commerce.

8. Data availability

Our data comes from a questionnaire survey designed by ourselves, which belongs to first-hand information. In terms of questionnaire design, our article has shown the content of the questionnaire to experts and doctoral students. We have conducted pre-tests in advance and complied with the ethical operation method.

CONFLICT OF INTERESTS

None.

ACKNOWLEDGMENTS

None.

REFERENCES

Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Akram, U., Hui, P., Khan, M. K., Yan, C., & Akram, Z. (2018). Factors Affecting Online Impulse Buying: Evidence from Chinese Social Commerce Environment. Sustainability, 10(2), 352. https://doi.org/10.3390/su10020352

Bianchi, C., Devenin, V., & Reyes, V. (2022). An Empirical Study of Consumer Purchase Intention for Responsible Enterprises in Chile. Journal of Environmental Planning and Management, 65(1), 105–125. https://doi.org/10.1080/09640568.2021.1879032

Brodeur, A., Grigoryeva, I., & Kattan, L. (2021). Stay-at-home Orders, Social Estrangement, and trust. Journal of Population Economics, 34(4), 1321–1354. https://doi.org/10.1007/s00148-021-00848-z

Cambra-Fierro, J. J., Fuentes-Blasco, M., Huerta-Álvarez, R., & Olavarría, A. (2021). Customer-based Brand Equity and Customer Engagement in Experiential Services: Insights from an Emerging Economy. Service Business, 15(3), 467–491. https://doi.org/10.1007/s11628-021-00448-7

Chen, M. H., & Tsai, K. M. (2020). An Empirical Study Of Brand Fan Page Engagement Behaviors. Sustainability, 12(2), 434. https://doi.org/10.3390/su12020434

Cheng, Y. Y., Tung, W. F., Yang, M. H., & Chiang, C. T. (2019). Linking Relationship Equity to Brand Resonance in a Social Networking Brand Community. Electronic Commerce Research and Applications, 35, 100849. https://doi.org/10.1016/j.elerap.2019.100849

Chiou, L., & Tucker, C. E. (2020). Social Estrangement, Internet Access and Inequality. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3568255

DAM, T. C. (2020). Influence of Brand Trust, Perceived Value on Brand Preference and Purchase Intention. Journal of Asian Finance, Economics and Business, 7(10), 939–947. https://doi.org/10.13106/jafeb.2020.vol7.no10.939

Dabbous, A., Aoun Barakat, K., & Merhej Sayegh, M. (2020). Social Commerce Success: Antecedents of purchase Intention and the Mediating Role of Trust. Journal of Internet Commerce, 19(3), 262–297. https://doi.org/10.1080/15332861.2020.1756190

Duman, T., Ozbal, O., & Duerod, M. (2018). The Role of Affective Factors on Brand Resonance: Measuring Customer-Based Brand Equity for the Sarajevo Brand. Journal of Destination Marketing & Management, 8,359–372. https://doi.org/10.1016/J.JDMM.2017.08.001

Flavián, C., Gurrea, R., & Orús, C. (2017). The Influence of Online Product Presentation Videos on Persuasion and Purchase Channel Preference: The Role of Imagery Fluency and Need for Touch. Electronic Commerce Research, 17(4), 661–700.

Helm, R., Möller, M., Mauroner, O., & Conrad, D. (2013). The Effects of a lack of Social Recognition on Online Communication Behavior. Computers in Human Behavior, 29(3), 1065–1077. https://doi.org/10.1016/j.chb.2012.09.007

Hu, S., Akram, U., Ji, F., Zhao, Y., & Song, J. (2023). Does Social Media Usage Contribute to Cross-Border Social Commerce? An Empirical Evidence from SEM and fsQCA analysis. Acta Psychologica, 241, 104083. https://doi.org/10.1016/j.actpsy.2023.104083

Husain, R., Paul, J., & Koles, B. (2022). The Role of Brand Experience, Brand Resonance and Brand Trust in luxury consumption. Journal of Retailing and Consumer Services, 66, 102895. https://doi.org/10.1016/J.JRETCONSER.2021.102895

Jang, K. K., Bae, J., & Kim, K. H. (2021). Servitization Experience Measurement and the Effect of Servitization Experience on Brand Resonance and Customer Retention. Journal of Business Research, 130, 384–397. https://doi.org/10.1016/J.JBUSRES.2020.03.012

Kang, I., Koo, J., Han, J. H., & Yoo, S. (2021). Millennial consumers' Perceptions on Luxury Goods: Capturing Antecedents For Brand Resonance In The Emerging Market Context. Journal Of International Consumer Marketing, 34(3), 214–230. https://doi.org/10.1080/08961530.2021.1944832

Kao, W. K., & André L'Huillier, E. (2022). The Moderating Role of Social Estrangement in Mobile Commerce Adoption. Electronic Commerce Research and Applications, 52, 101116. https://doi.org/10.1016/j.elerap.2021.101116

Koren, M., & Pető, R. (2020, March 31). Business Disruptions from Social Estrangement. arXiv. https://doi.org/10.48550/arXiv.2003.13983

Kumar, A., Shankar, A., Tiwari, A. K., & Hong, H. J. (2023). Understanding Dark Side of Online Community Engagement: An Innovation Resistance Theory Perspective. Information Systems and e-Business Management,1–27. https://doi.org/10.1007/s10257-023-00633-3

Lee, C. H., Chen, C. W., Chen, W. K., & Lin, K. H. (2021). Analyzing the Effect of Social Support and Customer Engagement on Stickiness and Repurchase Intention in Social Commerce: A Trust Transfer Perspective. Journal of Electronic Commerce Research, 22(4), 363–381.

Lăzăroiu, G., Neguriţă, O., Grecu, I., Grecu, G., & Mitran, P. C. (2020). Consumers' Decision-Making Process on Social Commerce Platforms: Online Trust, Perceived Risk, and Purchase Intentions. Frontiers in Psychology, 11,890. https://doi.org/10.3389/fpsyg.2020.00890

Meilatinova, N. (2021). Social Commerce: Factors Affecting Customer Repurchase and word-of-Mouth Intentions. International Journal of Information Management, 57,102300. https://doi.org/10.1016/j.ijinfomgt.2020.102300

Molinillo, S., Aguilar-Illesca, R., Anaya-Snachea, R., & Liebana-Cabanillas, F. (2021). Social Commerce Website Design, Perceived Value and Loyalty Behavior Intentions: The Moderating Roles of Gender, Age, and Frequency of use. Journal of Retailing and Consumer Services, 63, 102404. https://doi.org/10.1016/j.jretconser.2020.102404

Qalati, S. A., Vela, E. G., Li, W., Dakhan, S. A., Hong Thuy, T. T., & Merani, S. H. (2021). Effects of Perceived Service Quality, Website Quality, and Reputation on Purchase Intention: The Mediating and Moderating Roles of Trust and Perceived Risk in Online Shopping. Cogent Business & Management, 8(1), 1869363. https://doi.org/10.1080/23311975.2020.1869363

Qiao, Y., Yin, X., & Xing, G. (2022, June 30). Impact of Perceived Product Value on Customer-Based Brand Equity: Marx's Theory—Value-Based Perspective. Frontiers in Psychology, 13, 931064. https://doi.org/10.3389/fpsyg.2022.931064

Qin, C., Zeng, X., Liang, S., & Zhang, K. (2023). Do Live Streaming and Online Consumer Reviews Jointly Affect Purchase Intention? Sustainability, 15(8), 6992. https://doi.org/10.3390/su15086992

Reinikainen, H., Munnukka, J., Maity, D., & Luoma-aho, V. (2020). 'You Really are a Great Big Sister'—Parasocial Relationships, Credibility, and the Moderating Role of Audience Comments in Influencer Marketing. Journal of Marketing Management, 36(3–4), 279–298.

Riaz, M. U., Guang, L. X., Zafar, M., Shahzad, F., Shahbaz, M., & Lateef, M. (2021). Consumers' Purchase Intention and Decision-Making Process Through Social Networking Sites: A Social Commerce Construct. Behaviour and Information Technology, 40(1), 99–115. https://doi.org/10.1080/0144929x.2020.1846790

Shang, B., & Bao, Z. (2022). How Repurchase Intention is Affected in Social Commerce? An Empirical Study. Journal of Computer Information Systems, 62(2), 326–336. https://doi.org/10.1080/08874417.2020.1812133

Sohaib, M., Safeer, A. A., & Majeed, A. (2022, August 5). Role of Social Media Marketing Activities in China's E-Commerce Industry: A Stimulus Organism Response Theory Context. Frontiers in Psychology, 13, 941058. https://doi.org/10.3389/fpsyg.2022.941058

Xiang, H., Chau, K. Y., Iqbal, W., Irfan, M., & Dagar, V. (2022). Determinants of Social Commerce Usage and Online Impulse Purchase: Implications for Business and Digital Revolution. Frontiers in Psychology, 13, 837042. https://doi.org/10.3389/fpsyg.2022.837042

Yang, L., Niu, X., & Wu, J. (2021, September 2). RF-LighGBM: A Probabilistic Ensemble Way to Predict Customer Repurchase Behaviour in Community E-Commerce. arXiv. https://doi.org/10.48550/arXiv.2109.00724

Yu, F., Wenhao, Q., & Jinghong, Z. (2022). Nexus Between Consumer's Motivations and Online Purchase Intentions of Fashion Products: A perspective of Social Media Marketing. Frontiers in Psychology, 13, 892135. https://doi.org/10.3389/fpsyg.2022.892135

Zhang, X., & Yu, X. (2020). The impact of Perceived Risk on Consumers' Cross-Platform Buying Behavior. Frontiers in Psychology, 11,* 592246. https://doi.org/10.3389/fpsyg.2020.592246

Zhu, L., Li, H., Wang, F.-K., He, W., & Tian, Z. (2020). How oNline Reviews Affect Purchase Intention: A New Model Based on the Stimulus-Organism-Response Framework. Aslib Journal of Information Management, 72*(4), 463–488. https://doi.org/10.1108/AJIM-11-2019-0308

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		ABSTRACT
		This paper focuses on the field of social commerce, exploring the internal relationships among social estrangement, consumer perception, consumer resonance, and purchase intention. While the rapid development of social commerce has provided consumers with significant convenience, it has also introduced challenges such as social estrangement, encompassing aspects like information, emotion, and behavior, which hinder consumer interaction and negatively impact purchase intention. Consumer perception, influenced by factors such as product knowledge and brand trust, further affects purchase decisions. Consumer resonance, arising from interactions within the community, is fostered by elements like emotional resonance, information sharing, and group identity, and can significantly enhance purchase intention. Building upon the Theory of Planned Behavior and other frameworks, this study constructs a research model and posits hypotheses regarding the negative impact of social estrangement on purchase intention, and the positive effects of consumer perception and resonance on purchase intention. Through questionnaire surveys conducted on social commerce platforms in Zhejiang and employing data analysis methods including descriptive statistics, correlation analysis, and structural equation modeling, the findings reveal that social estrangement significantly inhibits purchase intention, whereas consumer perception and resonance positively promote it. Additionally, purchase intention enhances the conversion rate in social commerce. This research enriches the theoretical system of social commerce, addresses the research gap concerning social estrangement, and expands the application of consumer behavior theory. Practically, it offers guidance for enterprises to optimize platform functionalities, strengthen brand development, and foster consumer resonance to boost purchase intention and market competitiveness. Future research could delve into aspects such as broadening sample scope, integrating multiple methodologies, refining research models, and examining the impact of emerging technologies.
Received 22 February 2025 Accepted 26 March 2025 Published 16 April 2025 Corresponding Author Tai-Ge Yan, andrewaccept@163.com DOI 10.29121/granthaalayah.v13.i3.2025.6016 Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Copyright: © 2025 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License. With the license CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
		Keywords: Social Commerce, Social Estrangement, Consumer Perception, Consumer Resonance, Purchase Intention