Li Wan
Yuxi Lan- Chongqing University of Posts and Telecommunications, Chongqing, China
Introduction: Social media has a huge impact in marketing, as evidenced by the sheer number of users and the amount of time they spend on it each day. How online social networking sites (SNS) change shopping habits and even reshape the entire consumer market has become increasingly important topic in marketing field. Drawing upon the literature in user behavior, consumer purchase intention (CPI), and social media marketing, this research aims to investigate how social media involvement (i.e., make new friends, self-exhibition, enjoyment) affects CPI on online SNS and to examine the mediating role of self-disclosure in this relationship.
Methods: This study employs empirical regression analysis. Based on grounded coding results from interviews with 26 stakeholders, we developed measurement scales and collected 520 questionnaire responses. Using SPSS 21.0, we sequentially conducted reliability tests, correlation analyses, and regression modeling. Structural equation modeling via AMOS 26.0 was then employed to validate mediation pathways.
Results: The main findings indicate that making new friends (β = 0.130, p < 0.05) and self-exhibition (β = 0.246, p < 0.001) on SNS positively affect CPI. Furthermore, the mediating role of self-disclosure (β = 0.168, p < 0.01) strengthens this relationship. The study presents an effective theoretical model illustrating the relationship between social media involvement and CPI.
Discussion: The results suggest that SNS managers can promote CPI by encouraging social media involvement within legal boundaries, while consumers should screen for credible merchants on SNS and protect their personal data.
1 Introduction
Social media and online social networking sites (SNS) has grown to become essential component of the modern world and it has become a dominant and pervasive artifact for billions of people worldwide (Qin et al., 2024; Ramírez-Correa et al., 2021; Yang et al., 2025). According to the most recent study jointly issued by Meltwater on February 2, 2024, over 5 billion individuals use social media globally, accounting for 62.3% of the world’s population (Shi, 2024; Yang et al., 2024). Facebook leads with 2.19 billion users, followed by Instagram (1.65 billion), and TikTok ranks third with 1.56 billion users. WeChat, as the largest social media in China, ranks fifth with around 1.3 billion users (Wangyi, 2024). Given this reach, it is unsurprising that social media significantly impacts marketing by providing enhanced interactivity and rich consumer insights (Bazrkar et al., 2021). Companies can analyze consumer perceptions and purchasing power by examining social media data (Hu and Zhu, 2022; Wu and Song, 2019).
Previous research in social media marketing and consumer behavior has clearly shown how social media directly boosts consumer purchase intention (CPI). This happens in two main ways: repeating ads and sending highly targeted messages tailored to users’ interests, values, online habits, and lifestyles (Yang et al., 2023; Onofrei et al., 2022; Hussain et al., 2022). For example, PapiJiang successfully influenced CPI by posting short videos on social media platforms such as Weibo, WeChat, and Bilibili with content-implanted advertisements and creative interstitial advertisements (Sting, 2021). Similarly, companies advertise by utilizing platforms like Facebook, YouTube, and Twitter and collaborate with knowledgeable web celebrities for promotions, further boosting CPI (Sun et al., 2021; Ahsan and Senarath, 2023; Bazrkar et al., 2021). Indirectly, research has also shown that factors such as the usefulness of recommended content on SNS, word-of-mouth about information displayed on the platform, and language preferences set by the platform can influence CPI (Yao et al., 2021; Onofrei et al., 2022). Users are more inclined to purchase a brand or product if his friends have recommended it via social media (Yin et al., 2019). Theoretically, all data recorded on websites or social media—such as comments, purchasing history, and followed bloggers—can be analyzed in the information age (Mujahid and Mubarik, 2021; Tariq et al., 2021). We believe that companies can design targeted marketing strategies by analyzing aforementioned information.
Previous research has primarily focused on mechanisms like advertising placement, content pushing, and specific promotions (e.g., Netflix promotions) (De Keyzer et al., 2022) that impact CPI. These studies enhance our understanding of social media’s business function as a marketing channel. However, it is essential to recognize that the core function of social media remains fundamentally “social.” Users spend a great deal of time on the platforms engaging in spontaneous, non-commercial social activities such as making new friends, self-exhibition, and enjoyment (Growth black box, 2024; Buchholz, 2022). These user-driven, socially oriented (Xu, 2025) activities form the majority of the user experience on social media.
A key and under-explored research gap currently exists: understanding how and to what extent purely social user behaviors—such as making new friends, self-exhibition, and enjoyment—affect CPI. Some current studies on social media user behavior have explored the impact of consumption-related communication [e.g., Consumers interacting with trial products (Zhang, 2023), etc.] on CPI. While these studies reveal mechanisms like social influence and product awareness, there is still a lack of research and empirical evidence examining how non-commercial, purely social behaviors influence CPI and the specific pathways through which this effect occurs.
Therefore, we are very eager to know whether using social media without directly conducting activities related to shopping still influence CPI? In other words, this research aims to investigate how social media involvement (i.e., make new friends, self-exhibition, enjoyment) affects CPI on online SNS and to examine the mediating role of self-disclosure in this relationship.
The following is the study’s research topic:
RQ: Whether social media involvement affect consumer purchase intention on online social networking sites? If so, to what extent and how does social media involvement influence CPI, and how does self-disclosure mediate this relationship?
The rest of this article is as follows. First, we look at prior research on the effects of social media marketing and self-disclosure on CPI. Following this, we outlined the interview process and coding procedures, then detailed the steps of the questionnaire study. We developed the research model based on the analysis of the findings. Subsequently, we presented the conclusions from the measurement model assessment and hypothesized relationship assessment. Finally, we conclude out the study by talking about theoretical contributions and practical implications.
2 Literature review
2.1 Headings social media marketing, social media involvement and CPI
In the digital age, social media marketing is seen as a necessary option to engage consumers through social media platforms like Facebook, Instagram, Twitter and YouTube, facilitating product and service promotion, customer interaction, and brand awareness (Khanom, 2023). Within social media marketing research domain, studies have shown that CPI is influenced by a number of factors including viral advertisement (Jamali and Khan, 2018), brand familiarity (McClure and Seock, 2020), informativeness (Alalwan, 2018), consequences of consumer purchases (Wong, 2018) and perceived value (Zhang et al., 2023). For example, through multiple linear regression analyses, Wong (2018) find that product recommendations between social media users have a significant effect on CPI. Alalwan (2018) provides some theoretical and practical guidance on how merchants affect CPI by effectively planing and implementing advertisements on social media platforms. The research on social media’s effect on CPI is compiled in Appendix A. The table divides literature into two basic categories: strategies related to customer experience and methods connected to guidance.
It is worth noting that in addition to what has been said earlier, some scholars have been focusing on the impact of the make new friends and self-exhibition on CPI. For example, Hu et al. (2016) argue that in social media marketing, by making new friends to understand people’s similarities, professionalism and friendliness when interacting with each other can help to enhance the value felt by consumers, which in turn influences CPI. Chen and Chen (2022) suggests that people’s desire for self-exhibition, such as wanting to present an image of themselves externally, positively influences purchase intentions. However, these studies tend to focus on different aspects or broad concepts, and fail to adequately generalize these behaviors to user social behaviors on SNS as a whole to examine their relationship with CPI.
Sun and Xing (2022) study shows that social media interactions positively affect the CPI. Bilal et al. (2024) concluded that social media engagement and consumer experience are positively correlated, consumer experience improves consumer satisfaction, which in turn improves CPI. In their study, social media engagement refers to the behavior of consumers’ interaction with brands or companies on social media, including but not restricted to following brand accounts, liking, commenting, sharing content, etc. It can be seen that scholars are also rich in research on social media interactions and CPI, but their definition of social media interactions is still the behavior of users related to consumption activities rather than non-commercial social activities.
Notably, the meanings of the two words (i.e., social media engagement and social media involvement) are very similar but carry subtle distinctions in the literature. According to Zhang and Yoon (2018), social media ‘involvement’ between customers and companies is an interaction that needs to be understood. According to Ali et al. (2022), social media involvement (SMI) is defined as the behavioral acts of consumers’ interactions using brand-related social media platforms, including but not limited to browsing brand pages, liking, commenting, and sharing brand-related content. This perspective emphasizes observable user actions. In contrast, in Myers et al. (2024) study, social media engagement is conceptualized more broadly as a mindset that underpins and influences the way customers behave while dealing with brands on social media, representing the consumer orientation and the psychological antecedents of interactive social behaviors (e.g., liking, commenting, sharing) towards brands. Therefore, while social media involvement (Ali et al., 2022) focuses on observable behaviors (e.g., liking, commenting), social media engagement (Myers et al., 2024) reflects the psychological state driving them. The differences between social media engagement and social media involvement are detailed in Table 1.
Table 1. Difference between involvement and engagement.
Referring to previous literature, the definition of SMI was adopted in our study and detailed into making new friends, self-exhibition and enjoyment. Self-exhibition is defined as the act of sharing and posting various information and media content about oneself (Aguiton et al., 2009). By sharing specific types of photos and information, users may be more inclined to connect with users who share their interests, which may increase their purchase intention (Aguiton et al., 2009). Enjoyment is defined as ‘a state in which a customer makes a purchase in a digital marketplace’, specifically the customer’s satisfaction with the worth of a product or service (Masri et al., 2020). Enjoyment positively influences gaming users’ purchase intention through mediating factors such as enhanced social interaction, increased frequency of play and optimized item experience (Jang et al., 2021). Additionally, Enjoyment indirectly promotes CPI through trust (Masri et al., 2020).
2.2 Privacy computing theory, satisfaction theory and self-disclosure
For the theoretical foundation of this study we decided to use the Privacy Computing Theory and the Satisfaction Theory, both of which in previous empirical findings allow us to explanation of how SMI affects the CPI through key mechanisms of self-disclosure.
In a study by Dinev and Hart (2006), according to Privacy Computing Theory, while people are debating if they want to share their personal information, they first assess potential risks and potential benefits that may result from doing so, and then make decisions based on that assessment, which involves a series of beliefs and decision-making processes in which privacy risk beliefs, trust beliefs and personal interest Factors interacting with each other to influence. In this context, the privacy threat will act as the main expected cost to impede the users’ disclosure of their private information (Culnan and Armstrong, 1999). People will share personal information when the perceived benefits of accurate recommendations and offers outweigh the perceived risks of harassment and unauthorized use; on the contrary, they will not disclose their private information; and they are more likely to share personal information when the perceived benefits are matched by the risks (Min and Kim, 2015).
The Satisfaction Theory allows for an understanding of how individuals actively seek out and use certain media outlets to satisfy their specific needs (Dolan et al., 2016), which helps to understand and explain the reasons for the use of social media platforms (Khan, 2017). Dinev and Hart (2006) verified the impact of perceived privacy risks and benefits on willingness to making self-disclosure in e-commerce using the Privacy Computing Theory. Customers’ self-disclosure is an important source of information for retailers on the online platforms. Together, Privacy Calculus Theory and Uses and Satisfaction Theory form this study’s theoretical foundation. Uses Satisfaction Theory explains why users engage in SMI behaviors. Privacy Calculus Theory explains how users decide the extent of behaviors involving information sharing and self-disclosure by weighing potential benefits against risks. This directly determines their level of self-disclosure.
Taking self-disclosure into account, According to Wheeless’s (1978) definition, self-disclosure is a message one sends to others about oneself. It is a multidimensional construct with widely accepted aspects (Posey et al., 2010; Collins and Miller, 1994; Forgas, 2011; Wheeless, 1978). Generally, investigations of self-disclosure take a social psychology approach, utilizing theories like the notion of privacy computing (Cheung et al., 2015; Posey et al., 2010). In age of social media, self-disclosure is not only a common occurrence, but also a complex social behavior influenced by numerous elements, including technological characteristics, personal psychological states, and social environments. Studies have shown that SNS such as Facebook, Instagram, or TikTok have become active sites of personal narrative in online environments, where people share bits and pieces of their lives, often in front of a large group of strangers (Masur et al., 2023).
To ensure that the core constructs (SMI, self-disclosure, and CPI) are clearly defined in this study, Table 2 provides their specific definitions.
Table 2. Summary of interviews.
But there is still less systematic research on the mechanisms of how SMI, i.e., non-commercial social activities on SNS themselves (like making friends, self-exhibition, enjoyment), affect CPI. In addition, how self-disclosure, a key behavior promoted by SMI, mediates the relationship between SMI and CPI has not been fully explored. This gap motivates the present study.
3 Methodology
In order to investigate the underlying mechanisms that influence CPI in SNS, this study employs empirical regression analysis. This method is suitable for quantifying the relationship between multiple independent variables and the dependent variable (CPI). Regression analysis allows us to statistically validate the hypotheses in the theoretical framework, measure the extent and significance of the influence of each factor, and reveal how these factors work together to influence CPI.
The methodological design of the study was divided into two phases. Firstly, we conducted a qualitative study to interviewed and collected the opinions of 26 users from different backgrounds and lifestyles related to online shopping. Secondly, based on results of the first round, we design an research model and build hypotheses. Then we test our model by using the data collected from questionnaire.
4 Phase 1: qualitative study
Previous study has provided insights on the influencing factors of CPI on SNS, however, we know little about the relationship between SMI and CPI, the questions such as ‘Does the act of self-exhibition on social media make you more likely to buy certain products online? Why?’ ‘After making new friends through social media, do you feel inclined to consume certain online products because of their recommendations?’ has not been answered adequately. The answer to these questions were collected through semi-structured interviews (Liu et al., 2022).
To ensure diverse perspectives and mitigate respondent bias, participants were recruited using purposive sampling targeting individuals with (1) Experience or knowledge of online shopping; and (2) Diversity in gender, education, occupation, etc. These interviews involved 26 distinct individuals. Chinese was used for the interviews, which lasted between 30 min to an hour. Table 3 provides an overview of the interviewees’ demographics. An interview protocol (as presented in Appendix C) was pre-developed to guide the interviews. The protocol explicitly covered key domains including participants’ general SNS use, experiences with self-disclosure and self-exhibition online, interactions leading to new friendships, perceived enjoyment in SNS activities, and specific instances or attitudes towards online purchase intention influenced by these factors.
Table 3. Summary of interviews.
With the participants’ permission, all interviews were audio-recorded. Prior to the interviews, written informed consent was obtained from all participants, outlining the study purpose, procedures, confidentiality measures (anonymization), data usage, and their right to withdraw at any time.
In our analysis, coding was conducted using NVivo 12 software.
Firstly, we open coded the interviews of 26 individuals, totaling more than 14,000 words, involving three steps (Hoang et al., 2023): (1) labeling significant raw data excerpts, (2) conceptualizing by grouping similar labels into preliminary concepts, and (3) categorizing to refine these concepts into distinct subcategories. This process was iterative, employing constant comparison between data excerpts and emerging codes. From the 26 interview cases, we mined a total of 22 subcategories such as self-disclosure of beneficial information, refusal to disclose irrelevant information, self-exhibition, and gift-giving.
Secondly, performing spindle coding, by comparing the relationship between the subcategories, 8 more representative main categories were further obtained, namely: Expanded socialization, social expansion and individual needs.
Finally, we performed selective coding to simulate the process of consumer online shopping with respect to the existing results, linking the subcategories, main categories and core categories identified by open coding and principal axis coding, extracting the core categories to explain the influencing factors of online purchase intention, and ultimately refining four core categories: self-disclosure, making new friends, self-exhibition and enjoyment.
Based on the coding results, we constructed the data structure of the study, specifically, the three SMIs of making new friends, self-exhibition, and enjoyment are categorized as SMI, and self-disclosure is categorized as Slice of Life Sharing, Emotional expression, Disclosure, personal, and information, as detailed in Figure 1.
Figure 1. Data structure.
5 Phase 2: quantitative analysis
Our study drew on previously tested scales and adapted them for this study (see Appendix F) (Leganés-Lavall and Pérez-Aldeguer, 2016). All questions were scored on a Likert 7-point scale, with 1 being strongly disagree and 7 being strongly agree. For data collecting, we designed an online survey (see Appendix D for questionnaire). Our sample technique was consistent with our pre-study (see section 4). To confirm this example strategy, we used a number of pre-selected questions (e.g., “Have you ever shopped online?”, “Do you like to socialize online?”) to select participants.
Before conducting the formal research, five professionals in the field of consumer behavior were invited to assess the comprehensibility and clarity of the measurement items in the questionnaire. Then, in order to obtain valid and credible data, this paper is divided into a pre-survey and a formal survey with consumers who have experience in online shopping as the research target. In the pre-survey, the questionnaire was distributed to a total of 200 volunteers, and the questionnaire was returned one week later, and the scale was modified according to the results of the pre-survey and the opinions of experts.
In the pre-survey, first, we used SPSS 21.0 data analysis software to conduct the reliability test. The Cronbach’s α coefficients of meeting new friends, self-exhibition, hedonism, self-disclosure, and CPI were all above 0.7, indicating that the internal consistency of the variables was high. The combination reliability (CR) of each variable was greater than 0.7, indicating that the constructs had good combination reliability. Second, SPSS 21.0 data analysis software was used for differential validity, and the pre-study measured the variables using validated factor analysis. The standardized factor loadings (STD) of each variable ranged from 0.667 to 0.866, which was greater than 0.5. Meanwhile, the average variance of refinement (AVE) of meeting new friends, self-exhibition, enjoyment, and self-disclosure were higher than 0.5. The AVE of CPI was less than 0.5, so the measurement question items of CPI were adjusted before the formal research.
In the formal research, a total of 560 people engaged in the survey, of which 5 were eliminated because they did not know about or had not shopped online. In addition, 18 participants did not have complete answers to the questionnaire and were therefore dropped by us, and 12 participants were also dropped because they did not succeed in picking out the trap questions we had set up (specifically, we designed a clear instruction item as “Please select B in this question,” aiming to identify inattentive respondents and ensure data quality). The questionnaire platform can record the total time each person spends filling out the questionnaire. The questionnaire took about 10 min to complete, with a margin of error of 4 min. Five of the participants, who completed all the questionnaire options in less than 100 s, were eliminated because their answer time was too short. The ultimate sample size is 520 participants. The results of this survey provided data to support subsequent research.
Making new friends influences consumers’ willingness to buy by increasing their perceived social and purchase intention (Hu et al., 2016). SNS users purposefully share their photographs, experiences, and successes in order to convey their identity and establish impressions. Krasnova et al. (2010) shows that, SNS users are more inclined to demonstrate the benefits of self-exhibition and the benefits of using social media. Additionally, people communicate with their pals on SNS by posting their daily activities (Gibbs et al., 2006). Kankanhalli et al. (2005) shows that people will display more personal information online when they recognize the importance of the exchange. People use social media because they are both affordable and fun (Yu et al., 2022). Moreover, consumers who value the pleasure and enjoyment a product provides are less likely to consider the costs and benefits of the product when deciding to buy it (Lee and Murphy, 2008). This suggests that as users get a higher level of enjoyment from social media, their willingness to purchase a product may be enhanced. Therefore, we hypothesize:
H1a: Consumers' desire to make new friends is positively correlated with the CPI.
H1b: Consumers' desire to self-exhibition is positively correlated with the CPI.
H1c: Consumer perceived enjoyment is positively correlated with the CPI.
Lee (2017) argues that consumer disclosure of personal information, thoughts, feelings, etc. plays an important role in merchant-consumer interactions, and that the interaction process facilitates the formation of positive attitudes toward the brand, which then influences consumers’ purchase intentions. By examining consumers’ self-disclosure behaviors, the degree to they disclose information to companies, and the degree to which they respond to purchase online, Zeng et al. (2021) investigated how consumers respond to purchase online. The study’s findings indicated that consumers are more likely to purchase online when they share more information to companies. Kurtz et al. (2021) show that, in LBA, if customers are happy to provide information about their personal location, the chances of them purchasing goods and enjoying services related to it tend to be higher. According to Manchanda et al. (2022), social media encourages self-disclosure, which improves prosocial relations and increases customers’ sensitivity to suggestions from social media and tendency to buy. According to a study by Koay et al. (2023), congruence between customers and social media influenced the beneficial impact of self-disclosure on purchase intention, which was mediated by prosocial relationships. Research conducted by academics has shown that consumer positive self-disclosure influences their intention to purchase either directly or indirectly (Söderlund, 2020). Therefore, the following hypothesis is set out in this paper:
H2: Consumers' willingness to self-disclosure is positively correlated with the CPI.
As we mentioned earlier, the prevalence of self-disclosure on social media has been widely studied. Studies have shown that social networking sites (SNS) like Facebook, Instagram, or TikTok have become active sites of personal narrative and self-exhibition in online environments, where people share bits and pieces of their lives, occasionally in front of a great deal of strangers (Masur et al., 2023). In previous studies, self-disclosure is a very important factor influencing online CPI. A part of the research studies its direct impact on CPI. For example, examining the effect of negative information self-disclosure on CPI (Fennis and Stroebe, 2014). Self-disclosure of observer review records also has an impact on the CPI (Liu et al., 2021). As well as the impact of social media influencers’ self-disclosure on the CPI (Koay et al., 2023). Another part of the study focuses on its impact on CPI as a mediating variable. For instance, the association between social anxiety and online friendships was examined using self-disclosure as a mediating variable (Tian, 2013). In addition, individuals with high self-exhibition may be more inclined to attract attention and appreciation from others by posting selfies (Guo et al., 2018). Research has shown that if people enjoy themselves on SNS, they are more inclined to keep utilizing those SNS services and to provide personal information (Gwebu et al., 2014). This suggests that self-disclosure may play some mediating role between SMI and CPI. Appendix B summarizes the literature on the impact of self-disclosure, as an independent and mediating variable, on the CPI. Therefore, we posit that self-disclosure plays a mediating role in the SMI—CPI process. Specifically, these SMIs may promote consumers’ self-disclosure on online social media, which can be detected by merchants and use big data to push product pages related to consumers’ preferences, thus promoting CPI.
Additionally, research indicates that the behaviors stemming from self-exhibition desires (Guo et al., 2018) and the enjoyment derived from SNS use (Gwebu et al., 2014) are significant antecedents of self-disclosure tendencies. Therefore, making new friends, self-exhibition, and enjoyment of social media may influence consumers’ desire for self-disclosure (Tian, 2013; Guo et al., 2018). In summary, we hypothesize:
H3: The association between consumers' desire to self-exhibition and CPI is mediated by self-disclosure, so this relationship is stronger the greater the self-disclosure.
We design research model (Figure 2) and hypothesis in this phase.
Figure 2. Research model.
6 Results
We analyzed our survey data using SPSS 21.0 and AMOS 26.0. Consistent with previous study models. We investigated our model in two stages: first, we assessed the measurement model’s reliability and validity. Second, we entailed assessing the structural model (Yoon and Kim, 2023).
6.1 Assessment of the measurement model
When we conduct quantitative research, we attach great importance to the accuracy and reliability of the results to ensure that the conclusions drawn are valid (Venkatesh et al., 2016). First, we used SPSS 21.0 data analysis software to conduct a reliability test on the collected samples. In the reliability test, the Cronbach’s α of making new friends, self-exhibition, enjoyment, self-disclosure, and CPI were 0.881, 0.878, 0.868, 0.850, and 0.880, respectively, which were all above 0.7. The CR values were all greater than 0.7 (0.7823 to 0.9479), and AVE are all greater than 0.5 (0.5454 to 0.6855), indicating that the scale has good convergent validity (see Appendix E). This indicates that the internal consistency of the constructs is high. Secondly, in the validity test, the KMO value of structural validity was 0. 904, the Bartlett’s χ2 value was 10783.557, and the cumulative explained variance was 72.938%, which all met the criteria and were considered reliable. Thirdly, in the Harman one-way test, the first factor explained 14.903%, which is less than the 50% critical value, indicating no significant bias.
6.2 Assessment of the hypothesized relationships
In this study, structural equation modeling was constructed in the AMOS 26.0. First, in the model fit test, Table 4 details the main fit indicators obtained from the structural model test. After comparing with the recommended values of the indicators, the fitted values of all the indicators meet the criteria of the recommended values, except for AGFI and NFI, which are very close to the recommended values of 0.8 and 0.9. Therefore, the setting of this theoretical model is acceptable.
Table 4. Fitness index values for structural equation modeling.
Secondly, in the main effects test, the structural relationship between the latent variables and their standardized path coefficients’ estimates, T-values and hypothesis testing results are shown in Table 5, and the results show that making new friends, self-exhibition and self-disclosure produce significant positive effects. The results of hypothesis testing showed that meeting new people had a significant positive effect on CPI (β = 0.130, p < 0.05) self-exhibition had a significant positive effect on CPI (β = 0.246, p < 0.001), self-exhibition had a significant positive effect on self-disclosure (β = 0.182, p < 0.05), and self-disclosure has a significant positive effect on CPI (β = 0.168, p < 0.01). Notably, the non-significant negative path suggests that Enjoyment has no statistically meaningful impact on CPI (β = −0.002, p < 0.05) in this model.
Table 5. Main effects test (N = 520).
Thirdly, the mediation effect test for each path coefficient is as follows in Table 6, if the p-value is significant, the first condition for the existence of mediation effect is satisfied. The existence of mediation effect can be proved if the value between the upper and lower bounds is not zero at 95% confidence interval. From the table below, it is clear that consumer self-disclosure plays a fully mediating role in self-exhibition and CPI. The fully mediating role of self-disclosure between self-exhibition and CPI reflects Privacy Computing Theory’s core mechanism: self-exhibition triggers privacy disclosure calculus, which ultimately drives CPI decisions.
Table 6. Path coefficients for the mediated effects model.
Thus, our findings suggested that the hypotheses were generally confirmed, demonstrating how Satisfaction Theory and Privacy Computing Theory explains the cognitive trade-offs underlying CPI behaviors.
The model path coefficients of SMI affecting the CPI, which we derived after a series of analysis and tests of the data using AMOS 26.0, are shown in Figure 3. R2 is the coefficient of determination, which indicates the percentage of variation in the dependent variable CPI that is jointly explained by the independent variables SD, NF, SE, and EN in the model The closer the value of R2 is to 1, the stronger the effect of the independent variables on the dependent variable.
Figure 3. Model path coefficient diagram of factors influencing online purchase intention.
7 Discussion and conclusion
This study examines how SMI influences CPI through the perspective of Privacy Computing Theory and the Satisfaction Theory. We came to five important conclusions.
First, making new friends on the SNS positively affect CPI (H1a). This is because social networks make it easier for people to establish connections and make new friends (Angelini et al., 2024), resulting in more social interactions, and studies have shown that friends’ recommendations significantly influence purchase intentions. This result supports the key role of word-of-mouth marketing and user recommendations in social media on purchase decisions as emphasized by Bilal et al. (2022). Therefore, this result reveals that managers should pay attention to word-of-mouth marketing on social media, encourage interaction and recommendation among users, and build communities where users can share their product experience (Bilal et al., 2022). SNS, on the other hand, should optimize social features to encourage users to expand their social circles and provide more opportunities for interaction, thus supporting the positive impact of such social relationships on purchase decisions.
Second, self-exhibition on SNS (e.g., posting selfies, commenting, liking, etc.) also positively affects CPI (H1b). self-exhibition behaviors increase users’ activity on the platform and make it easier for them to be exposed to personalized advertising and marketing content; at the same time, presentation behaviors may also be influenced by others’ feedback (e.g., likes and comments) (Muyidi, 2025), which may change or strengthen their consumption perceptions. This suggests that social platforms should encourage users to actively participate in interaction and content creation to increase user stickiness, thereby increasing commercial realization opportunities; and Managers/brands should understand users’ self-exhibition styles and content preferences on social media in order to more accurately deliver advertisements and content. The study emphasizes “the importance of encouraging users to interact online,” suggesting that there is a correlation between user engagement and purchase intention, thus supporting the conclusion that self-exhibition, as an important form of user interaction, affects their purchase intention.
Third, enjoyment has little effect on CPI. These findings contradict hypothesis H1c. Specifically, the enjoyment experienced in social media use did not significantly increase CPI. This may occur because users’ inherent entertainment satisfaction with platforms is already high, reducing their motivation for additional online purchases. This indicates a complex relationship between enjoyment and CPI on SNS that requires deeper investigation. Future research should identify key factors enabling enjoyment to translate into CPI.
Fourth, self-disclosure has a positive effect on the CPI (H6). This suggests that facilitating authentic and engaging consumer sharing on social media is key to driving consumption. This strengthens Yin et al. (2019) view that creating a more humane and trustworthy information environment establishes long-term brand loyalty.
Finally, self-disclosure acts as a mediator, strengthening the effect of self-exhibition on CPI (H7b). This means that self-exhibition not only directly increases CPI but also boosts it further through the process of self-disclosure. This mediating mechanism likely involves factors like psychological satisfaction and social identity. Therefore, SNS should study the motivations and psychological processes behind users’ self-exhibition behavior to better guide them and meet business goals. Managers and brands should go beyond simple advertising (Wang et al., 2022). By creating engaging topics and encouraging users to share (fostering self-exhibition), they can indirectly raise CPI because these actions facilitate self-disclosure.
Taken together, these findings provide valuable insights into the specific mechanisms through which SMI influences CPI, advancing our understanding beyond generic platform effects or advertising-centric views.
7.1 Contributions to theory
Theoretically, this paper extends the literature on CPI in the context of social media by providing a new research component of users’ non-consumption related behavior on SNS. Prior studies demonstrated that many factors affecting CPI including viral advertisement, brand familiarity, informativeness, perceived risk, perceived value and so on (Alalwan, 2018; Wong, 2018; Jamali and Khan, 2018; McClure and Seock, 2020; Zhang et al., 2023). Notably, our study is different from the previous ones focusing on the advertisements on social media or social media platform itself, we pay special attention to the user activities on SNS, specifically examining users’ behavior on SNS is not related to consumption. This study examines how making new friends and self-exhibition on social media significantly increase CPI, thus providing empirical support for Satisfaction Theory. The theory posits that individuals actively use media to fulfill specific needs (Islam et al., 2020). Although SMI is essential to the development of social media marketing, it is unclear how SMI affects CPI. Our study fills this gap by explaining self-disclosure as a mediating variable. Crucially, this mediation mechanism is illuminated through the lens of Privacy Computing Theory. Privacy Computing Theory suggests that individuals weigh the perceived benefits against the perceived risks of disclosing personal information (Jiang et al., 2022). Notably, our study shows that self-exhibition have a significant positive effect on CPI. Our findings demonstrate that when perceived benefits (such as achieving Privacy Computing Theory gratifications like self-exhibition) outweigh perceived risks, self-disclosure acts as a critical pathway leading to CPI.
In addition, our unexpected finding that enjoyment had no significant effect on CPI raises important theoretical questions. Specifically, we found that hedonic satisfaction from SNS use by itself was not enough to drive CPI in the context we studied. This underscores the need to identify boundary conditions, mediators, and moderating mechanisms to deepen our understanding of this relationship. Thus, the study contributes to theory by highlighting the need for more nuanced models in satisfaction theory.
7.2 Implications for practice
Given the significance of the management and our findings, we recommend that managers pay careful attention to SMI data on social media. Where legal, merchant management can go about acquiring and analyzing data so as to influence CPI to buy by influencing their friends on SNS. Specifically, our results highlight the powerful role of self-exhibition and self-disclosure in driving CPI. Managers should leverage this by designing campaigns that encourage authentic user sharing and content creation (self-exhibition), which in turn fosters deeper self-disclosure. Crucially, this data utilization must be transparent and respect user privacy to build and maintain trust. What was once an ad push is gradually becoming disliked by users, it always appears anywhere we do not really want to see it, such as WeChat’s ad push. But since people stay for a long time on social media, businessmen can switch to a different strategy and use online friend influence, selfie influence, etc. to design marketing strategies. For instance, create engaging hashtag challenges or user-generated content campaigns that incentivize self-exhibition and peer sharing, capitalizing on the positive effect of meeting new friends and social validation. SNS managers can maintain consumer trust while using self-disclosed data to boost CPI by providing a clear privacy setting switch for users to “optimize ads based on my interests,” and only collecting essential interaction data (e.g., likes, views), clearly stating the benefit is “fewer irrelevant ads”.
And, again, our research should remind consumers that their every move is watched online, therefore, it’s critical to increase awareness of need for consumers to be disciplined about their online behavior and how to identify and choose trustworthy merchants. Businesses can support this by implementing robust privacy controls, offering transparency tools that show users what data is collected, and displaying trust badges or clear privacy policies. In addition, consumers should be proactive in protecting their personal data by using strong passwords, transacting through secure networks and regularly checking their favourite shops for inappropriate behavior.
Our finding that Enjoyment had no significant impact on CPI further underscores that simply creating fun experiences is insufficient; managers must focus on facilitating meaningful self-expression and connection to effectively influence purchase decisions while prioritizing ethical data practices.
7.3 Limitations and future research
While this study offers novel insights by uniquely examining the roles of specific Satisfaction Theory gratifications (making new friends, self-exhibition, enjoyment) and the mediating mechanism of self-disclosure through the lens of Privacy Computing Theory, it also faces several limitations that warrant attention.
Future research can address the shortcomings of our study. As shown in this paper, there are various types of SNS. For example, Facebook, Jitterbug, and WeChat are considered different business formats, and the extent and direction of their influence on users may vary. We have just generalized all of them and categorized them as social media for the research study. Therefore, it is meaningful and necessary to systematically understand users’ specific participation in SNS before subsequent research.
Our study can only provide data for a specified period of time, and cannot record changes in user behavior on SNS over time. Therefore it is unable to describe users’ behavior and intentions over time or how behavior and intentions change over time. In future research, a longitudinal study design could be used to monitor how customer behavior and intentions change over time.
Future research, as suggested above, can build upon this foundation by addressing these contextual and temporal constraints to refine and extend our model.
Data availability statement
The data analysed in this study is available in online repositories. This data can be found here: https://doi.org/10.6084/m9.figshare.27619986.v1. Further inquiries can be directed to the corresponding authors.
Ethics statement
Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the (patients/participants or patients/participants legal guardian/next of kin) was not required to participate in this study in accordance with the national legislation and the institutional requirements.
Author contributions
LW: Funding acquisition, Writing – review & editing. YL: Writing – original draft. JJ: Funding acquisition, Writing – review & editing. KL: Investigation, Writing – original draft.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the National Social Science Foundation (grant number 21CGL003); the Scientific and Technological Research Project of Chongqing Municipal Education Commission (grant number KJQN202200611); the Chongqing Major Decision-Making Consultancy Research Key Issues (grant number 2024ZB07); and the National Social Science Foundation Project (grant number 20XSH001).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Gen AI was used in the creation of this manuscript.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcomm.2025.1604727/full#supplementary-material
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Keywords: self-disclosure, consumer purchase intention, social media marketing, social media, social media involvement
Citation: Wan L, Lan Y, Jian J and Liu K (2025) How social media involvement affect consumer purchase intention on online social networking sites: examining the mediating role of self-disclosure. Front. Commun. 10:1604727. doi: 10.3389/fcomm.2025.1604727
Edited by:
Tereza Semerádová, Technical University of Liberec, CzechiaReviewed by:
Kwadwo Boateng Prempeh, Sunyani Technical University, GhanaYunpeng Yang, Shanghai Jiao Tong University, China
Copyright © 2025 Wan, Lan, Jian and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yuxi Lan, MTAyMzY0ODAxNkBxcS5jb20=; Jie Jian, amlhbmppZUBjcXVwdC5lZHUuY24=