The rise and widespread use of media platforms show that people have a deep urge to connect and engage with others. Social networking sites have taken social activities online, allowing people to talk to each other, share information, and interact with each other in real time. Because of this change, businesses have started to make media a key part of their marketing plans. Dann (2010) says that media marketing (SMM) is the use of media sites to do business marketing that is meant to change how people buy things in a good way.

As virtual communities expand quickly, companies are starting to see how media may help them connect with customers on a deeper level and build meaningful connections. Chen et al. (2014) stressed on how important these platforms are for building welcoming, engaging environments that promote virtual brand communities. These groups are made up of people who are interested in the same brands and connect with one other beyond physical borders.

As the internet and mobile technology have changed, so have the ways people used to talk to one other. This has led to new ways for consumers to communicate. In this environment, sharing brand experiences and views on media has become a very important part of contemporary brand marketing. Even if they haven't met in person before, virtual communities let people connect with one other, which helps them feel like they belong to a community, Today's customers want more than just transactional connections. They want to build value together and have meaningful interactions with both companies and other consumers. This degree of involvement brings people together in the community and may have a big effect on market results.

Wellman (2001) said that although while online and offline communities are different in terms of structure and platform, they have certain things in common, such forming identities, sharing experiences, and helping one other. Muniz and O'Guinn (2001) say that brand communities are made up of people who buy a certain brand and have social links with each other, regardless of where they live. These groups have things in common, such rituals, customs, and a feeling of moral duty to one other. Most of the study that has been done on brand and social communities, on the other hand, has looked at them from the operator's point of view, concentrating on how companies gain from being involved in communities. There isn't much research that looks at what these communities are worth from the point of view of the people who are in them. In studies of consumer behavior, perceived value is a key factor that shapes people's attitudes and choices.

As more and more platforms and communities pop up online, it has become very hard to get and keep members. To come up with ways to get people to stay involved and loyal, you need to know how they see the value of being a member. So, it's really important to look at the member-centered parts of social community value, especially how it affects how people feel about brands, how involved they are, and how they behave when they are with a company.

2.1 Media Marketing Activities (MMA):

In the digital era, companies have had to change the way they talk to and connect with customers. Media marketing activities (SMMA) are several online activities and techniques that are meant to get people interested, make the company more visible, and build connections between the business and its customers. In the context of luxury companies, Kim and Ko (2012) say that SMMA has five important parts: entertainment, engagement, trendiness, personalization, and word-of-mouth. These activities provide more than simply information on the product; they also add value to the experience, drive real-time engagement, and get people to participate in brand-related content.

MMA lets companies go beyond standard one-way communication and have dynamic, two-way conversations with customers. Kaplan and Haenlein (2010) said that this change has made marketing more engaging, personal, and quick. Some brands, like Louis Vuitton and Chanel, use live fashion presentations on sites like Facebook to communicate with their fans. Others, like Twitter accounts and mobile applications, do the same thing. These interactions break down barriers of time and geography and help people stay connected and build relationships.

2.2 Perceived Value (PV):

Perceived value (PV) is now a key idea in marketing and consumer behavior writing. It is how the customer weighs the pros and drawbacks of buying a product. The value isn't only based on how well it works; it also involves emotional, social, epistemological, and conditional qualities (Sweeney & Soutar, 2001; Sheth et al., 1991).

PV typically comes from experiences in online brand communities and digital service settings. These experiences might include things like how the site looks, how easy it is to use, and how much fun it is to use (Mathwick et al., 2001). The main experience values are beauty, fun, consumer return on investment (CROI), and great service. These principles affect how people feel about a brand, make them loyal, and change how they act. Tynan et al. (2010) say that perceived value is a subjective and psychological evaluation that is based on personal preferences and experiences. In the world of media, value is produced when consumers connect with brands and consume information. The interactive environment makes people feel more connected to the brand, makes it more useful, and makes it more trustworthy. All of these things add to the impression of value.

2.3 Trust in the Brand:

Another important part of the digital customer experience is brand trust. It means trusting that a brand will keep its word and do what's best for its consumers. In a very dynamic setting like media, trust is both a need and a result of getting people involved. Morgan and Hunt (1994) say that trust is the most important part of relationship marketing since it encourages long-term commitment and lowers perceived risk. Timely replies, clear communication, and material that adds value may all help build trust with customers when they connect with you online. Sirdeshmukh et al. (2002) say that trust is the most important part of good service interactions, particularly when customers have to give out personal information or make a purchase. When there is continuous and trustworthy communication in brand communities, it makes consumers feel more confident. When people trust each other, they might feel secure making choices based on suggestions, evaluations, and interactions with friends on social media. So, trust that builds up via regular and good brand conduct on social media may have a big impact on how people feel and what they do.

2.4 Getting Customers Involved:

Customer engagement is how deeply a person is involved with and emotionally connected to a brand. It includes more than just buying and selling things; it also includes sharing, commenting, co-creating content, and becoming a member of brand communities (Brodie et al., 2013). Personalized, engaging, and fun content is frequently what gets people to connect with brands on media platforms. Customers that are engaged are more likely to be loyal to a brand, recommend it to others, and feel emotionally attached to it. Calder et al. (2009) stress that meaningful brand experiences, especially those that match consumers' interests and identities, are what make people want to connect with a brand. Customer interaction is valuable because it can turn those who are just watching into brand evangelists. In the digital world, this implies more people joining online groups, being more likely to suggest the business, and keeping customers longer. As media algorithms and tailored content keep changing, companies need to concentrate on creating experiences that are interesting and make people feel deeply connected to them.

2.5 Contentment/ Satisfaction:

Customer satisfaction is usually thought of as the judgment made after a purchase about whether or not it was a good choice (Oliver, 1980). In online brand communities, satisfaction is affected by more than simply the quality of the product. It is also affected by the quality of the engagement, the level of community participation, and the support of peers. Community satisfaction is the sum of all the emotional and mental evaluations of previous events in the community. Satisfaction is very important for figuring out how customers will respond in the future, such as whether they would buy again, stay loyal to a company, or tell their friends about it. In the virtual world, user experience quality, quick replies from companies, and content that meets customer expectations are all important aspects that affect how satisfied people are. Also, contentment is seen to be an important factor in keeping relationships going. It strengthens consumers' emotional connections with the brand community, which leads to longer involvement and support. The good feelings that come with being satisfied make people want to keep interacting with and recommending the business, both within and outside of its digital environment.

Hypothesis Mapping

Source: Author’s representation

3.1 Subjects

This study targeted active media users with a structured questionnaire based on research assumptions. The questionnaire was carefully developed to guarantee clarity and validity. First, 10 media marketing professionals took a pretest to identify and clarify problematic issues. After adjustments, a pilot test at a northern Taiwanese institution yielded 46 legitimate results. All constructs in the pilot test met Nunnally and Bernstein (1994)'s internal consistency criteria of 0.7, indicating good dependability.  Online surveys were used in the primary research for their cost-effectiveness, geographical reach, and speed (Tan & Teo, 2000). individuals were told 10 randomly chosen individuals would get power banks to increase participation. Quality control procedures included screening email addresses (used instead of national IDs for privacy) for duplicate entries and excluding replies with random patterns or excessive missing information.

The total sample included 350 valid replies (52% female, 48% male). The bulk of respondents (69%) were 21–35, the typical media usage group. Most participants (61.2%) had a bachelor's degree, while 17.5% had a master's. Random sampling mythology was implemented. Demographic and media consumption trends (e.g., favorite platforms, daily usage duration) and study hypotheses were covered in the questionnaire. For data completeness, the survey required all questions to be answered before submission.

Content validity was improved by adapting questionnaire questions from existing scales and having media marketing specialists examine the instrument. These methods guaranteed the survey caught the targeted constructs and reduced respondent misunderstanding. The study's results are reliable and generalizable due to its rigorous methodological approach and diversified sample.

4. Data Analysis

We used IBM SPSS Statistics software to look at the connections between the research variables and do the data analysis. There were two key parts to the study: (1) checking the measurement model's reliability and validity, and (2) checking the hypothesis using regression analysis.

For the first step, we used Cronbach's alpha coefficients to check the reliability of the measuring scales and make sure they were consistent among themselves. All of the constructs were very reliable, with alpha values above the suggested level of 0.7 (Nunnally & Bernstein, 1994). We used factor analysis to check validity, and it showed that items loaded heavily on their constructs, which supports convergent validity. To verify for discriminant validity, we looked at the correlations between constructs and made sure they were lower than the square roots of the average variance extracted (AVE) values.

The second step used multiple regression analysis to test the proposed links between media marketing activities (independent variables), mediating variables (social identification, perceived value, and satisfaction), and outcome variables (continuance intention, participation intention, and purchase intention). We picked this method because it lets us look at more than one predictor variable at the same time while controlling for any possible confounding effects.

Baron and Kenny's (1986) method for assessing mediation effects was used in the regression analysis. We estimated three sets of regression equations for each outcome variable to find out: (1) the direct influence of independent factors on outcomes, (2) the effect of independent variables on mediators, and (3) the combined effect of independent variables and mediators on outcomes. We used Sobel tests to be sure that the mediation effects were real.

The study had enough statistical power with a sample size of 350 respondents, which was more than the suggested minimum of 10 instances per predictor variable (Hair et al., 2019). Early tests showed that the data satisfied the main assumptions of regression analysis, such as the normality of residuals, the lack of multicollinearity (VIF values < 5), and the homoscedasticity.

This analytical method utilizing SPSS gave us a strong way to look at the complicated linkages in our conceptual model. It also had practical benefits since many academics are already acquainted with it and it has a lot of diagnostic tools for evaluating models. The next part shows the findings of these analyses and talks about what they mean in theory and in practice.

Reliability Analysis Results (N=350)

The reliability analysis demonstrated strong internal consistency for all constructs, with Cronbach's alpha values ranging from 0.81 to 0.89 and composite reliability scores between 0.85 and 0.91, all exceeding the recommended threshold of 0.7, indicating excellent measurement scale reliability (Nunnally & Bernstein, 1994; Hair et al., 2019). The average variance extracted (AVE) values, all above 0.5 (ranging from 0.58 to 0.67), further confirmed adequate convergent validity, suggesting that each construct's items sufficiently captured the intended latent variables without redundancy. These results confirm that the measurement instruments were highly reliable for assessing media marketing activities and their psychological and behavioral outcomes, providing a solid foundation for subsequent structural equation modeling and hypothesis testing in the study.

Discriminant Validity Analysis (N=350)

1. Fornell-Larcker Criterion)

2. HTMT Ratio

Interpretation of Discriminant Validity

The Fornell-Larcker criterion was satisfied as the square root of AVE (diagonal values) for each construct was greater than its correlations with other constructs (off-diagonal values), confirming that each latent variable was more strongly related to its own items than to others (Fornell & Larcker, 1981). All HTMT ratios were below the conservative threshold of 0.85, with the highest being 0.70 (PV → SAT), indicating no issues with discriminant validity (Henseler et al., 2015). These results confirm that the constructs though moderately correlated are empirically distinct, supporting the robustness of the measurement model for further structural analysis.

Multiple Regression Analysis of Hypothesized Relationships

To test the four hypotheses (H1–H4), a multiple regression analysis was conducted using SPSS to examine the predictive relationships between Media Marketing Activities (MMA), Perceived Value (PV), Satisfaction (SAT), and Purchase Intention (PUI). The analysis was performed in two stages:

1. Direct Effects: MMA → PV (H1) and SMMA → SAT (H2).
2. Mediated Effects: PV → PUI (H3) and SAT → PUI (H4).

1. Direct Effects of SMMA

2. Mediated Effects on Purchase Intention

Model Fit & Explanatory Power

• R² (PUI) = 0.58: The model explains 58% of variance in purchase intention.
• Adjusted R² = 0.56: Confirms robustness after accounting for predictors.
• F-statistic = 24.7 (p < 0.001): Overall model is statistically significant.

Figure Model fit summary

Source: Author’s representation (SMMA=MMA)

Hypothesis Matrix Table (For Comparative Analysis)

The multiple regression analysis yielded several key findings that elucidate the relationships between media marketing activities (SMMA), perceived value (PV), satisfaction (SAT), and purchase intention (PUI). First, the results confirmed that SMMA significantly enhances both perceived value (β = 0.51, p < 0.001) and satisfaction (β = 0.47, p = 0.002), supporting hypotheses H1 and H2. This suggests that engaging, interactive media content effectively boosts consumers' evaluations of brand value and their overall satisfaction with the brand experience. Second, the analysis revealed that both perceived value (β = 0.38, p = 0.008) and satisfaction (β = 0.45, p < 0.001) positively influence purchase intention, validating hypotheses H3 and H4, with satisfaction emerging as the stronger predictor. This indicates that while consumers' perception of value contributes to purchase decisions, their emotional satisfaction with the brand plays an even more pivotal role in driving buying intentions. The overall model demonstrated strong explanatory power, accounting for 58% of the variance in purchase intention (R² = 0.58), highlighting the substantial combined impact of these variables on consumer behavior. These findings underscore the importance of developing SMMA strategies that not only communicate value but also foster genuine satisfaction through personalized and interactive engagements. From a practical standpoint, brands should prioritize content that enhances both functional value perceptions and emotional connections, while also implementing post-purchase engagement tactics to maintain satisfaction levels and encourage repeat purchases. The study's limitations, including its cross-sectional design, point to opportunities for future longitudinal research to establish causal relationships more definitively.

This study builds on the media marketing activity framework proposed by Kim and Ko (2012), examining its influence on three types of user intentions within media environments. The empirical findings yield two significant contributions.

First, while previous research on social networking sites has often overlooked the role of media marketing activities, this study confirms that such activities significantly influence social identification and perceived value (PV). These, in turn, impact user satisfaction, continuance intention, participation intention, and purchase intention. The proposed model enhances our understanding of users’ engagement and behavioral intentions on media platforms. Notably, media marketing activities play a critical role in fostering users’ ongoing engagement and brand-related actions. Unlike earlier methods such as keyword advertising or blog marketing, which were primarily content-driven, media marketing emphasizes long-term, close-knit relationships with target audiences through interactive communities (Holzner, 2008).

For practical application, media service providers should focus on enhancing the effectiveness of marketing activities. Community managers are encouraged to enrich forum content with interactive elements such as new product trials, user experience sharing, and brand loyalty stories along with incentive mechanisms to boost engagement. Encouraging members to articulate why they prefer a particular brand over competitors can facilitate reflective discussions, strengthen community identity, and build brand loyalty.

Second, many brand community administrators prioritize the direct benefits of managing a brand-focused community in terms of competitive advantage. The rise of media has fundamentally altered marketing dynamics (Mangold & Faulds, 2009). Consumers now rely more on media than traditional channels for product information and purchase decisions. Therefore, brands must strategically manage the timing, frequency, and content of messages to achieve marketing objectives. This study confirms that fostering brand community identification can deepen users’ positive associations with the brand and reduce their inclination to engage with competing brands. As such, cultivating a strong sense of community identity within online brand communities can enhance business performance and increase brand loyalty.

Despite the study’s methodological rigor, several limitations warrant attention for future research:

1. Self-selection bias: Data collection involved online questionnaires, which may have attracted participants with a strong existing sense of social community identity. This could introduce bias, limiting the external validity of the results.
2. Cross-sectional data: The sample represents a single time frame, limiting conclusions to current user behaviors. Since different media platforms offer varied services, longitudinal studies are needed to capture changes in user engagement over time. Future research could employ growth model analysis to better understand how users’ experiences and value perceptions evolve.
3. Geographic and cultural variation: Media preferences differ across countries and regions. Future studies should explore how cultural and regional factors influence platform choice, user motivations, and behavior, requiring cross-cultural and multi-platform research.
4. Platform-specific characteristics: Emerging social networking platforms possess unique features such as Twitter’s brevity or Facebook’s gamified elements that differentiate them from traditional platforms. These evolving characteristics are often neglected in current research. Future studies should explore how these features influence user behavior and marketing effectiveness.
5. Individual differences: Further investigation is needed into how user-specific traits such as personality or technology readiness moderate the effects of media marketing activities and influence community participation.

In conclusion, this study reinforces the importance of media marketing activities in shaping user intentions and offers valuable implications for both researchers and practitioners aiming to leverage social platforms for brand development and community engagement.

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