Beyond the Platform: A Comparative Analysis of How Consumer Characteristics and Valuation of Brand Attributes Moderate the Drivers of Consumer Behaviour on Facebook, TikTok, Instagram, and LinkedIn

Beyond the Platform: A Comparative Analysis of How Consumer Characteristics and Valuation of Brand Attributes Moderate the Drivers of Consumer Behaviour on Facebook, TikTok, Instagram, and LinkedIn | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Beyond the Platform: A Comparative Analysis of How Consumer Characteristics and Valuation of Brand Attributes Moderate the Drivers of Consumer Behaviour on Facebook, TikTok, Instagram, and LinkedIn N.M. Mohamed Fazal, Prasad Neelawela This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7602641/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In today’s fragmented digital landscape, a one-size-fits-all approach to social media marketing is ineffective, as platform-specific conditions critically shape consumer responses. This study moves beyond general examinations of social media’s impact to provide a comparative analysis of how consumer characteristics moderate behaviour across four major platforms. Using survey data from 435 Sri Lankan social media users—368 on Facebook, 103 on TikTok, 187 on Instagram, and 194 on LinkedIn—moderation analyses were conducted with the PROCESS macro for SPSS (Model 1). The results highlight clear and theoretically significant contrasts. On mature platforms such as Facebook and LinkedIn, age emerged as a dominant direct predictor of consumer behaviour, while on Instagram it functioned as a moderator, amplifying the role of personal interests and brand characteristics among older users. In contrast, age was negligible on TikTok, where spending played a more powerful role, uniquely moderating interactions with “heavy user” status and psychosocial cues. Similar spending-based activation effects were observed on Instagram but absent on Facebook and LinkedIn, underscoring platform-contingent consumer psychology. These findings carry important managerial implications: Facebook and LinkedIn demand age-based segmentation, Instagram strategies should target older users with interest-driven and brand-focused content, while TikTok strategies should prioritize commercial intent over demographics, with emphasis on high-spending segments. Tests including Social Media Platform Characteristics indicated no age-contingent effects across platforms and a marginal, age-invariant association on LinkedIn. The study cautions against directly porting strategies across platforms and contributes by empirically demonstrating that psychological mechanisms of influence are not universal but shaped by each platform’s structural purpose and user dynamics. This study advances a platform contingency perspective , offering a nuanced model for effective platform-specific marketing strategies. Marketing Social Media Marketing Consumer Behaviour Moderation Analysis Comparative Analysis Facebook TikTok Instagram LinkedIn Age Spending Platform Contingency Perspective Figures Figure 1 Figure 2 1. Introduction The proliferation of social media has fundamentally reshaped modern commerce, transforming not only how brands communicate with consumers but also the very fabric of decision-making processes. Platforms such as Facebook, Instagram, TikTok, and LinkedIn have evolved into global ecosystems where commercial activity, content consumption, and social interaction are deeply intertwined (Kaplan & Haenlein, 2010 ). For businesses, a strategic and well-executed social media presence is no longer optional but a core element of brand building, customer relationship management, and sales channel development. The scale of this transformation is evident in the projected annual social media advertising spend of over $ 200 billion, with emerging economies like Sri Lanka showing rapid digital penetration and e-commerce adoption (Statista, 2025 ; We Are Social, 2024 ). Yet, as algorithms governing visibility become more complex and the digital environment more saturated, the effectiveness of a “one-size-fits-all” approach is rapidly diminishing. What resonates with consumers on one platform may fail on another, and what persuades one demographic may alienate another (Tuten & Solomon, 2017 ). Earlier research validated social media as a powerful marketing channel, showing links to outcomes such as brand loyalty, purchase intention, and electronic word-of-mouth (e.g., Schivinski & Dabrowski, 2016 ; De Vries, Gensler, & Leeflang, 2012 ). However, much of this work treated social media and its users as homogeneous, overlooking the structural and cultural differences between platforms and the heterogeneity of the consumer base. This oversight has led many organizations to a strategic plateau, where rising investments no longer guarantee improved returns (Kumar & Mirchandani, 2012 ). Against this backdrop, a significant gap remains: while social media marketing is demonstrably effective, less is known about the boundary conditions under which it works best and how these conditions differ across platforms. This study addresses this gap by investigating how consumer characteristics—specifically age and spending—moderate the drivers of consumer behaviour on Facebook, TikTok, Instagram, and LinkedIn. By analyzing these ecosystems in parallel, the research shifts the discussion from whether social media works to under what conditions it is most effective. Grounded in the Sri Lankan context, a rapidly digitizing emerging economy, the study also provides insights that extend beyond Western markets, offering a nuanced, platform-sensitive model of social media marketing effectiveness. 2. Literature Review and Hypothesis Development 2.1 Social Media Marketing and Consumer Behaviour: A Foundational Relationship Consumer behaviour, defined as the processes by which individuals or groups select, purchase, use, and dispose of products or experiences (Solomon, 2020 ), has been fundamentally reshaped by social media. These platforms create dynamic, interactive, and socially embedded environments for commerce (Mangold & Faulds, 2009 ; Dwivedi, Ismagilova, & Hughes, 2023 ), where algorithms and affordances strongly shape outcomes (Liu et al., 2022 ). TikTok’s short-form video environment, for instance, fosters impulse-driven purchasing, while LinkedIn supports trust-based, professional decision-making (Li & Xie, 2021 ; Sharma et al., 2023 ). This shift has replaced linear AIDA models with cyclical ones where consumers are in continuous dialogue with brands (Edelman & Singer, 2015 ). SMM effectiveness is underpinned by classic persuasion theories. The Elaboration Likelihood Model (ELM) distinguishes between central routes requiring cognitive effort and peripheral routes driven by heuristic cues (Petty & Cacioppo, 1986 ). Social media enables both: detailed product tutorials engage high-involvement consumers, while visually appealing posts or influencer endorsements appeal peripherally (Tafesse & Wien, 2018 ). Uses and Gratifications Theory (UGT) further emphasizes that users actively select media to satisfy information, entertainment, or social interaction needs (Katz, Blumler, & Gurevitch, 1973 ; Whiting & Williams, 2013 ). Empirical studies confirm SMM’s positive impact on brand equity, purchase intention, and loyalty (Godey et al., 2016 ; Schivinski & Dabrowski, 2016 ), but early research often treated “social media” as homogeneous, overlooking critical platform-level differences. 2.2 A Typology of Social Media Platforms: The Need for a Contingency Approach Social media platforms are not interchangeable; their architectures, purposes, and cultures create distinct consumer dynamics. A contingency approach (Fiedler, 1964 ) highlights the need to tailor strategies by platform. Four key archetypes illustrate this: Facebook – The Social Graph : Built on explicit social connections across diverse demographics, Facebook emphasizes trust and reciprocity. Marketing effectiveness often derives from leveraging social capital, authenticity, and peer recommendations (Ellison, Steinfield, & Lampe, 2007 ; Lu, Fan, & Zhou, 2016 ). TikTok – The Content Graph : Powered by algorithmic recommendations, TikTok delivers serendipitous short-form content based on behavioural signals rather than social ties. Its youthful culture values authenticity and entertainment, making trend-based, natively engaging content critical for brand success (Kaye, 2021; Omar & Dequan, 2020 ; Liu, Zhang, & Zhao, 2022 ). This aligns with emerging theories of algorithmic personalization (Liu et al., 2022 ; Sharma et al., 2023 ), which suggest that algorithmic curation minimizes demographic effects and foregrounds behavioural signals. Explicitly integrating this perspective helps explain why variables such as age exert negligible influence on TikTok compared to social-graph platforms. Instagram – The Interest & Lifestyle Graph : Instagram’s visually driven environment prioritizes aspiration and lifestyle discovery. Users follow brands and influencers based on aesthetics and identity projection, making influencer marketing and brand storytelling particularly persuasive (Zarouali et al., 2020 ; Hughes, Swaminathan, & Brooks, 2019 ). LinkedIn – The Professional Graph : Anchored in career identity and credibility, LinkedIn connects users by professional networks and expertise. Consumer-like behaviour is oriented toward B2B purchasing and professional services, where authority and knowledge are primary drivers (van Dijck, 2013 ). These structural distinctions imply that consumer drivers vary by platform, necessitating platform-specific hypotheses. 2.3 Age as a Generational Cohort Moderator Age functions as a proxy for generational cohorts, reflecting differences in digital literacy, motivations, and platform affinities (Bolton et al., 2013 ; Djafarova & Bowes, 2021 ). Gen Z typically seeks identity expression and entertainment, aligning with UGT’s focus on peer recognition (Phua et al., 2017 ), while older cohorts gravitate toward information-seeking or professional networking (Vaterlaus et al., 2021 ). Thus, Age is expected to moderate platform-specific behaviours. H1a : Age will be a significant direct predictor of consumer behaviour on mature platforms (Facebook, LinkedIn). H1b : Age will moderate the relationship between antecedents (e.g., brand characteristics, personal interest) and consumer behaviour differently across platforms. 2.4 Spending as Consumer Involvement and Economic Capital Moderator Spending reflects both purchasing power and consumer involvement. Higher disposable income often correlates with brand consciousness and materialistic values, increasing responsiveness to brand-related content (Richins, 2004 ; Shrum et al., 2013 ). From a Self-Congruity perspective (Sirgy, 1986 ), high-spending consumers align self-concept with aspirational brands, while cross-cultural values such as collectivism and power distance shape how economic capital translates into engagement (Hofstede, 2001 ). H2a : Spending will significantly moderate the relationship between usage intensity (“Heavy User” identification) and consumer behaviour. H2b : Spending will significantly moderate the relationship between psychosocial factors (e.g., Peer Influence, Brand Characteristics) and consumer behaviour across platforms. 2.5 Conceptual and Cultural Lens Positioning Age and Spending as moderators captures both demographic and broader psychographic orientations. Age reflects generational gratifications and social identity salience, while Spending captures economic capacity, materialism, and cultural hierarchy. Together, they provide a baseline for understanding how structural factors condition consumer behaviour on social media. At the same time, this framework highlights the need for future research to incorporate additional psychographic moderators—such as innovativeness, collectivist orientation, or need for uniqueness—for a more complete theoretical account. 2.6 Conceptual Framework Rather than proposing a single, overarching model, this study's conceptual framework is grounded in a platform contingency approach. It tests a consistent set of baseline moderation models across four distinct platform subgroups (Facebook, TikTok, Instagram, and LinkedIn) to empirically determine if and how the influence of key antecedents (e.g., Peer Influence, Brand Characteristics) on Consumer Behaviour is altered by consumer characteristics (Age, Spending). The central thesis is that the significance and nature of these moderated relationships are contingent on the platform's unique structural and cultural environment. Figure 2 in the results section serves as a visual summary of these contingent findings, mapping the divergent patterns of influence across the platforms. 3. Methodology 3.1. Research Design and Sample This study employed a quantitative, cross-sectional research design to investigate the relationships between consumer characteristics, social media marketing antecedents, and consumer behaviour across four distinct platforms. A survey-based methodology was chosen as it provides an efficient and effective means of collecting data on attitudes, perceptions, and self-reported behaviours from a large and diverse sample, allowing for robust statistical analysis and the testing of moderation hypotheses (Hair et al., 2016 ). The cross-sectional nature of the design provides a snapshot of the prevailing dynamics at a single point in time, which is appropriate for exploring the current state of consumer behaviour on these rapidly evolving platforms. The data were collected via a structured online questionnaire. The survey was created using a digital platform and distributed through various online channels to ensure a broad and heterogeneous reach. These channels included social media platforms (such as Facebook groups and LinkedIn posts), online forums, and email lists targeting a general population of internet users in Sri Lanka. Although convenience sampling introduces potential bias, it is widely used in exploratory social media research (Etikan et al., 2016 ). To mitigate limitations, the study ensured heterogeneity by distributing the survey across multiple platforms and demographic groups. The target population for this study was active social media users residing in Sri Lanka. This context was deliberately chosen to explore the dynamics of consumer behaviour on global social media platforms within a non-Western, emerging economy. Sri Lanka—an emerging economy with rapid digital penetration—offers a non-Western testbed to examine platform-contingent consumer behaviour, providing a valuable counterpoint to predominantly North American and European evidence (We Are Social, 2024 ). A total of 435 valid responses were collected. For the purpose of this comparative analysis, the sample was filtered into four distinct, non-exclusive subgroups based on self-reported regular usage of each platform: respondents who identified as active users of Facebook ( N = 368 ), TikTok ( N = 103 ), Instagram ( N = 187 ), and LinkedIn ( N = 194 ). The demographic profile of the final sample reflected a diverse range of social media users, with a majority falling between the ages of 18 and 34, which is consistent with the general user base of most social media platforms. 3.2. Measures and Instrumentation The survey instrument was developed based on a comprehensive review of existing literature in the fields of marketing, consumer behaviour, and communication. All constructs were measured using items on a 5-point Likert scale (1 = Strongly Disagree, 5 = Strongly Agree), unless otherwise specified, a widely used and validated method for capturing attitudinal and perceptual data (Likert, 1932 ). Consumer Behaviour (DV) : This dependent variable, the core outcome of the study, was operationalized as a multi-faceted construct capturing social media-influenced commercial intentions and actions. It was measured using a 5-item scale developed for this study to reflect modern digital consumerism. Sample items included: "Social media platforms influence my purchase intentions" and "I feel loyal to brands that engage with me on social media." The items were averaged to create a composite Consumer_Behaviour_Score. The scale demonstrated high internal consistency and reliability, as indicated by a Cronbach’s alpha of .88, which is well above the acceptable threshold of .70 for exploratory research (Nunnally, 1978 ). Predictor Variables (X-variables) : These variables represent the antecedents whose effects on consumer behaviour were hypothesized to be moderated. Given the exploratory and comparative nature of this study, which examines multiple predictors across four distinct platforms, single-item measures were employed for the predictor variables. While this approach reduces participant fatigue and is justified for narrow constructs (Bergkvist & Rossiter, 2007), it also limits construct reliability. Therefore, findings should be interpreted with caution and validated in future research using multi-item scales. While multi-item scales are often the standard for latent constructs, methodological research supports the use of single-item measures when the construct being measured is concrete, unambiguous, and easily understood by the respondent (Bergkvist & Rossiter, 2007; Sackett & Larson, 1990). For variables such as "Peer Influence" or "Heavy User" identification, the concepts are sufficiently narrow that a single, direct question can capture the construct with high face validity. This approach was strategically chosen to reduce the cognitive load on participants, minimize survey fatigue, and maintain high data quality across the lengthy questionnaire, a critical consideration in multi-platform research designs (Fuchs & Diamantopoulos, 2009). Furthermore, in a comparative study examining four distinct platform contexts, using concrete, single-item measures ensured conceptual consistency, reducing the risk that multi-item scales would be interpreted differently by users with different platform mindsets (e.g., professional on LinkedIn vs. entertainment on TikTok). The priority was to establish a comparable baseline effect across platforms, a goal for which this parsimonious approach is well-suited, despite the acknowledged trade-off in construct depth. Nevertheless, future studies should validate these constructs using multi-item scales (e.g., peer influence scales by Chu & Kim, 2011 ; brand evaluation scales by Yoo & Donthu, 2001 ) to strengthen construct reliability. Peer Influence : Measured with the item, "Peer influences affect my engagement with social media content." Perceived Importance of Brand Characteristics : Measured with the item, "Brand characteristics (e.g., brand reputation, product quality) influence my interaction with social media content." Personal Interest : Measured with the item, “My personal interests strongly influence what I engage with on this platform.” (5-point Likert: 1 = Strongly Disagree to 5 = Strongly Agree). Heavy User Identification : Measured with the item, "I am a heavy user of social media." Several constructs were operationalised using single-item measures, consistent with prior studies where the construct is concrete, unidimensional, and easily understood by respondents (Bergkvist & Rossiter, 2007; Drolet & Morrison, 2001). While multi-item validated scales are often preferred, single-item measures are widely accepted for parsimonious operationalisation of focal variables in consumer behaviour contexts. The dependent variable, Consumer Behaviour, was self-developed to capture platform-specific engagement and purchase orientation; the strong internal consistency (Cronbach’s α = .88) suggests reliability, but future studies should further validate the scale against established behavioural measures to enhance external validity. Moderating Variables (W-variables) : These are the key consumer characteristics hypothesized to alter the relationship between the predictors and the outcome. Age : Measured as a categorical variable in the survey (1 = Under 18, 2 = 18–24, 3 = 25–34, etc.) and treated as a continuous variable in the regression analyses. This is a common and accepted practice in moderation analysis, as it allows for the examination of linear interaction effects (Hayes, 2022 ). Age was collected in categories but analysed as continuous, following standard practice where ordinal categories approximate an interval scale with sufficient range (Norman, 2010). As a robustness check, we also ran models with dummy-coded categories. The results did not differ substantively, strengthening confidence in the chosen analytic approach (see Appendix B for details). Spending : Measured on a scale reflecting different levels of self-reported transactional activity on social media platforms, providing a direct behavioural indicator of commercial engagement. Similarly, Spending should not be interpreted narrowly as financial outlay but as an indicator of consumer involvement and purchasing power. Prior studies show that consumers with higher disposable income tend to exhibit stronger brand consciousness and materialistic values, which condition their responsiveness to brand-related content on social media (Richins, 2004 ; Shrum et al., 2013 ). From the perspective of Self-Congruity Theory (Sirgy, 1986 ), individuals with higher spending capacity may align their self-concept with aspirational brand identities, thereby moderating the influence of social media stimuli on behavioural outcomes. Furthermore, economic capital interacts with cultural values such as collectivism and power distance (Hofstede, 2001 ), shaping how spending power translates into engagement or advocacy. In this sense, Spending functions as a structural moderator that captures the extent to which consumers’ financial capacity and material orientations condition their behavioural responses. Although a convenience sampling approach was employed through online groups and networks, demographic balancing was monitored to reduce skew, and the final sample reflected a wide spread across gender, age categories, and provinces. Future research should further validate these findings using probability sampling or weighting techniques to enhance representativeness. 3.3. Analytical Strategy The data analysis was conducted using IBM SPSS Statistics Version 28. The first phase involved data screening and preparation. This included checking for missing values, assessing the normality of distributions, and creating the necessary composite scores for multi-item constructs (e.g., Consumer_Behaviour_Score). Binary filter variables (e.g., IsFacebookUser, IsTikTokUser) were also generated to facilitate the subgroup analyses. Preliminary analyses included descriptive statistics to summarize the demographic and behavioural characteristics of each platform's user sample and a Pearson correlation analysis to examine the bivariate relationships between the key variables. This step was crucial for identifying initial patterns and ensuring that the assumptions of multicollinearity were not violated for the subsequent regression analyses. The main hypotheses were tested using a series of moderation analyses conducted with the PROCESS macro for SPSS (Model 1) developed by Hayes ( 2022 ). This tool is specifically designed for testing moderation, mediation, and conditional process models and provides robust estimates of interaction effects. For each of the four platform subgroups, a series of models was run. In each model, Consumer_Behaviour_Score was set as the dependent variable (Y), a predictor (e.g., Peer Influence) was set as the independent variable (X), and a consumer characteristic (Age or Spending) was set as the moderator (W). The significance of the interaction term (X × W) was used to determine whether moderation occurred, with a significance level of p < .05 as the primary threshold for accepting a hypothesis. P-values between .05 and .10 were noted as exploratory and were not used to claim support for hypotheses or to make inferential conclusions. Conditional effects for such models are reported descriptively only. When a significant interaction was found, a conditional effects analysis (also known as a simple slopes analysis) was conducted to probe the nature of the interaction, examining the effect of the predictor at low (16th percentile), mean, and high (84th percentile) levels of the moderator. 4. Results 4.1 Preliminary Analysis Prior to hypothesis testing, data quality was verified and descriptive statistics calculated. Table 1 presents means, standard deviations, and Pearson correlations for each platform-specific subgroup. Distinct demographic profiles emerged: TikTok users were the youngest (M = 2.15, SD = 1.05), followed by Instagram (M = 2.45, SD = 1.15), Facebook (M = 2.89, SD = 1.21), and LinkedIn (M = 3.10, SD = 1.25). Consumer Behaviour scores were generally positive and similar across platforms, ranging from 3.45 (LinkedIn) to 3.68 (TikTok). As expected, Age correlated negatively with Consumer Behaviour on Facebook (r = –.35, p < .01) and LinkedIn (r = –.29, p < .01), providing initial support for H1a, while the relationship was weaker and non-significant for TikTok and Instagram. Spending correlated positively with Consumer Behaviour across all four platforms. Multicollinearity checks confirmed that predictor intercorrelations were well below the .80 threshold (Hair et al., 2016 ). Table 1 Descriptive Statistics for Facebook, TikTok, Instagram, and LinkedIn Samples Variable Facebook (N = 368) TikTok (N = 103) Instagram (N = 187) LinkedIn (N = 194) M (SD) M (SD) M (SD) M (SD) 1. Consumer Behaviour 3.55 (0.73) 3.68 (0.69) 3.62 (0.70) 3.45 (0.76) 2. Age 2.89 (1.21) 2.15 (1.05) 2.45 (1.15) 3.10 (1.25) 3. Spending 2.50 (1.15) 2.30 (1.10) 2.40 (1.12) 2.65 (1.18) 4. Peer Influence 3.88 (0.95) 3.95 (0.92) 3.91 (0.93) 4.05 (0.90) 5. Brand Characteristics 4.15 (0.88) 4.20 (0.85) 4.18 (0.86) 4.25 (0.82) 6. Heavy User 3.95 (1.01) 4.05 (0.99) 4.01 (1.00) 3.85 (1.05) Note. M = Mean; SD = Standard Deviation. 4.2 Moderating Role of Age The first hypothesis tested Age as a moderator. Table 2 summarizes the cross-platform results, with additional PROCESS output and conditional effects provided in Appendix A. On Facebook and LinkedIn , Age was a significant direct negative predictor of Consumer Behaviour, confirming H1a for mature platforms. This indicates younger users are more commercially responsive in these ecosystems. However, moderation tests were largely non-significant, with exploratory trends observed for the Age × Brand Characteristics interaction (Facebook: p = .090; LinkedIn: p = .091). On Instagram , Age functioned primarily as a moderator rather than a direct predictor. Two significant interactions emerged: Age × Personal Interest (p = .035) and Age × Brand Characteristics (p = .016). These results indicate that the influence of interests and brand values becomes more salient for older users, consistent with ELM’s involvement shift—central-route processing dominating among older cohorts, peripheral cues among younger cohorts. On TikTok , Age showed neither direct nor moderating effects, underscoring the limited role of demographics in a content-graph ecosystem where algorithmic personalization overrides generational differences. This provides support for H1b, highlighting platform-contingent roles of Age. 4.3 Moderating Role of Spending The second hypothesis tested Spending as a moderator. Results are summarized in Table 2 , with conditional effects provided in Appendix A. A key convergence appeared on Facebook and TikTok , where the Heavy User × Spending interaction was significant (Facebook: p = .018; TikTok: p = .011). In both cases, heavy usage predicted stronger consumer behaviour as spending increased, supporting H2a. By contrast, the same interaction was not significant on Instagram or LinkedIn , suggesting that high engagement does not universally translate into greater responsiveness for high-spending consumers. Spending also moderated psychosocial factors selectively. While interpreting the following results with caution due to the smaller sample size (N = 103) for this subgroup, on TikTok, significant interactions were found for Peer Influence × Spending (p = .006) and Brand Characteristics × Spending (p = .035). On Instagram , Peer Influence × Spending was significant (p = .043), but Brand Characteristics × Spending was not (p = .419). On Facebook , psychosocial interactions were not moderated by Spending, and on LinkedIn , Peer Influence × Spending showed only an exploratory trend (p = .067). This mixed pattern provides nuanced support for H2b: spending enhances responsiveness to psychosocial cues primarily in entertainment- and lifestyle-oriented platforms. Social Media Platform Characteristics (X5). Across platforms, the X5 × Age interaction was not significant on Facebook (ΔR² = .0039, p = .2019), Instagram (ΔR² = .0068, p = .2346), or TikTok (ΔR² = .0110, p = .2791). on LinkedIn, the interaction was marginal (ΔR² = .0117, p = .0915), and the conditional slopes of X5 at the 16th, 50th, and 84th percentiles of Age were positive (Appendix A), suggesting an age-invariant association rather than a meaningful age contingency. In all cases, the main effect of X5 was not significant in the interaction models. Although some coefficients were statistically significant, effect sizes were small (ΔR² typically ≤ .07), consistent with moderation norms in consumer research (Hayes, 2022 ). 4.4 Comparative Cross-Platform Summary Table 2 consolidates all moderation tests across platforms, while Fig. 2 visually maps significant, exploratory, and non-significant effects. Together, they highlight platform-specific contingencies: Facebook : direct Age effects; Heavy User × Spending significant. TikTok : multiple Spending-based moderations; negligible Age influence. Instagram : Age activates Personal Interest and Brand Characteristics; Spending moderates Peer Influence. LinkedIn : direct Age effects; exploratory moderation trends only. This synthesis underscores the central claim: consumer moderators operate differently across platform archetypes, reinforcing the need for platform-contingent marketing strategies. Table 2 Comparative Summary of Key Moderation Effects Across Four Platforms Platform Predictor (X) Moderator (W) Interaction (β) p-value Key Finding Summary Facebook Brand Characteristics Age .06† .090 Exploratory (0.05 ≤ p < 0.10) SM Platform Characteristics Age .051 .202 Non-Significant Heavy User Spending .07 .018 Significant Interaction Peer Influence Spending .05 .134 Non-Significant TikTok Brand Characteristics Age .12† .076 Exploratory (0.05 ≤ p < 0.10) SM Platform Characteristics Age .072 .279 Non-Significant Heavy User Spending .14 * .011 Significant Interaction Peer Influence Spending .19 ** .006 Significant Interaction Brand Characteristics Spending .16 * .035 Significant Interaction Instagram Brand Characteristics Age .13 * .016 Significant Interaction Personal Interest Age .11 * .035 Significant Interaction SM Platform Characteristics Age .061 .235 Non-Significant Brand Characteristics Spending .036 .419 Non-Significant Heavy User Spending .05 .170 Non-Significant Peer Influence Spending .08 * .043 Significant Interaction LinkedIn Brand Characteristics Age .09† .091 Exploratory (0.05 ≤ p < 0.10) SM Platform Characteristics Age .087 .092 Exploratory (0.05 ≤ p < 0.10) Heavy User Spending .05 .282 Non-Significant Peer Influence Spending .07† .067 Marginally Significant Trend Note. Table displays standardized beta coefficients (β) for the interaction term. † 0.05 ≤ p < 0.10: exploratory only; not used to infer moderation ; results are reported descriptively. Table 3 Summary of Key Findings and Consumer Dynamics by Social Media Platform Platform Key Finding Summary Facebook Consumer behaviour is primarily driven by Age , with younger users being more commercially active. The incremental value of ‘Heavy User’ is small but significant for low spenders and stronger for high spenders ( Heavy User × Spending, p = .018 ). Psychosocial factors like peer influence and brand perception are not significantly moderated by spending or age. SM Platform Characteristics: no evidence of age-contingent effect. TikTok Consumer behaviour is largely independent of Age . Instead, the platform's commercial dynamics are notably moderated by Spending . Psychosocial factors (Peer Influence, Brand Characteristics) and high engagement ("Heavy User") are "activated" and become significant drivers of consumer behaviour only for users who are also spenders. SM Platform Characteristics: age showed no moderation; platform features did not predict differences in consumer behaviour. Instagram Consumer behaviour is uniquely moderated by Age , which "activates" the importance of Personal Interest and Brand Characteristics for older users. Unlike on TikTok, these factors are not relevant for the youngest users. Peer Influence is also activated by spending, but the link between being a "Heavy User" and spending is broken, suggesting usage is more aspirational. SM Platform Characteristics: not significant across age groups. LinkedIn Consumer behaviour is driven by different factors than on social/entertainment platforms. Age is a direct predictor (younger users are more active), but the tested psychosocial factors are not significantly moderated by age or spending. Peer (professional) influence and brand characteristics show marginal trends, suggesting a professional context where credibility is universally important but amplified for spenders. SM Platform Characteristics: marginal but consistently positive slopes across ages, suggesting an age-invariant effect. 5. Discussion 5.1 Summary and Interpretation of Findings This study examined how Age and Spending moderate consumer behaviour across four major social media platforms—Facebook, TikTok, Instagram, and LinkedIn—each representing a distinct structural archetype (social graph, content graph, interest/lifestyle graph, professional graph). The findings confirm that social media marketing is not a universal phenomenon but is shaped by the interplay between user characteristics and platform architecture. Rather than reiterating statistical results, this section interprets what the findings mean conceptually, how they compare to existing literature, and what they suggest for marketing theory and practice. The discussion is organized around three themes: the divergent role of Age, the activating role of Spending, and the theoretical significance of non-significant findings. These are followed by theoretical contributions, managerial implications, broader implications for emerging markets, and directions for future research. 5.1.1 Age as Contextual Involvement and Identity Cue Age emerged as a moderator that varies substantially by platform. On Facebook and LinkedIn , younger users were consistently more responsive to commercial cues, while older users displayed lower levels of consumer behaviour. This finding is consistent with generational cohort theory (Bolton et al., 2013 ), where younger cohorts are characterized by digital nativity and greater comfort blending social or professional interactions with commercial activity. For Facebook, this suggests that younger users view the platform as an integrated ecosystem where entertainment, social connection, and commerce co-exist. Older users, by contrast, may retain a narrower view of the platform as primarily social or professional, resisting overt commercial cues. On Instagram , Age functioned less as a direct predictor and more as a moderator. Commercial relevance of personal interests and brand characteristics increased significantly among older users, a phenomenon that can be described as commercial maturation . While younger users may engage with Instagram for aesthetic gratification or fleeting trends, older users increasingly weigh their stable interests and evaluations of brand reputation when deciding whether to act commercially. This is consistent with the Elaboration Likelihood Model (ELM) , where involvement determines the route of persuasion. Younger users process primarily through the peripheral route , influenced by visuals or trendiness, whereas older users process centrally, paying attention to diagnostic cues such as brand credibility or alignment with interests. From a Uses and Gratifications Theory (UGT) perspective, gratifications also shift: entertainment and peer recognition dominate among younger users, while information, stability, and identity coherence gain prominence as users age. In TikTok’s case , Age had no measurable role—neither as a direct predictor nor as a moderator. This highlights the influence of algorithmic personalization . Unlike social-graph or interest-graph platforms, TikTok curates feeds primarily from behavioural signals (e.g., watch time, interactions). The algorithm surfaces content irrespective of user demographics, thereby flattening generational differences. In effect, TikTok demonstrates how algorithmic environments can diminish the predictive power of demographics . Instead, platform-native behavioural markers, not chronological age, become the more relevant predictors of consumer action. Overall, the findings support the claim that Age is not a static predictor but a platform-conditioned signal of involvement and identity salience. On some platforms (Facebook, LinkedIn), Age aligns with generational differences in adoption and use. On others (Instagram), it activates latent motivations and processing routes. On still others (TikTok), its influence is eclipsed by personalization systems. 5.1.2 Spending as a Marker of Commercial Intent Spending consistently distinguished between two types of engagement: one that is purely performative and one that translates into commercial behaviour. On Facebook and TikTok, the Heavy User × Spending interaction confirmed that high usage translates into consumer behaviour primarily among high spenders. In other words, a heavy user who does not spend represents an engaged but commercially inactive participant, while a heavy user who spends reflects activated commercial intent . The divergence across platforms is equally revealing. On Instagram, heavy usage was not moderated by Spending, suggesting that frequent interactions may represent aspirational browsing or aesthetic consumption rather than transactional readiness. On LinkedIn, heavy usage was tied more to professional development and networking, again decoupling platform engagement from consumer-like behaviour. This distinction reinforces the need to evaluate engagement quality rather than relying on raw volume metrics. Spending also moderated psychosocial drivers in meaningful ways. On TikTok, both Peer Influence and Brand Characteristics were more predictive among high spenders, whereas on Instagram only Peer Influence showed a spending-based effect; Brand Characteristics × Spending was not significant. This indicates that social validation and brand trust are not inherently powerful—they require the enabling condition of financial intent. Put differently, Spending transforms peer influence and brand values from passive perceptions into active behavioural drivers. On Facebook, spending did not moderate psychosocial factors like peer influence or brand characteristics. However, it did significantly activate the link between being a "Heavy User" and consumer behaviour, consistent with findings on TikTok. This suggests that on mature social-graph platforms, high engagement translates to commercial action primarily when the user has a pre-existing commercial intent, which is reflected by their spending habits. On LinkedIn, exploratory trends suggest that Peer Influence may matter for some users, but the effect does not cross conventional significance thresholds. Overall, while statistically significant, the effect sizes were modest (ΔR² ≤ .07), which is consistent with moderation norms (Hayes, 2022 ). These results highlight nuanced rather than large-scale moderating effects, underscoring the need for cautious interpretation. This evidence supports a two-step model of commercial activation : Platforms generate engagement through structural or cultural drivers (social ties, viral content, aspirational images). Engagement translates into consumer behaviour only when moderated by a user’s financial intent, captured here as Spending. This interpretation positions Spending not only as economic capital but as a psychological signal of willingness to act on social influence. 5.1.3 Theoretical Value of Non-Significance The consistent non-significance of general usage metrics (e.g., Heavy User, Usage Level) is itself an important finding. These results challenge both academic assumptions and industry practices that treat engagement volume as a reliable proxy for consumer value. The implication is clear: not all engagement is equal. Users may be active for social, performative, or informational reasons that do not translate into purchasing or advocacy. For theory, this supports calls to refine models of social media influence by incorporating behavioural intent variables (e.g., past spending, willingness to pay, transactional history). For practice, it underscores the limitations of “vanity metrics” such as time spent or number of likes. Marketing strategies should prioritize behavioural signals aligned with ROI , such as demonstrated spending patterns, in-app purchase activity, or engagement with commerce features. 5.1.4 Platform Affordances and Age-Invariant Effects We found no evidence of age-contingent moderation on Facebook, TikTok, or Instagram, and only a marginal pattern on LinkedIn. Notably, on LinkedIn the conditional slopes of X5 were consistently positive at typical age values despite the non-significant interaction, which implies that platform affordances (e.g., verification, professional profiles, ad formats) relate to consumer behaviour uniformly across ages rather than differentially by age. This complements our broader conclusion that who the user is (age) matters less for X5 than what the platform affords. This finding aligns with research on platform affordances, where algorithmic curation (Liu et al., 2022 ) and verification cues (Sharma et al., 2023 ) shape user trust and engagement irrespective of demographics. In the LinkedIn context, professional credibility mechanisms appear to operate consistently across cohorts, underscoring the design power of platform-level features. 5.2 Theoretical Implications This research advances marketing theory on three fronts: Platform-Contingent Persuasion. Findings extend ELM and UGT by demonstrating that persuasion routes and gratifications are not stable but vary by platform. On Instagram, the same brand cues are peripheral for younger users but central for older ones; on TikTok, demographic cues are bypassed altogether. These patterns necessitate platform-contingent refinements of persuasion theory. Conditional Social Influence. Traditional social influence theory assumes relatively stable peer effects. Here, peer influence was significant only when coupled with Spending on certain platforms. This suggests that social validation is contingent on both cultural norms and commercial intent , adding nuance to how influence should be theorized. Platform Contingency Perspective. Building on contingency theory, this study introduces a middle-range perspective: platforms themselves act as contingencies shaping which consumer characteristics matter. The structural archetype (social, content, interest, professional graph) determines whether Age, Spending, or other moderators activate. This provides a framework for future comparative research across platforms and cultures. 5.3 Managerial Implications The results provide a roadmap for managers seeking platform-sensitive strategies : Avoid one-to-one porting. Campaigns must be aligned with platform archetypes. Social-graph platforms like Facebook reward authenticity and trust, content-graph platforms like TikTok require culturally relevant entertainment, Instagram thrives on aspirational visual storytelling, and LinkedIn demands professional credibility. Segment by Age on mature/professional platforms. Younger cohorts are more commercially responsive on Facebook and LinkedIn. Strategies should focus on early-career professionals and younger consumers, while older users may need content framed around trust, authority, or long-term value. Leverage Age on Instagram to activate interests. Interests and brand cues are most effective among older users. Younger audiences require more trend-based, aesthetic content, though conversion may be slower. Segment by Spending on TikTok. Age-based segmentation is ineffective on TikTok; instead, focus on identifying high-spending users. These consumers respond strongly to peer validation, brand cues, and in-app commerce features. Rethink engagement metrics. High frequency of use does not guarantee value. Marketers should prioritize transactional signals, high-intent behaviours, and spending history over superficial engagement indicators. Optimize platform-level affordances for all age groups. Our findings on Social Media Platform Characteristics (X5) show age-invariant returns : optimizing affordances such as clarity of calls-to-action, trust/verification cues, and friction-light purchase paths will benefit users across cohorts. Age-based tailoring of platform design is therefore a lower priority than tailoring content and messaging , which remain more sensitive to generational differences. 5.4 Broader Implications for Emerging Markets Situating this study in Sri Lanka provides additional insight. Emerging markets are characterized by rapid digital adoption, diverse income distributions, and collectivist orientations. Findings suggest that spending-based segmentation may be more predictive than demographics in such contexts, where income disparities strongly shape online behaviour. Additionally, platforms like TikTok may democratize influence by emphasizing behavioural signals over demographics, allowing first-generation digital users to engage on equal footing. For policymakers and businesses in emerging economies, this points to opportunities for inclusive digital commerce strategies that do not rely solely on traditional demographic targeting. 5.5 Limitations and Directions for Future Research This study provides a robust comparative analysis, yet several limitations must be acknowledged. Addressing these in future work will strengthen theoretical development and practical insight. Methodological Enhancements. The study's primary methodological limitation is the use of single-item measures for key predictors (e.g., Peer Influence, Brand Characteristics). While strategically employed to ensure comparability and reduce participant fatigue in a multi-platform design, this approach inherently limits construct validity and reliability. The findings for these predictors should therefore be considered preliminary, establishing a baseline for future research that must employ validated, multi-item scales to confirm these relationships with greater fidelity.. Future research should employ multi-item validated scales (e.g., Chu & Kim, 2011 ; Yoo & Donthu, 2001 ) to capture latent constructs with greater fidelity. Additionally, longitudinal designs are needed to capture how moderators evolve as platforms mature and user cohorts age. Future research should also employ structural equation modelling (SEM) to simultaneously test measurement validity and structural paths, offering more rigorous insights. Finally, replication across cultural contexts (e.g., Western, collectivist Asian, African markets) would clarify whether these patterns are universal or context-specific, particularly regarding the proposed platform contingency perspective. Finally, the study used a convenience sample of Sri Lankan users. While stratified across provinces, it may not fully represent the national population. Future research should employ probability sampling or panel-based recruitment for stronger external validity. Moreover, platform algorithms and features evolve rapidly, so findings should be revalidated over time. Second, the sample size for the TikTok subgroup (N = 103) was considerably smaller than for other platforms. Moderation analyses, which test for interaction effects, typically require larger samples to achieve adequate statistical power. Consequently, while several significant interactions were found for TikTok, these results should be considered provisional until replicated with a larger, more robust sample. Longitudinal Validation. This study’s cross-sectional design provides a valuable snapshot but cannot capture how consumer responses evolve over time. Future studies should adopt longitudinal or panel data approaches to track how Age and Spending effects shift as users mature with platforms or as platform cultures themselves change. Such designs would also allow testing for cohort effects, disentangling whether differences are due to generational traits or lifecycle stages. Cross-Cultural Replication. The Sri Lankan context provides valuable insights from a non-Western emerging economy, but cultural values (e.g., collectivism, power distance) may limit generalizability. Future research should replicate this model across diverse cultural contexts to examine whether platform-contingent effects hold in Western, collectivist Asian, or African markets. Comparative studies could also incorporate cultural moderators (e.g., individualism vs. collectivism) to refine the proposed Platform Contingency perspective. Additional Moderators and Content-Level Analysis. Beyond Age and Spending, psychographic traits such as innovativeness, need for uniqueness, or collectivist orientation warrant exploration. Moreover, examining content-level factors—for instance, message framing, influencer type, or interactivity—would enrich understanding of how user characteristics intersect with content features within platform ecosystems. By addressing these areas, future research can move beyond cross-sectional description to develop a more dynamic, culturally sensitive, and theoretically rigorous model of platform-contingent consumer behaviour. 6. Conclusion In conclusion, this research sought to move beyond the foundational question of if social media marketing is effective, to the more nuanced and strategically vital questions of how , for whom , and on which platform it operates. The comparative analysis of four distinct social media archetypes—Facebook, TikTok, Instagram, and LinkedIn—revealed a clear and compelling verdict: the rules of consumer engagement are not universal. The psychological and behavioural mechanisms that drive consumer action are fundamentally contingent upon the specific digital ecosystem in which they are deployed. The influence of a demographic reality like age was found to be clear and direct on mature and professional platforms (Facebook and LinkedIn), but its role shifted to that of a moderator on the lifestyle-oriented Instagram, and faded almost entirely on the youth-centric TikTok. This demonstrates that a marketer's reliance on demographic segmentation must be adapted to the demographic variance and cultural norms of the platform itself. Conversely, the influence of a behavioural pattern like spending emerged as a critical key, unlocking the commercial potential of user engagement and psychosocial cues, but only within specific platform contexts. The finding that high engagement translates into consumer action primarily among spenders on Facebook and TikTok— but not on Instagram or LinkedIn— underscores platform-specific conversion dynamics, challenging the validity of universal engagement metrics and pointing towards a more sophisticated, context-aware understanding of user value. For businesses navigating the fragmented and ever-evolving digital world, the message is clear: success lies not in having a singular social media strategy, but in having a portfolio of platform-specific strategies. These strategies must be grounded in a deep and empirical understanding of the unique user dynamics of each digital ecosystem. This study provides a foundational framework for such an approach, demonstrating that by analyzing the conditional effects of key consumer characteristics, marketers can move from broad-stroke campaigns to nuanced, targeted, and ultimately more effective digital engagement. Declarations Ethics Approval Statement Ethical review and approval were waived for this study as per the guidelines of Lincoln University College of Malaysia and First Friends Campus, Sri Lanka, since it involved anonymized survey responses and posed no potential harm to participants. The study complied with the ethical standards of the Lincoln University College Malaysia and with the 1964 Helsinki Declaration and its later amendments. Participant Consent Statement Informed consent was obtained from all participants prior to their inclusion in the study. Participation was voluntary, and respondents were informed about the purpose of the research, their right to withdraw at any time, and the confidentiality of their responses. References Aral S, Walker D (2012) Identifying influential and susceptible individuals in social networks. Science 337(6092):337–341 Bolton RN, Parasuraman A, Hoefnagels A, Migchels N, Kabadayi S, Gruber T, Solnet D (2013) Understanding Generation Y and their use of social media: a review and research agenda. J Service Manage 24(3):245–267 Chu S-C, Kim Y (2011) Determinants of consumer engagement in electronic word-of-mouth (eWOM) in social networking sites. Int J Advertising 30(1):47–75. https://doi.org/10.2501/IJA-30-1-047-075 De Vries L, Gensler S, Leeflang PS (2012) Popularity of brand posts on brand fan pages: An investigation of the effects of social media marketing. J Interact Mark 26(2):83–91 Djafarova E, Bowes T (2021) Instagram made me buy it: Generation Z impulse purchases in fashion industry. 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J Bus Res 52(1):1–14. https://doi.org/10.1016/S0148-2963(99)00098-3 Zarouali B, Van der Goot M, de Vries DA (2020) What makes you click? The impact of news factors and sourcing on the consumption of sponsored content on Instagram. New Media Soc 22(10):1850–1869 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7602641","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":514345768,"identity":"08c07eef-7512-451f-bafa-0a46d8730d65","order_by":0,"name":"N.M. Mohamed Fazal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYJCCD0Asww9iJRQQp4NxBpDgkWwAaTEgRYvBARCbGC3mYqcTG378seMxPr868cMDAwZ5frED+LVYzs7d2NjDk8xjduPtZgmgwwxnzk7Ar8Xgdu72BzwSzEAtZzeAtCQY3CasZWPjH4N6HuMZZzf/IFpLM0/CYR4D/t5txNvSLHPgOI/EDd5tFgkGEkT5ZWPjmz/Vcvz9Zzff/FFhI88vTUALAkiAVUoQqxwE+A+QonoUjIJRMApGEgAA6y9GAbfK8sYAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0007-5981-5836","institution":"Insight Institute of Management and Technology","correspondingAuthor":true,"prefix":"","firstName":"N.M.","middleName":"Mohamed","lastName":"Fazal","suffix":""},{"id":514345769,"identity":"4a18706f-812f-402e-8af3-c45d1eb17824","order_by":1,"name":"Prasad Neelawela","email":"","orcid":"","institution":"Uwa Wellassa University","correspondingAuthor":false,"prefix":"","firstName":"Prasad","middleName":"","lastName":"Neelawela","suffix":""}],"badges":[],"createdAt":"2025-09-12 17:25:25","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7602641/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7602641/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91548169,"identity":"f20c264a-d4e2-4d69-8003-d9f27a75b248","added_by":"auto","created_at":"2025-09-17 15:09:37","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":401845,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual moderation framework applied to each platform. Predictors (X1–X4) influence Consumer Behaviour (Y), with Age and Spending as moderators (W) tested across Facebook, TikTok, Instagram, and LinkedIn.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7602641/v1/bef06538055f9bb4d380b51b.jpg"},{"id":91548171,"identity":"f543e24b-e9f9-4a80-87b0-67802b8fefd3","added_by":"auto","created_at":"2025-09-17 15:09:37","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":722803,"visible":true,"origin":"","legend":"\u003cp\u003ePlatform-Specific Moderation Map\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: Figure 2 complements Table 2 by providing a visual summary of which interactions are significant, marginal, or non-significant. It is intended as a quick comparative overview rather than a substitute for numerical detail.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7602641/v1/f7f836aa5114b98635972c40.jpg"},{"id":91550629,"identity":"991c6f2e-5bc0-41ce-812f-e40e80c4a4cf","added_by":"auto","created_at":"2025-09-17 15:41:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3692154,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7602641/v1/e19e9f47-8a96-48ce-a5f5-a561f4d16073.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eBeyond the Platform: A Comparative Analysis of How Consumer Characteristics and Valuation of Brand Attributes Moderate the Drivers of Consumer Behaviour on Facebook, TikTok, Instagram, and LinkedIn\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe proliferation of social media has fundamentally reshaped modern commerce, transforming not only how brands communicate with consumers but also the very fabric of decision-making processes. Platforms such as Facebook, Instagram, TikTok, and LinkedIn have evolved into global ecosystems where commercial activity, content consumption, and social interaction are deeply intertwined (Kaplan \u0026amp; Haenlein, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). For businesses, a strategic and well-executed social media presence is no longer optional but a core element of brand building, customer relationship management, and sales channel development. The scale of this transformation is evident in the projected annual social media advertising spend of over \u003cspan\u003e$\u003c/span\u003e200\u0026nbsp;billion, with emerging economies like Sri Lanka showing rapid digital penetration and e-commerce adoption (Statista, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; We Are Social, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eYet, as algorithms governing visibility become more complex and the digital environment more saturated, the effectiveness of a \u0026ldquo;one-size-fits-all\u0026rdquo; approach is rapidly diminishing. What resonates with consumers on one platform may fail on another, and what persuades one demographic may alienate another (Tuten \u0026amp; Solomon, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Earlier research validated social media as a powerful marketing channel, showing links to outcomes such as brand loyalty, purchase intention, and electronic word-of-mouth (e.g., Schivinski \u0026amp; Dabrowski, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; De Vries, Gensler, \u0026amp; Leeflang, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, much of this work treated social media and its users as homogeneous, overlooking the structural and cultural differences between platforms and the heterogeneity of the consumer base. This oversight has led many organizations to a strategic plateau, where rising investments no longer guarantee improved returns (Kumar \u0026amp; Mirchandani, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAgainst this backdrop, a significant gap remains: while social media marketing is demonstrably effective, less is known about the boundary conditions under which it works best and how these conditions differ across platforms. This study addresses this gap by investigating how consumer characteristics\u0026mdash;specifically age and spending\u0026mdash;moderate the drivers of consumer behaviour on Facebook, TikTok, Instagram, and LinkedIn. By analyzing these ecosystems in parallel, the research shifts the discussion from \u003cem\u003ewhether\u003c/em\u003e social media works to \u003cem\u003eunder what conditions\u003c/em\u003e it is most effective. Grounded in the Sri Lankan context, a rapidly digitizing emerging economy, the study also provides insights that extend beyond Western markets, offering a nuanced, platform-sensitive model of social media marketing effectiveness.\u003c/p\u003e"},{"header":"2. Literature Review and Hypothesis Development","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Social Media Marketing and Consumer Behaviour: A Foundational Relationship\u003c/h2\u003e\u003cp\u003eConsumer behaviour, defined as the processes by which individuals or groups select, purchase, use, and dispose of products or experiences (Solomon, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), has been fundamentally reshaped by social media. These platforms create dynamic, interactive, and socially embedded environments for commerce (Mangold \u0026amp; Faulds, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Dwivedi, Ismagilova, \u0026amp; Hughes, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), where algorithms and affordances strongly shape outcomes (Liu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). TikTok\u0026rsquo;s short-form video environment, for instance, fosters impulse-driven purchasing, while LinkedIn supports trust-based, professional decision-making (Li \u0026amp; Xie, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This shift has replaced linear AIDA models with cyclical ones where consumers are in continuous dialogue with brands (Edelman \u0026amp; Singer, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSMM effectiveness is underpinned by classic persuasion theories. The Elaboration Likelihood Model (ELM) distinguishes between central routes requiring cognitive effort and peripheral routes driven by heuristic cues (Petty \u0026amp; Cacioppo, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Social media enables both: detailed product tutorials engage high-involvement consumers, while visually appealing posts or influencer endorsements appeal peripherally (Tafesse \u0026amp; Wien, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Uses and Gratifications Theory (UGT) further emphasizes that users actively select media to satisfy information, entertainment, or social interaction needs (Katz, Blumler, \u0026amp; Gurevitch, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1973\u003c/span\u003e; Whiting \u0026amp; Williams, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Empirical studies confirm SMM\u0026rsquo;s positive impact on brand equity, purchase intention, and loyalty (Godey et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Schivinski \u0026amp; Dabrowski, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), but early research often treated \u0026ldquo;social media\u0026rdquo; as homogeneous, overlooking critical platform-level differences.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 A Typology of Social Media Platforms: The Need for a Contingency Approach\u003c/h2\u003e\u003cp\u003eSocial media platforms are not interchangeable; their architectures, purposes, and cultures create distinct consumer dynamics. A contingency approach (Fiedler, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1964\u003c/span\u003e) highlights the need to tailor strategies by platform. Four key archetypes illustrate this:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eFacebook \u0026ndash; The Social Graph\u003c/b\u003e: Built on explicit social connections across diverse demographics, Facebook emphasizes trust and reciprocity. Marketing effectiveness often derives from leveraging social capital, authenticity, and peer recommendations (Ellison, Steinfield, \u0026amp; Lampe, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Lu, Fan, \u0026amp; Zhou, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eTikTok \u0026ndash; The Content Graph\u003c/b\u003e: Powered by algorithmic recommendations, TikTok delivers serendipitous short-form content based on behavioural signals rather than social ties. Its youthful culture values authenticity and entertainment, making trend-based, natively engaging content critical for brand success (Kaye, 2021; Omar \u0026amp; Dequan, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu, Zhang, \u0026amp; Zhao, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This aligns with emerging theories of \u003cb\u003ealgorithmic personalization\u003c/b\u003e (Liu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which suggest that algorithmic curation minimizes demographic effects and foregrounds behavioural signals. Explicitly integrating this perspective helps explain why variables such as age exert negligible influence on TikTok compared to social-graph platforms.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eInstagram \u0026ndash; The Interest \u0026amp; Lifestyle Graph\u003c/b\u003e: Instagram\u0026rsquo;s visually driven environment prioritizes aspiration and lifestyle discovery. Users follow brands and influencers based on aesthetics and identity projection, making influencer marketing and brand storytelling particularly persuasive (Zarouali et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hughes, Swaminathan, \u0026amp; Brooks, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eLinkedIn \u0026ndash; The Professional Graph\u003c/b\u003e: Anchored in career identity and credibility, LinkedIn connects users by professional networks and expertise. Consumer-like behaviour is oriented toward B2B purchasing and professional services, where authority and knowledge are primary drivers (van Dijck, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThese structural distinctions imply that consumer drivers vary by platform, necessitating platform-specific hypotheses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Age as a Generational Cohort Moderator\u003c/h2\u003e\u003cp\u003eAge functions as a proxy for generational cohorts, reflecting differences in digital literacy, motivations, and platform affinities (Bolton et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Djafarova \u0026amp; Bowes, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Gen Z typically seeks identity expression and entertainment, aligning with UGT\u0026rsquo;s focus on peer recognition (Phua et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), while older cohorts gravitate toward information-seeking or professional networking (Vaterlaus et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, Age is expected to moderate platform-specific behaviours.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH1a\u003c/b\u003e: \u003cem\u003eAge will be a significant direct predictor of consumer behaviour on mature platforms (Facebook, LinkedIn).\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH1b\u003c/b\u003e: \u003cem\u003eAge will moderate the relationship between antecedents (e.g., brand characteristics, personal interest) and consumer behaviour differently across platforms.\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Spending as Consumer Involvement and Economic Capital Moderator\u003c/h2\u003e\u003cp\u003eSpending reflects both purchasing power and consumer involvement. Higher disposable income often correlates with brand consciousness and materialistic values, increasing responsiveness to brand-related content (Richins, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Shrum et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). From a Self-Congruity perspective (Sirgy, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), high-spending consumers align self-concept with aspirational brands, while cross-cultural values such as collectivism and power distance shape how economic capital translates into engagement (Hofstede, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH2a\u003c/b\u003e: \u003cem\u003eSpending will significantly moderate the relationship between usage intensity (\u0026ldquo;Heavy User\u0026rdquo; identification) and consumer behaviour.\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH2b\u003c/b\u003e: \u003cem\u003eSpending will significantly moderate the relationship between psychosocial factors (e.g., Peer Influence, Brand Characteristics) and consumer behaviour across platforms.\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Conceptual and Cultural Lens\u003c/h2\u003e\u003cp\u003ePositioning Age and Spending as moderators captures both demographic and broader psychographic orientations. Age reflects generational gratifications and social identity salience, while Spending captures economic capacity, materialism, and cultural hierarchy. Together, they provide a baseline for understanding how structural factors condition consumer behaviour on social media. At the same time, this framework highlights the need for future research to incorporate additional psychographic moderators\u0026mdash;such as innovativeness, collectivist orientation, or need for uniqueness\u0026mdash;for a more complete theoretical account.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Conceptual Framework\u003c/h2\u003e\u003cp\u003eRather than proposing a single, overarching model, this study's conceptual framework is grounded in a platform contingency approach. It tests a consistent set of baseline moderation models across four distinct platform subgroups (Facebook, TikTok, Instagram, and LinkedIn) to empirically determine \u003cem\u003eif\u003c/em\u003e and \u003cem\u003ehow\u003c/em\u003e the influence of key antecedents (e.g., Peer Influence, Brand Characteristics) on Consumer Behaviour is altered by consumer characteristics (Age, Spending). The central thesis is that the significance and nature of these moderated relationships are contingent on the platform's unique structural and cultural environment. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e in the results section serves as a visual summary of these contingent findings, mapping the divergent patterns of influence across the platforms.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Research Design and Sample\u003c/h2\u003e\u003cp\u003eThis study employed a quantitative, cross-sectional research design to investigate the relationships between consumer characteristics, social media marketing antecedents, and consumer behaviour across four distinct platforms. A survey-based methodology was chosen as it provides an efficient and effective means of collecting data on attitudes, perceptions, and self-reported behaviours from a large and diverse sample, allowing for robust statistical analysis and the testing of moderation hypotheses (Hair et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The cross-sectional nature of the design provides a snapshot of the prevailing dynamics at a single point in time, which is appropriate for exploring the current state of consumer behaviour on these rapidly evolving platforms.\u003c/p\u003e\u003cp\u003eThe data were collected via a structured online questionnaire. The survey was created using a digital platform and distributed through various online channels to ensure a broad and heterogeneous reach. These channels included social media platforms (such as Facebook groups and LinkedIn posts), online forums, and email lists targeting a general population of internet users in Sri Lanka. Although convenience sampling introduces potential bias, it is widely used in exploratory social media research (Etikan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). To mitigate limitations, the study ensured heterogeneity by distributing the survey across multiple platforms and demographic groups.\u003c/p\u003e\u003cp\u003eThe target population for this study was active social media users residing in Sri Lanka. This context was deliberately chosen to explore the dynamics of consumer behaviour on global social media platforms within a non-Western, emerging economy. Sri Lanka\u0026mdash;an emerging economy with rapid digital penetration\u0026mdash;offers a non-Western testbed to examine platform-contingent consumer behaviour, providing a valuable counterpoint to predominantly North American and European evidence (We Are Social, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A total of 435 valid responses were collected. For the purpose of this comparative analysis, the sample was filtered into four distinct, non-exclusive subgroups based on self-reported regular usage of each platform: respondents who identified as active users of Facebook (\u003cb\u003eN\u0026thinsp;=\u0026thinsp;368\u003c/b\u003e), TikTok (\u003cb\u003eN\u0026thinsp;=\u0026thinsp;103\u003c/b\u003e), Instagram (\u003cb\u003eN\u0026thinsp;=\u0026thinsp;187\u003c/b\u003e), and LinkedIn (\u003cb\u003eN\u0026thinsp;=\u0026thinsp;194\u003c/b\u003e). The demographic profile of the final sample reflected a diverse range of social media users, with a majority falling between the ages of 18 and 34, which is consistent with the general user base of most social media platforms.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Measures and Instrumentation\u003c/h2\u003e\u003cp\u003eThe survey instrument was developed based on a comprehensive review of existing literature in the fields of marketing, consumer behaviour, and communication. All constructs were measured using items on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;Strongly Disagree, 5\u0026thinsp;=\u0026thinsp;Strongly Agree), unless otherwise specified, a widely used and validated method for capturing attitudinal and perceptual data (Likert, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1932\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eConsumer Behaviour (DV)\u003c/b\u003e: This dependent variable, the core outcome of the study, was operationalized as a multi-faceted construct capturing social media-influenced commercial intentions and actions. It was measured using a 5-item scale developed for this study to reflect modern digital consumerism. Sample items included: \"Social media platforms influence my purchase intentions\" and \"I feel loyal to brands that engage with me on social media.\" The items were averaged to create a composite Consumer_Behaviour_Score. The scale demonstrated high internal consistency and reliability, as indicated by a Cronbach\u0026rsquo;s alpha of .88, which is well above the acceptable threshold of .70 for exploratory research (Nunnally, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1978\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePredictor Variables (X-variables)\u003c/b\u003e: These variables represent the antecedents whose effects on consumer behaviour were hypothesized to be moderated. Given the exploratory and comparative nature of this study, which examines multiple predictors across four distinct platforms, single-item measures were employed for the predictor variables. While this approach reduces participant fatigue and is justified for narrow constructs (Bergkvist \u0026amp; Rossiter, 2007), it also limits construct reliability. Therefore, findings should be interpreted with caution and validated in future research using multi-item scales. While multi-item scales are often the standard for latent constructs, methodological research supports the use of single-item measures when the construct being measured is concrete, unambiguous, and easily understood by the respondent (Bergkvist \u0026amp; Rossiter, 2007; Sackett \u0026amp; Larson, 1990). For variables such as \"Peer Influence\" or \"Heavy User\" identification, the concepts are sufficiently narrow that a single, direct question can capture the construct with high face validity. This approach was strategically chosen to reduce the cognitive load on participants, minimize survey fatigue, and maintain high data quality across the lengthy questionnaire, a critical consideration in multi-platform research designs (Fuchs \u0026amp; Diamantopoulos, 2009). Furthermore, in a comparative study examining four distinct platform contexts, using concrete, single-item measures ensured conceptual consistency, reducing the risk that multi-item scales would be interpreted differently by users with different platform mindsets (e.g., professional on LinkedIn vs. entertainment on TikTok). The priority was to establish a comparable baseline effect across platforms, a goal for which this parsimonious approach is well-suited, despite the acknowledged trade-off in construct depth. Nevertheless, future studies should validate these constructs using multi-item scales (e.g., peer influence scales by Chu \u0026amp; Kim, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; brand evaluation scales by Yoo \u0026amp; Donthu, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) to strengthen construct reliability.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePeer Influence\u003c/b\u003e: Measured with the item, \"Peer influences affect my engagement with social media content.\"\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePerceived Importance of Brand Characteristics\u003c/b\u003e: Measured with the item, \"Brand characteristics (e.g., brand reputation, product quality) influence my interaction with social media content.\"\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePersonal Interest\u003c/b\u003e: Measured with the item, \u0026ldquo;My personal interests strongly influence what I engage with on this platform.\u0026rdquo; (5-point Likert: 1\u0026thinsp;=\u0026thinsp;Strongly Disagree to 5\u0026thinsp;=\u0026thinsp;Strongly Agree).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eHeavy User Identification\u003c/b\u003e: Measured with the item, \"I am a heavy user of social media.\"\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eSeveral constructs were operationalised using single-item measures, consistent with prior studies where the construct is concrete, unidimensional, and easily understood by respondents (Bergkvist \u0026amp; Rossiter, 2007; Drolet \u0026amp; Morrison, 2001). While multi-item validated scales are often preferred, single-item measures are widely accepted for parsimonious operationalisation of focal variables in consumer behaviour contexts. The dependent variable, Consumer Behaviour, was self-developed to capture platform-specific engagement and purchase orientation; the strong internal consistency (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.88) suggests reliability, but future studies should further validate the scale against established behavioural measures to enhance external validity.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eModerating Variables (W-variables)\u003c/b\u003e: These are the key consumer characteristics hypothesized to alter the relationship between the predictors and the outcome.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e: Measured as a categorical variable in the survey (1\u0026thinsp;=\u0026thinsp;Under 18, 2\u0026thinsp;=\u0026thinsp;18\u0026ndash;24, 3\u0026thinsp;=\u0026thinsp;25\u0026ndash;34, etc.) and treated as a continuous variable in the regression analyses. This is a common and accepted practice in moderation analysis, as it allows for the examination of linear interaction effects (Hayes, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAge was collected in categories but analysed as continuous, following standard practice where ordinal categories approximate an interval scale with sufficient range (Norman, 2010). \u003cb\u003eAs a robustness check, we also ran models with dummy-coded categories. The results did not differ substantively, strengthening confidence in the chosen analytic approach (see Appendix B for details).\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSpending\u003c/b\u003e: Measured on a scale reflecting different levels of self-reported transactional activity on social media platforms, providing a direct behavioural indicator of commercial engagement.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eSimilarly, Spending should not be interpreted narrowly as financial outlay but as an indicator of consumer involvement and purchasing power. Prior studies show that consumers with higher disposable income tend to exhibit stronger brand consciousness and materialistic values, which condition their responsiveness to brand-related content on social media (Richins, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Shrum et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). From the perspective of Self-Congruity Theory (Sirgy, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), individuals with higher spending capacity may align their self-concept with aspirational brand identities, thereby moderating the influence of social media stimuli on behavioural outcomes. Furthermore, economic capital interacts with cultural values such as collectivism and power distance (Hofstede, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), shaping how spending power translates into engagement or advocacy. In this sense, Spending functions as a structural moderator that captures the extent to which consumers\u0026rsquo; financial capacity and material orientations condition their behavioural responses.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAlthough a convenience sampling approach was employed through online groups and networks, demographic balancing was monitored to reduce skew, and the final sample reflected a wide spread across gender, age categories, and provinces. Future research should further validate these findings using probability sampling or weighting techniques to enhance representativeness.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Analytical Strategy\u003c/h2\u003e\u003cp\u003eThe data analysis was conducted using IBM SPSS Statistics Version 28. The first phase involved data screening and preparation. This included checking for missing values, assessing the normality of distributions, and creating the necessary composite scores for multi-item constructs (e.g., Consumer_Behaviour_Score). Binary filter variables (e.g., IsFacebookUser, IsTikTokUser) were also generated to facilitate the subgroup analyses.\u003c/p\u003e\u003cp\u003ePreliminary analyses included descriptive statistics to summarize the demographic and behavioural characteristics of each platform's user sample and a Pearson correlation analysis to examine the bivariate relationships between the key variables. This step was crucial for identifying initial patterns and ensuring that the assumptions of multicollinearity were not violated for the subsequent regression analyses.\u003c/p\u003e\u003cp\u003eThe main hypotheses were tested using a series of moderation analyses conducted with the PROCESS macro for SPSS (Model 1) developed by Hayes (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This tool is specifically designed for testing moderation, mediation, and conditional process models and provides robust estimates of interaction effects. For each of the four platform subgroups, a series of models was run. In each model, Consumer_Behaviour_Score was set as the dependent variable (Y), a predictor (e.g., Peer Influence) was set as the independent variable (X), and a consumer characteristic (Age or Spending) was set as the moderator (W). The significance of the interaction term (X \u0026times; W) was used to determine whether moderation occurred, with a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;.05 as the primary threshold for accepting a hypothesis. P-values between .05 and .10 were noted \u003cb\u003eas exploratory\u003c/b\u003e and \u003cb\u003ewere not used to claim support\u003c/b\u003e for hypotheses or to make inferential conclusions. Conditional effects for such models are reported descriptively only. When a significant interaction was found, a conditional effects analysis (also known as a simple slopes analysis) was conducted to probe the nature of the interaction, examining the effect of the predictor at low (16th percentile), mean, and high (84th percentile) levels of the moderator.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Preliminary Analysis\u003c/h2\u003e\u003cp\u003ePrior to hypothesis testing, data quality was verified and descriptive statistics calculated. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents means, standard deviations, and Pearson correlations for each platform-specific subgroup. Distinct demographic profiles emerged: TikTok users were the youngest (M\u0026thinsp;=\u0026thinsp;2.15, SD\u0026thinsp;=\u0026thinsp;1.05), followed by Instagram (M\u0026thinsp;=\u0026thinsp;2.45, SD\u0026thinsp;=\u0026thinsp;1.15), Facebook (M\u0026thinsp;=\u0026thinsp;2.89, SD\u0026thinsp;=\u0026thinsp;1.21), and LinkedIn (M\u0026thinsp;=\u0026thinsp;3.10, SD\u0026thinsp;=\u0026thinsp;1.25). Consumer Behaviour scores were generally positive and similar across platforms, ranging from 3.45 (LinkedIn) to 3.68 (TikTok).\u003c/p\u003e\u003cp\u003eAs expected, Age correlated negatively with Consumer Behaviour on Facebook (r = \u0026ndash;.35, p\u0026thinsp;\u0026lt;\u0026thinsp;.01) and LinkedIn (r = \u0026ndash;.29, p\u0026thinsp;\u0026lt;\u0026thinsp;.01), providing initial support for H1a, while the relationship was weaker and non-significant for TikTok and Instagram. Spending correlated positively with Consumer Behaviour across all four platforms. Multicollinearity checks confirmed that predictor intercorrelations were well below the .80 threshold (Hair et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics for Facebook, TikTok, Instagram, and LinkedIn Samples\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFacebook (N\u0026thinsp;=\u0026thinsp;368)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTikTok (N\u0026thinsp;=\u0026thinsp;103)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInstagram (N\u0026thinsp;=\u0026thinsp;187)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLinkedIn (N\u0026thinsp;=\u0026thinsp;194)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM (SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM (SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eM (SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM (SD)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1. Consumer Behaviour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.55 (0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.68 (0.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.62 (0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.45 (0.76)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2. Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.89 (1.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.15 (1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.45 (1.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.10 (1.25)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3. Spending\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.50 (1.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.30 (1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.40 (1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.65 (1.18)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4. Peer Influence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.88 (0.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.95 (0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.91 (0.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.05 (0.90)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5. Brand Characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.15 (0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.20 (0.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.18 (0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.25 (0.82)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6. Heavy User\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.95 (1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.05 (0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.01 (1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.85 (1.05)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e\u0026nbsp;M\u0026thinsp;=\u0026thinsp;Mean; SD\u0026thinsp;=\u0026thinsp;Standard Deviation.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Moderating Role of Age\u003c/h2\u003e\u003cp\u003eThe first hypothesis tested Age as a moderator. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the cross-platform results, with additional PROCESS output and conditional effects provided in Appendix A.\u003c/p\u003e\u003cp\u003eOn \u003cb\u003eFacebook\u003c/b\u003e and \u003cb\u003eLinkedIn\u003c/b\u003e, Age was a significant direct negative predictor of Consumer Behaviour, confirming H1a for mature platforms. This indicates younger users are more commercially responsive in these ecosystems. However, moderation tests were largely non-significant, with exploratory trends observed for the Age \u0026times; Brand Characteristics interaction (Facebook: p\u0026thinsp;=\u0026thinsp;.090; LinkedIn: p\u0026thinsp;=\u0026thinsp;.091).\u003c/p\u003e\u003cp\u003eOn \u003cb\u003eInstagram\u003c/b\u003e, Age functioned primarily as a moderator rather than a direct predictor. Two significant interactions emerged: Age \u0026times; Personal Interest (p\u0026thinsp;=\u0026thinsp;.035) and Age \u0026times; Brand Characteristics (p\u0026thinsp;=\u0026thinsp;.016). These results indicate that the influence of interests and brand values becomes more salient for older users, consistent with ELM\u0026rsquo;s involvement shift\u0026mdash;central-route processing dominating among older cohorts, peripheral cues among younger cohorts.\u003c/p\u003e\u003cp\u003eOn \u003cb\u003eTikTok\u003c/b\u003e, Age showed neither direct nor moderating effects, underscoring the limited role of demographics in a content-graph ecosystem where algorithmic personalization overrides generational differences. This provides support for H1b, highlighting platform-contingent roles of Age.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Moderating Role of Spending\u003c/h2\u003e\u003cp\u003eThe second hypothesis tested Spending as a moderator. Results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, with conditional effects provided in Appendix A.\u003c/p\u003e\u003cp\u003eA key convergence appeared on \u003cb\u003eFacebook\u003c/b\u003e and \u003cb\u003eTikTok\u003c/b\u003e, where the Heavy User \u0026times; Spending interaction was significant (Facebook: p\u0026thinsp;=\u0026thinsp;.018; TikTok: p\u0026thinsp;=\u0026thinsp;.011). In both cases, heavy usage predicted stronger consumer behaviour as spending increased, supporting H2a.\u003c/p\u003e\u003cp\u003eBy contrast, the same interaction was not significant on \u003cb\u003eInstagram\u003c/b\u003e or \u003cb\u003eLinkedIn\u003c/b\u003e, suggesting that high engagement does not universally translate into greater responsiveness for high-spending consumers.\u003c/p\u003e\u003cp\u003eSpending also moderated psychosocial factors selectively. While interpreting the following results with caution due to the smaller sample size (N\u0026thinsp;=\u0026thinsp;103) for this subgroup, on TikTok, significant interactions were found for Peer Influence \u0026times; Spending (p\u0026thinsp;=\u0026thinsp;.006) and Brand Characteristics \u0026times; Spending (p\u0026thinsp;=\u0026thinsp;.035). On \u003cb\u003eInstagram\u003c/b\u003e, Peer Influence \u0026times; Spending was significant (p\u0026thinsp;=\u0026thinsp;.043), but Brand Characteristics \u0026times; Spending was not (p\u0026thinsp;=\u0026thinsp;.419). On \u003cb\u003eFacebook\u003c/b\u003e, psychosocial interactions were not moderated by Spending, and on \u003cb\u003eLinkedIn\u003c/b\u003e, Peer Influence \u0026times; Spending showed only an exploratory trend (p\u0026thinsp;=\u0026thinsp;.067). This mixed pattern provides nuanced support for H2b: spending enhances responsiveness to psychosocial cues primarily in entertainment- and lifestyle-oriented platforms.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSocial Media Platform Characteristics (X5).\u003c/b\u003e Across platforms, the X5 \u0026times; Age interaction was \u003cb\u003enot significant\u003c/b\u003e on Facebook (ΔR\u0026sup2; = .0039, p\u0026thinsp;=\u0026thinsp;.2019), Instagram (ΔR\u0026sup2; = .0068, p\u0026thinsp;=\u0026thinsp;.2346), or TikTok (ΔR\u0026sup2; = .0110, p\u0026thinsp;=\u0026thinsp;.2791). on LinkedIn, the interaction was marginal (ΔR\u0026sup2; = .0117, p\u0026thinsp;=\u0026thinsp;.0915), \u003cb\u003eand the conditional slopes of X5 at the 16th, 50th, and 84th percentiles of Age were positive\u003c/b\u003e (Appendix A), suggesting an age-invariant association rather than a meaningful age contingency. In all cases, the main effect of X5 was not significant in the interaction models.\u003c/p\u003e\u003cp\u003eAlthough some coefficients were statistically significant, effect sizes were small (ΔR\u0026sup2; typically\u0026thinsp;\u0026le;\u0026thinsp;.07), consistent with moderation norms in consumer research (Hayes, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Comparative Cross-Platform Summary\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e consolidates all moderation tests across platforms, while Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e visually maps significant, exploratory, and non-significant effects. Together, they highlight platform-specific contingencies:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eFacebook\u003c/b\u003e: direct Age effects; Heavy User \u0026times; Spending significant.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eTikTok\u003c/b\u003e: multiple Spending-based moderations; negligible Age influence.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eInstagram\u003c/b\u003e: Age activates Personal Interest and Brand Characteristics; Spending moderates Peer Influence.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eLinkedIn\u003c/b\u003e: direct Age effects; exploratory moderation trends only.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThis synthesis underscores the central claim: consumer moderators operate differently across platform archetypes, reinforcing the need for platform-contingent marketing strategies.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparative Summary of Key Moderation Effects Across Four Platforms\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatform\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePredictor (X)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerator (W)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInteraction (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eKey Finding Summary\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFacebook\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBrand Characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.06\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eExploratory (0.05\u0026thinsp;\u0026le;\u0026thinsp;p\u0026thinsp;\u0026lt;\u0026thinsp;0.10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSM Platform Characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNon-Significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eHeavy User\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eSpending\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e.07\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e.018\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eSignificant Interaction\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePeer Influence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpending\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNon-Significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTikTok\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBrand Characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.12\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eExploratory (0.05\u0026thinsp;\u0026le;\u0026thinsp;p\u0026thinsp;\u0026lt;\u0026thinsp;0.10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSM Platform Characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNon-Significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eHeavy User\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eSpending\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e.14\u003c/b\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e.011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eSignificant Interaction\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePeer Influence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eSpending\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e.19\u003c/b\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eSignificant Interaction\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBrand Characteristics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eSpending\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e.16\u003c/b\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e.035\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eSignificant Interaction\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInstagram\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBrand Characteristics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e.13\u003c/b\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e.016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eSignificant Interaction\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePersonal Interest\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e.11\u003c/b\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e.035\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eSignificant Interaction\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSM Platform Characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNon-Significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBrand Characteristics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eSpending\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e.036\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e.419\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNon-Significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHeavy User\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpending\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNon-Significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePeer Influence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eSpending\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e.08\u003c/b\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e.043\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eSignificant Interaction\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLinkedIn\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBrand Characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.09\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eExploratory (0.05\u0026thinsp;\u0026le;\u0026thinsp;p\u0026thinsp;\u0026lt;\u0026thinsp;0.10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSM Platform Characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eExploratory (0.05\u0026thinsp;\u0026le;\u0026thinsp;p\u0026thinsp;\u0026lt;\u0026thinsp;0.10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHeavy User\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpending\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNon-Significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePeer Influence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpending\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.07\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMarginally Significant Trend\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e\u0026nbsp;Table displays standardized beta coefficients (β) for the interaction term.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026dagger; 0.05\u0026thinsp;\u0026le;\u0026thinsp;p\u0026thinsp;\u0026lt;\u0026thinsp;0.10:\u0026nbsp;\u003cb\u003eexploratory only; not used to infer moderation\u003c/b\u003e; results are reported descriptively.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of Key Findings and Consumer Dynamics by Social Media Platform\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatform\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKey Finding Summary\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFacebook\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConsumer behaviour is primarily driven by\u0026nbsp;\u003cb\u003eAge\u003c/b\u003e, with younger users being more commercially active. The incremental value of \u0026lsquo;Heavy User\u0026rsquo; is\u0026nbsp;small but significant for low spenders and stronger for high spenders (\u003cb\u003eHeavy User \u0026times; Spending, p\u0026thinsp;=\u0026thinsp;.018\u003c/b\u003e). Psychosocial factors like peer influence and brand perception are not significantly moderated by spending or age. SM Platform Characteristics: no evidence of age-contingent effect.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTikTok\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConsumer behaviour is largely independent of\u0026nbsp;\u003cb\u003eAge\u003c/b\u003e. Instead, the platform's commercial dynamics are notably moderated by\u0026nbsp;\u003cb\u003eSpending\u003c/b\u003e. Psychosocial factors (Peer Influence, Brand Characteristics) and high engagement (\"Heavy User\") are \"activated\" and become significant drivers of consumer behaviour\u0026nbsp;\u003cem\u003eonly\u003c/em\u003e\u0026nbsp;for users who are also spenders. SM Platform Characteristics: age showed no moderation; platform features did not predict differences in consumer behaviour.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInstagram\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConsumer behaviour is uniquely moderated by\u0026nbsp;\u003cb\u003eAge\u003c/b\u003e, which \"activates\" the importance of\u0026nbsp;\u003cb\u003ePersonal Interest\u003c/b\u003e and\u0026nbsp;\u003cb\u003eBrand Characteristics\u003c/b\u003e\u0026nbsp;for older users. Unlike on TikTok, these factors are not relevant for the youngest users.\u0026nbsp;\u003cb\u003ePeer Influence\u003c/b\u003e\u0026nbsp;is also activated by spending, but the link between being a \"Heavy User\" and spending is broken, suggesting usage is more aspirational. SM Platform Characteristics: not significant across age groups.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLinkedIn\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConsumer behaviour is driven by different factors than on social/entertainment platforms.\u0026nbsp;\u003cb\u003eAge\u003c/b\u003e\u0026nbsp;is a direct predictor (younger users are more active), but the tested psychosocial factors are not significantly moderated by age or spending. Peer (professional) influence and brand characteristics show marginal trends, suggesting a professional context where credibility is universally important but amplified for spenders. SM Platform Characteristics: marginal but consistently positive slopes across ages, suggesting an age-invariant effect.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Summary and Interpretation of Findings\u003c/h2\u003e\u003cp\u003eThis study examined how \u003cb\u003eAge\u003c/b\u003e and \u003cb\u003eSpending\u003c/b\u003e moderate consumer behaviour across four major social media platforms\u0026mdash;Facebook, TikTok, Instagram, and LinkedIn\u0026mdash;each representing a distinct structural archetype (social graph, content graph, interest/lifestyle graph, professional graph). The findings confirm that \u003cb\u003esocial media marketing is not a universal phenomenon\u003c/b\u003e but is shaped by the interplay between user characteristics and platform architecture.\u003c/p\u003e\u003cp\u003eRather than reiterating statistical results, this section interprets what the findings mean conceptually, how they compare to existing literature, and what they suggest for marketing theory and practice. The discussion is organized around three themes: the divergent role of Age, the activating role of Spending, and the theoretical significance of non-significant findings. These are followed by theoretical contributions, managerial implications, broader implications for emerging markets, and directions for future research.\u003c/p\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e5.1.1 Age as Contextual Involvement and Identity Cue\u003c/h2\u003e\u003cp\u003eAge emerged as a moderator that varies substantially by platform. On \u003cb\u003eFacebook and LinkedIn\u003c/b\u003e, younger users were consistently more responsive to commercial cues, while older users displayed lower levels of consumer behaviour. This finding is consistent with generational cohort theory (Bolton et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), where younger cohorts are characterized by \u003cb\u003edigital nativity\u003c/b\u003e and greater comfort blending social or professional interactions with commercial activity. For Facebook, this suggests that younger users view the platform as an integrated ecosystem where entertainment, social connection, and commerce co-exist. Older users, by contrast, may retain a narrower view of the platform as primarily social or professional, resisting overt commercial cues.\u003c/p\u003e\u003cp\u003eOn \u003cb\u003eInstagram\u003c/b\u003e, Age functioned less as a direct predictor and more as a moderator. Commercial relevance of personal interests and brand characteristics increased significantly among older users, a phenomenon that can be described as \u003cb\u003ecommercial maturation\u003c/b\u003e. While younger users may engage with Instagram for aesthetic gratification or fleeting trends, older users increasingly weigh their stable interests and evaluations of brand reputation when deciding whether to act commercially. This is consistent with the \u003cb\u003eElaboration Likelihood Model (ELM)\u003c/b\u003e, where involvement determines the route of persuasion. Younger users process primarily through the \u003cb\u003eperipheral route\u003c/b\u003e, influenced by visuals or trendiness, whereas older users process centrally, paying attention to diagnostic cues such as brand credibility or alignment with interests. From a \u003cb\u003eUses and Gratifications Theory (UGT)\u003c/b\u003e perspective, gratifications also shift: entertainment and peer recognition dominate among younger users, while information, stability, and identity coherence gain prominence as users age.\u003c/p\u003e\u003cp\u003eIn \u003cb\u003eTikTok\u0026rsquo;s case\u003c/b\u003e, Age had no measurable role\u0026mdash;neither as a direct predictor nor as a moderator. This highlights the influence of \u003cb\u003ealgorithmic personalization\u003c/b\u003e. Unlike social-graph or interest-graph platforms, TikTok curates feeds primarily from behavioural signals (e.g., watch time, interactions). The algorithm surfaces content irrespective of user demographics, thereby flattening generational differences. In effect, TikTok demonstrates how \u003cb\u003ealgorithmic environments can diminish the predictive power of demographics\u003c/b\u003e. Instead, platform-native behavioural markers, not chronological age, become the more relevant predictors of consumer action.\u003c/p\u003e\u003cp\u003eOverall, the findings support the claim that Age is not a static predictor but a \u003cb\u003eplatform-conditioned signal of involvement and identity salience.\u003c/b\u003e On some platforms (Facebook, LinkedIn), Age aligns with generational differences in adoption and use. On others (Instagram), it activates latent motivations and processing routes. On still others (TikTok), its influence is eclipsed by personalization systems.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003e5.1.2 Spending as a Marker of Commercial Intent\u003c/h2\u003e\u003cp\u003eSpending consistently distinguished between two types of engagement: one that is purely performative and one that translates into commercial behaviour. On Facebook and TikTok, the Heavy User \u0026times; Spending interaction confirmed that high usage translates into consumer behaviour primarily among high spenders. In other words, a heavy user who does not spend represents an \u003cb\u003eengaged but commercially inactive\u003c/b\u003e participant, while a heavy user who spends reflects \u003cb\u003eactivated commercial intent\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eThe divergence across platforms is equally revealing. On Instagram, heavy usage was not moderated by Spending, suggesting that frequent interactions may represent aspirational browsing or aesthetic consumption rather than transactional readiness. On LinkedIn, heavy usage was tied more to professional development and networking, again decoupling platform engagement from consumer-like behaviour. This distinction reinforces the need to evaluate engagement quality rather than relying on raw volume metrics.\u003c/p\u003e\u003cp\u003eSpending also moderated psychosocial drivers in meaningful ways. On TikTok, both Peer Influence and Brand Characteristics were more predictive among high spenders, whereas on Instagram only Peer Influence showed a spending-based effect; Brand Characteristics \u0026times; Spending was not significant. This indicates that social validation and brand trust are not inherently powerful\u0026mdash;they require the enabling condition of financial intent. Put differently, Spending transforms peer influence and brand values from passive perceptions into active behavioural drivers.\u003c/p\u003e\u003cp\u003eOn Facebook, spending did not moderate psychosocial factors like peer influence or brand characteristics. However, it did significantly activate the link between being a \"Heavy User\" and consumer behaviour, consistent with findings on TikTok. This suggests that on mature social-graph platforms, high engagement translates to commercial action primarily when the user has a pre-existing commercial intent, which is reflected by their spending habits. On LinkedIn, exploratory trends suggest that Peer Influence may matter for some users, but the effect does not cross conventional significance thresholds.\u003c/p\u003e\u003cp\u003eOverall, while statistically significant, the effect sizes were modest (ΔR\u0026sup2; \u0026le; .07), which is consistent with moderation norms (Hayes, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These results highlight nuanced rather than large-scale moderating effects, underscoring the need for cautious interpretation.\u003c/p\u003e\u003cp\u003eThis evidence supports a \u003cb\u003etwo-step model of commercial activation\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePlatforms generate engagement through structural or cultural drivers (social ties, viral content, aspirational images).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eEngagement translates into consumer behaviour only when moderated by a user\u0026rsquo;s financial intent, captured here as Spending.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThis interpretation positions Spending not only as economic capital but as a \u003cb\u003epsychological signal of willingness to act on social influence.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003e5.1.3 Theoretical Value of Non-Significance\u003c/h2\u003e\u003cp\u003eThe consistent \u003cb\u003enon-significance of general usage metrics\u003c/b\u003e (e.g., Heavy User, Usage Level) is itself an important finding. These results challenge both academic assumptions and industry practices that treat engagement volume as a reliable proxy for consumer value. The implication is clear: \u003cb\u003enot all engagement is equal.\u003c/b\u003e Users may be active for social, performative, or informational reasons that do not translate into purchasing or advocacy.\u003c/p\u003e\u003cp\u003eFor theory, this supports calls to refine models of social media influence by incorporating \u003cb\u003ebehavioural intent variables\u003c/b\u003e (e.g., past spending, willingness to pay, transactional history). For practice, it underscores the limitations of \u0026ldquo;vanity metrics\u0026rdquo; such as time spent or number of likes. Marketing strategies should prioritize \u003cb\u003ebehavioural signals aligned with ROI\u003c/b\u003e, such as demonstrated spending patterns, in-app purchase activity, or engagement with commerce features.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e5.1.4 Platform Affordances and Age-Invariant Effects\u003c/h2\u003e\u003cp\u003eWe found no evidence of age-contingent moderation on Facebook, TikTok, or Instagram, and only a marginal pattern on LinkedIn. Notably, on LinkedIn the conditional slopes of X5 were consistently positive at typical age values despite the non-significant interaction, which implies that platform affordances (e.g., verification, professional profiles, ad formats) relate to consumer behaviour uniformly across ages rather than differentially by age. This complements our broader conclusion that who the user is (age) matters less for X5 than what the platform affords. This finding aligns with research on platform affordances, where algorithmic curation (Liu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and verification cues (Sharma et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) shape user trust and engagement irrespective of demographics. In the LinkedIn context, professional credibility mechanisms appear to operate consistently across cohorts, underscoring the design power of platform-level features.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Theoretical Implications\u003c/h2\u003e\u003cp\u003eThis research advances marketing theory on three fronts:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePlatform-Contingent Persuasion.\u003c/b\u003e Findings extend \u003cb\u003eELM\u003c/b\u003e and \u003cb\u003eUGT\u003c/b\u003e by demonstrating that persuasion routes and gratifications are not stable but vary by platform. On Instagram, the same brand cues are peripheral for younger users but central for older ones; on TikTok, demographic cues are bypassed altogether. These patterns necessitate platform-contingent refinements of persuasion theory.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eConditional Social Influence.\u003c/b\u003e Traditional social influence theory assumes relatively stable peer effects. Here, peer influence was significant only when coupled with Spending on certain platforms. This suggests that \u003cb\u003esocial validation is contingent on both cultural norms and commercial intent\u003c/b\u003e, adding nuance to how influence should be theorized.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePlatform Contingency Perspective.\u003c/b\u003e Building on contingency theory, this study introduces a middle-range perspective: \u003cb\u003eplatforms themselves act as contingencies\u003c/b\u003e shaping which consumer characteristics matter. The structural archetype (social, content, interest, professional graph) determines whether Age, Spending, or other moderators activate. This provides a framework for future comparative research across platforms and cultures.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Managerial Implications\u003c/h2\u003e\u003cp\u003eThe results provide a roadmap for managers seeking \u003cb\u003eplatform-sensitive strategies\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eAvoid one-to-one porting.\u003c/b\u003e Campaigns must be aligned with platform archetypes. Social-graph platforms like Facebook reward authenticity and trust, content-graph platforms like TikTok require culturally relevant entertainment, Instagram thrives on aspirational visual storytelling, and LinkedIn demands professional credibility.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSegment by Age on mature/professional platforms.\u003c/b\u003e Younger cohorts are more commercially responsive on Facebook and LinkedIn. Strategies should focus on early-career professionals and younger consumers, while older users may need content framed around trust, authority, or long-term value.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eLeverage Age on Instagram to activate interests.\u003c/b\u003e Interests and brand cues are most effective among older users. Younger audiences require more trend-based, aesthetic content, though conversion may be slower.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSegment by Spending on TikTok.\u003c/b\u003e Age-based segmentation is ineffective on TikTok; instead, focus on identifying high-spending users. These consumers respond strongly to peer validation, brand cues, and in-app commerce features.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eRethink engagement metrics.\u003c/b\u003e High frequency of use does not guarantee value. Marketers should prioritize transactional signals, high-intent behaviours, and spending history over superficial engagement indicators.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eOptimize platform-level affordances for all age groups.\u003c/b\u003e Our findings on Social Media Platform Characteristics (X5) show \u003cb\u003eage-invariant returns\u003c/b\u003e: optimizing affordances such as clarity of calls-to-action, trust/verification cues, and friction-light purchase paths will benefit users across cohorts. Age-based tailoring of \u003cb\u003eplatform design\u003c/b\u003e is therefore a \u003cb\u003elower priority\u003c/b\u003e than tailoring \u003cb\u003econtent and messaging\u003c/b\u003e, which remain more sensitive to generational differences.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e5.4 Broader Implications for Emerging Markets\u003c/h2\u003e\u003cp\u003eSituating this study in Sri Lanka provides additional insight. Emerging markets are characterized by rapid digital adoption, diverse income distributions, and collectivist orientations. Findings suggest that \u003cb\u003espending-based segmentation\u003c/b\u003e may be more predictive than demographics in such contexts, where income disparities strongly shape online behaviour.\u003c/p\u003e\u003cp\u003eAdditionally, platforms like TikTok may democratize influence by emphasizing behavioural signals over demographics, allowing first-generation digital users to engage on equal footing. For policymakers and businesses in emerging economies, this points to opportunities for \u003cb\u003einclusive digital commerce strategies\u003c/b\u003e that do not rely solely on traditional demographic targeting.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e5.5 Limitations and Directions for Future Research\u003c/h2\u003e\u003cp\u003eThis study provides a robust comparative analysis, yet several limitations must be acknowledged. Addressing these in future work will strengthen theoretical development and practical insight.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethodological Enhancements.\u003c/b\u003e The study's primary methodological limitation is the use of single-item measures for key predictors (e.g., Peer Influence, Brand Characteristics). While strategically employed to ensure comparability and reduce participant fatigue in a multi-platform design, this approach inherently limits construct validity and reliability. The findings for these predictors should therefore be considered preliminary, establishing a baseline for future research that must employ validated, multi-item scales to confirm these relationships with greater fidelity.. Future research should employ multi-item validated scales (e.g., Chu \u0026amp; Kim, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Yoo \u0026amp; Donthu, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) to capture latent constructs with greater fidelity. Additionally, longitudinal designs are needed to capture how moderators evolve as platforms mature and user cohorts age. Future research should also employ structural equation modelling (SEM) to simultaneously test measurement validity and structural paths, offering more rigorous insights. Finally, replication across cultural contexts (e.g., Western, collectivist Asian, African markets) would clarify whether these patterns are universal or context-specific, particularly regarding the proposed platform contingency perspective. Finally, the study used a convenience sample of Sri Lankan users. While stratified across provinces, it may not fully represent the national population. Future research should employ probability sampling or panel-based recruitment for stronger external validity. Moreover, platform algorithms and features evolve rapidly, so findings should be revalidated over time.\u003c/p\u003e\u003cp\u003eSecond, the sample size for the TikTok subgroup (N\u0026thinsp;=\u0026thinsp;103) was considerably smaller than for other platforms. Moderation analyses, which test for interaction effects, typically require larger samples to achieve adequate statistical power. Consequently, while several significant interactions were found for TikTok, these results should be considered provisional until replicated with a larger, more robust sample.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLongitudinal Validation.\u003c/b\u003e This study\u0026rsquo;s cross-sectional design provides a valuable snapshot but cannot capture how consumer responses evolve over time. Future studies should adopt longitudinal or panel data approaches to track how Age and Spending effects shift as users mature with platforms or as platform cultures themselves change. Such designs would also allow testing for cohort effects, disentangling whether differences are due to generational traits or lifecycle stages.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCross-Cultural Replication.\u003c/b\u003e The Sri Lankan context provides valuable insights from a non-Western emerging economy, but cultural values (e.g., collectivism, power distance) may limit generalizability. Future research should replicate this model across diverse cultural contexts to examine whether platform-contingent effects hold in Western, collectivist Asian, or African markets. Comparative studies could also incorporate cultural moderators (e.g., individualism vs. collectivism) to refine the proposed Platform Contingency perspective.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAdditional Moderators and Content-Level Analysis.\u003c/b\u003e Beyond Age and Spending, psychographic traits such as innovativeness, need for uniqueness, or collectivist orientation warrant exploration. Moreover, examining content-level factors\u0026mdash;for instance, message framing, influencer type, or interactivity\u0026mdash;would enrich understanding of how user characteristics intersect with content features within platform ecosystems.\u003c/p\u003e\u003cp\u003eBy addressing these areas, future research can move beyond cross-sectional description to develop a more dynamic, culturally sensitive, and theoretically rigorous model of platform-contingent consumer behaviour.\u003c/p\u003e\u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eIn conclusion, this research sought to move beyond the foundational question of \u003cem\u003eif\u003c/em\u003e social media marketing is effective, to the more nuanced and strategically vital questions of \u003cem\u003ehow\u003c/em\u003e, \u003cem\u003efor whom\u003c/em\u003e, and \u003cem\u003eon which platform\u003c/em\u003e it operates. The comparative analysis of four distinct social media archetypes\u0026mdash;Facebook, TikTok, Instagram, and LinkedIn\u0026mdash;revealed a clear and compelling verdict: the rules of consumer engagement are not universal. The psychological and behavioural mechanisms that drive consumer action are fundamentally contingent upon the specific digital ecosystem in which they are deployed.\u003c/p\u003e\u003cp\u003eThe influence of a demographic reality like \u003cb\u003eage\u003c/b\u003e was found to be clear and direct on mature and professional platforms (Facebook and LinkedIn), but its role shifted to that of a moderator on the lifestyle-oriented Instagram, and faded almost entirely on the youth-centric TikTok. This demonstrates that a marketer's reliance on demographic segmentation must be adapted to the demographic variance and cultural norms of the platform itself.\u003c/p\u003e\u003cp\u003eConversely, the influence of a behavioural pattern like \u003cb\u003espending\u003c/b\u003e emerged as a critical key, unlocking the commercial potential of user engagement and psychosocial cues, but only within specific platform contexts. The finding that high engagement translates into consumer action primarily among spenders on Facebook and TikTok\u0026mdash; but not on Instagram or LinkedIn\u0026mdash; underscores platform-specific conversion dynamics, \u003cb\u003echallenging the validity of universal engagement metrics and pointing towards a more sophisticated, context-aware understanding of user value.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor businesses navigating the fragmented and ever-evolving digital world, the message is clear: success lies not in having a singular social media strategy, but in having a portfolio of platform-specific strategies. These strategies must be grounded in a deep and empirical understanding of the unique user dynamics of each digital ecosystem. This study provides a foundational framework for such an approach, demonstrating that by analyzing the conditional effects of key consumer characteristics, marketers can move from broad-stroke campaigns to nuanced, targeted, and ultimately more effective digital engagement.\u003c/p\u003e"},{"header":"Declarations","content":"\u003col\u003e\n \u003cli\u003e\n \u003cp\u003eEthics Approval Statement Ethical review and approval were waived for this study as per the guidelines of Lincoln University College of Malaysia and First Friends Campus, Sri Lanka, since it involved anonymized survey responses and posed no potential harm to participants. The study complied with the ethical standards of the Lincoln University College Malaysia and with the 1964 Helsinki Declaration and its later amendments.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eParticipant Consent Statement Informed consent was obtained from all participants prior to their inclusion in the study. Participation was voluntary, and respondents were informed about the purpose of the research, their right to withdraw at any time, and the confidentiality of their responses.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAral S, Walker D (2012) Identifying influential and susceptible individuals in social networks. Science 337(6092):337\u0026ndash;341\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBolton RN, Parasuraman A, Hoefnagels A, Migchels N, Kabadayi S, Gruber T, Solnet D (2013) Understanding Generation Y and their use of social media: a review and research agenda. J Service Manage 24(3):245\u0026ndash;267\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChu S-C, Kim Y (2011) Determinants of consumer engagement in electronic word-of-mouth (eWOM) in social networking sites. 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New Media Soc 22(10):1850\u0026ndash;1869\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Insight Institute of Management and Technology","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Social Media Marketing, Consumer Behaviour, Moderation Analysis, Comparative Analysis, Facebook, TikTok, Instagram, LinkedIn, Age, Spending, Platform Contingency Perspective","lastPublishedDoi":"10.21203/rs.3.rs-7602641/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7602641/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn today\u0026rsquo;s fragmented digital landscape, a one-size-fits-all approach to social media marketing is ineffective, as platform-specific conditions critically shape consumer responses. This study moves beyond general examinations of social media\u0026rsquo;s impact to provide a comparative analysis of how consumer characteristics moderate behaviour across four major platforms. Using survey data from 435 Sri Lankan social media users\u0026mdash;368 on Facebook, 103 on TikTok, 187 on Instagram, and 194 on LinkedIn\u0026mdash;moderation analyses were conducted with the PROCESS macro for SPSS (Model 1). The results highlight clear and theoretically significant contrasts. On mature platforms such as Facebook and LinkedIn, age emerged as a dominant direct predictor of consumer behaviour, while on Instagram it functioned as a moderator, amplifying the role of personal interests and brand characteristics among older users. In contrast, age was negligible on TikTok, where spending played a more powerful role, uniquely moderating interactions with \u0026ldquo;heavy user\u0026rdquo; status and psychosocial cues. Similar spending-based activation effects were observed on Instagram but absent on Facebook and LinkedIn, underscoring platform-contingent consumer psychology. These findings carry important managerial implications: Facebook and LinkedIn demand age-based segmentation, Instagram strategies should target older users with interest-driven and brand-focused content, while TikTok strategies should prioritize commercial intent over demographics, with emphasis on high-spending segments. \u003cb\u003eTests including Social Media Platform Characteristics indicated no age-contingent effects across platforms and a marginal, age-invariant association on LinkedIn.\u003c/b\u003e The study cautions against directly porting strategies across platforms and contributes by empirically demonstrating that psychological mechanisms of influence are not universal but shaped by each platform\u0026rsquo;s structural purpose and user dynamics. This study advances a \u003cb\u003eplatform contingency perspective\u003c/b\u003e, offering a nuanced model for effective platform-specific marketing strategies.\u003c/p\u003e","manuscriptTitle":"Beyond the Platform: A Comparative Analysis of How Consumer Characteristics and Valuation of Brand Attributes Moderate the Drivers of Consumer Behaviour on Facebook, TikTok, Instagram, and LinkedIn","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-17 15:09:32","doi":"10.21203/rs.3.rs-7602641/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1c7be54b-5497-4c07-bff9-820f3d07e88c","owner":[],"postedDate":"September 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54648121,"name":"Marketing"}],"tags":[],"updatedAt":"2025-09-17T15:09:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-17 15:09:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7602641","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7602641","identity":"rs-7602641","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}