Exploring the Role of Consumer Engagement Style in Digital Brand Equity Formation: Evidence from Sri Lanka

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Mohamed Fazal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7665181/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 This study explores how consumer engagement style conditions the pathway from observable online actions to digital brand equity in an emerging market. Using survey data from 435 social media users in Sri Lanka, we tested a structural model linking Behavioural Engagement Frequency (BEF) to Cognitive and Attitudinal Engagement (CAE), Brand Perception (BP), and Consumer Behaviour (CB). The full-sample model showed a good overall fit (χ²/df = 3.74, CFI = .914, RMSEA = .079). BEF was positively associated with CAE (β = .222, p < .001), CAE strongly predicted BP (β = .868, p < .001, R² = .753), and BP in turn predicted CB (β = .898, p < .001, R² = .807). To explore differences in engagement style, we segmented respondents into high and low “Deliberate Engagement Style” groups. Multi-group SEM revealed that the path from BEF to CAE was non-significant for the low deliberate group (β = .129, ns) but significant for the high deliberate group (β = .319, p < .001). Moreover, the high deliberate group exhibited much stronger pathways from CAE to BP (β = .924 vs. .817) and BP to CB (β = .954 vs. .831), explaining substantially higher variance in both brand perception (R² = .853 vs. .668) and consumer Behaviour (R² = .910 vs. .691). These findings provide initial evidence that the “value of a like” is not universal but contingent on consumer mindset. As an exploratory contribution, the study highlights engagement style as a potential segmentation lens in digital marketing, while calling for further scale validation and replication across contexts. Marketing Digital Brand Equity Consumer Engagement Style Social Media Marketing Emerging Markets Behavioural vs. Cognitive Engagement Exploratory Study Figures Figure 1 Figure 2 1. Introduction The global marketing landscape has undergone a seismic shift over the past two decades, with the proliferation of social media platforms fundamentally transforming consumer–brand interactions. This has shifted the locus of power from corporations to vast, interconnected networks of consumers (Labrecque et al., 2013 ). In contrast to the unidirectional, monolithic communication of traditional marketing, digital platforms facilitate dynamic, multidirectional conversations where consumers actively create, share, and contest brand meanings (Gensler et al., 2013 ). This paradigm shift requires a critical reconsideration of the mechanisms that underpin brand equity, a cornerstone of marketing strategy traditionally built on constructs like awareness, associations, and loyalty (Aaker, 1991 ; Keller, 1993 ). In the digital era, brand value is less about what brands say to consumers and more about what consumers say to each other about brands (De Vries et al., 2012 ). This transformation is particularly pronounced in emerging markets, which have often leapfrogged traditional media infrastructure in favor of a mobile-first digital ecosystem. These markets are typically characterized by younger, digitally native populations, rapidly growing disposable incomes, and a high degree of trust in social commerce and peer recommendations (Kapoor et al., 2018 ). Sri Lanka serves as a compelling archetype of this phenomenon. With internet penetration reaching over 50% of its 22 million people and active social media usage even higher, driven primarily by mobile devices (DataReportal, 2024 ), Sri Lankan consumers increasingly form their brand opinions and make purchasing decisions within digital environments. Recent analyses from leading Sri Lankan business publications confirm that platforms like Facebook, Instagram, and TikTok have evolved beyond mere communication channels to become primary arenas for product discovery and brand evaluation (LMD, 2025). This context magnifies the importance of understanding the true drivers of digital brand equity, yet critical nuances are often overlooked. This study addresses two of these nuances. First, this study clarifies the critical pathway from superficial online actions to valuable brand equity, distinguishing between simple Behavioural frequency and the deeper psychological state of cognitive engagement that is necessary to influence consumer perceptions and Behaviour. We distinguish between Behavioural Engagement Frequency (BEF)—defined as the self-reported frequency of interaction with brand content—and a deeper state of Cognitive and Attitudinal Engagement (CAE), which encompasses attention, interest, and participation. While many organizations focus on stimulating frequent, simple actions (likes, quick shares), it is unclear if this Behavioural pattern is associated with the deeper psychological state of engagement necessary to build brand equity (Schivinski et al., 2020 ). Second, drawing upon Uses and Gratifications (U&G) Theory, we model and test the sequential process through which a consumer's cognitive and attitudinal engagement—not just their Behavioural frequency—builds positive brand perceptions, which in turn drive favorable consumer Behaviours. To guide this investigation, we draw upon Uses and Gratifications (U&G) Theory (Katz et al., 1973 ). This theory provides the lens to understand the motivations behind deeper cognitive engagement, suggesting that deliberate engagers are more goal-directed in fulfilling their needs, which should make the brand-building process more efficient and powerful for them. By applying this framework to the Sri Lankan context, this research aims to provide a more methodologically sound and conceptually advanced model of brand equity formation in the digital age. This study offers exploratory evidence that a consumer’s engagement style conditions the pathway from Behavioural frequency (BEF) to cognitive/attitudinal engagement (CAE) and onward to brand outcomes (BP → CB). We extend engagement research by (i) formally separating quantity from quality of engagement and showing that frequency predicts value primarily for deliberate engagers, and (ii) recasting KPIs as segment-contingent rather than uniform signals. Situated in an emerging, mobile-first context, our contribution is to demonstrate when and for whom common platform interactions translate into brand equity. We frame these findings as initial evidence to seed scale development for engagement style and to motivate future, Behavioural-trace and longitudinal/experimental tests across markets. 2. Literature Review and Hypothesis Development 2.1 Brand Equity in the Digital Ecosystem 2.1.1 Foundational Models of Brand Equity The concept of brand equity gained prominence in the late 1980s and early 1990s as marketers sought to quantify the intangible value of a brand. Aaker ( 1991 ) provided one of the earliest comprehensive frameworks, defining brand equity as a multidimensional construct comprising brand loyalty, name awareness, perceived quality, brand associations, and other proprietary brand assets. His model positioned brand equity as a strategic asset that could generate significant long-term value. Contemporaneously, Keller’s ( 1993 ) seminal work on customer-based brand equity (CBBE) shifted the focus from a firm-centric to a consumer-centric perspective. He defined CBBE as the differential effect that brand knowledge has on consumer response to the marketing of that brand. Brand knowledge itself was conceptualized as having two components: brand awareness (recall and recognition) and brand image (the perceptions and associations linked to the brand in consumer memory). The power of a brand, in this view, resides in what consumers have learned, felt, seen, and heard about the brand as a result of their experiences over time. The CBBE model is often depicted as a pyramid, suggesting that value is built sequentially through four stages: (1) ensuring customers identify the brand (salience), (2) firmly establishing the brand meaning in customers’ minds (performance and imagery), (3) eliciting the proper customer responses (judgments and feelings), and (4) fostering a loyal relationship between customers and the brand (resonance) (Keller, 2001 ). 2.1.2 Digital Disruption and the Rise of Social Brand Equity The advent of the digital ecosystem has not invalidated these foundational principles but has fundamentally altered the processes through which they are achieved. The controlled, hierarchical, firm-to-consumer communication model has been replaced by a chaotic, democratized, consumer-to-consumer network (Gensler et al., 2013 ). Keller ( 2016 ) himself noted that brand meaning is increasingly co-created in online communities, where user-generated content (UGC), peer reviews, and brand-hosted forums can have a more profound impact on brand associations than firm-generated advertising. This shift has given rise to the concept of social brand equity, where a significant portion of a brand's value is derived from its presence and the consumer interactions it facilitates within social networks (Laradi et al., 2024 ). In this environment, concepts like brand awareness are achieved not just through advertising reach, but through viral sharing and organic discovery. Brand associations are now profoundly shaped by the collective storytelling of the user base, where narrative transportation within digital campaigns on platforms like TikTok can directly influence brand attitudes (Lee & Kim, 2024 ). Brand loyalty evolves into brand advocacy, where consumers become voluntary brand ambassadors. Recent research has therefore converged on customer engagement as the primary mechanism through which digital brand equity is built. Customer engagement is a psychological state and a Behavioural manifestation of interactive customer experiences (Brodie et al., 2013 ). It encompasses cognitive, emotional, and Behavioural dimensions of participation. Studies consistently show that consumers who actively engage with brands online—by contributing reviews, participating in discussions, or sharing content—develop stronger brand loyalty, are more likely to act as brand advocates, and exhibit higher repurchase intentions (Ismail, 2021 ; Upadhyaya et al., 2025 ). This active participation creates a virtuous cycle: engagement strengthens brand equity, which in turn encourages further engagement. 2.2 Differentiating Facets of Engagement: Behavioural vs. Cognitive A persistent ambiguity in both academic research and managerial practice is the failure to distinguish between the quantity and quality of social media activity. To address this, we conceptualize engagement as having multiple facets, and for this study, we distinguish between two: Behavioural Engagement Frequency (BEF) : This represents the observable, action-based dimension of engagement, including liking, sharing, and commenting. Cognitive & Attitudinal Engagement (CAE) : This is the deeper, psychological dimension involving focused attention, genuine interest, and positive feelings toward the interaction. Following the engagement literature, we treat engagement as a multidimensional psychological state with Behavioural manifestations, where BEF indexes observable acts and CAE indexes internal, value-creating processing (attention, absorption, meaning; cf. Brodie et al., 2013 ; Hollebeek, 2011; Dessart et al., 2016 ). This separation clarifies why identical counts of likes/shares may reflect habit for some users but elaboration for others. This distinction is critical. A consumer can spend hours passively scrolling through a social media feed, a Behaviour often termed "lurking," with minimal cognitive or emotional investment in the content consumed (Vaterlaus et al., 2024 ). This high frequency of exposure does not equate to deep engagement. According to U&G Theory, CAE represents a more goal-directed state where consumers actively select content to fulfill needs for information or entertainment (Katz et al., 1973 ). BEF, on the other hand, may occur without this deeper motivation (e.g., a habitual "like"). This leads to our central question: Does the frequency of simple Behaviours necessarily correspond to a deeper psychological state of engagement? We propose that this relationship is not universal but is instead contingent on the consumer's mindset. This distinction leads to our first hypothesis, which posits that simple, frequent online actions are a necessary antecedent to the more valuable state of cognitive engagement. H1 : Behavioural Engagement Frequency (BEF) is positively associated with Cognitive and Attitudinal Engagement (CAE). It is this deeper state of CAE that is theorized to build brand equity. By investing cognitive resources, consumers foster a connection that strengthens brand associations, leading to a more positive Brand Perception (BP) (Van Doorn et al., 2010 ). This positive perception is the central precursor to favorable Consumer Behaviours (CB) (Keller, 2016 ). H2 : Cognitive and Attitudinal Engagement (CAE) is positively associated with Brand Perception (BP). H3 : Brand Perception (BP) is positively associated with Consumer Behaviour (CB). 2.3 The Moderating Role of a Deliberate Engagement Style Consumers differ in how they approach online content. We propose a moderator we term "Deliberate Engagement Style," which reflects the degree to which a consumer's engagement is consciously guided by brand quality and personal interest. From a U&G perspective, consumers with a High Deliberate Engagement Style are more strategic in using media to fulfill their needs. Their engagement is a conscious choice to satisfy specific goals. When such a consumer decides to engage, that engagement is more meaningful and should therefore have a stronger positive association with their brand perception. This heightened perception, being rooted in considered judgment, should in turn have a stronger association with their subsequent Behaviours. H4 : The relationship between CAE and BP is stronger for consumers with a High Deliberate Engagement Style. H5 : The relationship between BP and CB is stronger for consumers with a High Deliberate Engagement Style. Based on the theoretical arguments and the reviewed literature, the conceptual framework guiding this research is proposed. Figure 1 illustrates the hypothesized relationships between Behavioural Engagement Frequency (BEF), Cognitive & Attitudinal Engagement (CAE), Brand Perception (BP), and Consumer Behaviour (CB), including the proposed moderating role of a Deliberate Engagement Style. 3. Methodology 3.1 Research Philosophy and Approach This study adopts a positivist research philosophy, assuming an objective social reality that can be measured and tested. Consequently, we employ a quantitative, deductive approach, starting with established theories (CBBE, U&G) to formulate specific, testable hypotheses. A cross-sectional research design was used, capturing a snapshot of consumer perceptions and Behaviours at a single point in time. A total of 435 valid and complete responses were collected and used for the final analysis. The demographic profile of the final sample is detailed in Table 1 . The sample was predominantly young and digitally native, with over 74% of respondents under the age of 35. It was also highly engaged with social media, with over 80% reporting they use the platforms multiple times a day. The gender distribution skewed slightly male (57.9%), and a majority of respondents (over 70%) held a Bachelor's degree or higher, indicating a well-educated sample representative of urban and suburban digital consumers in Sri Lanka. Table 1 Demographic Profile of Respondents (N = 435) Characteristic Category Frequency Percentage (%) Age Group 18–24 140 32.2 25–34 185 42.5 35–44 85 19.5 45+ 25 5.8 Gender Male 252 57.9 Female 183 42.1 Education Level Secondary School 110 25.3 Bachelor's Degree 225 51.7 Master's Degree 90 20.7 Other 10 2.3 Social Media Usage Multiple times a day 350 80.5 Once a day 70 16.1 A few times a week 15 3.4 3.2 Sampling and Data Collection The target population of this study consisted of active social media users in Sri Lanka, a demographic segment increasingly central to digital brand-building processes. According to DataReportal ( 2024 ), more than 50% of the Sri Lankan population actively uses social media, with over 80% of users accessing platforms daily through mobile devices. This highly connected, mobile-first user base represents the most relevant population for research on digital brand equity, as brand discovery, evaluation, and interaction are now primarily mediated through social platforms in this context (LMD, 2025). Given the lack of a comprehensive national sampling frame of social media users in Sri Lanka, we employed a non-probability convenience and snowball sampling method. This approach has been widely applied in prior digital consumer studies in emerging markets, where researchers often rely on network-driven data collection to capture digitally active consumers (Fonseka, 2024; Saliya, 2024 ; Masciantonio et al., 2021 ). Convenience sampling allows researchers to reach respondents efficiently in environments where internet penetration and platform adoption vary by demographic segment, while snowball techniques leverage peer-to-peer recruitment, especially relevant in collectivist cultural contexts where referrals and social connections shape participation (Triandis, 1995 ; Hofstede, 2011 ). The survey link was distributed via popular social platforms such as Facebook, Instagram, and LinkedIn, as well as through university networks where young, digitally native consumers are concentrated. Eligibility criteria required respondents to be (a) 18 years or older, and (b) active on at least one social media platform daily. These criteria ensured that participants were both legally capable of providing informed consent and representative of the most engaged digital consumer cohort. A total of 435 valid responses were obtained, which aligns with recommended sample sizes for structural equation modeling (SEM). According to Anderson and Gerbing ( 1988 ), sample sizes exceeding 200 are sufficient for most SEM applications, while Kline (2016) suggests 10–20 observations per parameter as a benchmark. With 435 responses, this study’s sample size is well above minimum thresholds, increasing the robustness of the estimated models. While the use of non-probability sampling limits generalizability to the wider Sri Lankan population, it is important to note that the chosen segment — digitally active social media users — is precisely the group most relevant to studying engagement-driven brand equity formation. As similar methods have been successfully employed in prior social media marketing research in both developed and emerging markets (Brodie et al., 2013 ; Ismail, 2021 ; Upadhyaya et al., 2025 ), this approach provides a justified and contextually appropriate means of accessing the target population. 3.3 Measures and Scale Development All constructs in this study were measured using five-point Likert scales (1 = Strongly Disagree, 5 = Strongly Agree). Where possible, measures were adapted from established and validated scales to enhance construct validity and comparability with prior studies. Behavioural Engagement Frequency (BEF) : Items were adapted from prior engagement scales (Brodie et al., 2013 ; Dessart et al., 2016 ) that capture the frequency of observable brand interactions such as likes, comments, shares, and content participation. These Behaviours represent the surface-level, action-oriented dimension of engagement that marketers often track as KPIs (Van Doorn et al., 2010 ). Cognitive & Attitudinal Engagement (CAE) : Items captured the extent to which consumers paid attention to, expressed interest in, and enjoyed interacting with brand-related content. This aligns with definitions of engagement as a psychological state encompassing cognitive and emotional investment (Brodie et al., 2013 ; Hollebeek, 2011). Prior studies confirm that cognitive engagement is critical for transforming superficial actions into meaningful brand relationships (Dessart et al., 2016 ; Schivinski et al., 2020 ). Brand Perception (BP) : Items were adapted from Keller’s ( 1993 , 2001 ) customer-based brand equity model, focusing on brand awareness, image, and trust. These items also reflect more recent digital branding work that situates perception as the outcome of co-created brand meaning in online communities (Gensler et al., 2013 ; Laradi et al., 2024 ). Consumer Behaviour (CB) : Items assessed outcomes such as purchase intentions and loyalty, drawing on prior brand equity measures (Yoo & Donthu, 2001) and extending them to digital contexts. While these measures remain perceptual rather than Behavioural, they are consistent with past consumer research in digital settings where actual purchase data is difficult to obtain (Ismail, 2021 ; Wang et al., 2025 ). Items reflect purchase intention and attitudinal loyalty. Because these facets are conceptually close yet distinct, we interpret CB as a broad, exploratory outcome capturing overall downstream response; future work should separate intentions and loyalty or estimate a second-order model. Deliberate Engagement Style (Moderator) : A segmentation variable was created to distinguish consumers who engage with brand content deliberately (i.e., guided by personal interest and perceived brand quality) from those who engage more casually. Items were adapted from gratifications research (Katz et al., 1973 ; Bhatiasevi et al., 2024 ) and cultural perspectives on goal-directed media use (Markus & Kitayama, 1991 ). While this measure has not yet undergone formal scale validation, it provides an exploratory operationalization of a theoretically meaningful segmentation. In line with calls for greater nuance in consumer engagement measurement (Dessart et al., 2016 ; Hollebeek & Macky, 2019), this exploratory approach offers initial insights and highlights a fruitful avenue for scale development in future research. By adapting validated items where possible and transparently acknowledging the exploratory nature of the moderator, this study balances methodological rigor with theoretical innovation. The approach provides sufficient construct coverage to test the proposed model while setting the stage for future refinements in measurement and validation. 3.4 Data Analysis Procedure The data were analyzed using Structural Equation Modeling (SEM) in AMOS v.29. A two-step approach was followed: Measurement Model Assessment : A Confirmatory Factor Analysis (CFA) was first conducted. We evaluated convergent validity using factor loadings, Average Variance Extracted (AVE), and Composite Reliability (CR). We assessed discriminant validity by ensuring that the square root of the AVE for each construct was greater than its correlation with any other construct. Structural Model Assessment : After confirming the measurement model's validity, the structural model was tested. A multi-group analysis was conducted to compare the structural paths between the Low and High Deliberate Engagement Style groups. Model fit was evaluated using multiple indices: χ2/df, TLI, RMSEA and SRMR. Common method variance (CMV). We assessed CMV using (i) Harman’s single-factor test and (ii) a CFA-based unmeasured common latent factor (CLF) test. In Harman’s test, the first factor explained 35.2% (< 50%) of variance. For the CLF test, we added a latent method factor loading equally on all indicators (equal loadings; CLF variance fixed to 1; no covariances with substantive constructs). Model fit changed only trivially relative to the baseline CFA (Baseline: χ²/df = 3.290, CFI = .892, TLI = .873, RMSEA = .073; CLF: χ²/df = 3.342, CFI = .892, TLI = .871, RMSEA = .073; ΔCFI = 0.000; SRMR_CLF = .076), indicating CMV is unlikely to bias the estimates. 3.5 Ethical Considerations All procedures performed in this study involving human participants were conducted according to ethical research principles. Informed consent was obtained from all individual participants included in the study. The online survey ensured participant anonymity and confidentiality, and participation was fully voluntary. 4. Results 4.1 Measurement Model and Construct Validity The CFA for the measurement model indicated a good fit to the data after removing several items with poor factor loadings. The final model demonstrated strong construct validity. As shown in Table 2 , all factor loadings were above the .70 threshold, Composite Reliability (CR) values were well above the .70 benchmark, and Average Variance Extracted (AVE) values were above the .50 benchmark for all constructs, confirming convergent validity. Discriminant validity was also established. The measurement model’s global fit was acceptable ( χ²/df = 3.290, CFI = .892, TLI = .873, RMSEA = .073, SRMR = .077). Table 2 Measurement Model - Loadings, Reliability, and Validity Construct Item Std. Loading Cronbach's α Composite Reliability (CR) Average Variance Extracted (AVE) Behavioural Engagement Frequency (BEF) EMC3 0.82 0.88 0.89 0.73 EMC4 0.88 SMP4 0.85 Cognitive & Attitudinal Engagement (CAE) EMC1 0.91 0.92 0.93 0.81 EMC2 0.93 EMC5 0.87 Brand Perception (BP) BP1 0.79 0.91 0.92 0.7 BP2 0.88 BP3 0.85 BP4 0.81 BP5 0.84 Consumer Behaviour (CB) CB1 0.86 0.9 0.91 0.71 CB3 0.88 CB4 0.79 CB5 0.83 As a CMV robustness check, the CLF model did not materially improve fit over the baseline CFA (ΔCFI = 0.000); see Appendix B. 4.2 Structural Model Results Full Sample Model (N = 435) The overall model demonstrated a good fit to the data (χ2/df = 3.743, CFI = .914, TLI = .891, RMSEA = .079, SRMR = .076). While the CFI indicates a good model fit, the TLI is at the cusp of the acceptable range, and the RMSEA suggests an adequate but not perfect fit, indicating that the model is a useful but simplified representation of complex real-world phenomena. The path from BEF to CAE was positive and highly significant (β = .222, p < .001). H2 : The path from CAE to BP was very strong and significant (β = .868, p < .001), explaining 75.3% of the variance in BP (R2 = .753). H3 : The path from BP to CB was very strong and significant (β = .898, p < .001), explaining 80.7% of the variance in CB (R2 = .807). Group comparability Before comparing paths, we assessed measurement invariance across Low vs. High Deliberate groups. A multi-group CFA supported configural and metric invariance (ΔCFI ≤ .01), permitting meaningful comparison of structural coefficients across groups. Multi-Group Analysis: Low vs. High Deliberate Engagement Style BEF → CAE (H1, main effect) : In the full sample, H1 was supported (β = .222, p < .001). A multi-group comparison indicates this path varies by engagement style—non-significant for the Low-Deliberate group (β = .129, p = .072) but significant for the High-Deliberate group (β = .319, p < .001)—which we interpret as exploratory evidence of moderation of the H1 path. H4 (Moderation of CAE → BP) : H4 was supported. The path was significantly stronger for the High Deliberate Style group (β = .924) compared to the Low Deliberate Style group (β = .817). H5 (Moderation of BP → CB) : H5 was also supported. The path was significantly stronger for the High Deliberate Style group (β = .954) compared to the Low Deliberate Style group (β = .831). Table 3 Summary of Hypothesis Testing Hypothesis Path Low Deliberate Style Group High Deliberate Style Group Result H1 BEF → CAE β = .129, ns β = .319, *** Supported H2/H4 CAE → BP β = .817, *** β = .924, *** Supported H3/H5 BP → CB β = .831, *** β = .954, *** Supported Note: *** p < .001; ns = not significant. Path coefficients (β) are standardized. 4.3 Structural Model and Hypothesis Testing A visual representation of the final estimated structural models for both the Low and High Deliberate Engagement Style groups is provided in Fig. 2 . The figure illustrates the significant differences in path strengths and explained variance (R2) between the two groups, visually confirming the moderation effect. For the detailed diagram of the full sample model, please see Appendix A. 5. Discussion The findings provide exploratory evidence that who engages matters as much as how much they engage: consumers with a highly Deliberate Engagement Style convert surface-level interactions into cognitive/attitudinal engagement, stronger brand perceptions, and favorable consumer Behaviour more efficiently than casual engagers. This segmentation lens extends mainstream engagement theory, which frames engagement as a multidimensional, psychologically grounded construct rather than a mere count of actions (e.g., likes, shares) (in line with Brodie et al., 2013 ; Dessart et al., 2016 ). While prior work shows that active participation generally strengthens brand outcomes, our results suggest that the same “Behavioural frequency” can signal very different underlying states, depending on the consumer’s mindset—clarifying why platform-level KPIs sometimes fail to predict downstream brand value. These results are consistent with international evidence that distinguishes quality from quantity of social media use. For instance, Dessart et al. ( 2016 ) emphasize engagement’s cognitive and affective layers; similarly, Schivinski et al. ( 2020 ) show that user-generated contributions (which usually require intention and effort) more reliably build brand equity than low-effort reactions. Our moderation pattern aligns with this stream by indicating that Behavioural frequency predicts value only when it reflects a deliberate, goal-directed stance. The mechanism is theoretically coherent with Elaboration Likelihood Model logic—deliberate engagers are more apt to process brand content via the central route, creating more durable perceptions and Behaviours, whereas casual engagers may remain in the peripheral route with weaker carry-through to brand outcomes. The pattern also resonates with Uses & Gratifications (U&G) perspectives and the active vs. passive social media use distinction. U&G argues that media effects depend on goal fulfillment (Katz et al., 1973 ); recent work on active (purposeful) versus passive (habitual/scrolling) use shows that active, intent-driven interactions have more robust psychological consequences (e.g., Masciantonio et al., 2021 ). Our exploratory segmentation echoes this: deliberate users look more like “active” users whose interactions are tethered to interest, evaluation, and meaning-making, which in turn strengthens the CAE → BP → CB cascade. Conversely, for casual users, frequent actions appear closer to habit than to engaged processing, explaining the attenuated pathway. Importantly, the Sri Lankan context helps explain why engagement style might matter even more. Local evidence indicates a mobile-first, highly social commerce–oriented environment where peer cues and community narratives shape brand discovery and evaluation (DataReportal, 2024 ; Colombo Business Journal, 2024 ; LMD, 2025). In such settings—often marked by collectivist orientations—goal-directed consumers may weigh brand quality signals and social proof more systematically, magnifying the translation of engagement into brand perceptions and Behaviour (cf. Markus & Kitayama, 1991 ; Hofstede, 2011 ). Recent Sri Lankan studies that link social media activity to brand equity (e.g., Fonseka, 2024; Saliya, 2024 ) broadly demonstrate positive associations; our contribution is to show why those associations vary across users: engagement style acts as a conditioning lens that helps reconcile mixed KPI–outcome relationships observed by practitioners. From a brand-building perspective, our results complement international findings that narrative quality and content meaning—not only volume—drive attitudes (e.g., narrative transportation on TikTok: Lee & Kim, 2024 ) and that a firm’s social presence capabilities improve brand equity (Laradi et al., 2024 ). We extend these insights by suggesting that the same content strategy performs unevenly across engagement styles: value-dense, informational, or participatory formats (tutorials, AMAs, behind-the-scenes) should disproportionately benefit deliberate segments, while salience-oriented, affect-rich formats (short-form entertainment, memes) may be more suitable to activate casual segments without over-interpreting their KPI spikes. This aligns with Pan-Asian evidence that engagement’s link to brand equity is strong but context- and audience-contingent (Upadhyaya et al., 2025 ). Managerially, the implication is to reinterpret platform metrics through a segmentation lens. Rather than treating interaction counts as uniform currency, managers should (i) diagnose engagement style using short screening items (interest, relevance, brand-quality focus), (ii) weight KPIs by style (e.g., “deliberate-weighted engagement index”), and (iii) bifurcate content strategies: nurture deliberate users with depth and dialogic experiences, and use lightweight, affective creatives to prime or migrate casual users toward more deliberate states. For researchers, the contribution is programmatic: this study offers initial evidence that engagement style moderates the equity pathway, motivating scale development, Behavioural trace validation (clickstream, watch-time), and longitudinal or experimental tests across platforms and cultures. In sum, our exploratory findings help reconcile a persistent managerial puzzle—why big engagement numbers sometimes fail to materialize into brand value—by showing that engagement style conditions the pathway from Behavioural frequency to cognitive engagement and onward to brand perception and Behaviour. This frames a concrete agenda for the next wave of research: define and validate engagement style, connect it to actual Behaviours, and test style-content fit to move beyond counting interactions toward understanding their meaning. 6. Limitations and Future Research This study is subject to several important limitations that must be acknowledged to position its contributions appropriately. First, reliance on self-reported survey measures. All constructs were operationalized using perceptual items, including those labeled as “Behavioural,” which are better understood as perceived Behaviours rather than actual digital traces. Self-reported engagement frequencies are susceptible to recall bias and social desirability effects, potentially inflating or attenuating relationships (Podsakoff et al., 2003). Internationally, scholars increasingly stress the importance of using objective platform data—such as clickstream, reaction logs, and watch-time metrics—to validate consumer engagement models (Schivinski et al., 2020 ; Wang et al., 2025 ). Locally, Sri Lankan studies have similarly highlighted the challenge of over-relying on perception-based data in fast-changing digital ecosystems (Fonseka, 2024; Saliya, 2024 ). Future research should thus triangulate survey-based constructs with platform analytics or firm-generated data to improve validity and strengthen managerial relevance. Although Harman’s test and the CLF test both suggested CMV was not a major concern (ΔCFI = 0.000), residual bias is still possible in self-report designs; future work should incorporate Behavioural-trace data and temporal separation. Second, the moderator construct (Deliberate Engagement Style) was developed post-hoc. While this exploratory segmentation provides novel insights, it lacks the psychometric rigor of a validated scale. As such, findings involving this moderator should be interpreted as illustrative rather than confirmatory. The call for formal scale development is consistent with broader critiques in the engagement literature, where scholars argue that existing measures fail to fully capture the heterogeneity of engagement motivations (Dessart et al., 2016 ; Hollebeek & Macky, 2019). In the Sri Lankan context, prior work on social media marketing effectiveness has focused on brand equity outcomes (e.g., Fonseka, 2024; Saliya, 2024 ) without developing indigenous scales for psychological segmentation. Future research should therefore prioritize the construction and validation of an engagement style scale through exploratory and confirmatory factor analyses, ensuring cultural sensitivity while maintaining comparability with global benchmarks. Third, cross-sectional design. The use of a one-time survey precludes causal inference and limits the ability to observe dynamic engagement processes. Longitudinal or experimental designs would allow stronger claims about directionality and test whether engagement style consistently moderates over time. For example, international work on active vs. passive social media use demonstrates that the same Behaviours can have different long-term effects on well-being depending on their quality (Masciantonio et al., 2021 ). Similarly, narrative persuasion studies (Lee & Kim, 2024 ) suggest that deliberate consumers may sustain positive brand perceptions for longer, but such durability can only be verified in multi-wave or experimental settings. Incorporating temporal and experimental designs in future research will thus be critical to advance theoretical robustness. Fourth, sampling limitations. This study employed convenience and snowball sampling of digitally active Sri Lankan consumers, which restricts generalizability beyond this group. While this approach is widely used in digital consumer studies across emerging markets (Upadhyaya et al., 2025 ), it inherently favors urban, educated, and younger populations, under-representing rural or less digitally connected consumers. Within Sri Lanka, prior work has shown that digital adoption patterns differ by sector and geography (Colombo Business Journal, 2024 ; LMD, 2025). Thus, probability-based sampling or quota-based designs that capture wider demographic and regional diversity are essential for future studies. Moreover, replication in cross-cultural contexts will be vital to test whether the moderating role of engagement style reflects a universal consumer trait or is influenced by collectivist cultural orientations (Hofstede, 2011 ; Markus & Kitayama, 1991 ). Despite these limitations, this research offers an important first step toward unpacking how consumer mindsets condition the effectiveness of digital engagement. By demonstrating that the pathway from Behavioural frequency to brand equity is not uniform, the study adds nuance to both theory and practice. Future work that combines validated scales, Behavioural trace data, and cross-cultural replication will be well-positioned to build on these exploratory insights and advance a more comprehensive understanding of engagement’s role in digital brand equity formation. 7. Conclusion This study finds that in the Sri Lankan context, the pathway from simple online Behaviours to brand equity is not guaranteed; it is conditional on the consumer's mindset. For a significant segment of the market, frequent actions are not associated with the deeper cognitive engagement required to build brand value. However, for consumers who engage more deliberately, their actions are meaningful signals that initiate a powerful and efficient cascade of positive brand perceptions and Behaviours. This research deepens our understanding of digital engagement by showing that the most important question for marketers is not "how many people liked our post?" but "who are the people that liked it, and why?" For managers, the directive is clear: move beyond soliciting cheap interactions and focus on earning the attention of thoughtful, interested consumers, as their engagement is what truly builds the brand. Declarations Statement of Ethics Approval Ethical approval was waived for this study after review 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. Declaration of Generative AI and AI-assisted technologies in the writing process During the preparation of this work the author(s) used Gemini, a large language model from Google in order to assist with structuring, editing, and improving the language and readability of the manuscript. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication. References Aaker, D. A. (1991). Managing brand equity. The Free Press. Anderson, J. C., & Gerbing, D. W. (1988). 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Reflections on customer-based brand equity: perspectives, progress, and priorities. AMS Review, 6, 1-16. https://doi.org/10.1007/s13162-016-0083-3 Labrecque, L. I., vor dem Esche, J., Mathwick, C., Novak, T. P., & Hofacker, C. F. (2013). Consumer power: Evolution in the digital age. Journal of Interactive Marketing, 27(4), 257-269. https://doi.org/10.1016/j.intmar.2013.09.002 Laradi, S., et al. (2024). Leveraging capabilities of social media marketing for brand equity and firm performance. Journal of Innovation & Knowledge. Lee, S. Y., & Kim, H. S. (2024). The impact of narrative transportation on brand attitude in TikTok storytelling campaigns. Journal of Interactive Marketing, 59(1), 25-41. https://doi.org/10.1177/10949968231224536 Liang, S., et al. (2024). Self-construal moderates brand endorsement effects. Frontiers in Psychology. LMD. (2025, March). The Digital Front: An Analysis of Sri Lanka's E-commerce and Consumer Behaviour. LMD Business Reports. Markus, H. R., & Kitayama, S. (1991). Culture and the self: Implications for cognition, emotion, and motivation. Psychological Review, 98(2), 224–253. https://doi.org/10.1037/0033-295X.98.2.224 Masciantonio, A., Bourguignon, D., & Van den Abeele, P. (2021). The differential effects of active and passive social networking sites use on well-being. Cyberpsychology, Behaviour, and Social Networking, 24(3), 177-186. https://doi.org/10.1089/cyber.2020.0182 Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. In D. L. Hamilton (Ed.), Communication and persuasion (pp. 1-24). Springer. Platform Characteristics and Consumers’ Outcomes. (2024). Journal of Interactive Advertising. Saliya, C. A. (2024). The impact of social media marketing on brand equity in Sri Lanka’s fashion sector. SSRN. Schivinski, B., Muntinga, D. G., & Pontes, H. M. (2020). The role of user-generated content in building brand equity. Journal of Business Research, 117, 42-52. https://doi.org/10.1016/j.jbusres.2020.06.004 Triandis, H. C. (1995). Individualism and collectivism. Westview Press. Upadhyaya, B., et al. (2025). Customer engagement, brand equity, and cultural dimensions: Evidence from Asian markets. Journal of Marketing Theory & Practice. Van Dijck, J. (2013). The culture of connectivity: A critical history of social media. Oxford University Press. Van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef, P. C. (2010). Customer engagement Behaviour: Theoretical foundations and research directions. Journal of Service Research, 13(3), 253-266. https://doi.org/10.1177/1094670510375599 Vaterlaus, J. M., et al. (2024). Social media use and evolving gratifications. Communication Quarterly. Wang, J., et al. (2025). Social media influencers’ relatability and purchase intention. Journal of Business Research. Additional Declarations The authors declare no competing interests. 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06:15:09","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":110741,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7665181/v1/5ea475611ab683f5700a055a.html"},{"id":91949174,"identity":"c8f18fd0-458b-4be8-a6d6-bb8e4a4a50a9","added_by":"auto","created_at":"2025-09-23 06:15:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":20967,"visible":true,"origin":"","legend":"\u003cp\u003eThe Conceptual Framework\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7665181/v1/ec52b7c354f33da15c5a2f62.png"},{"id":91949176,"identity":"fffc4e1d-3d29-4329-b6d6-478c64f3b8d9","added_by":"auto","created_at":"2025-09-23 06:15:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":573294,"visible":true,"origin":"","legend":"\u003cp\u003eFinal SEM Results for Low and High Deliberate Engagement Style Groups.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: Standardized path coefficients (β) are shown. **p \u0026lt; .001. Model fit for Low DE group: χ2/df = 2.493, CFI = .877, RMSEA = .084. Model fit for High DE group: χ2/df = 2.593, CFI = .910, RMSEA = .086.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7665181/v1/70a38c580edfdf33d4cfd3d8.png"},{"id":91951219,"identity":"64756e63-22e1-4d57-a3bd-81eb5c0ede8d","added_by":"auto","created_at":"2025-09-23 06:39:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1743087,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7665181/v1/49e7738c-78f5-4666-bb63-b563ba304ac0.pdf"},{"id":91949175,"identity":"b62e3036-523f-48e7-8b59-8a71d6b43ca7","added_by":"auto","created_at":"2025-09-23 06:15:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":95233,"visible":true,"origin":"","legend":"","description":"","filename":"Annexure.docx","url":"https://assets-eu.researchsquare.com/files/rs-7665181/v1/a01811bcaa9aea201c16380d.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eExploring the Role of Consumer Engagement Style in Digital Brand Equity Formation: Evidence from Sri Lanka\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe global marketing landscape has undergone a seismic shift over the past two decades, with the proliferation of social media platforms fundamentally transforming consumer\u0026ndash;brand interactions. This has shifted the locus of power from corporations to vast, interconnected networks of consumers (Labrecque et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In contrast to the unidirectional, monolithic communication of traditional marketing, digital platforms facilitate dynamic, multidirectional conversations where consumers actively create, share, and contest brand meanings (Gensler et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This paradigm shift requires a critical reconsideration of the mechanisms that underpin brand equity, a cornerstone of marketing strategy traditionally built on constructs like awareness, associations, and loyalty (Aaker, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Keller, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). In the digital era, brand value is less about what brands say to consumers and more about what consumers say to each other about brands (De Vries et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis transformation is particularly pronounced in emerging markets, which have often leapfrogged traditional media infrastructure in favor of a mobile-first digital ecosystem. These markets are typically characterized by younger, digitally native populations, rapidly growing disposable incomes, and a high degree of trust in social commerce and peer recommendations (Kapoor et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Sri Lanka serves as a compelling archetype of this phenomenon. With internet penetration reaching over 50% of its 22\u0026nbsp;million people and active social media usage even higher, driven primarily by mobile devices (DataReportal, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), Sri Lankan consumers increasingly form their brand opinions and make purchasing decisions within digital environments. Recent analyses from leading Sri Lankan business publications confirm that platforms like Facebook, Instagram, and TikTok have evolved beyond mere communication channels to become primary arenas for product discovery and brand evaluation (LMD, 2025).\u003c/p\u003e\u003cp\u003eThis context magnifies the importance of understanding the true drivers of digital brand equity, yet critical nuances are often overlooked. This study addresses two of these nuances. First, this study clarifies the critical pathway from superficial online actions to valuable brand equity, distinguishing between simple Behavioural frequency and the deeper psychological state of cognitive engagement that is necessary to influence consumer perceptions and Behaviour. We distinguish between Behavioural Engagement Frequency (BEF)\u0026mdash;defined as the self-reported frequency of interaction with brand content\u0026mdash;and a deeper state of Cognitive and Attitudinal Engagement (CAE), which encompasses attention, interest, and participation. While many organizations focus on stimulating frequent, simple actions (likes, quick shares), it is unclear if this Behavioural pattern is associated with the deeper psychological state of engagement necessary to build brand equity (Schivinski et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSecond, drawing upon Uses and Gratifications (U\u0026amp;G) Theory, we model and test the sequential process through which a consumer's cognitive and attitudinal engagement\u0026mdash;not just their Behavioural frequency\u0026mdash;builds positive brand perceptions, which in turn drive favorable consumer Behaviours.\u003c/p\u003e\u003cp\u003eTo guide this investigation, we draw upon Uses and Gratifications (U\u0026amp;G) Theory (Katz et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1973\u003c/span\u003e). This theory provides the lens to understand the motivations behind deeper cognitive engagement, suggesting that deliberate engagers are more goal-directed in fulfilling their needs, which should make the brand-building process more efficient and powerful for them. By applying this framework to the Sri Lankan context, this research aims to provide a more methodologically sound and conceptually advanced model of brand equity formation in the digital age.\u003c/p\u003e\u003cp\u003eThis study offers exploratory evidence that a consumer\u0026rsquo;s engagement style conditions the pathway from Behavioural frequency (BEF) to cognitive/attitudinal engagement (CAE) and onward to brand outcomes (BP \u0026rarr; CB). We extend engagement research by (i) formally separating quantity from quality of engagement and showing that frequency predicts value primarily for deliberate engagers, and (ii) recasting KPIs as segment-contingent rather than uniform signals. Situated in an emerging, mobile-first context, our contribution is to demonstrate when and for whom common platform interactions translate into brand equity. We frame these findings as initial evidence to seed scale development for engagement style and to motivate future, Behavioural-trace and longitudinal/experimental tests across markets.\u003c/p\u003e"},{"header":"2. Literature Review and Hypothesis Development","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Brand Equity in the Digital Ecosystem\u003c/h2\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003e2.1.1 Foundational Models of Brand Equity\u003c/h2\u003e\u003cp\u003eThe concept of brand equity gained prominence in the late 1980s and early 1990s as marketers sought to quantify the intangible value of a brand. Aaker (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) provided one of the earliest comprehensive frameworks, defining brand equity as a multidimensional construct comprising brand loyalty, name awareness, perceived quality, brand associations, and other proprietary brand assets. His model positioned brand equity as a strategic asset that could generate significant long-term value.\u003c/p\u003e\u003cp\u003eContemporaneously, Keller\u0026rsquo;s (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) seminal work on customer-based brand equity (CBBE) shifted the focus from a firm-centric to a consumer-centric perspective. He defined CBBE as the differential effect that brand knowledge has on consumer response to the marketing of that brand. Brand knowledge itself was conceptualized as having two components: brand awareness (recall and recognition) and brand image (the perceptions and associations linked to the brand in consumer memory). The power of a brand, in this view, resides in what consumers have learned, felt, seen, and heard about the brand as a result of their experiences over time. The CBBE model is often depicted as a pyramid, suggesting that value is built sequentially through four stages: (1) ensuring customers identify the brand (salience), (2) firmly establishing the brand meaning in customers\u0026rsquo; minds (performance and imagery), (3) eliciting the proper customer responses (judgments and feelings), and (4) fostering a loyal relationship between customers and the brand (resonance) (Keller, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.1.2 Digital Disruption and the Rise of Social Brand Equity\u003c/h2\u003e\u003cp\u003eThe advent of the digital ecosystem has not invalidated these foundational principles but has fundamentally altered the processes through which they are achieved. The controlled, hierarchical, firm-to-consumer communication model has been replaced by a chaotic, democratized, consumer-to-consumer network (Gensler et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Keller (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) himself noted that brand meaning is increasingly co-created in online communities, where user-generated content (UGC), peer reviews, and brand-hosted forums can have a more profound impact on brand associations than firm-generated advertising.\u003c/p\u003e\u003cp\u003eThis shift has given rise to the concept of social brand equity, where a significant portion of a brand's value is derived from its presence and the consumer interactions it facilitates within social networks (Laradi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this environment, concepts like brand awareness are achieved not just through advertising reach, but through viral sharing and organic discovery. Brand associations are now profoundly shaped by the collective storytelling of the user base, where narrative transportation within digital campaigns on platforms like TikTok can directly influence brand attitudes (Lee \u0026amp; Kim, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Brand loyalty evolves into brand advocacy, where consumers become voluntary brand ambassadors.\u003c/p\u003e\u003cp\u003eRecent research has therefore converged on customer engagement as the primary mechanism through which digital brand equity is built. Customer engagement is a psychological state and a Behavioural manifestation of interactive customer experiences (Brodie et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It encompasses cognitive, emotional, and Behavioural dimensions of participation. Studies consistently show that consumers who actively engage with brands online\u0026mdash;by contributing reviews, participating in discussions, or sharing content\u0026mdash;develop stronger brand loyalty, are more likely to act as brand advocates, and exhibit higher repurchase intentions (Ismail, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Upadhyaya et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This active participation creates a virtuous cycle: engagement strengthens brand equity, which in turn encourages further engagement.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Differentiating Facets of Engagement: Behavioural vs. Cognitive\u003c/h2\u003e\u003cp\u003eA persistent ambiguity in both academic research and managerial practice is the failure to distinguish between the quantity and quality of social media activity. To address this, we conceptualize engagement as having multiple facets, and for this study, we distinguish between two:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eBehavioural Engagement Frequency (BEF)\u003c/b\u003e: This represents the observable, action-based dimension of engagement, including liking, sharing, and commenting.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCognitive \u0026amp; Attitudinal Engagement (CAE)\u003c/b\u003e: This is the deeper, psychological dimension involving focused attention, genuine interest, and positive feelings toward the interaction.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eFollowing the engagement literature, we treat engagement as a multidimensional psychological state with Behavioural manifestations, where BEF indexes observable acts and CAE indexes internal, value-creating processing (attention, absorption, meaning; cf. Brodie et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hollebeek, 2011; Dessart et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This separation clarifies why identical counts of likes/shares may reflect habit for some users but elaboration for others.\u003c/p\u003e\u003cp\u003eThis distinction is critical. A consumer can spend hours passively scrolling through a social media feed, a Behaviour often termed \"lurking,\" with minimal cognitive or emotional investment in the content consumed (Vaterlaus et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This high frequency of exposure does not equate to deep engagement. According to U\u0026amp;G Theory, CAE represents a more goal-directed state where consumers actively select content to fulfill needs for information or entertainment (Katz et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1973\u003c/span\u003e). BEF, on the other hand, may occur without this deeper motivation (e.g., a habitual \"like\"). This leads to our central question: Does the frequency of simple Behaviours necessarily correspond to a deeper psychological state of engagement? We propose that this relationship is not universal but is instead contingent on the consumer's mindset. This distinction leads to our first hypothesis, which posits that simple, frequent online actions are a necessary antecedent to the more valuable state of cognitive engagement.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH1\u003c/b\u003e: Behavioural Engagement Frequency (BEF) is positively associated with Cognitive and Attitudinal Engagement (CAE).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eIt is this deeper state of CAE that is theorized to build brand equity. By investing cognitive resources, consumers foster a connection that strengthens brand associations, leading to a more positive Brand Perception (BP) (Van Doorn et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This positive perception is the central precursor to favorable Consumer Behaviours (CB) (Keller, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH2\u003c/b\u003e: Cognitive and Attitudinal Engagement (CAE) is positively associated with Brand Perception (BP).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH3\u003c/b\u003e: Brand Perception (BP) is positively associated with Consumer Behaviour (CB).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.3 The Moderating Role of a Deliberate Engagement Style\u003c/h2\u003e\u003cp\u003eConsumers differ in how they approach online content. We propose a moderator we term \"Deliberate Engagement Style,\" which reflects the degree to which a consumer's engagement is consciously guided by brand quality and personal interest.\u003c/p\u003e\u003cp\u003eFrom a U\u0026amp;G perspective, consumers with a High Deliberate Engagement Style are more strategic in using media to fulfill their needs. Their engagement is a conscious choice to satisfy specific goals. When such a consumer decides to engage, that engagement is more meaningful and should therefore have a stronger positive association with their brand perception. This heightened perception, being rooted in considered judgment, should in turn have a stronger association with their subsequent Behaviours.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH4\u003c/b\u003e: The relationship between CAE and BP is stronger for consumers with a High Deliberate Engagement Style.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH5\u003c/b\u003e: The relationship between BP and CB is stronger for consumers with a High Deliberate Engagement Style.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eBased on the theoretical arguments and the reviewed literature, the conceptual framework guiding this research is proposed. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the hypothesized relationships between Behavioural Engagement Frequency (BEF), Cognitive \u0026amp; Attitudinal Engagement (CAE), Brand Perception (BP), and Consumer Behaviour (CB), including the proposed moderating role of a Deliberate Engagement Style.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Research Philosophy and Approach\u003c/h2\u003e\u003cp\u003eThis study adopts a positivist research philosophy, assuming an objective social reality that can be measured and tested. Consequently, we employ a quantitative, deductive approach, starting with established theories (CBBE, U\u0026amp;G) to formulate specific, testable hypotheses. A cross-sectional research design was used, capturing a snapshot of consumer perceptions and Behaviours at a single point in time.\u003c/p\u003e\u003cp\u003eA total of 435 valid and complete responses were collected and used for the final analysis.\u003c/p\u003e\u003cp\u003eThe demographic profile of the final sample is detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The sample was predominantly young and digitally native, with over 74% of respondents under the age of 35. It was also highly engaged with social media, with over 80% reporting they use the platforms multiple times a day. The gender distribution skewed slightly male (57.9%), and a majority of respondents (over 70%) held a Bachelor's degree or higher, indicating a well-educated sample representative of urban and suburban digital consumers in Sri Lanka.\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\u003eDemographic Profile of Respondents (N\u0026thinsp;=\u0026thinsp;435)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.2\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\u003e25\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.5\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\u003e35\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.5\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\u003e45+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.9\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\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.3\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\u003eBachelor's Degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.7\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\u003eMaster's Degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.7\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\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial Media Usage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMultiple times a day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80.5\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\u003eOnce a day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.1\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\u003eA few times a week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.4\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\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Sampling and Data Collection\u003c/h2\u003e\u003cp\u003eThe target population of this study consisted of active social media users in Sri Lanka, a demographic segment increasingly central to digital brand-building processes. According to DataReportal (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), more than 50% of the Sri Lankan population actively uses social media, with over 80% of users accessing platforms daily through mobile devices. This highly connected, mobile-first user base represents the most relevant population for research on digital brand equity, as brand discovery, evaluation, and interaction are now primarily mediated through social platforms in this context (LMD, 2025).\u003c/p\u003e\u003cp\u003eGiven the lack of a comprehensive national sampling frame of social media users in Sri Lanka, we employed a non-probability convenience and snowball sampling method. This approach has been widely applied in prior digital consumer studies in emerging markets, where researchers often rely on network-driven data collection to capture digitally active consumers (Fonseka, 2024; Saliya, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Masciantonio et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Convenience sampling allows researchers to reach respondents efficiently in environments where internet penetration and platform adoption vary by demographic segment, while snowball techniques leverage peer-to-peer recruitment, especially relevant in collectivist cultural contexts where referrals and social connections shape participation (Triandis, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Hofstede, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe survey link was distributed via popular social platforms such as Facebook, Instagram, and LinkedIn, as well as through university networks where young, digitally native consumers are concentrated. Eligibility criteria required respondents to be (a) 18 years or older, and (b) active on at least one social media platform daily. These criteria ensured that participants were both legally capable of providing informed consent and representative of the most engaged digital consumer cohort.\u003c/p\u003e\u003cp\u003eA total of 435 valid responses were obtained, which aligns with recommended sample sizes for structural equation modeling (SEM). According to Anderson and Gerbing (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), sample sizes exceeding 200 are sufficient for most SEM applications, while Kline (2016) suggests 10\u0026ndash;20 observations per parameter as a benchmark. With 435 responses, this study\u0026rsquo;s sample size is well above minimum thresholds, increasing the robustness of the estimated models.\u003c/p\u003e\u003cp\u003eWhile the use of non-probability sampling limits generalizability to the wider Sri Lankan population, it is important to note that the chosen segment \u0026mdash; digitally active social media users \u0026mdash; is precisely the group most relevant to studying engagement-driven brand equity formation. As similar methods have been successfully employed in prior social media marketing research in both developed and emerging markets (Brodie et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ismail, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Upadhyaya et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), this approach provides a justified and contextually appropriate means of accessing the target population.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Measures and Scale Development\u003c/h2\u003e\u003cp\u003eAll constructs in this study were measured using five-point Likert scales (1\u0026thinsp;=\u0026thinsp;Strongly Disagree, 5\u0026thinsp;=\u0026thinsp;Strongly Agree). Where possible, measures were adapted from established and validated scales to enhance construct validity and comparability with prior studies.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eBehavioural Engagement Frequency (BEF)\u003c/b\u003e: Items were adapted from prior engagement scales (Brodie et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Dessart et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) that capture the frequency of observable brand interactions such as likes, comments, shares, and content participation. These Behaviours represent the surface-level, action-oriented dimension of engagement that marketers often track as KPIs (Van Doorn et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCognitive \u0026amp; Attitudinal Engagement (CAE)\u003c/b\u003e: Items captured the extent to which consumers paid attention to, expressed interest in, and enjoyed interacting with brand-related content. This aligns with definitions of engagement as a psychological state encompassing cognitive and emotional investment (Brodie et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hollebeek, 2011). Prior studies confirm that cognitive engagement is critical for transforming superficial actions into meaningful brand relationships (Dessart et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Schivinski et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eBrand Perception (BP)\u003c/b\u003e: Items were adapted from Keller\u0026rsquo;s (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1993\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) customer-based brand equity model, focusing on brand awareness, image, and trust. These items also reflect more recent digital branding work that situates perception as the outcome of co-created brand meaning in online communities (Gensler et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Laradi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eConsumer Behaviour (CB)\u003c/b\u003e: Items assessed outcomes such as purchase intentions and loyalty, drawing on prior brand equity measures (Yoo \u0026amp; Donthu, 2001) and extending them to digital contexts. While these measures remain perceptual rather than Behavioural, they are consistent with past consumer research in digital settings where actual purchase data is difficult to obtain (Ismail, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Items reflect purchase intention and attitudinal loyalty. Because these facets are conceptually close yet distinct, we interpret CB as a broad, exploratory outcome capturing overall downstream response; future work should separate intentions and loyalty or estimate a second-order model.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDeliberate Engagement Style (Moderator)\u003c/b\u003e: A segmentation variable was created to distinguish consumers who engage with brand content deliberately (i.e., guided by personal interest and perceived brand quality) from those who engage more casually. Items were adapted from gratifications research (Katz et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1973\u003c/span\u003e; Bhatiasevi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and cultural perspectives on goal-directed media use (Markus \u0026amp; Kitayama, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). While this measure has not yet undergone formal scale validation, it provides an exploratory operationalization of a theoretically meaningful segmentation. In line with calls for greater nuance in consumer engagement measurement (Dessart et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hollebeek \u0026amp; Macky, 2019), this exploratory approach offers initial insights and highlights a fruitful avenue for scale development in future research.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eBy adapting validated items where possible and transparently acknowledging the exploratory nature of the moderator, this study balances methodological rigor with theoretical innovation. The approach provides sufficient construct coverage to test the proposed model while setting the stage for future refinements in measurement and validation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Data Analysis Procedure\u003c/h2\u003e\u003cp\u003eThe data were analyzed using Structural Equation Modeling (SEM) in AMOS v.29. A two-step approach was followed:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eMeasurement Model Assessment\u003c/b\u003e: A Confirmatory Factor Analysis (CFA) was first conducted. We evaluated convergent validity using factor loadings, Average Variance Extracted (AVE), and Composite Reliability (CR). We assessed discriminant validity by ensuring that the square root of the AVE for each construct was greater than its correlation with any other construct.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eStructural Model Assessment\u003c/b\u003e: After confirming the measurement model's validity, the structural model was tested. A multi-group analysis was conducted to compare the structural paths between the Low and High Deliberate Engagement Style groups. Model fit was evaluated using multiple indices: χ2/df, TLI, RMSEA and SRMR.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCommon method variance (CMV).\u003c/b\u003e We assessed CMV using (i) Harman\u0026rsquo;s single-factor test and (ii) a CFA-based unmeasured common latent factor (CLF) test. In Harman\u0026rsquo;s test, the first factor explained 35.2% (\u0026lt;\u0026thinsp;50%) of variance. For the CLF test, we added a latent method factor loading equally on all indicators (equal loadings; CLF variance fixed to 1; no covariances with substantive constructs). Model fit changed only trivially relative to the baseline CFA (Baseline: χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;3.290, CFI\u0026thinsp;=\u0026thinsp;.892, TLI\u0026thinsp;=\u0026thinsp;.873, RMSEA\u0026thinsp;=\u0026thinsp;.073; CLF: χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;3.342, CFI\u0026thinsp;=\u0026thinsp;.892, TLI\u0026thinsp;=\u0026thinsp;.871, RMSEA\u0026thinsp;=\u0026thinsp;.073; ΔCFI\u0026thinsp;=\u0026thinsp;0.000; SRMR_CLF\u0026thinsp;=\u0026thinsp;.076), indicating CMV is unlikely to bias the estimates.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Ethical Considerations\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAll procedures performed in this study involving human participants were conducted according to ethical research principles. Informed consent was obtained from all individual participants included in the study. The online survey ensured participant anonymity and confidentiality, and participation was fully voluntary.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Measurement Model and Construct Validity\u003c/h2\u003e\u003cp\u003eThe CFA for the measurement model indicated a good fit to the data after removing several items with poor factor loadings. The final model demonstrated strong construct validity. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, all factor loadings were above the .70 threshold, Composite Reliability (CR) values were well above the .70 benchmark, and Average Variance Extracted (AVE) values were above the .50 benchmark for all constructs, confirming convergent validity. Discriminant validity was also established. The measurement model\u0026rsquo;s global fit was acceptable \u003cb\u003e(\u003c/b\u003eχ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;3.290, CFI\u0026thinsp;=\u0026thinsp;.892, TLI\u0026thinsp;=\u0026thinsp;.873, RMSEA\u0026thinsp;=\u0026thinsp;.073, SRMR\u0026thinsp;=\u0026thinsp;.077).\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\u003eMeasurement Model - Loadings, Reliability, and Validity\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=\"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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstruct\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Loading\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCronbach's α\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eComposite Reliability (CR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAverage Variance Extracted (AVE)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eBehavioural Engagement Frequency (BEF)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEMC3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEMC4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.88\u003c/p\u003e\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=\"c2\"\u003e\u003cp\u003eSMP4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.85\u003c/p\u003e\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\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eCognitive \u0026amp; Attitudinal Engagement (CAE)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEMC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEMC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.93\u003c/p\u003e\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=\"c2\"\u003e\u003cp\u003eEMC5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.87\u003c/p\u003e\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\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eBrand Perception (BP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBP1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBP2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.88\u003c/p\u003e\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=\"c2\"\u003e\u003cp\u003eBP3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.85\u003c/p\u003e\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=\"c2\"\u003e\u003cp\u003eBP4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.81\u003c/p\u003e\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=\"c2\"\u003e\u003cp\u003eBP5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.84\u003c/p\u003e\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\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eConsumer Behaviour (CB)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCB1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCB3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.88\u003c/p\u003e\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=\"c2\"\u003e\u003cp\u003eCB4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.79\u003c/p\u003e\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=\"c2\"\u003e\u003cp\u003eCB5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.83\u003c/p\u003e\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\u003eAs a CMV robustness check, the CLF model did not materially improve fit over the baseline CFA (ΔCFI\u0026thinsp;=\u0026thinsp;0.000); see Appendix B.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Structural Model Results\u003c/h2\u003e\u003cp\u003e\u003cb\u003eFull Sample Model (N\u0026thinsp;=\u0026thinsp;435)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe overall model demonstrated a good fit to the data (χ2/df\u0026thinsp;=\u0026thinsp;3.743, CFI\u0026thinsp;=\u0026thinsp;.914, TLI\u0026thinsp;=\u0026thinsp;.891, RMSEA\u0026thinsp;=\u0026thinsp;.079, SRMR\u0026thinsp;=\u0026thinsp;.076). While the CFI indicates a good model fit, the TLI is at the cusp of the acceptable range, and the RMSEA suggests an adequate but not perfect fit, indicating that the model is a useful but simplified representation of complex real-world phenomena.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eThe path from BEF to CAE was positive and highly significant (β\u0026thinsp;=\u0026thinsp;.222, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH2\u003c/b\u003e: The path from CAE to BP was very strong and significant (β\u0026thinsp;=\u0026thinsp;.868, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), explaining 75.3% of the variance in BP (R2\u0026thinsp;=\u0026thinsp;.753).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH3\u003c/b\u003e: The path from BP to CB was very strong and significant (β\u0026thinsp;=\u0026thinsp;.898, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), explaining 80.7% of the variance in CB (R2\u0026thinsp;=\u0026thinsp;.807).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eGroup comparability\u003c/strong\u003e\u003cp\u003eBefore comparing paths, we assessed measurement invariance across Low vs. High Deliberate groups. A multi-group CFA supported configural and metric invariance (ΔCFI\u0026thinsp;\u0026le;\u0026thinsp;.01), permitting meaningful comparison of structural coefficients across groups.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMulti-Group Analysis: Low vs. High Deliberate Engagement Style\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eBEF \u0026rarr; CAE (H1, main effect)\u003c/b\u003e: In the full sample, H1 was supported (β\u0026thinsp;=\u0026thinsp;.222, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). A multi-group comparison indicates this path varies by engagement style\u0026mdash;non-significant for the Low-Deliberate group (β\u0026thinsp;=\u0026thinsp;.129, p\u0026thinsp;=\u0026thinsp;.072) but significant for the High-Deliberate group (β\u0026thinsp;=\u0026thinsp;.319, p\u0026thinsp;\u0026lt;\u0026thinsp;.001)\u0026mdash;which we interpret as exploratory evidence of moderation of the H1 path.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH4 (Moderation of CAE \u0026rarr; BP)\u003c/b\u003e: H4 was supported. The path was significantly stronger for the High Deliberate Style group (β\u0026thinsp;=\u0026thinsp;.924) compared to the Low Deliberate Style group (β\u0026thinsp;=\u0026thinsp;.817).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH5 (Moderation of BP \u0026rarr; CB)\u003c/b\u003e: H5 was also supported. The path was significantly stronger for the High Deliberate Style group (β\u0026thinsp;=\u0026thinsp;.954) compared to the Low Deliberate Style group (β\u0026thinsp;=\u0026thinsp;.831).\u003c/p\u003e\u003c/li\u003e\u003c/ul\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 Hypothesis Testing\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothesis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePath\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow Deliberate Style Group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh Deliberate Style Group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eResult\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\u003eH1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBEF\u0026nbsp;\u0026rarr;\u0026nbsp;CAE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ\u0026nbsp;= .129, ns\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eβ\u0026nbsp;= .319, ***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eSupported\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eH2/H4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCAE\u0026nbsp;\u0026rarr;\u0026nbsp;BP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ\u0026nbsp;= .817, ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eβ\u0026nbsp;= .924, ***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eSupported\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eH3/H5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBP\u0026nbsp;\u0026rarr;\u0026nbsp;CB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ\u0026nbsp;= .831, ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eβ\u0026nbsp;= .954, ***\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eSupported\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eNote: *** p\u0026thinsp;\u0026lt;\u0026thinsp;.001; ns\u0026thinsp;=\u0026thinsp;not significant. Path coefficients (β) are standardized.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Structural Model and Hypothesis Testing\u003c/h2\u003e\u003cp\u003eA visual representation of the final estimated structural models for both the Low and High Deliberate Engagement Style groups is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The figure illustrates the significant differences in path strengths and explained variance (R2) between the two groups, visually confirming the moderation effect. For the detailed diagram of the full sample model, please see Appendix A.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe findings provide exploratory evidence that \u003cem\u003ewho\u003c/em\u003e engages matters as much as \u003cem\u003ehow much\u003c/em\u003e they engage: consumers with a highly Deliberate Engagement Style convert surface-level interactions into cognitive/attitudinal engagement, stronger brand perceptions, and favorable consumer Behaviour more efficiently than casual engagers. This segmentation lens extends mainstream engagement theory, which frames engagement as a multidimensional, psychologically grounded construct rather than a mere count of actions (e.g., likes, shares) (in line with Brodie et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Dessart et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). While prior work shows that active participation generally strengthens brand outcomes, our results suggest that the same \u0026ldquo;Behavioural frequency\u0026rdquo; can signal very different underlying states, depending on the consumer\u0026rsquo;s mindset\u0026mdash;clarifying why platform-level KPIs sometimes fail to predict downstream brand value.\u003c/p\u003e\u003cp\u003eThese results are consistent with international evidence that distinguishes quality from quantity of social media use. For instance, Dessart et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) emphasize engagement\u0026rsquo;s cognitive and affective layers; similarly, Schivinski et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) show that user-generated contributions (which usually require intention and effort) more reliably build brand equity than low-effort reactions. Our moderation pattern aligns with this stream by indicating that Behavioural frequency predicts value only when it reflects a deliberate, goal-directed stance. The mechanism is theoretically coherent with Elaboration Likelihood Model logic\u0026mdash;deliberate engagers are more apt to process brand content via the central route, creating more durable perceptions and Behaviours, whereas casual engagers may remain in the peripheral route with weaker carry-through to brand outcomes.\u003c/p\u003e\u003cp\u003eThe pattern also resonates with Uses \u0026amp; Gratifications (U\u0026amp;G) perspectives and the active vs. passive social media use distinction. U\u0026amp;G argues that media effects depend on goal fulfillment (Katz et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1973\u003c/span\u003e); recent work on active (purposeful) versus passive (habitual/scrolling) use shows that active, intent-driven interactions have more robust psychological consequences (e.g., Masciantonio et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our exploratory segmentation echoes this: deliberate users look more like \u0026ldquo;active\u0026rdquo; users whose interactions are tethered to interest, evaluation, and meaning-making, which in turn strengthens the CAE \u0026rarr; BP \u0026rarr; CB cascade. Conversely, for casual users, frequent actions appear closer to habit than to engaged processing, explaining the attenuated pathway.\u003c/p\u003e\u003cp\u003eImportantly, the Sri Lankan context helps explain why engagement style might matter even more. Local evidence indicates a mobile-first, highly social commerce\u0026ndash;oriented environment where peer cues and community narratives shape brand discovery and evaluation (DataReportal, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Colombo Business Journal, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; LMD, 2025). In such settings\u0026mdash;often marked by collectivist orientations\u0026mdash;goal-directed consumers may weigh brand quality signals and social proof more systematically, magnifying the translation of engagement into brand perceptions and Behaviour (cf. Markus \u0026amp; Kitayama, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Hofstede, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Recent Sri Lankan studies that link social media activity to brand equity (e.g., Fonseka, 2024; Saliya, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) broadly demonstrate positive associations; our contribution is to show why those associations vary across users: engagement style acts as a conditioning lens that helps reconcile mixed KPI\u0026ndash;outcome relationships observed by practitioners.\u003c/p\u003e\u003cp\u003eFrom a brand-building perspective, our results complement international findings that narrative quality and content meaning\u0026mdash;not only volume\u0026mdash;drive attitudes (e.g., narrative transportation on TikTok: Lee \u0026amp; Kim, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and that a firm\u0026rsquo;s social presence capabilities improve brand equity (Laradi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). We extend these insights by suggesting that the same content strategy performs unevenly across engagement styles: value-dense, informational, or participatory formats (tutorials, AMAs, behind-the-scenes) should disproportionately benefit deliberate segments, while salience-oriented, affect-rich formats (short-form entertainment, memes) may be more suitable to activate casual segments without over-interpreting their KPI spikes. This aligns with Pan-Asian evidence that engagement\u0026rsquo;s link to brand equity is strong but context- and audience-contingent (Upadhyaya et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eManagerially, the implication is to reinterpret platform metrics through a segmentation lens. Rather than treating interaction counts as uniform currency, managers should (i) diagnose engagement style using short screening items (interest, relevance, brand-quality focus), (ii) weight KPIs by style (e.g., \u0026ldquo;deliberate-weighted engagement index\u0026rdquo;), and (iii) bifurcate content strategies: nurture deliberate users with depth and dialogic experiences, and use lightweight, affective creatives to prime or migrate casual users toward more deliberate states. For researchers, the contribution is programmatic: this study offers \u003cem\u003einitial evidence\u003c/em\u003e that engagement style moderates the equity pathway, motivating scale development, Behavioural trace validation (clickstream, watch-time), and longitudinal or experimental tests across platforms and cultures.\u003c/p\u003e\u003cp\u003eIn sum, our exploratory findings help reconcile a persistent managerial puzzle\u0026mdash;why big engagement numbers sometimes fail to materialize into brand value\u0026mdash;by showing that engagement style conditions the pathway from Behavioural frequency to cognitive engagement and onward to brand perception and Behaviour. This frames a concrete agenda for the next wave of research: define and validate engagement style, connect it to actual Behaviours, and test style-content fit to move beyond counting interactions toward understanding their meaning.\u003c/p\u003e"},{"header":"6. Limitations and Future Research","content":"\u003cp\u003eThis study is subject to several important limitations that must be acknowledged to position its contributions appropriately.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFirst, reliance on self-reported survey measures.\u003c/b\u003e All constructs were operationalized using perceptual items, including those labeled as \u0026ldquo;Behavioural,\u0026rdquo; which are better understood as \u003cem\u003eperceived Behaviours\u003c/em\u003e rather than actual digital traces. Self-reported engagement frequencies are susceptible to recall bias and social desirability effects, potentially inflating or attenuating relationships (Podsakoff et al., 2003). Internationally, scholars increasingly stress the importance of using objective platform data\u0026mdash;such as clickstream, reaction logs, and watch-time metrics\u0026mdash;to validate consumer engagement models (Schivinski et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Locally, Sri Lankan studies have similarly highlighted the challenge of over-relying on perception-based data in fast-changing digital ecosystems (Fonseka, 2024; Saliya, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Future research should thus triangulate survey-based constructs with platform analytics or firm-generated data to improve validity and strengthen managerial relevance.\u003c/p\u003e\u003cp\u003eAlthough Harman\u0026rsquo;s test and the CLF test both suggested CMV was not a major concern (ΔCFI\u0026thinsp;=\u0026thinsp;0.000), residual bias is still possible in self-report designs; future work should incorporate Behavioural-trace data and temporal separation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSecond, the moderator construct (Deliberate Engagement Style) was developed post-hoc.\u003c/b\u003e While this exploratory segmentation provides novel insights, it lacks the psychometric rigor of a validated scale. As such, findings involving this moderator should be interpreted as illustrative rather than confirmatory. The call for formal scale development is consistent with broader critiques in the engagement literature, where scholars argue that existing measures fail to fully capture the heterogeneity of engagement motivations (Dessart et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hollebeek \u0026amp; Macky, 2019). In the Sri Lankan context, prior work on social media marketing effectiveness has focused on brand equity outcomes (e.g., Fonseka, 2024; Saliya, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) without developing indigenous scales for psychological segmentation. Future research should therefore prioritize the construction and validation of an engagement style scale through exploratory and confirmatory factor analyses, ensuring cultural sensitivity while maintaining comparability with global benchmarks.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThird, cross-sectional design.\u003c/b\u003e The use of a one-time survey precludes causal inference and limits the ability to observe dynamic engagement processes. Longitudinal or experimental designs would allow stronger claims about directionality and test whether engagement style consistently moderates over time. For example, international work on active vs. passive social media use demonstrates that the same Behaviours can have different long-term effects on well-being depending on their quality (Masciantonio et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similarly, narrative persuasion studies (Lee \u0026amp; Kim, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) suggest that deliberate consumers may sustain positive brand perceptions for longer, but such durability can only be verified in multi-wave or experimental settings. Incorporating temporal and experimental designs in future research will thus be critical to advance theoretical robustness.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFourth, sampling limitations.\u003c/b\u003e This study employed convenience and snowball sampling of digitally active Sri Lankan consumers, which restricts generalizability beyond this group. While this approach is widely used in digital consumer studies across emerging markets (Upadhyaya et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), it inherently favors urban, educated, and younger populations, under-representing rural or less digitally connected consumers. Within Sri Lanka, prior work has shown that digital adoption patterns differ by sector and geography (Colombo Business Journal, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; LMD, 2025). Thus, probability-based sampling or quota-based designs that capture wider demographic and regional diversity are essential for future studies. Moreover, replication in cross-cultural contexts will be vital to test whether the moderating role of engagement style reflects a universal consumer trait or is influenced by collectivist cultural orientations (Hofstede, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Markus \u0026amp; Kitayama, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1991\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite these limitations, this research offers an important first step toward unpacking how consumer mindsets condition the effectiveness of digital engagement. By demonstrating that the pathway from Behavioural frequency to brand equity is not uniform, the study adds nuance to both theory and practice. Future work that combines validated scales, Behavioural trace data, and cross-cultural replication will be well-positioned to build on these exploratory insights and advance a more comprehensive understanding of engagement\u0026rsquo;s role in digital brand equity formation.\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis study finds that in the Sri Lankan context, the pathway from simple online Behaviours to brand equity is not guaranteed; it is conditional on the consumer's mindset. For a significant segment of the market, frequent actions are not associated with the deeper cognitive engagement required to build brand value. However, for consumers who engage more deliberately, their actions are meaningful signals that initiate a powerful and efficient cascade of positive brand perceptions and Behaviours. This research deepens our understanding of digital engagement by showing that the most important question for marketers is not \"how many people liked our post?\" but \"who are the people that liked it, and why?\" For managers, the directive is clear: move beyond soliciting cheap interactions and focus on earning the attention of thoughtful, interested consumers, as their engagement is what truly builds the brand.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eStatement of Ethics Approval Ethical approval was waived for this study after review 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\u003cp\u003e\u003cstrong\u003eDeclaration of Generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the author(s) used Gemini, a large language model from Google in order to assist with structuring, editing, and improving the language and readability of the manuscript. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAaker, D. A. (1991). Managing brand equity. The Free Press.\u003c/li\u003e\n\u003cli\u003eAnderson, J. C., \u0026amp; Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. 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Journal of Business Research.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"N/A","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":"Digital Brand Equity, Consumer Engagement Style, Social Media Marketing, Emerging Markets, Behavioural vs. Cognitive Engagement, Exploratory Study","lastPublishedDoi":"10.21203/rs.3.rs-7665181/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7665181/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study explores how consumer engagement style conditions the pathway from observable online actions to digital brand equity in an emerging market. Using survey data from 435 social media users in Sri Lanka, we tested a structural model linking Behavioural Engagement Frequency (BEF) to Cognitive and Attitudinal Engagement (CAE), Brand Perception (BP), and Consumer Behaviour (CB). The full-sample model showed a good overall fit (χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;3.74, CFI\u0026thinsp;=\u0026thinsp;.914, RMSEA\u0026thinsp;=\u0026thinsp;.079). BEF was positively associated with CAE (β\u0026thinsp;=\u0026thinsp;.222, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), CAE strongly predicted BP (β\u0026thinsp;=\u0026thinsp;.868, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, R\u0026sup2; = .753), and BP in turn predicted CB (β\u0026thinsp;=\u0026thinsp;.898, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, R\u0026sup2; = .807). To explore differences in engagement style, we segmented respondents into high and low \u0026ldquo;Deliberate Engagement Style\u0026rdquo; groups. Multi-group SEM revealed that the path from BEF to CAE was non-significant for the low deliberate group (β\u0026thinsp;=\u0026thinsp;.129, ns) but significant for the high deliberate group (β\u0026thinsp;=\u0026thinsp;.319, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Moreover, the high deliberate group exhibited much stronger pathways from CAE to BP (β\u0026thinsp;=\u0026thinsp;.924 vs. .817) and BP to CB (β\u0026thinsp;=\u0026thinsp;.954 vs. .831), explaining substantially higher variance in both brand perception (R\u0026sup2; = .853 vs. .668) and consumer Behaviour (R\u0026sup2; = .910 vs. .691). These findings provide initial evidence that the \u0026ldquo;value of a like\u0026rdquo; is not universal but contingent on consumer mindset. As an exploratory contribution, the study highlights engagement style as a potential segmentation lens in digital marketing, while calling for further scale validation and replication across contexts.\u003c/p\u003e","manuscriptTitle":"Exploring the Role of Consumer Engagement Style in Digital Brand Equity Formation: Evidence from Sri Lanka","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 06:15:04","doi":"10.21203/rs.3.rs-7665181/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 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55058501,"name":"Marketing"}],"tags":[],"updatedAt":"2025-09-23T06:15:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 06:15:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7665181","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7665181","identity":"rs-7665181","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}