Attention-Driven Rendering And Interface Adaptation Using Eye-Tracking Data

Managing cognitive load in interactive digital environments requires understanding how users allocate visual attention to interface elements. This study investigates human-system interaction in digital fashion platforms, where high-fidelity images of luxury garments create complex visual networks that challenge usability and engagement. Using eye-tracking, we empirically assess how users prioritize aesthetic details over brand identifiers, and how individual materialist values filter these patterns—offering direct implications for designing intuitive, attention-aware interfaces.

Eye-tracking has transformed HCI research by objectively revealing gaze behaviours in contexts like online advertising, social media, and mobile apps (e.g., De Keyser et al., 2023; Pieters & Wedel, 2008). In digital fashion interfaces, users navigate dense stimuli blending aesthetics, textures, and logos, yet little is known about how these elements compete for attention or influence user experience. We address this gap through three experiments testing selective attention theory (Yiend, 2010): unbranded images (Exp. 1), branded vs. unbranded comparisons (Exp. 2), and materialism moderation via the Material Values Scale (Richins, 2004; Exp. 3). Results guide interface designers toward usability enhancements, such as optimizing visual hierarchies for faster brand discovery and reduced cognitive strain.​

Harnessing the proven value of ocular tracking, this study investigates individual responses to fashion imagery by focusing on the mapping of visual attention and brand recognition. We conducted three experiments to test this model: analyzing fixations on unbranded images, comparing branded versus unbranded stimuli, and uniquely exploring the link between biometric search patterns and personal value systems using the Materialism Values Scale (Richins, 2004). By reframing the fashion environment as a complex network, we aim to refine how digital platforms optimize for human cognitive constraints.

Theoretical Background

Attention in Digital Interfaces

Selective Attention Theory (SAT; Yiend, 2010) explains how users filter interface clutter, prioritizing salient elements amid competing stimuli—a core usability concern in HCI. In digital fashion environments (e.g., e-commerce apps, Instagram feeds), eye-tracking metrics like fixations and saccades reveal processing depth, informing design for accessibility and engagement (Rayner, 1998; Pieters & Wedel, 2004). Eye-tracking provides an objective and indispensable method for assessing visual attention and understanding consumer decision-making, outperforming subjective self-reported measures that often misrepresent latent gaze patterns (Aribarg et al., 2010; Chandon et al., 2007, 2009; Valenzuela & Raghubir, 2009, 2010). This empirical objectivity is crucial for discerning phenomena such as the "gaze cascade effect," where attention intensifies on a chosen option before a decision is finalized, profoundly influencing internal preferences (Shimojo et al., 2003; Glaholt & Reingold, 2011; Russo, 2011). Despite its broad utility in general marketing, the application of eye-tracking in mapping the visually dense digital displays of fashion image perception remains under-explored.

The present research utilizes Selective Attention Theory (SAT) (Yiend, 2010) to explore how individuals manage cognitive load and prioritize visual information in luxury fashion. This theory posits that people do not ingest all available data but instead actively prioritize relevant information while filtering out distractions, a process observable through eye-tracking fixation metrics (Reeck & Egner, 2015; Stevens & Bavelier, 2012). For example, longer fixations on complex or emotionally resonant fashion visuals indicate deeper cognitive processing and the strategic deployment of selective attention.

While Attention Engagement Theory (AET) (Duncan and Humphreys, 1989, 1992) has historically provided insights into how visual elements initially capture attention, our study shifts its emphasis. AET explains the initial "pull" of a visual signal, where longer fixations indicate the processing of complexity (Alemdag & Cagiltay, 2018; Kim & Lee, 2021; Sperling & Weichselgartner, 1995). However, this study moves beyond initial capture to focus on the active filtering and prioritization of information detailed by SAT. By utilizing the principles of selective attention, we aim to better understand how consumers not only notice, but also deeply process specific visual cues in luxury fashion to evoke emotional responses, enhance brand recall, and influence purchase intent.

Eye-tracking directly measures this selective attention, yielding empirical data crucial for understanding the nexus of memory and perception (Kessinger & Corkin, 2023; Ferre, 2002). This provides insight into how attention shapes consumer preferences, particularly concerning materialistic values. As SAT suggests that personally relevant stimuli capture a disproportionate share of attention (Yiend, 2010), individuals with higher materialistic values may focus more intensely on status-associated elements, which the Materialism Values Scale (MVS) quantifies (Richins, 2004, 2017).

Studies on Fixations and Visual Imagery

Guided by Selective Attention Theory (SAT), this study explores how clothing design elements function as "attentional anchors." Prior research on web layouts (Hsieh & Chen, 2011) and product-specific features, such as handbag handles (Ho, 2013), demonstrates that specific features draw primary focus. Eye-tracking reveals that complex stimuli necessitate more eye movements and longer fixations for deeper processing (Rayner, 1998; Henderson & Hollingworth, 1999). Furthermore, studies in reading and visual search confirm that intricate visual scenes elicit longer fixations and more saccades to resolve information (Rayner, 1978; Wolfe, 2007), informing the hypotheses below. Longer fixations on complete images, indicating deeper processing, are expected to extend overall viewing times as the user navigates the high-fidelity details of the image. This aligns with research showing that attention is linked to aesthetic preference (Calvo & Lang, 2004), and aesthetically pleasing elements elicit prolonged fixations (Reber, Schwarz, and Winkielman, 2004) and increased fixation counts (Park & Lee, 2016), reflecting heightened engagement.

Hypothesis 1a: A positive relationship exists between the duration of whole fixations and the number of saccades predict user engagement in fashion interfaces.

Cumulative research posits that aesthetically appealing luxury fashion imagery elicits prolonged and increased fixations, signaling heightened engagement and deeper processing density (Parkhurst and Niebur, 2002; Leder et al., 2004). Research consistently confirms that visually complex or aesthetically pleasing stimuli drive longer, more frequent fixations (Nummenmaa et al., 2009; Leder et al., 2004).

Hypothesis 1b: A positive relationship exists between the duration of whole fixations and the average duration of whole fixations predict user engagement in fashion interfaces.

Interdisciplinary findings support the idea that longer fixations correlate with frequent saccades in complex processing (Rayner, 1998; Wolfe, 2007), and aesthetically pleasing advertisements elicit prolonged fixations (Pieters and Wedel, 2007). Given luxury fashion's inherent visual emphasis, viewers are expected to prioritize aesthetically striking elements as high-value nodes in their search.

Hypothesis 1c: A positive relationship exists between the duration and number of whole fixations on aesthetics predict user engagement in fashion interfaces.

Biometric Processing of Brand Nodes and Aesthetic Hierarchy

In visually dense digital displays, brands function as pivotal anchors for identity and self-expression, particularly within materialistic cognitive frameworks (Kim & Kramer, 2015). These brand identifiers act as high-value data points that foster systemic loyalty and drive trend adoption (Ferreira et al., 2019; Kautish & Sharma, 2018; De Mooij, 2011). From an  HCI design perspective, the intricate designs inherent in luxury branding necessitate an increase in ocular activity, specifically higher fixation counts and saccadic frequency, signaling deeper engagement with the stimulus (Wedel et al., 2008; Pieters and Wedel, 2004). Eye-tracking thus serves as a sensor for quantifying how these aesthetic nodes modulate user behavior. Gaze patterns, attention, aesthetics, and brand perception are intricately linked and inform the following hypotheses:

Hypothesis 2a: There is a positive relationship between the number of brand fixations and the number of saccades, highlighting interface salience needs.

Hypothesis 2b: There is a positive relationship between the number of brand fixations and the average duration of whole fixations, highlighting interface salience needs.

Hypothesis 2c: There is a positive relationship between the number of brand fixations and the number of whole fixations on aesthetics, highlighting interface salience needs.

Materialism as a Cognitive Filter in Human attention model for digital interfaces

This study integrates the Materialism Values Scale (MVS) (Richins, 2004) with ocular biometrics (Pesa et al., 2024) to explore how internal psychometric states function as filters for visual attention. Guided by Selective Attention Theory, we propose that the user's level of materialism dictates their "attentional priority" within a luxury network. Individuals who internalize materialism as a primary value for self-validation and status (Audrin et al., 2018; O’Cass, 2004; Richins, 2017) are expected to exhibit a specialized ocular "tuning," characterized by heightened sensitivity to brand identifiers and aesthetic luxury cues.This informs the following hypotheses:

Hypothesis 3a: There is a positive relationship between fixations on aesthetics and materialism associated with happiness, centrality, and success, suggesting interface adaptations.

Hypothesis 3b: There is a positive relationship between fixations on brand labels and material preferences linked to happiness, centrality, and success, suggesting personalized interface adaptations.

The experiments

This research, across three experiments each with sixty participants, explored visual attention, materialism, aesthetics, and brand perception in luxury fashion using eye-tracking.  Participants were compensated with a £6.00 Amazon voucher for their involvement. Experiment 1 analyzed fixations on unbranded images and showed aesthetics drive consumer choice, validating eye-tracking over self-reports and drawing on Selective Attention Theory (Yiend, 2010). Experiment 2 examined brand identity's influence on visual attention using branded images. Experiment 3 investigated materialism's impact, linking higher materialism (Richins, 2004) to increased focus on luxury cues, aligning with Selective Attention Theory (Audrin et al., 2018). Eye-tracking on diverse imagery (Figure 1a, 1b) revealed how materialism shapes engagement with luxury aesthetics and brands, correlating fixations on brands and aesthetics with materialism dimensions.

Source: Figures taken by Steele and Menkes (2016) and modified by the authors

Figure 1(a). Bespoke Fashion designs

Source: Figures taken by Steele and Menkes (2016) and modified by the authors

Figure 1(b). Bespoke Fashion designs

Methods

Experimental Design and Data Acquisition: Experiment 1

The research architecture was designed to map the intersection of human ocular behavior and digital information networks using a cohort of sixty participants (N=60). This group, primarily composed of London-based professionals and academic researchers, provided a demographic distribution (75% female, 25% male; 73.8% under 34 years old) optimized for analyzing the cognitive processing of high-fidelity luxury fashion stimuli. To ensure the integrity of the ocular data, the experimental protocol was conducted in a controlled environment designed to minimize external noise and interference. All procedures received formal approval from the London College of Fashion Ethics Review Board (2023-3002023sv), adhering strictly to British Psychological Society (BPS) ethical standards, with all participants providing written informed consent prior to the initialization of the sensor arrays.

Figure 2. Tobii X2 Experiment set-up Tobi X2-30 Eye-tracker

High-Frequency Infrared Ocular Sensor Array

Experiments used a Tobii Pro X2-30 eye-tracker (figure 2) to capture gaze in controlled settings simulating digital viewing (8 seconds exposures). Tobii Pro X2 is high-frequency infrared ocular sensor, a specialized hardware component utilizing dark-pupil tracking to capture gaze data. Operating at a sampling rate of 30 Hz, the sensor ensures the precise measurement of micro-fixations (with a minimum duration threshold of 0.05s) and rapid saccadic transitions. The specifications of this array allow for a high-accuracy tracking range within a 50 x 36 cm head-motion box, system-validated at a 70 cm viewing distance to maintain the spatial reliability of coordinates within the visual field. Data processing was subsequently executed via Tobii Pro Lab - Full Edition (2023), which translated raw infrared reflections into actionable biometric metrics required for the development of our predictive statistical model.

Stimuli were static luxury images (Steele & Menkes, 2016), defined as Areas of Interest (AOIs) for aesthetics and brands. mirroring real e-commerce layouts.

Stimuli and Materialist Value Orientations

During the testing phase, participants were exposed to nine high-fidelity, static visual stimuli featuring unbranded garments from premier luxury fashion designers including Chanel, Christian Dior, Dolce and Gabbana, Giorgio Armani, Gucci, Jean Paul Gaultier, Versace, Vivienne Westwood, and Valentino for an 8-second exposure cycle (Steele & Menkes, 2016). Within this computational model, the images are framed as visually dense digital displays, while the user’s psychometric profile, quantified by the Materialism Values Scale (MVS), is treated as a Materialist Value Orientations Parameter. This parameter functions as a Materialist value orientation that facilitates the categorization of user behavior based on internal value systems. By correlating these Materialism value parameters with ocular data, specifically fixation duration, frequency, and saccadic velocity, this experiment developed a predictive statistical model for visual throughout to determine how different users prioritize specific nodes of information within the digital fashion environment.

To reduce potential confounding influences, brand familiarity was treated as a source of bias because prior exposure to luxury labels may affect fixation behavior independently of the visual properties of the stimuli. In addition, the presentation order of the nine stimuli was randomized across participants to minimize order effects such as habituation, fatigue, and carryover from one image to the next. This procedure increased the likelihood that observed fixation patterns reflected the visual characteristics of each stimulus rather than its position in the sequence. Because each image was presented for a fixed interval, temporal consistency was maintained across trials, supporting comparability across conditions.

System Performance and Data Output Analysis

Dependent Variable: Total duration of whole fixations

B represents the unstandardized coefficients

Table 1. Stepwise Regression Results Predicting Total Duration of Whole Fixations

To evaluate the efficiency of the visual information network and validate the predictive capacity of our model, a Stepwise Multiple Regression was executed. Stepwise regression was adopted in the first two experiments as an exploratory variable-selection strategy because the eye-tracking indicators were likely intercorrelated and potentially redundant, a common issue in regression models with multiple related predictors. In particular, fixation-based metrics often overlap conceptually and statistically, making it useful to identify which variable contributes unique explanatory variance beyond the others. Prior methodological work recognizes that stepwise procedures can be useful for regression model building when the goal is to isolate predictors with incremental value (Babyak, 2004; Harrell, 2015). This analysis functioned as a diagnostic for visual throughout, identifying which biometric variables served as the primary drivers of total system engagement. The regression architecture was developed across three incremental stages, establishing a high-performance statistical model (see Table 1).

Predictive Modelling for Total Visual Throughput

The first phase of the analysis (Model 1) integrated the "Number of Saccades" as the primary independent variable. The results supported Hypothesis 1a, with the model demonstrating high statistical significance, F(1, 58) = 276.12, p < .001. This single variable accounted for 82.5% of the variance (Adj. R² = .825), confirming that saccadic frequency is a dominant positive predictor of processing time (ß = .91, p < .001). From an HCI standpoint, this validates that more frequent gaze shifts represent the increased computational effort required to map the high-fidelity features of the fashion network.

In the second stage (Model 2), the "Average Duration of Whole Fixations" was introduced to the system, which successfully increased the explained variance to 90.8% (Adj. R² = .908). This supported Hypothesis 1b, identifying average fixation duration as a significant positive predictor (ß = .289, p < .001) even when controlling for saccade frequency. This indicates that while the system "scans" via saccades, the dwell time on specific data packets substantially extends the overall duration of the processing cycle.

The final iteration (Model 3) incorporated the "Number of Whole Fixations on Aesthetics" as a targeted feature vector. The resulting model was significant and accounted for 91.4% of the total variance (Adj. R² = .914). Supporting Hypothesis 1c, fixations on aesthetic nodes significantly predicted total throughput duration (ß = .244, p = .028). Although aesthetic focus contributed a smaller portion of unique variance (Part correlation = .086) compared to primary scanning metrics, it represents a critical incremental value in explaining the "stickiness" of the visual stimuli.

The data output demonstrates that visual engagement within a luxury fashion network is a multifaceted computational process. While the frequency of gaze shifts (saccades) remains the most robust technical predictor of total processing time, the temporal length of individual fixations and the specific targeting of aesthetic nodes provide vital insights into the user’s cognitive priority. These findings suggest that aesthetic appeal effectively "hooks" the processor, extending the bandwidth dedicated to resolving the visual network. Having established this baseline for general stimuli, the subsequent phase of our system analysis will investigate visual fixations on branded data nodes to quantify brand-specific interaction.

Experimental Design and Data Acquisition: Branded Node Integration for Experiment 2

Experiment 2 utilized sixty London-based professionals (N=60) 48 females (75%)/12 males (25%); 73.8% under 34 and 30.3% over 35. The visual stimuli were upgraded to test the system's response to Brand Interference within the information network. Participants were exposed to a dual-state stimuli set. Nine high-fidelity unbranded images Chanel, Dior, Dolce and Gabbana, Armani, Gucci, Gaultier, Versace, Westwood, and Valentino (Steele and Menkes, 2016) and nine corresponding images integrated with brand/designer wordmarks.

To minimize potential confounding influences, there was randomization of brand presentation, ensuring each brand appears in both branded and unbranded conditions across participants in balanced order. Stimuli order was counterbalanced across participants to control for order and habituation effects. The data acquisition phase maintained the 30 Hz sampling frequency via the Tobii infrared ocular sensor. To ensure spatial precision in this more complex network, the hardware was validated through a 5-point calibration and 4-point validation protocol. Each visual node was presented for a standard 8-second exposure cycle to monitor the transition of attention between aesthetic features and brand identifiers.

Predictive Modelling for Brand-Specific Data Throughput

A second Stepwise Multiple Regression was executed to determine which biometric features successfully predict the number of fixations on brand-specific nodes (Hypotheses 2a–2c). The results, summarized in Table 2, highlight a significant shift in how the visual system prioritizes branded data.

Dependent Variable: Number of fixations on brands

B represents the unstandardized coefficients and the ß the standardized coefficients, *p<0.05 and **p<0.001

Table 2. Stepwise Regression Results Predicting the Number of Brand Fixations

Predictive Modeling for Brand-Specific Data

Throughout the initial phase of the predictive analysis, Model 1, tested Hypothesis 2a by entering the "Number of Saccades" as the primary predictor for brand-node engagement. However, the resulting relationship was not statistically significant (ß = .196, p = .133), suggesting that the raw frequency of rapid eye movements, or generalized scanning behavior, does not reliably correlate with brand-specific attention. From an HCI perspective, this implies that high-speed data scanning across the visual field does not automatically translate into the successful detection of brand-specific nodes.The subsequent iteration, Model 2, incorporated the "Average Duration of Fixations" to test Hypothesis 2b.

This significant negative correlation was observed between the number of brand fixations and the average duration of fixations across stimuli,

β=-0.263,t57

=-2.10,p

=.040 accounting for 7.6% of the system variance

(Adjusted R² = .076)

This result indicates that participants who spent longer viewing general visual features tended to fixate less frequently on branded elements. Conceptually, this reflects a cognitive trade-off in attentional allocation, that is extended dwell time on non-branded or aesthetic details reduces the temporal bandwidth available for detecting brand identifiers. In attentional network terms, prolonged fixation denotes deeper visual processing of perceptual details, which may divert resources from brand wordmarks. Hence, fixation duration inversely predicts engagement with brand-specific nodes, illustrating how sustained aesthetic inspection limits rapid brand detection within complex visual fields.

Model 3, testing Hypothesis 2c, emerged as the most robust architecture for the predictive model by incorporating the "Number of Whole Fixations on Aesthetics" as the primary driver. This final model successfully accounted for 40.3% of the variance in brand fixations (Adjusted R² = .403), with the aesthetic fixation count functioning as a highly significant positive predictor (ß = .658, p < .001).

A positive correlation was found between aesthetic engagement and brand fixations, (β=0.658, t(57)=5.68, p<.001), explaining approximately 40% of the variance in brand fixations (Radj2=.403 ). This finding demonstrates that aesthetic processing acts as a primary attentional predictor. When viewers are visually attracted to aesthetic nodes such as composition, color harmony, and design sophistication, those same regions guide the gaze toward associated brand identifiers. Thus, aesthetic appeal does not operate independently of brand processing; rather, it enhances it by spatially and cognitively channeling attention toward brand features embedded within aesthetically pleasing visual contexts. In luxury design networks, aesthetic salience serves as the key mechanism for brand recognition and engagement.

Crucially, the introduction of the aesthetic feature vector caused the previous variables, saccadic frequency and average duration, to lose their statistical significance (p > .05). This indicates that aesthetic engagement serves as the primary driver of brand attention within the luxury information network. The analysis demonstrates that visual appeal is not merely a decorative or secondary factor; rather, it functions as the primary vehicle for directing user attention toward brand-specific elements. Within this Human attention model for digital interfaces, the "aesthetic node" acts as a critical predictor, facilitating the cognitive prioritization and successful processing of brand identifiers.

Experimental Design and Data Acquisition: Experiment 3

Integrating Materialism Value Orientations into the Computational Model

Experiment 3 extends previous behavioral research (Audrin et al., 2018) by investigating how Materialism functions as a cognitive filter, directing attentional priority toward specific brand and aesthetic nodes. While traditional literature suggests that high materialism prioritizes brand signifiers over product quality, this study utilizes the Materialism Values Scale (MVS) (Richins, 2004) in conjunction with high-frequency ocular sensors (Pesa et al., 2024) to generate deeper systemic insights. Building on the data output from Experiment 2, which identified increased brand-node fixations and reduced pupil diameter as indicators of cognitive engagement, this research models the relationship between internal value systems and external brand perception. This is particularly critical given the established link between materialism and the use of luxury acquisitions as markers of success and fulfillment (Fitzmaurice, 2008; Richins, 2017; Richins and Dawson, 1992).

The pursuit of fashionable attire within a visually dense digital displays is heavily influenced by a user’s drive for self-identity and social validation (Dittmar, 2005; Lertwannawit & Mandhachitara, 2012). For users with a high materialistic feature vector, fashion serves as a primary tool for self-expression and social approval (O’Cass, 2000, 2004; Segev et al., 2015). However, the specific biometric mechanics of this dynamic remain largely unexplored in luxury consumption. By employing infrared ocular tracking, this study aims to bridge this research gap, revealing the complex interplay between materialist value systems, brand-node perception, and status-seeking behavior in the luxury market.

Participants and Biometric Setup

The experimental architecture utilized a cohort of sixty participants (N=60), maintaining demographic consistency with previous phases (48 females, 12 males; 73.8% under 34 years old). Participants were recruited via targeted digital advertisements and social media campaigns aimed at art and commerce communities to ensure a sample with varied sensitivity to luxury stimuli. The data acquisition process involved the presentation of nine fashion imagery sets, incorporating high-prestige brand identifiers (Figure Plate 1), while synchronized with the 30 Hz Tobii infrared ocular sensor array. This setup allowed for the simultaneous tracking of visual search patterns and the quantification of the user's psychometric parameters.

Psychometric Materialist Value Orientations

To quantify the internal user parameters, we employed the Material Values Scale - Short Form (Richins, 2004; 2017; Appendix 1). This instrument measures materialism across three distinct sub-dimensions: Success, Centrality, and Happiness, utilizing a 5-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). The 18-item scale demonstrated a high level of statistical reliability for measuring the materialism feature vector, yielding a Cronbach’s Alpha (alpha) of .72. This validated psychometric data was then integrated into our predictive model to determine if high scores in materialistic centrality and success predictably alter the hierarchy of visual attention when navigating branded information networks.

System Performance and Data Output Analysis

Table 3 presents the descriptive statistics and Pearson correlation coefficients for the primary biometric and psychometric variables. The data output indicates a significant divergence in how the visual system allocates bandwidth within the information network. Participants exhibited a substantially higher frequency of fixations on aesthetic nodes (M = 5.64, SD = 2.63) compared to brand-specific data nodes (M = 0.61, SD = 0.37). This disparity suggests that while brand identifiers are critical anchors, the vast majority of the "computational load" during visual search is dedicated to resolving the high-fidelity aesthetic features of the garment.

To evaluate the predictive relationship between visual attention and consumer value orientations, a correlation analysis was conducted, treating the sub-dimensions of the Materialism Values Scale Orientations. Regarding Hypothesis 3a, which proposed a link between aesthetic engagement and materialist values, the Pearson correlations analysis revealed no significant correlations with MVQ-Success (r = .13), MVQ-Centrality (r = .08), or MVQ-Happiness (r = .22). Consequently, the data fails to support the hypothesis that a user’s internal materialist framework predictably alters their processing of general aesthetic nodes. In contrast, Hypothesis 3b received partial empirical support. Brand fixations were significantly and positively related to the parameters of perceived MVQ-Success (r = .26, p < .05) and MVQ-Happiness (r = .33, p < .05), while the association with MVQ-Centrality was not significant, (r=.07, p > .05).

Aesthetic fixations were strongly correlated with brand fixations, r=.63 , indicating a robust relationship between aesthetic and brand attention. These effects should be interpreted cautiously because multiple comparisons were tested and the significant associations may not survive correction, and the sample size may have been underpowered for weak correlations.

These findings indicate that the visual processing of brands is not a generic response but is specifically moderated by the user’s internal value system regarding status and achievement. Within our Human attention model for digital interfaces, the "brand node" serves as a high-priority target for users whose  parameters emphasize success and happiness. This suggest that visual engagement with branding, rather than general aesthetic appeal, is the primary biometric marker for status-aligned cognitive processing. This correlation allows us to refine the predictive model, identifying how specific psychological "Materialist Value Orientations" can be used to forecast user interaction with branded information networks.

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Table 3. Descriptive Statistics and Bivariate Correlations

RESULTS

A hierarchical multiple regression analysis was used to assess whether MVQSuccess, MVQCentrality, and MVQHappiness predicted brand outcomes. The overall model was not statistically significant, F (3, 52) = 2.416, p = .077 and accounted for 12.2% of the variance in brand fixation outcomes (R² =.122,Adjusted R² = 0.072; Table 3). MVQSuccess significantly and positively predicted brand fixations (β=.722,p=.012;Table 4), whereas MVQHappiness significantly and negatively predicted time to first fixation on brand elements (β=-.696,p=.013). MVQCentrality was not significant (p=.790). As the overall model did not reach statistical significance, these coefficients should be interpreted cautiously.

a Predictors: (Constant), MVQSuccess, MVQCentrality, MVQHappiness.

b Dependent Variable: Time to first events on Brands

Table 4. Hierarchical Multiple Regression Model Summary on Brand Fixations as outcome variable.

a Dependent Variable: Time_to_ first_ events.brands.

Table 5. Multiple regression of Time to First events on Brands.

A multiple regression analysis was conducted to examine whether materialism dimensions (success, centrality, and happiness) predict the time to first event on aesthetics. The overall model was not statistically significant, F(3, 97) = 2.180, p = .095, explaining 6.3% of the variance (R² = .063; Adjusted R² = .034; Table 7). This indicates that, collectively, materialism variables provide limited explanatory power for predicting initial visual engagement with aesthetic elements.

Despite the non-significant overall model, two predictors made significant individual contributions. MVQ Success emerged as a positive predictor (ß = .423, p = .020; Table 6), suggesting that individuals who associate material possessions with success tend to take longer to fixate on aesthetic elements. This may indicate more deliberate or evaluative processing of visual stimuli among success-oriented individuals.

In contrast, MVQ Happiness was a significant negative predictor (ß = −.444, p = .015; Table 6), indicating that participants who associate material possessions with happiness tend to fixate more quickly on aesthetic elements. This suggests a more immediate attentional response to visual appeal among individuals driven by happiness-related material values.

a Dependent Variable: Time_to_first_Event.aesthetics.

Table 6. Regression Coefficients for the Prediction of Time to First Event (Aesthetics) Using MVQ Dimensions.

a Predictors: (Constant), MVQHappiness, MVQCentrality, MVQSuccess .

b Dependent Variable: Time_to_first_Event.aesthetics

Table 7. Multiple Regression Model for MVQ Predictors on aesthetics.