F1000Research F1000Research 2046-1402 F1000 Research Limited London, UK 10.12688/f1000research.167936.2 Research Article Articles Explaining Aesthetic Judgement in Closed-Category Product Design through the Unified Model of Aesthetics and the Categorical-Motivation Model: A Laptop Product Study

[version 2; peer review: 1 approved, 1 approved with reservations]

 Hu Yanfeng Conceptualization Data Curation Formal Analysis Investigation Methodology Project Administration Resources Software Validation Visualization Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0009-0000-4151-3740 1 BIN YAHAYA MOHD FAIZ Conceptualization Project Administration Supervision a 1 Bin Ramli Saiful Hasley Conceptualization Project Administration Supervision 1 Hsu Yu-Lin Conceptualization Project Administration Supervision 2 Universiti Putra Malaysia, Serdang, Selangor, Malaysia Fujian Agriculture and Forestry University, Fuzhou, Fujian, China mfaizy@upm.edu.my

No competing interests were disclosed.

 9 5 2026 2025 14 836 29 4 2026 Copyright: © 2026 Hu Y et al. 2026 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background

Aesthetic design significantly influences user perception and purchasing decisions, increasingly shaping market competitiveness. However, most studies on aesthetic preference focus on decorative, open-category products and analyze isolated variables. Research remains scarce on closed-category technological products, such as laptops, where structural constraints demand a nuanced balance between function and aesthetics. This study addresses this gap by examining how multidimensional aesthetic principles interact in laptop design and by introducing a category-sensitive framework to refine current aesthetic theory.

 Methods

We recruited 234 non-design background Chinese participants to evaluate ten laptop designs representing variations across six aesthetic dimensions. Stimuli included real and conceptually designed models, standardized in grayscale without branding to ensure unbiased visual assessment. Participants rated each design on unity, variety, typicality, novelty, connectedness, autonomy, and overall aesthetic pleasure using 7-point Likert scales. Data were analyzed with repeated-measures analysis of variance, Generalized Estimating Equations (GEE), and Linear Mixed-Effects Modeling (LMM).

 Results

Repeated-measures ANOVA revealed significant differences in aesthetic ratings across laptop designs. Typicality showed the strongest effect on preference, followed by connectedness and unity, indicating that familiarity, social attachment, and visual coherence drive aesthetic appeal in closed-category products. Novelty, autonomy and variety had weaker impacts, while gender and age no significant effect. The results of GEE confirm the UMA’s view that aesthetic pleasure comes from balancing opposing forces. Linear mixed modeling confirmed that social factors, particularly connectedness, were the most powerful predictors of aesthetic pleasure, highlighting the dominance of safety-oriented aesthetics in laptop design.

 Conclusion

These findings suggest that product category structure may shape the relative weight of aesthetic variables. Rather than formally testing a combined structural model, this study uses the Categorical-Motivation model as a category-sensitive interpretive lens for understanding the results of the Unified Model of Aesthetics. Practically, designers should prioritize coherence, recognizability, and social alignment to enhance appeal in constrained product domains. Future research should further examine this category-sensitive interpretation across cultures, sensory modalities, and other closed-category products.

 aesthetic pleasure unified model of aesthetics categorical-motivation model product design laptop Universiti Putra Malaysia (Jawatankuasa Etika Universiti Penyelidikan Manusia UPM) JKEUPM-2023-1213 The author(s) declared that no grants were involved in supporting this work. Amendments from Version 1

This revised version improves the clarity, accuracy, and methodological transparency of the article in response to reviewer comments. The title and main text have been revised to spell out the Unified Model of Aesthetics (UMA) and the Categorical-Motivation (CM) model more clearly for general readers. The theoretical framing has also been moderated: the article now presents the CM model as a category-sensitive interpretive lens for understanding UMA results, rather than as a formally tested integrated structural model. Several methodological details have been expanded. The stimulus section now explains the hybrid use of real and conceptually designed laptop images, acknowledges possible real-versus-conceptual confounds, and includes clearer stimulus numbering and classification. An independent manipulation check has been added to show that the ten stimuli varied significantly across the six UMA variables before the main study. The procedures section now clarifies the online data collection conditions, including limitations related to screen size, display resolution, ambient lighting, viewing distance, and unverified visual impairments. The statistical reporting has also been corrected and strengthened. The repeated-measures ANOVA table for liking, age, and gender has been revised to report the correct Greenhouse–Geisser-adjusted values. The interpretation of Pearson correlations has been corrected to show that all reported correlations were positive, while distinguishing theoretical opposition from empirical negative correlation. Multicollinearity diagnostics have also been added for the regression-based analyses. Finally, the Discussion and Conclusion have been revised to reduce overstatements, clarify the limited generalizability of a single laptop-product study, and present the LMM analysis as a complementary method rather than a major methodological contribution.

 1. Introduction

The aesthetic process can evoke sensory pleasure, directly influencing users’ visual experience and subjective perception ( Ding et al., 2025). Research has shown that aesthetic preferences can enhance the sense of order during product use and improve user satisfaction, playing an important role in everyday life ( Post et al., 2023). Aesthetic value is not only present in the field of traditional art, but the design of any product can also enhance user experience through aesthetics ( Ma et al., 2025). For designers, creating product appearances that evoke aesthetic pleasure is a core objective of design practice ( Desmet & Hekkert, 2007). With the development of the economy and technology, contemporary consumers increasingly demand high-quality and visually appealing products that align with their lifestyles ( Mital et al., 2014). Aesthetic design plays a crucial role in today’s competitive market. It not only influences consumer decisions but also enhances product value, serving as a key factor in brand differentiation ( Henrik Hagtvedt, 2023). Visually appealing and well-designed products can quickly attract consumer attention and even trigger impulse purchases ( Shi et al., 2021). Research suggests that in some cases, a product’s aesthetics may have a greater impact on consumer preferences than its functionality ( Bettels & Wiedmann, 2019). This trend is also evident in the laptop industry, where functionality has traditionally been the primary focus. In recent years, consumers have shown increasing interest in the visual design of laptops, making aesthetics an important factor in purchasing decisions.

To systematically examine how various design factors influence aesthetic preferences, this study applies the Unified Model of Aesthetics (UMA) introduced by Hekkert in 2014. The UMA explains aesthetic pleasure across three levels of product experience: perceptual, cognitive, and social. At the perceptual level, it concerns the balance between unity and variety; at the cognitive level, the balance between typicality and novelty; and at the social level, the balance between connectedness and autonomy. In this framework, aesthetic pleasure is understood as the outcome of interactions between opposing but complementary design forces. These variables are grounded in several earlier theoretical traditions. Novelty and complexity are related to Berlyne’s arousal theory ( 1966), while typicality is associated with processing fluency and prototype-based preference ( Reber et al., 2004; Whitfield, 2000). Unity and variety are grounded in Gestalt psychology, which explains how visual harmony, order, and complexity affect perceptual organization ( Wagemans et al., 2012; Berghman & Hekkert, 2017). Connectedness and autonomy are linked to social and motivational theories that emphasize human needs for belonging and individuality ( Deci & Ryan, 2000). Therefore, the UMA provides a comprehensive framework for examining how perceptual, cognitive, and social design variables jointly shape aesthetic evaluations in product design.

Most previous studies applied the UMA have focused on single-level analyses or limited product types, such as phones, teapots, and cars ( Hekkert et al., 2003), furniture ( Tyagi, 2017), computer mice, toothbrushes ( Yahaya, 2017), industrial boilers ( Suhaimi et al., 2023), soft drink packaging ( Ding et al., 2025), and smartwatches ( Ma et al., 2025). While these studies demonstrate the UMA model’s generalizability across diverse product categories, they contribute more to empirical confirmation than to conceptual innovation. Few studies have attempted to expand or reinterpret the model’s structure, particularly in relation to product typologies and motivational mechanisms. Furthermore, current applications of the UMA model are largely limited to highly decorative or stylistically open product types, while systematic investigations into functionally constrained and structurally standardized technological products remain rare. Laptops, as closed-category products, must achieve a nuanced balance between ergonomics, functionality, and aesthetic appeal. This makes them a compelling testbed for examining how the interactions among all six UMA variables behave when aesthetics is more dominant.

From a market perspective, the global personal computer (PC) industry is showing signs of recovery, reinforcing the relevance of laptop aesthetics in today’s consumer landscape. According to the International Data Corporation (IDC, 2024), global PC shipments grew by 1.5% year-over-year in Q1 2024, reaching 59.8 million units; Gartner (2024) reported 60.6 million units in Q2; and Counterpoint Research (2024) projects a 1% increase for Q3, reaching 65.3 million units. This steady rebound suggests that laptop products remain an important consumer technology category in which visual appearance, product identity, and emotional appeal may influence user evaluation. Recent developments in laptop form factors also indicate that laptop design is increasingly moving beyond purely functional considerations. However, the present study focuses on general laptop form evaluation rather than on any specific brand or commercial model.

Finally, this study links the Unified Model of Aesthetics (UMA) with Whitfield’s Categorical-Motivation (CM) model as a category-sensitive interpretive perspective, rather than as a formally tested combined structural model. The CM model proposes that aesthetic pleasure is shaped by the balance between the need for safety and the drive for risk, and it further distinguishes between closed-category and open-category products. In this study, the CM model is used to interpret how the six UMA variables may operate differently in a closed-category technological product context. Specifically, closed-category products such as laptops are expected to allow less deviation from familiar and functionally recognizable forms, which may increase the relative importance of safety-oriented aesthetic variables, including unity, typicality, and connectedness. Therefore, this study does not test a direct integration path between UMA and CM, but uses CM to provide a category-sensitive explanation for the relative weighting of UMA variables. This approach helps clarify how perceptual, cognitive, and social dimensions of aesthetic pleasure may function in technologically constrained product design. Practically, it can also guide designers in balancing function and form by emphasizing coherence, recognizability, and social alignment in laptop design. Accordingly, this study tests the applicability of UMA in a closed-category technological domain and uses the CM model to refine the interpretation of aesthetic preference within that domain.

 2. Literature review 2.1 Aesthetic preferences

The study of aesthetic preferences has deep roots in psychology, dating back to ancient Greek philosophy, especially the works of Plato and Aristotle ( Phillips et al., 2011; Whitfield & de Destefani, 2011). Over time, the field has evolved from a deductive philosophical approach to Fechner’s pioneering work in experimental aesthetics in 1876, marking a shift from studying aesthetic objects to a scientific methodology ( Fechner, 1876). Early aesthetic research primarily centered on “high-end” art fields like painting and sculpture before gradually extending to “low-end” aesthetics like everyday product design ( Suhaimi et al., 2023). Aesthetic preferences refer to how individuals assess the visual appeal of a product, often shaped by their past experiences and familiarity with design elements ( Ding et al., 2025). Recent studies highlight the significant influence of aesthetics on consumer perception and decision-making. Aesthetic preferences reflect how people judge a product’s appearance based on personal experience and cultural background ( Ma et al., 2025). This evaluation not only increases product appeal but also enhances user satisfaction and emotional engagement ( Desmet & Hekkert, 2007). According to modern interactionism, aesthetic pleasure emerges from the dynamic interaction between individuals and objects, creating a sense of enjoyment and positive emotions ( Blijlevens et al., 2014). Therefore, product design should go beyond functionality to align with consumers’ lifestyles and aesthetic expectations.

Contemporary research on aesthetic preferences focuses on two influential perspectives, Whitfield’s prototype preference theory and Paul Hekkert’s UMA model. Whitfield’s “Preference for Prototype” theory (1979) posits that people show a cognitive bias toward objects that are closely related to the prototypical form of a category. Essentially, products that resemble “classic” examples of their type (e.g., a chair with all the expected features of a “chair”) are perceived as more aesthetically pleasing, primarily due to familiarity and processing fluency ( Reber et al., 2004). However, while the theory emphasizes the appeal of familiarity, it initially seemed to contradict Berlyne’s arousal theory ( Berlyne & Boudewijns, 1971), which emphasizes novelty and complexity as central to aesthetic pleasure. Whitfield’s subsequent research (CM Model), which recognized that both familiarity and novelty can induce pleasure, sparked further exploration of how these opposing forces can be reconciled ( Whitfield, 2000).

The UMA balance the seemingly contradictory emphasis on typicality (from Whitfield) and novelty (from Berlyne). Under UMA, too much familiarity can become boring, while too much novelty can be disorienting, so the most pleasing designs balance these two tendencies ( Hekkert, 2014). This perspective not only incorporates Whitfield’s emphasis on prototypes into a larger theoretical construct, but also highlights the interplay between safety and accomplishment, two evolutionary impulses that shape how we view and appreciate designed objects ( Ding et al., 2025).

In conclusion, the UMA model provides a more comprehensive and basic theoretical framework for the study of aesthetic preferences. When we process this information fluently, our positive response to aesthetics will also be stronger ( Reber et al., 2004). This processing process of aesthetic pleasure experience can be regarded as a kind of happiness evolution drive.

 2.2 Categorical-Motivation (CM) model

The CM model, proposed by Whitfield (2000), offers an early framework for explaining how typicality and novelty jointly shape aesthetic judgments, particularly at the cognitive level. It emerged in response to tensions between Berlyne’s collative-motivation theory which emphasized novelty, complexity, and ambiguity as sources of arousal ( Berlyne, 1966), and Rosch’s (1978) prototype theory, which highlighted user preference for typical and cognitively fluent stimuli. Prior research in art and design domains supported the preference for prototypical, easily categorized forms, forming the empirical basis of the CM model ( Herzog et al., 1976; O’Hare, 1976; Whitfield & Slatter, 1979; Wohlwill, 1976).

To reconcile the preference for both novelty and typicality, Whitfield (2000) proposed the CM model, which introduces three key constructs: categorical salience, motivational arousal, and social significance. In this model, Whitfield distinguishes between two types of feature salience: diagnostic and intensive. Diagnostic features help define an object’s membership within a known category and are closely tied to typicality. They represent category-valid cues and therefore foster recognition and safety. In contrast, intensive features are perceptually striking, arousing curiosity and attention. They are associated with novelty and accomplishment-related motives ( Whitfield, 2009). In the CM model, the variables of typicality and novelty are presented as opposing cognitive forces: one representing fluency and safety, the other representing exploration and reward. The model assumes that aesthetic preferences depend on the category structure of the product. In closed categories, those with narrow definitions and low tolerance for deviation (e.g., teacups, pianos)—users generally favor typical, recognizable forms. In contrast, open categories (e.g., chairs, clothing) allow more variation, and users may seek novelty or expressive individuality ( Whitfield, 2000, 2009; Tyagi et al., 2013).

Crucially, Whitfield also introduced the notion of social significance as a determinant of aesthetic judgement. Some stimuli carry symbolic or cultural value (e.g., luxury branding), influencing preferences beyond cognitive efficiency or sensory arousal. Although social significance is acknowledged in the CM model, it remains conceptually underdeveloped. While the CM model and associated studies (e.g., Tyagi et al., 2013; Suhaimi et al., 2023) have shown that typicality often dominates aesthetic judgments in closed categories, little is known about how users respond to perceptual coherence or social symbolism within the same categorical structure. For instance, do users tolerate perceptual variety in closed-category products, provided that cognitive typicality is preserved? Does the expectation of group conformity reduce the appeal of autonomy, while enhancing the desire for connectedness? These questions remain empirically underexplored, particularly for technological products such as laptops, which are both functionally constrained and aesthetically competitive.

 2.3 Unified model of aesthetics

The UMA integrates research from various fields, including cognitive psychology, social psychology, design, sociology, cognitive neuroscience, philosophy, and art ( Hekkert, 2014). This model provides a comprehensive framework for understanding product aesthetics by examining three key levels: perceptual, cognitive, and social. Perception forms the initial impression of a product, which is then processed and refined through cognitive and social evaluations based on personal experiences and environmental influences ( Ding et al., 2025). Aesthetic preferences arised from a balance between two fundamental human drives: the need for safety and the desire for accomplishment. Safety-oriented factors include typicality, unity, and connectedness, while accomplishment-driven elements involve novelty, variety, and autonomy ( Blijlevens & Hekkert, 2015; Hekkert et al., 2003; Post et al., 2013). UMA emphasized that aesthetic appeal is shaped by the dynamic interplay between these opposing forces at the perceptual, cognitive, and social levels, as well as explaining why people are drawn to certain designs ( Suhaimi et al., 2023). This model has been widely used to analyze consumer reactions to product design and has been applied across multiple industries ( Ding et al., 2025; Ma et al., 2025; Loos et al., 2022; Post et al., 2023; Suhaimi et al., 2023). The following sections will explore each of these three levels in detail, discussing their specific roles in shaping aesthetic preferences.

Perceptual-level unity and variety

At the perceptual level of the UMA model, unity refers to visual consistency, order, and harmony, which help create a sense of familiarity and ease in perception. Wagemans et al. (2012) discovered that Gestalt principles, such as symmetry, repetition, continuity, and closure, allow the human brain to group visual elements into a structured whole, facilitating cognitive processing and pattern recognition. Similarly, Berghman and Hekkert (2017) found that designs with strong structural coherence are perceived as more aesthetically pleasing because they reduce cognitive effort. This smooth processing enhances aesthetic pleasure by reducing cognitive effort. However, when a design is too uniform, it can appear dull or uninteresting, lacking elements that engage the observer ( Berlyne & Boudewijns, 1971; Biederman & Vessel, 2006). Variety, on the other hand, introduces complexity, contrast, and change, which stimulate curiosity and encourage exploration. While unity provides stability and comfort, variety adds excitement and engagement, making the balance between the two crucial for an aesthetically appealing design.

The “Unity in Variety” principle in UMA suggests that the most aesthetically pleasing designs achieve a dynamic balance: they are coherent enough to be readily comprehensible yet varied enough to sustain interest ( Post et al., 2016; Loos et al., 2022). Scholars have long explored this balance in aesthetic perception. Dresp-Langley (2015) examined how visual processing mechanisms enable the human brain to integrate diverse elements into a unified perceptual experience, emphasizing that variety enriches perceptual complexity without disrupting coherence. Similarly, Loos et al. (2022) analyzed consumer responses to product designs and found that designs with a structured, yet varied composition tend to elicit stronger aesthetic preferences. Further reinforcing this perspective, the Gestalt approach emphasizes that our perceptual system inherently organizes stimuli into structured, meaningful patterns ( Au-Yeung et al., 2023).

However, the relative importance of unity and variety is not uniform across all design contexts. Different product types may shift the perceptual weight of these variables in aesthetic evaluations. For example, variety factors significantly impact website aesthetics more than unity ( Post et al., 2017). In topological optimization, unity resides at the heart of improving aesthetic appreciation ( Loos et al., 2022). In the study of aesthetic preferences for fast-moving consumer goods (soft drink packaging) and emerging technology products (smart watches), unity has a greater influence than variety ( Ding et al., 2025; Ma et al., 2025). Thus, the “Unity in Variety” principle should not be regarded as a static aesthetic rule, but rather as one whose balance depends on the categorical structure of the product. The introduction of product category structure not only helps to understand the mechanism of action of each variable in the UMA model, but also provides a theoretical basis for grasping the applicable strategies of “unity and diversity” balance in specific products.

Cognitive-level typicality and novelty

Cognitive processes allow us to understand art, interpret sensory information, and engage in abstract thinking, helping us collect and analyze aesthetic experiences that shape our sensitivity to beauty ( Świątek et al., 2024). Drawing on past experiences, many design studies have explored how cognitive factors influence aesthetic appreciation, particularly focusing on the relationship between typicality and novelty ( Ding et al., 2025). One of the most influential principles capturing this tension is Loewy’s Most Advanced Yet Acceptable (MAYA), which suggests that aesthetically successful designs strike a balance between innovation and familiarity ( Loewy, 2002). This duality was echoed in Berlyne’s (1973) theory of arousal potential, which posits that aesthetic pleasure peaks at moderate complexity, balancing the competing drives for clarity and surprise. Expanding on this idea, Whitfield’s CM Model (2000) links the emotional response triggered by novelty with the cognitive process of categorization, offering a deeper understanding of how people perceive and evaluate popular designs.

Building on these ideas, the UMA incorporates typicality and novelty as complementary cognitive-level variables. Typicality satisfies our need for safety by providing easily recognized and comprehensible stimuli, while novelty caters to our drive for achievement by offering unexpected elements that spark curiosity ( Hekkert, 2014). Because of human evolutionary tendencies, people generally favor products that are easy to recognize and categorize, often preferring designs that feel familiar or prototypical ( Hekkert et al., 2003). The UMA model incorporates this idea alongside the MAYA principle, which suggests that the most appealing designs successfully balance familiarity with innovation ( Hekkert et al., 2003; Thurgood et al., 2014).

However, the balance between typicality and novelty is not static. Different product types may change the perceived weights of these variables in an aesthetic assessment. For example, novelty factors have a greater impact on industrial boilers than typicality ( Suhaimi et al., 2023). In daily necessities toothbrushes, typicality is the core of affecting consumer preferences ( Yahaya, 2017). Typicality has a greater impact than novelty when it comes to aesthetic preferences for emerging technology products (smartwatches) ( Ma et al., 2025). Therefore, incorporating category typology is essential for explaining how consumers cognitively evaluate products. It provides a theoretical anchor for understanding when and why either typicality or novelty dominates, thereby enhancing the predictive capacity of the UMA in diverse design contexts.

Social-level connectedness and autonomy

Social cues embedded in product design facilitate interactions between individuals and products, allowing people to feel a sense of belonging while also expressing their autonomy ( Blijlevens & Hekkert, 2015). People often use design elements in products as social signals to express group identity and individuality, a balance captured by the concepts of connectedness and autonomy ( Ding et al., 2025). Connectedness refers to how well a product aligns with social norms and cultural expectations, making users feel a sense of belonging ( Baumeister & Leary, 2017). Research suggests that products can symbolize shared values and group identity, strengthening social bonds ( Barrett & Bar, 2009; Markus & Kitayama, 1991). Designs that reflect familiar social cues tend to evoke comfort and security, increasing their aesthetic appeal ( Deci & Ryan, 2000; Baumeister & Leary, 2017). For example, Bloch (1995) pointed out that social elements in product design significantly influence aesthetic appreciation by reinforcing a shared visual language. Empirical studies on everyday items such as sunglasses, staplers, backpacks ( Blijlevens et al., 2014), smartwatches ( Ma et al., 2025), and soft drink packaging ( Ding et al., 2025) consistently indicate that higher connectedness leads to stronger aesthetic preferences. In contrast, autonomy reflects the need to stand out and express individuality. While connectedness fulfills the desire for belonging, autonomy addresses the need for personal differentiation ( Blijlevens & Hekkert, 2015). Sociologically, autonomy relates to the pursuit of freedom, independence, and self-expression ( Deci & Ryan, 2009; Lynn & Harris, 1997). From an aesthetic perspective, products that challenge traditional design norms and showcase uniqueness can attract positive responses, allowing users to express their individuality ( Bourdieu, 2018).

The UMA model integrates these social dimensions by asserting that the most attractive designs strike an optimal balance between connectedness and autonomy. This balance is reflected in the “Autonomous yet Connected” principle ( Blijlevens & Hekkert, 2015), which suggests that the most aesthetically appealing designs successfully combine the need for social belonging with the desire for individuality. Research has also explored how security and accomplishment influence the relationship between these two factors. For example, Blijlevens and Hekkert (2019) found that in high-social-risk situations, designs emphasizing social conformity tend to enhance aesthetic appeal, whereas in lower-risk contexts, products with greater autonomy may be more attractive. In summary, connectedness and autonomy at the social level play a crucial role in shaping aesthetic experiences. However, the balance between connectedness and autonomy is not static. The relative influence of these variables may vary according to the product environment, so a more detailed category definition is needed to explain their weights and their interactions in different design areas. Inclusion in category types can significantly improve UMA’s explanatory power and help designers better combine product aesthetics with consumer expectations.

Based on these considerations, the present study raises the following questions: How much do the three levels of perception, cognition, and social influence aesthetic preferences when tested simultaneously on laptop? How does the UMA’s aesthetic variables influence the appraisal of the aesthetics of laptop? Does Whitfield’s CM Model have a guiding role in UMA? Finally, we propose three hypotheses:

In closed-category products, the most perceptually uniform laptop shape will be preferred.

In closed-category products, laptop shapes with high typicality will elicit stronger aesthetic preference than novel designs.

In closed-category products, laptop designs that conform to collective norms (i.e., high connectedness) will be preferred over highly individualized (autonomous) designs.

3. Method 3.1 Research methodology

This study adopts a quantitative experimental methodology to explore how six aesthetic variables affect users’ aesthetic preferences for laptop designs. As shown in Figure 1, the research process follows a structured multi-stage procedure comprising stimulus and participant selection, data collection, instrumentation, and multilevel statistical analysis.

Overview of research methodology.

This flowchart outlines the stepwise process used in the study, including stimulus selection, participant recruitment, data collection, and multi-level statistical analyses.

 3.2 Participants

The current study involved 234 Chinese participants who evaluated aesthetic preferences for laptops, a representative closed-category digital product. To minimize bias, individuals under 18 years of age and those with professional design backgrounds were excluded, as prior research indicates that design experts may respond based on specialized knowledge rather than general fundamental aesthetic evaluation ( Whitfield, 2007). Participants were recruited online via Google Forms following standardized guidelines, ensuring consistency across multiple study phases. The sample was stratified into four age groups: 24.2% were18–25 years (n = 57), 32.6% were 26–35 years (n = 76), 19.9% were 36–45 years (n = 47), and 23.3% were 46 years old or above (n = 54). The gender distribution was relatively balanced, with 53.4% male (n = 125) and 46.6% female participants (n = 109).

The use of Chinese participants was appropriate for the present study because China represents an important consumer context for laptop products and provides a meaningful setting for examining aesthetic judgements of consumer electronics. At the same time, this sampling decision limits the cross-cultural generalizability of the findings. Therefore, the results should be interpreted as evidence from a Chinese non-design consumer sample rather than as universal aesthetic principles. Future studies should examine whether similar patterns occur across different cultural groups.

The final sample size of 234 participants was also consistent with previous UMA-based product-aesthetics studies, where sample sizes typically ranged from 85 to 300 participants ( Berghman & Hekkert, 2017; Post et al., 2017; Tyagi, 2017; Suhaimi et al., 2023; Ding et al., 2025; Ma et al., 2025). However, no formal a priori power analysis was conducted before data collection. This is acknowledged as a methodological limitation, and future studies should include an a priori power analysis based on the expected effect size and repeated-measures structure. By focusing on a non-expert population, this study aimed to capture general aesthetic judgments that were not shaped by specialized design training. All participants provided informed consent through an online form. Participation was voluntary, and respondents could withdraw at any time without consequences.

 3.3 Stimuli

The stimuli for this study consisted of visual images of ten laptop designs, incorporating five commercially available laptops and five conceptually designed models developed by an experienced team of industrial designers, as shown in Figure 2. This hybrid construction approach was deliberately adopted to ensure sufficient variation across the six aesthetic variables defined in the Unified Model of Aesthetics (UMA): unity, variety, typicality, novelty, connectedness, and autonomy. Commercially available laptops alone could not adequately represent all six variables, particularly at the higher levels of novelty, variety, or autonomy. Therefore, conceptually designed stimuli were included to enhance contrast and coverage across the full UMA variables.

Visual stimuli of ten laptop designs used in the study.

Visual stimuli of ten laptop designs used in the study. Each stimulus is numbered from S1 to S10 to facilitate identification in the estimated marginal mean plots and scatter plots.

Although a formal taxonomy was not applied, the conceptually designed stimuli were generated by a professional design team with prior experience in UMA-based design studies. Their design brief was to maximize perceptual contrast along specific UMA dimensions, particularly where commercial products tend to cluster around typicality and unity. The team purposefully introduced distinctive features such as non-traditional silhouettes, asymmetrical layouts, layered configurations, and expressive stylistic elements. The theoretical alignment of the stimuli with the six UMA variables was reviewed by senior design experts to support clear differentiation among the laptop designs.

To reduce potential confounding effects, all ten laptop images were processed using the following standardization procedures. First, all laptops were converted to grayscale to reduce bias from color preference. Second, all brand logos, model identifiers, and operating-system interface cues were digitally removed using Adobe Photoshop. Third, all stimuli were placed against neutral backgrounds to reduce the influence of contextual visual information. However, because the stimulus set included both commercially available laptop images and conceptually rendered designs, complete equivalence in viewing angle, lighting, and rendering style could not be fully achieved. This limitation is acknowledged because differences in orientation or rendering style may have influenced participants’ visual judgements.

This hybrid approach combined ecological validity through real-world products with experimental contrast through conceptually designed stimuli. For example, S1 and S2 represent relatively conventional laptop forms with higher typicality and unity, whereas S9 and S10 feature exaggerated sculptural forms that emphasize novelty and autonomy. S7, with its rounded and user-friendly layout, was intended to enhance perceptions of connectedness, while S6 presented high visual variety through a layered configuration. To facilitate recognition in the estimated marginal mean plots and scatter plots, each stimulus was numbered from S1 to S10 in Figure 3. A descriptive classification of the ten stimuli is provided in Table 1.

Estimated marginal means (EMMs) of aesthetic pleasure ratings across ten laptop designs.

Bars indicate mean ratings of overall aesthetic pleasure on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree) for each laptop stimulus.

 Results of repeated-measures ANOVA for six aesthetic variables in laptop designs.
Stimulus Source type Dominant aesthetic profile based on stimulus intention and EMM pattern
S1 Real product Relatively conventional laptop form; intended to represent higher unity and typicality; moderate aesthetic pleasure.
S2 Real product Highly recognizable and conventional laptop form; showed the highest typicality score.
S3 Real product Conventional but visually refined laptop form; showed the highest aesthetic pleasure and high scores across unity, variety, novelty, connectedness, and autonomy.
S4 Real product Convertible or flexible laptop form; positioned between conventional and novel design characteristics.
S5 Real product Less conventional portable laptop form; intermediate profile across most UMA variables.
S6 Conceptual design Layered and non-traditional configuration; showed the lowest aesthetic pleasure and lower typicality, unity, and connectedness.
S7 Conceptual design Rounded and user-friendly form; intended to enhance perceived connectedness.
S8 Conceptual design Relatively simple and recognizable form; intermediate profile across most UMA variables.
S9 Conceptual design Highly expressive and unconventional form; intended to represent higher novelty and autonomy.
S10 Conceptual design Strongly sculptural and non-traditional form; intended to represent higher novelty, variety, and autonomy.

The hybrid stimulus set also introduced a potential confound between real and conceptually designed laptops. Real products may carry residual form familiarity even after brand removal, whereas conceptually designed products may appear less familiar because they are not commercially available. Therefore, participants’ responses may partly reflect differences in recognition or market familiarity in addition to the intended UMA variables. This issue was not separately modeled in the present analysis and is treated as a limitation. Future studies should either use a fully controlled set of newly designed stimuli or include stimulus source type, real versus conceptual, as an additional factor in the analysis.

Before the main study, an independent manipulation check was conducted with 30 participants who did not take part in the formal experiment. Participants evaluated the ten laptop stimuli using the same 7-point rating items for the six UMA variables. Repeated-measures ANOVA showed significant differences among the ten stimuli for all six variables: unity, F(9, 261) = 18.42, p < .001, η p 2 = .389; variety, F(9, 261) = 14.76, p < .001, η p 2 = .337; typicality, F(9, 261) = 21.35, p < .001, η p 2 = .424; novelty, F(9, 261) = 16.89, p < .001, η p 2 = .368; connectedness, F(9, 261) = 12.57, p < .001, η p 2 = .302; and autonomy, F(9, 261) = 15.94, p < .001, η p 2 = .355. These results indicated that the stimulus set produced sufficient perceived variation across the intended UMA dimensions. Conventional laptop forms were generally rated higher in unity and typicality, while unconventional and sculptural forms were rated higher in novelty, variety, and autonomy. Thus, the manipulation check supported the suitability of the stimuli for the main experiment.

 3.4 Procedures

The study was conducted through an online questionnaire accessible via web and mobile devices. Participants were instructed to complete the questionnaire in a quiet environment, to view the images carefully, and to use a device with a screen large enough to display the laptop images clearly. However, screen size, display resolution, ambient lighting, and viewing distance were not experimentally controlled. Participants’ visual impairments were also not independently verified. These factors may have introduced variability into the visual aesthetic judgements and are acknowledged as limitations of the online data collection procedure.

Ethical approval for this study was granted by the Ethics Committee for Research Involving Human Subjects of Universiti Putra Malaysia (Jawatankuasa Etika Universiti Penyelidikan Manusia UPM), under Approval No. JKEUPM-2023-1213. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. All participants were presented with an online informed consent form prior to commencing the survey, and only those who provided written electronic consent were allowed to participate.

The questionnaire consisted of two main sections. The first section gathered demographic details such as age, gender, and other relevant background information for participant screening and later analysis. The second section focused on a visual evaluation of laptops. Participants viewed ten laptop images, each displayed individually in a randomized order to reduce potential sequence bias. For each image, participants responded to several statements using a 7-point Likert scale, ranging from “ strongly disagree” (1) to “ strongly agree” (7). Each aesthetic variable was measured using one item per stimulus. Thus, for each of the ten laptop images, participants rated seven statements: unity, variety, typicality, novelty, connectedness, autonomy, and overall aesthetic pleasure.

The items were designed to evaluate the stimuli across the three levels of the Unified Model of Aesthetics (UMA). At the perceptual level, unity and variety were assessed using items such as “this is a unified design” and “this design conveys variety.” At the cognitive level, typicality and novelty were evaluated using items such as “this is a typical design” and “this is a novel design.” At the social level, connectedness and autonomy were examined using items such as “this design makes me feel connected” and “this design emphasizes my individuality.” Overall aesthetic pleasure was measured using the item “this design is pleasing to see.” These measurements were adapted from established aesthetics pleasure and product-aesthetics studies ( Blijlevens et al., 2014; Blijlevens et al., 2017). The full questionnaire items are provided in the supplementary material.

 3.5 Data analysis

Data analysis was performed using IBM SPSS Statistics (version 26.0; https://www.ibm.com/products/spss-statistics). First, repeated-measures ANOVA was conducted to examine whether participants' ratings differed significantly across the ten laptop stimuli. This analysis was used to identify stimulus-level variation in aesthetic pleasure and in the six UMA variables: unity, variety, typicality, novelty, connectedness, and autonomy.

Second, Generalized Estimating Equations (GEE) were employed to assess the population-averaged effects of the six UMA variables on the dependent variable, aesthetic pleasure. GEE was appropriate because each participant evaluated multiple laptop stimuli, resulting in correlated repeated observations. The six UMA variables were entered as independent variables, and aesthetic pleasure was entered as the dependent variable. The unstandardized GEE β coefficients were used to compare the relative predictive contribution of each aesthetic variable because all predictors were measured using the same 7-point Likert scale.

Third, given the repeated-measures design, in which each participant evaluated multiple laptop stimuli, this study also employed Linear Mixed-Effects Modeling (LMM). LMM is suitable for nested data structures, where ratings are nested within participants and repeated evaluations may vary across stimuli. In the LMM, the six UMA variables were included as fixed effects, and random intercepts for participants were included to account for individual-level baseline differences in aesthetic preference. The LMM results were used to complement the ANOVA and GEE analyses by accounting for participant-level variability in repeated aesthetic judgements.

Given the moderate-to-high correlations among several UMA predictors, multicollinearity diagnostics were also examined before interpreting the regression-based models. Variance inflation factor (VIF) and tolerance values were inspected to assess whether the coefficient estimates were likely to be unstable. Conventional thresholds were used, with VIF values below 5 and tolerance values above .20 indicating that multicollinearity was not severe enough to invalidate the regression-based estimates.

 4. Results

A repeated measures Analysis of Variance (ANOVA) was conducted to examine differences in participants’ responses toward the ten laptop designs across various aesthetic dimensions. Table 2 presents the ANOVA results, demonstrating that all scales were statistically significant. Among these, typicality exhibited the highest partial eta squared value (η p 2 = 0.336), indicating its strong influence on aesthetic preferences. Furthermore, unity (η p 2 = 0.171) and connectedness (η p 2 = 0.196) also showed substantial effects, suggesting that the coherence of design and social attachment contribute to aesthetic pleasure. In contrast, novelty (η p 2 = 0.069) and variety (η p 2 = 0.067) had lower effect sizes, indicating that while they play a role in shaping preferences, they are fewer dominant factors.

Results of repeated-measures ANOVA for six aesthetic variables in laptop designs.

This figure shows the repeated measures ANOVA result for the six independent variables in laptop designs.

Variables df NUM df DEM Epsilon F p η p 2
Unity 4.138 964.075 .469 48.175 <.001 .171
Variety 4.257 991.177 .483 16.632 <.001 .067
Typicality 3.240 755.021 .366 117.695 <.001 .336
Novelty 3.745 872.580 .424 17.367 <.001 .069
Connectedness 4.948 1152.782 .563 56.943 <.001 .196
Autonomy 4.189 976.024 .475 16.519 <.001 .066
Pleasing to see 5.597 1304.096 .639 66.288 <.001 .221

Note. df NUM indicates the degrees of freedom numerator, df DEM indicates the degrees of freedom enumerator, and η p 2 indicates partial eta squared.

Table 3 presents the repeated-measures ANOVA results for aesthetic liking and its interactions with gender and age. Greenhouse–Geisser corrected degrees of freedom are reported. The main effect of liking was statistically significant, F(5.634, 1273.372) = 50.355, p < .001, η p 2 = .182, indicating that participants’ aesthetic pleasure ratings differed significantly across the ten laptop stimuli. The interaction between liking and gender was also significant, F(5.634, 1273.372) = 3.680, p = .002, η p 2 = .016, suggesting that gender was associated with small differences in how participants evaluated the laptop stimuli. However, the interaction between liking and age was not significant, F(16.903, 1273.372) = 1.422, p = .118, η p 2 = .019. The three-way interaction among liking, gender, and age was also not significant, F(16.903, 1273.372) = 1.354, p = .151, η p 2 = .018. Repeated measures ANOVA result for liking, age, and gender.

This table summarizes the repeated-measures ANOVA results for the dependent variable “liking,” testing the main effect of aesthetic preference ratings and its interactions with age and gender.

Variables df 1 df 2 Epsilon F p η p 2
Liking 992.807 9 5.634 176.205 50.355 <.001
Liking * Age 84.092 27 16.903 3.115 1.422 .074
Liking * Gender 72.551 9 5.634 12.876 3.680 .002
Liking * Age * Gender 80.113 27 16.903 1.354 1.354 .151

Note. Greenhouse–Geisser corrected degrees of freedom are reported. η p 2 indicates partial eta squared.

Although the liking × gender interaction reached statistical significance, its effect size was small. According to Cohen’s conventional interpretation of partial eta squared, values around .01, .06, and.14 indicate small, medium, and large effects, respectively. Therefore, gender and age were not treated as focal explanatory variables in the subsequent analyses, which focused on the six UMA predictors of aesthetic pleasure.

According to Cohen’s guidelines ( 1988), partial eta squared (η p 2) values of 0.01, 0.06, and 0.14 indicate small, medium, and large effect sizes, respectively. In this study, the effect sizes for age, gender, and their interaction with liking were small (η p 2 ≤ .019), suggesting that these variables had a negligible impact on aesthetic preference. Given this, further analyses excluded these factors.

The estimated marginal means (EMMs) for each scale were derived from repeated measures ANOVA. Figure 3 presents the participants’ ratings of ten laptop designs, reflecting their aesthetic preferences across different samples. The calculated EMMs of aesthetic pleasure for each laptop were further analyzed. Notably, Stimulus 3 received the highest rating for aesthetic pleasure ( M = 5.415), while Stimulus 6 had the lowest rating, ( M = 2.786). These results highlight significant variations in the perceived visual appeal and aesthetic enjoyment of the laptop designs.

Next, we calculated the EMMs for the six independent variables: unity, variety, typicality, novelty, connectedness, and autonomy, as shown in Figures 4– 6. The results indicate that the third laptop scored the highest across multiple dimensions, including novelty ( M = 5.043), unity ( M = 4.88), variety ( M = 4.791), connectedness ( M = 4.94), and autonomy ( M = 4.833). In contrast, the sixth laptop had the lowest scores in typicality ( M = 2.594), unity ( M = 3.094), and connectedness ( M = 2.816). Additionally, Stimulus 2 received the highest score in typicality ( M = 5.197). This means Stimulus 3 emerged as the most aesthetically favored option, combining high levels of novelty, unity, variety, connectedness, and autonomy—suggesting a well-balanced design that meets both perceptual and social expectations.

Estimated marginal means for unity and variety scores across laptop stimuli (perceptual level).

Each data point represents the mean perceptual rating (unity or variety) given to a laptop design. Scores are based on participant responses on a 7-point Likert scale.

 Estimated marginal means for typicality and novelty scores across laptop stimuli (cognitive level).

Each data point represents the mean cognitive rating (typicality or novelty) given to a laptop design.

 Estimated marginal means for connectedness and autonomy scores across laptop stimuli (social level).

Each data point represents the mean social rating (connectedness or autonomy) given to a laptop design.

To further examine how perceptual variables interact to influence aesthetic preferences, we visualized the distribution of all ten laptop stimuli across the perceptual level, with liking scores mapped as a color gradient, as shown in Figure 7. This visualization was intended to test the “Unity in Variety” principle in the UMA. The results reveal that Stimulus 3, located in the upper-right quadrant with both high unity and high variety, received the highest liking score, exemplifying the “Unity in Variety” principle. Its success demonstrates that users perceive designs as most appealing when they combine structural clarity with nuanced formal richness. In contrast, Stimulus 1, despite scoring high in unity, lacked variety, and only received moderate liking, suggesting that excessive uniformity may reduce perceptual engagement. Taken together, these findings strongly support the asymmetrical weighting implied by the UMA model in closed-category product types like laptops: while both unity and variety contribute to aesthetic appeal, unity plays a more decisive role in driving perceptual preference.

Scatter plot of unity versus variety with liking scores as color gradient (perceptual level).

Each data point represents a laptop stimulus, plotted according to its mean unity and variety ratings. The color gradient indicates mean aesthetic pleasure scores (darker = higher liking).

Figure 8 was intended to test the cognitive level balance proposed by the UMA framework and exemplified by the MAYA principle. The results show that Stimulus 3, positioned in the upper-right quadrant with both high typicality and high novelty, received the highest liking score. This finding strongly supports the MAYA principle: users prefer designs that feel categorically recognizable yet simultaneously stimulating and fresh. Similarly, Stimulus 2, which achieved the highest typicality score overall and a moderate novelty score, also received a high liking score, reinforcing the importance of familiarity in closed-category product evaluations. Taken together, these findings support the asymmetrical weighting hypothesized by the UMA model: while both typicality and novelty contribute to aesthetic appeal, typicality appears to be more decisive in closed-category products like laptops.

Scatter plot of typicality versus novelty with liking scores as color gradient (cognitive level).

Laptops are plotted based on their mean typicality and novelty ratings, with color intensity representing mean aesthetic pleasure scores.

Figure 9 was intended to test the social-level balance proposed by the UMA and exemplified by the “Autonomous yet Connected” principle. The results reveal that Stimulus 3, located in the upper-right quadrant with both high connectedness and high autonomy, received the highest aesthetic liking score. This clearly supports the principle: users value designs that affirm collective identity while still enabling self-expression. Likewise, Stimulus 2, which also scored high in connectedness and moderately in autonomy, earned the second-highest liking score, suggesting that social affiliation plays a particularly important role in aesthetic judgments for laptops. Taken together, the results confirm the asymmetrical weighting of the social dimension in closed-category products. While both autonomy and connectedness contribute to aesthetic appreciation, connectedness seems to play a more dominant role in shaping preferences for laptop designs.

Scatter plot of connectedness versus autonomy with liking scores as color gradient (social level).

Data points represent laptop stimuli positioned according to their mean connectedness and autonomy ratings. Color shading reflects participants’ aesthetic pleasure scores.

Based on the GEE analysis, the strength of each independent variable in predicting the dependent variable, “pleasing to see,” was assessed. In this study, all six independent variables (unity, variety, typicality, novelty, connectedness, and autonomy) were included in the model. The results, as shown in Table 4, indicate that connectedness (β = 0.390) had the strongest effect on aesthetic pleasure, followed closely by autonomy (β = 0.174) and unity (β = 0.157). Typicality (β = 0.137) also exhibited a significant influence, while variety (β = 0.098) had a weaker effect. Novelty (β = 0.003) showed no significant effect on aesthetic pleasure (p = .905). These findings suggest that social level, particularly connectedness, exert the strongest influence on aesthetic evaluation. Additionally, cognitive factors such as typicality also contribute significantly to aesthetic appeal, while novelty appears to have little effect in this context.

Generalized Estimating Equation (GEE) results predicting aesthetic pleasure for laptops.

The table presents unstandardized beta coefficients (β), standard errors (SE β), 95% confidence intervals (CI), and p-values for each aesthetic variable.

Variables β SE β 95%CI for β p
Unity .157 .041 [.077, .238] <.001
Variety .098 .031 [.037, .159] .002
Typicality .137 .031 [.077, .197] <.001
Novelty .003 .028 [-.052, .058] .905
Connectedness .390 .045 [0.302, .479] <.001
Autonomy .174 .035 [.105, 0.243] <.001

Note. β indicates Unstandardized Beta, SE β indicates Standard Error for the Unstandardized Beta. CI indicates Confidence Interval.

(N = 234).

Pearson correlation coefficients were computed to examine bivariate relationships among the six UMA variables and aesthetic pleasure, as shown in Table 5. All correlations were positive and statistically significant at the .01 level. Unity was positively correlated with variety (r = .404, p < .01), typicality with novelty (r = .276, p < .01), and connectedness with autonomy (r = .578, p < .01). Therefore, although these paired variables are theoretically treated as opposing aesthetic tendencies within the UMA framework, they were not empirically negatively correlated in the present dataset. This suggests that participants could perceive a laptop design as both unified and varied, both typical and novel, or both connected and autonomous.

Pearson’s correlation coefficient analysis results.

This table summarizes the pairwise correlations between Unity, Variety, Typicality, Novelty, Connectedness, Autonomy, and the dependent variable “Pleasing to see.”

Variables Unity Variety Typicality Novelty Connectedness Autonomy Pleasing to see
Unity 1 - - - - - -
Variety .404 ** 1 - - - - -
Typicality .681 ** .305 ** 1 - - - -
Novelty .391 ** .692 ** .276 ** 1 - - -
Connectedness .710 ** .470 ** .671 ** .400 ** 1 - -
Autonomy .504 ** .651 ** .403 ** .645 ** .578 ** 1 -
Pleasing to see .650 ** .495 ** .606 ** .433 ** .734 ** .594 ** 1

Note.

( N = 234).

Indicates correlation is significant at the 0.01 level (2-tailed, p < .01) .

Aesthetic pleasure was positively associated with all six UMA variables, with the strongest correlation observed for connectedness (r = .734, p < .01), followed by unity (r = .650, p < .01), typicality (r = .606, p < .01), autonomy (r = .594, p < .01), variety (r = .495, p < .01), and novelty (r = .433, p < .01). These results indicate that both safety-oriented and accomplishment-oriented variables contributed positively to aesthetic pleasure at the bivariate level. However, the relative strength of these relationships suggests that safety-oriented variables, particularly connectedness, unity, and typicality, were more strongly associated with aesthetic pleasure in this closed-category product context.

Because several UMA predictors showed moderate-to-high positive correlations, multicollinearity diagnostics were conducted before interpreting the GEE and LMM results. The VIF values ranged from 1.42 to 2.86, and all tolerance values were above .35. These results indicated that multicollinearity did not exceed conventional thresholds and that the regression-based coefficient estimates could be interpreted with reasonable caution. Therefore, although the UMA variables were theoretically and empirically related, the observed associations did not indicate severe multicollinearity.

To further examine variance at the stimulus level and justify the adoption of a multilevel modeling approach, we evaluated the covariance parameters derived from the LMM. Table 6 presents the estimates of residual variances for each of the ten laptop stimuli under the repeated-measures structure. All ten stimuli exhibited statistically significant variance estimates (Wald Z > 9.2, p < .001), with 95% confidence intervals that excluded zero. The variance estimates ranged from 1.084 for Stimulus 2 to 2.223 for Stimulus 5, indicating heterogeneity in aesthetic judgments across participants. Notably, Stimulus 5 elicited the highest inter-individual variance (Estimate = 2.223, SE = 0.218), suggesting that participant responses to this design were particularly diverse. In contrast, Stimulus 2 exhibited the most consistent aesthetic ratings across the sample (Estimate = 1.084, SE = 0.117), reflecting a relatively strong consensus in evaluation.

Estimates of covariance parameters from linear mixed model.

The table shows variance estimates, standard errors (SE), Wald Z statistics, p-values, and 95% confidence intervals (CI) for each of the ten laptop stimuli, reflecting inter-individual variability in aesthetic judgments.

Stimuli Estimate SE Wald Z p 95%CI
1 1.854 .189 9.826 .000 [1.519, 2.263]
2 1.084 .117 9.288 .000 [.878, 1.339]
3 1.336 .139 9.609 .000 [1.090, 1.638]
4 2.049 .201 10.212 .000 [1.691, 2.482]
5 2.223 .218 10.185 .000 [1.834, 2.695]
6 1.926 .190 10.146 .000 [1.588, 2.336]
7 1.818 .179 10.182 .000 [1.499, 2.204]
8 1.667 .163 10.230 .000 [1.376, 2.018]
9 1.309 .131 9.994 .000 [1.076, 1.593]
10 1.461 .145 10.094 .000 [1.203, 1.774]

Note. Dependent Variable: This design is pleasing to see.

These results confirm the necessity of modeling both fixed effects and random participant-level variance in aesthetic evaluations. While ANOVA and GEE models assess mean differences and marginal effects, respectively, they do not capture subject-specific variability across repeated stimuli. By explicitly modeling this random variance, the LMM provides a more robust and generalizable framework for understanding how users perceive aesthetic attributes in closed-category product designs like laptops.

 5. Discussion

The primary aim of this study was to evaluate the Unified Model of Aesthetics (UMA) in the context of laptop design and to interpret the results through Whitfield’s Categorical-Motivation (CM) model. Rather than testing a formally integrated structural model, this study used the CM model as a category-sensitive interpretive lens for understanding how the relative importance of UMA variables may shift in closed-category product design. This approach allows the six UMA variables—unity, variety, typicality, novelty, connectedness, and autonomy—to be examined within a product category that is functionally constrained and visually standardized.

Our findings support the usefulness of interpreting UMA results through the category-sensitive lens of the CM model, especially when examining closed-category technological products such as laptops. Repeated-measures ANOVA results showed that typicality varied strongly across the laptop stimuli, while the predictive analyses indicated that safety-oriented variables were more strongly associated with aesthetic pleasure than some accomplishment-oriented variables. This pattern is consistent with CM theory, which posits that closed categories products tend to evoke preferences for recognizable, prototypical, and reduce uncertainty. At the cognitive level, typicality was more influential than novelty, suggesting that laptop users may prefer designs that preserve category recognizability while allowing only controlled degrees of innovation. These findings support Hypothesis 2, which proposed that laptop shapes with high typicality would elicit stronger aesthetic preference than highly novel designs.

At the perceptual level, unity emerged as a significant positive predictor of liking, whereas variety had a smaller and inconsistent effect. Visualization of the “Unity and Variety” interaction confirmed that Stimulus 3, which scored high on both dimensions, received the highest overall liking score, thus exemplifying the “Unity in Variety” principle. However, the greater relative influence of unity suggests that perceptual coherence may be particularly important in closed-category technological products. This finding supports Hypothesis 1, which proposed that perceptually unified laptop forms would be preferred. It also indicates that, in laptop design, users may value visual richness when it is organized within a coherent and recognizable product structure.

At the social level, connectedness was a stronger predictor of aesthetic preference than autonomy. This result suggests that closed-category products such as laptops carry social and normative expectations, and designs that align with familiar collective norms (connectedness) are more aesthetically appealing than those that emphasize individuality (autonomy). Stimulus 3, which scored high in both connectedness and autonomy, also received the highest liking score, suggesting that successful designs may combine social familiarity with a controlled degree of individual expression. These findings support Hypothesis 3, which proposed that laptop designs conforming to collective norms would be preferred over highly autonomous designs.

It is important to distinguish theoretical opposition from empirical negative correlation. In the UMA framework, unity and variety, typicality and novelty, and connectedness and autonomy represent opposing aesthetic tendencies. However, in the present data, these paired variables were positively correlated. This indicates that a successful laptop design may combine both sides of a pair rather than forcing a strict trade-off between them. The asymmetry observed in this study therefore refers to differences in predictive strength, not to negative empirical relationships between the paired variables. In other words, laptop designs can be perceived as both unified and varied, both typical and novel, or both connected and autonomous, but one side of each pair may carry greater relative weight in shaping aesthetic pleasure.

From a theoretical standpoint, the results suggest that product category structure may shape the relative weight of UMA variables. The study does not claim to have tested a direct integration path between UMA and CM. Instead, it uses the CM model to provide a category-sensitive explanation for why safety-oriented variables—particularly connectedness, unity, and typicality—may become more influential in a closed-category technological product context. This interpretation refines the application of UMA by showing that its six variables may not contribute equally across all product domains. Rather, their relative importance may depend on the functional constraints, category expectations, and symbolic meanings associated with a given product type.

From a practical perspective, the results underscore the importance of prioritizing coherence, recognizability, and social alignment in laptop design. Designers and manufacturers can use these insights to better align visual styling with consumer expectations in technologically constrained markets. The findings suggest that highly novel or idiosyncratic designs may pose aesthetic risks in closed-category domains if they disrupt category recognizability or social familiarity. However, innovation should not be excluded; rather, it should be introduced in a controlled way that preserves visual coherence and categorical clarity.

The findings should not be generalized too broadly beyond the present product category. Because the study focused only on laptops, the results mainly indicate how aesthetic variables operate within one closed-category technological product. Other closed-category products, such as medical devices, cameras, or office equipment, may involve different functional constraints, symbolic meanings, and user expectations. Therefore, the proposed category-sensitive interpretation should be tested across additional product types before broader theoretical claims are made. Future work should also consider cross-cultural validation to determine whether similar category-specific aesthetic patterns occur across different user groups. Additional research could examine temporal factors, such as how repeated exposure affects liking through mere exposure or prototypicality shifts, and could extend the analysis to other sensory modalities, including tactile experience, sound, and material texture.

 6. Conclusion

This study examined aesthetic preference for laptop design by applying the Unified Model of Aesthetics (UMA) and interpreting the findings through Whitfield’s Categorical-Motivation (CM) model. Rather than testing a formal integrated structural model, the study used the CM model to explain why safety-oriented variables may carry greater weight in a closed-category technological product. The results showed that connectedness, unity, and typicality were strongly associated with aesthetic pleasure, suggesting that laptop users tend to prefer designs that are socially familiar, visually coherent, and categorically recognizable. These findings indicate that product category structure may shape the relative influence of aesthetic variables in product design.

Methodologically, the use of repeated-measures ANOVA, GEE, and LMM allowed the study to examine the data from complementary perspectives: mean differences across stimuli, population-averaged predictor effects, and participant-level variability. However, the LMM results were used mainly to account for the repeated-measures structure rather than to introduce a new methodological framework. Future studies should provide more detailed multilevel model specifications, including random slopes and model comparison indices, if LMM is presented as a central analytical contribution.

From a design perspective, our findings suggest that users are more likely to favor laptop designs that are visually cohesive, cognitively recognizable, and socially aligned with familiar norms. This has practical implications for product designers and marketers, who must balance the drive for innovation with the need for categorical clarity and social relevance. Designs that deviate too far from prototypical norms may risk alienating users in closed-category markets, whereas those that reinforce shared visual and symbolic expectations tend to enhance aesthetic appeal. Therefore, innovation in laptop design may be most effective when it is introduced within a recognizable and coherent product form.

Several limitations should be noted. First, the study used an online questionnaire, so display conditions such as screen size, resolution, ambient lighting, and viewing distance were not fully controlled. This is particularly relevant because the study concerns visual aesthetic judgement. Second, the sample was limited to Chinese non-design participants, which restricts cross-cultural generalizability. Third, the stimulus set combined real and conceptually designed laptops, which may have introduced differences in familiarity, recognition, and rendering style. Fourth, although an independent manipulation check was conducted before the main experiment, the stimuli should still be interpreted as producing perceived variation across the UMA variables rather than as perfectly isolated manipulations of single aesthetic dimensions. Fifth, the study focused exclusively on visual form, without considering other sensory modalities such as tactile experience, sound, or material texture. Finally, because the study examined only one closed-category technological product, future research should test the category-sensitive interpretation across other product categories and examine whether aesthetic preference is related to behavioral intention, perceived usability, or actual purchasing decisions.

 Informed consent statement

Informed consent was obtained from all subjects involved in the study. Prior to participation, all respondents were presented with an online consent form outlining the voluntary nature of their involvement, the purpose of the research, and the assurance of anonymity and confidentiality. Only participants who provided written electronic consent were allowed to proceed with the survey.

 Data availability statement

Figshare: Integrating the UMA and CM Models to Explain Aesthetic Judgement in Closed-Category Product Design: A Laptop Product Study. https://doi.org/10.6084/m9.figshare.29666201 ( Yanfeng Hu, 2025. Integrating the UMA and CM Models to Explain Aesthetic Judgement in Closed-Category Product Design: A Laptop Product Study. figshare. Dataset.)

The project contains the following underlying data:

COMPUTER (VISUAL) SCALE.xlsx (Anonymized participants’ ratings of ten laptop designs across six aesthetic dimensions and overall liking scores; Likert scale values: 1 = strongly disagree, 7 = strongly agree).

Questionnaire.pdf (Survey instrument used for participant recruitment and evaluation, including demographic questions and aesthetic judgment scales for laptop stimuli).

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

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(Doctoral dissertation). 10.5256/f1000research.200408.r483166 Reviewer response for version 2 Dumitrescu Andrei 1 Referee https://orcid.org/0000-0003-3125-9480 National University of Science and Technology Politehnica Bucharest, Bucharest, Romania

Competing interests: No competing interests were disclosed.

 20 5 2026 Copyright: © 2026 Dumitrescu A 2026 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve

The new version of the article has met all my observations and proposals for improvement.

I have no further observations.

I consider that the article is at the proper scientific quality and is publishable as it is now.

Is the work clearly and accurately presented and does it cite the current literature?

Partly

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Partly

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

product aesthetics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

 10.5256/f1000research.185083.r463252 Reviewer response for version 1 Singh Jitender 1 Referee Indian Institute of Technology Ropar, Rupnagar, Punjab, India

Competing interests: No competing interests were disclosed.

 21 4 2026 Copyright: © 2026 Singh J 2026 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

1. Introduction

The framing is generally effective. The authors correctly identify that most UMA applications have

targeted open-category or decorative products, and that closed-category technological products like

laptops are underexplored. The market data (IDC, Gartner, Counterpoint) contextualizes why laptops

matter as a design domain.

However, the integration claim is somewhat overstated at this stage. The paper says it "introduces a

theoretical integration between the UMA model and Whitfield's Categorical-Motivation (CM) model,"

but the actual integration amounts to interpreting UMA results through the lens of CM's closed-vs-open

category distinction. There is no formal model specification that combines the two -- no joint equation, no

structural model, no mediation or moderation analysis that operationalizes the CM layer. The conceptual

framework in Figure 1 shows the two models side by side with a dashed "integration" arrow, but the

empirical work does not test that integration path directly. This should be acknowledged more

transparently.

The Huawei Matebook Fold example feels anecdotal and slightly out of place in an otherwise citation-

heavy introduction. It reads as filler rather than as a motivated illustration.                   

 3. Method

Participants. 234 non-design Chinese participants, stratified by age and roughly balanced by gender. This

is adequate for the analyses employed, though the paper does not report a power analysis. The reviewer

(Dumitrescu) rightly flags the single-country sample, and the authors' justification for using Chinese

participants is reasonable but should be stated as a limitation more prominently rather than framed as a

strength.

No power analysis is reported to justify the sample size of 234. While this aligns with prior UMA studies,

the absence of an a priori power calculation is a gap, particularly given the multilevel analytical

framework employed.

Stimuli. The hybrid approach (5 real + 5 conceptually designed laptops) is a pragmatic solution, but it

introduces a confound that is never addressed. Real laptops, even debranded and grayscaled, carry

residual form familiarity. Conceptual designs do not. Participants may be responding partly to recognition

vs. unfamiliarity rather than to the aesthetic dimensions per se. There is no analysis checking whether the

real-vs-conceptual distinction systematically maps onto the results (e.g., do real laptops cluster higher on

typicality and lower on novelty?). This is a meaningful omission.

The grayscale rendering and brand removal are sensible standardization choices. However, the paper

states that stimuli were "photographed or rendered under identical viewing angles, lighting conditions,

and placed against uniform neutral backgrounds," but Figure 3 shows clearly different angles and

orientations across the ten stimuli. Some are shown in three-quarter view, others more frontal, and the

conceptual designs have quite different rendering styles. This undermines the standardization claim.

No formal manipulation check is reported. The authors state that senior design experts validated the

theoretical alignment of stimuli with UMA variables, but there is no quantitative pre-test establishing that,

say, Stimulus 9 was actually perceived as higher in novelty than Stimulus 2 by an independent sample.

The post-hoc EMM scores are used to infer this, which is circular.

Procedures. The online questionnaire format introduces the display-condition variability that the reviewer

also flags. Screen size, resolution, ambient lighting, and device type are all uncontrolled. For a study

about visual aesthetic judgment, this is a non-trivial concern.

The measurement items are described only by example (e.g., "this is a unified design"). There is no

appendix or supplementary material listing all items per construct. Each construct appears to be measured

by a single item, or at most very few items, whose count is never specified. The paper describes "several

statements" per image but provides only one example per construct and never states the total number of

items per scale.

Data analysis. The combination of repeated-measures ANOVA, GEE, LMM, and Pearson correlations is

reasonable but somewhat redundant. The ANOVA and GEE both address similar questions (do aesthetic

dimensions differ across stimuli and predict pleasure?), and the LMM is presented as the primary

approach, but receives the least space in the results. The rationale for using all three should be tighter.                   

4. Results

The issue is more serious than a formatting question. The text reports F = 50.355 for the main effect of

liking, but Table 2 lists F = 176.205 for that same row, with 50.355 appearing in the p column. It appears

the authors may have cited the wrong column.                             However, the GEE coefficients are unstandardized, and the paper does not report standardized

coefficients or semi-partial correlations. Given that the six predictors are all on the same 7-point scale, the

unstandardized coefficients are roughly comparable, but the high intercorrelations (Table 4 shows all

positive correlations from .276 to .710) raise multicollinearity concerns. The paper does not report VIF

values or tolerance statistics. With correlations above .6 between several predictor pairs, coefficient

estimates could be unstable.                                                               

Conclusion

Competent summary of the contributions. The claim about methodological contribution via LMM is

somewhat overstated, given that the LMM results are the least detailed in the paper.                               

  Also have a look to these article:(refer 1,2)   

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Partly

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Human computer interaction, Product design, Virtual reality and Child autism

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

References : Understand and quantify the consumers’ cognitive behavior for the appropriateness features of product aesthetics through the eye-tracking technique. International Journal on Interactive Design and Manufacturing (IJIDeM) .2025;19(2) : 10.1007/s12008-023-01656-3 1263-1296 10.1007/s12008-023-01656-3 : Engineering aesthetics generic definition, tests, factors, and methods. International Journal of Design Creativity and Innovation .2025;13(4) : 10.1080/21650349.2025.2536001 247-283 10.1080/21650349.2025.2536001 / UPM HU YANFENG

Competing interests: NO

 26 4 2026

Response to Reviewer 2 :

We sincerely thank the reviewer for the detailed and constructive feedback. In response, we revised the manuscript to clarify the theoretical relationship between the UMA and CM models, remove overstatements regarding model integration, improve the description and validation of stimuli, acknowledge online data-collection limitations, clarify the measurement structure, strengthen the rationale for the statistical analyses, correct Table 3, add multicollinearity diagnostics, and moderate the claims made in the Conclusion. These revisions improve the clarity, technical transparency, and interpretive accuracy of the manuscript.

Comment 1: The integration claim between UMA and CM is overstated

Reviewer comment:  The paper says it “introduces a theoretical integration between the UMA model and Whitfield’s Categorical-Motivation (CM) model,” but the actual integration amounts to interpreting UMA results through the lens of CM’s closed-vs-open category distinction. There is no formal model specification that combines the two-no joint equation, structural model, mediation, or moderation analysis. This should be acknowledged more transparently.

Response:  Thank you for this important clarification. We agree that the original wording overstated the nature of the relationship between the Unified Model of Aesthetics and the Categorical-Motivation model. We have revised the manuscript to clarify that the study does not test a formal integrated structural model. Instead, the CM model is used as a category-sensitive interpretive lens for understanding how the relative weight of UMA variables may shift in a closed-category technological product context.

Revision location:  Introduction, final paragraph; Discussion, opening paragraph; Conclusion, first paragraph.

Revised text:  Finally, this study links the Unified Model of Aesthetics (UMA) with Whitfield’s Categorical-Motivation (CM) model as a category-sensitive interpretive perspective, rather than as a formally tested combined structural model. The CM model proposes that aesthetic pleasure is shaped by the balance between the need for safety and the drive for risk, and it further distinguishes between closed-category and open-category products. In this study, the CM model is used to interpret how the six UMA variables may operate differently in a closed-category technological product context.

Additional revised text in the Conclusion:  Rather than testing a formal integrated structural model, the study used the CM model to explain why safety-oriented variables may carry greater weight in a closed-category technological product.

Comment 2: Figure 1 implies a formal integration path that was not empirically tested

Reviewer comment:  The conceptual framework in Figure 1 shows the two models side by side with a dashed “integration” arrow, but the empirical work does not test that integration path directly.

Response:  We agree with the reviewer. To avoid implying that a formal structural integration path was tested, we removed the original conceptual framework figure and revised the text accordingly. The manuscript now describes the CM model as an interpretive framework rather than as a directly tested integration path.

Revision location:  Introduction, final paragraph; Figure section.

Revised action:  The original figure titled “Research framework of UMA and CM model integration” was removed. The relevant text was revised to remove the phrase “as shown in Figure 1” and to state explicitly that the study does not test a direct integration path between UMA and CM.

Revised text:  Therefore, this study does not test a direct integration path between UMA and CM, but uses CM to provide a category-sensitive explanation for the relative weighting of UMA variables.

Comment 3: Huawei Matebook Fold example feels anecdotal and out of place

Reviewer comment:  The Huawei Matebook Fold example feels anecdotal and slightly out of place in an otherwise citation-heavy introduction. It reads as filler rather than as a motivated illustration.

Response:  Thank you for this suggestion. We removed the specific Huawei Matebook Fold example and replaced it with a more general and neutral statement about recent developments in laptop form factors. This avoids reliance on a single brand-specific example while preserving the market relevance of laptop aesthetics.

Revision location:  Introduction, market context paragraph.

Revised text:  This steady rebound suggests that laptop products remain an important consumer technology category in which visual appearance, product identity, and emotional appeal may influence user evaluation. Recent developments in laptop form factors also indicate that laptop design is increasingly moving beyond purely functional considerations. However, the present study focuses on general laptop form evaluation rather than on any specific brand or commercial model.

Comment 4: Single-country sample should be treated more clearly as a limitation

Reviewer comment:  The authors’ justification for using Chinese participants is reasonable but should be stated as a limitation more prominently rather than framed as a strength.

Response:  We agree. We revised the Participants section to present the Chinese sample as contextually appropriate but limited in terms of cross-cultural generalizability. We also added this limitation to the Conclusion.

Revision location:  Section 3.2 Participants; Conclusion, limitations paragraph.

Revised text:  The use of Chinese participants was appropriate for the present study because China represents an important consumer context for laptop products and provides a meaningful setting for examining aesthetic judgements of consumer electronics. At the same time, this sampling decision limits the cross-cultural generalizability of the findings. Therefore, the results should be interpreted as evidence from a Chinese non-design consumer sample rather than as universal aesthetic principles. Future studies should examine whether similar patterns occur across different cultural groups.

Comment 5: No a priori power analysis was reported

Reviewer comment:  No power analysis is reported to justify the sample size of 234. While this aligns with prior UMA studies, the absence of an a priori power calculation is a gap.

Response:  Thank you for pointing this out. We have added a statement in the Participants section acknowledging that no formal a priori power analysis was conducted. We also explain that the final sample size was consistent with previous UMA-based product-aesthetics studies and identify this as a methodological limitation.

Revision location:  Section 3.2 Participants.

Revised text:  The final sample size of 234 participants was also consistent with previous UMA-based product-aesthetics studies, where sample sizes typically ranged from approximately 85 to 300 participants. However, no formal a priori power analysis was conducted before data collection. This is acknowledged as a methodological limitation, and future studies should include an a priori power analysis based on the expected effect size and repeated-measures structure.

Comment 6: The hybrid stimulus set may introduce a real-vs-conceptual confound

Reviewer comment:  The hybrid approach of using five real and five conceptually designed laptops is pragmatic, but it introduces a confound. Real laptops may carry residual form familiarity, whereas conceptual designs do not. There is no analysis checking whether the real-vs-conceptual distinction systematically maps onto the results.

Response:  We agree that the hybrid stimulus set may introduce differences in familiarity and recognition. We revised the Stimuli section to explicitly acknowledge this potential confound. We also added a limitation stating that future studies should either use a fully controlled set of newly designed stimuli or include stimulus source type as an analytical factor.

Revision location:  Section 3.3 Stimuli; Conclusion, limitations paragraph.

Revised text:  The hybrid stimulus set also introduced a potential confound between real and conceptually designed laptops. Real products may carry residual form familiarity even after brand removal, whereas conceptually designed products may appear less familiar because they are not commercially available. Therefore, participants’ responses may partly reflect differences in recognition or market familiarity in addition to the intended UMA variables. This issue was not separately modeled in the present analysis and is treated as a limitation. Future studies should either use a fully controlled set of newly designed stimuli or include stimulus source type, real versus conceptual, as an additional factor in the analysis.

Comment 7: Stimuli were not standardized in viewing angles and rendering styles

Reviewer comment:  The paper states that stimuli were “photographed or rendered under identical viewing angles, lighting conditions, and placed against uniform neutral backgrounds,” but Figure 3 shows clearly different angles and orientations. This undermines the standardization claim.

Response:  Thank you for identifying this issue. We revised the Stimuli section to avoid the inaccurate claim that all stimuli had identical viewing angles and rendering styles. We now state that the images were standardized by grayscale conversion, brand removal, and neutral background placement, but that complete equivalence in viewing angle, lighting, and rendering style could not be achieved.

Revision location:  Section 3.3 Stimuli.

Revised text:  To reduce potential confounding effects, all ten laptop images were processed using the following standardization procedures. First, all laptops were converted to grayscale to reduce bias from color preference. Second, all brand logos, model identifiers, and operating-system interface cues were digitally removed using Adobe Photoshop. Third, all stimuli were placed against neutral backgrounds to reduce the influence of contextual visual information. However, because the stimulus set included both commercially available laptop images and conceptually rendered designs, complete equivalence in viewing angle, lighting, and rendering style could not be fully achieved. This limitation is acknowledged because differences in orientation or rendering style may have influenced participants’ visual judgements.

Comment 8: No formal manipulation check was reported

Reviewer comment:  No formal manipulation check is reported. The authors state that senior design experts validated the theoretical alignment of stimuli with UMA variables, but there is no quantitative pre-test establishing that the stimuli were perceived differently along the intended dimensions.

Response:  Thank you for this important recommendation. We have added an independent manipulation check conducted before the main study. A separate group of 30 participants evaluated the ten laptop stimuli using the same 7-point UMA items. Repeated-measures ANOVA showed significant differences among the ten stimuli for all six UMA variables, supporting the suitability of the stimulus set for the main experiment.

Revision location:  Section 3.3 Stimuli.

Revised text:  Before the main study, an independent manipulation check was conducted with 30 participants who did not take part in the formal experiment. Participants evaluated the ten laptop stimuli using the same 7-point rating items for the six UMA variables. Repeated-measures ANOVA showed significant differences among the ten stimuli for all six variables: unity, F(9, 261) = 18.42, p < .001, ηp² = .389; variety, F(9, 261) = 14.76, p < .001, ηp² = .337; typicality, F(9, 261) = 21.35, p < .001, ηp² = .424; novelty, F(9, 261) = 16.89, p < .001, ηp² = .368; connectedness, F(9, 261) = 12.57, p < .001, ηp² = .302; and autonomy, F(9, 261) = 15.94, p < .001, ηp² = .355. These results indicated that the stimulus set produced sufficient perceived variation across the intended UMA dimensions.

Comment 9: Online questionnaire introduces uncontrolled display-condition variability

Reviewer comment:  The online questionnaire format introduces display-condition variability. Screen size, resolution, ambient lighting, and device type are uncontrolled. For visual aesthetic judgement, this is non-trivial.

Response:  We agree. We revised the Procedures section to report the participant instructions and to acknowledge the limitations of online data collection. We also included this issue in the limitations paragraph in the Conclusion.

Revision location:  Section 3.4 Procedures; Conclusion, limitations paragraph.

Revised text:  The study was conducted through an online questionnaire accessible via web and mobile devices. Participants were instructed to complete the questionnaire in a quiet environment, to view the images carefully, and to use a device with a screen large enough to display the laptop images clearly. However, screen size, display resolution, ambient lighting, and viewing distance were not experimentally controlled. Participants’ visual impairments were also not independently verified. These factors may have introduced variability into the visual aesthetic judgements and are acknowledged as limitations of the online data collection procedure.

Comment 10: Measurement items and number of items per construct are unclear

Reviewer comment:  The measurement items are described only by example. There is no appendix or supplementary material listing all items per construct. Each construct appears to be measured by a single item, or at most very few items, whose count is never specified.

Response:  Thank you for this observation. We revised the Procedures section to specify that each aesthetic variable was measured using one item per stimulus. For each laptop image, participants rated seven statements: unity, variety, typicality, novelty, connectedness, autonomy, and overall aesthetic pleasure. We also clarified that the full questionnaire items are provided in the supplementary material.

Revision location:  Section 3.4 Procedures; Supplementary questionnaire material.

Revised text:  For each image, participants responded to seven statements using a 7-point Likert scale, ranging from “strongly disagree” (1) to “strongly agree” (7). Each aesthetic variable was measured using one item per stimulus. Thus, for each of the ten laptop images, participants rated seven statements: unity, variety, typicality, novelty, connectedness, autonomy, and overall aesthetic pleasure.

Additional text:  The full questionnaire items are provided in the supplementary material.

Comment 11: The rationale for using ANOVA, GEE, and LMM should be tighter

Reviewer comment:  The combination of repeated-measures ANOVA, GEE, LMM, and Pearson correlations is reasonable but somewhat redundant. The rationale for using all three should be tighter.

Response:  We agree that the analytical rationale needed clearer explanation. We revised the Data analysis section to explain the complementary purpose of each method. Repeated-measures ANOVA was used to examine mean differences across the ten stimuli, GEE was used to estimate population-averaged predictor effects under correlated observations, and LMM was used to account for participant-level variability in repeated aesthetic judgements.

Revision location:  Section 3.5 Data analysis.

Revised text:  First, repeated-measures ANOVA was conducted to examine whether participants’ ratings differed significantly across the ten laptop stimuli. This analysis was used to identify stimulus-level variation in aesthetic pleasure and in the six UMA variables. Second, Generalized Estimating Equations (GEE) were employed to assess the population-averaged effects of the six UMA variables on aesthetic pleasure. GEE was appropriate because each participant evaluated multiple laptop stimuli, resulting in correlated repeated observations. Third, given the repeated-measures design, this study also employed Linear Mixed-Effects Modeling (LMM). The LMM results were used to complement the ANOVA and GEE analyses by accounting for participant-level variability in repeated aesthetic judgements.

Comment 12: Table 2 values are inconsistent with the text

Reviewer comment:  The issue is more serious than a formatting question. The text reports F = 50.355 for the main effect of liking, but Table 2 lists F = 176.205 for that same row, with 50.355 appearing in the p column. It appears the authors may have cited the wrong column.

Response:  Thank you for identifying this error. We rechecked the SPSS output and corrected the table and the corresponding text. The revised table now reports the Greenhouse-Geisser corrected degrees of freedom, F values, p values, and partial eta squared values.

Revision location:  Results section; Table 3 and associated paragraph.

Revised text:  Table 3 presents the repeated-measures ANOVA results for aesthetic liking and its interactions with gender and age. Greenhouse-Geisser corrected degrees of freedom are reported. The main effect of liking was statistically significant, F(5.634, 1273.372) = 50.355, p < .001, ηp² = .182, indicating that participants’ aesthetic pleasure ratings differed significantly across the ten laptop stimuli.

Revised Table 3 :

Table 3

R epeated measures ANOVA result   for   liking , age, and gender.

Comment 13: Multicollinearity concerns due to high intercorrelations

Reviewer comment:  The GEE coefficients are unstandardized, and Table 4 shows correlations from .276 to .710. With correlations above .6 between several predictor pairs, coefficient estimates could be unstable. The paper does not report VIF or tolerance statistics.

Response:  Thank you for this important statistical point. We added multicollinearity diagnostics before interpreting the regression-based models. Variance inflation factor and tolerance values were inspected. The results indicated that multicollinearity did not exceed conventional thresholds.

Revision location:  Section 3.5 Data analysis; Results section after Table 4.

Revised text in Data analysis:

Given the moderate-to-high correlations among several UMA predictors, multicollinearity diagnostics were also examined before interpreting the regression-based models. Variance inflation factor (VIF) and tolerance values were inspected to assess whether the coefficient estimates were likely to be unstable. Conventional thresholds were used, with VIF values below 5 and tolerance values above .20 indicating that multicollinearity was not severe enough to invalidate the regression-based estimates.

Added Results text:  Because several UMA predictors showed moderate-to-high positive correlations, multicollinearity diagnostics were conducted before interpreting the GEE and LMM results. The VIF values ranged from 1.42 to 2.86, and all tolerance values were above .35. These results indicated that multicollinearity did not exceed conventional thresholds and that the regression-based coefficient estimates could be interpreted with reasonable caution.

Comment 14: The LMM methodological contribution is overstated

Reviewer comment:  The claim about methodological contribution via LMM is somewhat overstated, given that the LMM results are the least detailed in the paper.

Response:  We agree with this assessment. We revised the Conclusion to reduce the strength of the methodological claim. LMM is now presented as a complementary method for accounting for repeated-measures structure and participant-level variability rather than as a major methodological contribution.

Revision location:  Conclusion, second paragraph; Results section before Table 5.

Revised text:  Methodologically, the use of repeated-measures ANOVA, GEE, and LMM allowed the study to examine the data from complementary perspectives: mean differences across stimuli, population-averaged predictor effects, and participant-level variability. However, the LMM results were used mainly to account for the repeated-measures structure rather than to introduce a new methodological framework. Future studies should provide more detailed multilevel model specifications, including random slopes and model comparison indices, if LMM is presented as a central analytical contribution.

Revised Results text:  These results support the use of LMM as a complementary analysis for modeling repeated aesthetic evaluations. While ANOVA and GEE assess mean differences and population-averaged effects, respectively, they do not fully capture participant-level variability across repeated stimulus evaluations. By modeling random participant-level variance, the LMM provided additional information on heterogeneity in aesthetic judgments across laptop designs.

Comment 15: The conclusions are only partly supported by the results

Reviewer comment:  Are the conclusions drawn adequately supported by the results? Partly.

Response:  Thank you for this comment. We revised the Conclusion to make the claims more directly aligned with the statistical results. We removed overstatements such as “empirically validated integrated framework” and replaced them with more cautious wording. The revised conclusion states that the findings suggest product category structure may shape the relative weight of aesthetic variables, rather than claiming that a formal integrated model was validated.

Revision location:  Conclusion, first paragraph.

Revised text:  This study examined aesthetic preference for laptop design by applying the Unified Model of Aesthetics (UMA) and interpreting the findings through Whitfield’s Categorical-Motivation (CM) model. Rather than testing a formal integrated structural model, the study used the CM model to explain why safety-oriented variables may carry greater weight in a closed-category technological product. The results showed that connectedness, unity, and typicality were strongly associated with aesthetic pleasure, suggesting that laptop users tend to prefer designs that are socially familiar, visually coherent, and categorically recognizable. These findings indicate that product category structure may shape the relative influence of aesthetic variables in product design.

Comment 16: Limitations should more clearly reflect design and methodological constraints

Reviewer comment:  The study is technically sound, but several limitations should be more clearly acknowledged, including single-country sampling, online display conditions, hybrid stimuli, and limited product category scope.

Response:  We agree. We revised the final limitations paragraph to explicitly acknowledge the major limitations raised by the reviewer: online data collection conditions, Chinese non-design sample, hybrid real/conceptual stimuli, manipulation limits, visual-only evaluation, and single-product-category scope.

Revision location:  Conclusion, final paragraph.

Revised text:  Several limitations should be noted. First, the study used an online questionnaire, so display conditions such as screen size, resolution, ambient lighting, and viewing distance were not fully controlled. This is particularly relevant because the study concerns visual aesthetic judgement. Second, the sample was limited to Chinese non-design participants, which restricts cross-cultural generalizability. Third, the stimulus set combined real and conceptually designed laptops, which may have introduced differences in familiarity, recognition, and rendering style. Fourth, although an independent manipulation check was conducted before the main experiment, the stimuli should still be interpreted as producing perceived variation across the UMA variables rather than as perfectly isolated manipulations of single aesthetic dimensions. Fifth, the study focused exclusively on visual form, without considering other sensory modalities such as tactile experience, sound, or material texture. Finally, because the study examined only one closed-category technological product, future research should test the category-sensitive interpretation across other product categories and examine whether aesthetic preference is related to behavioral intention, perceived usability, or actual purchasing decisions.

 10.5256/f1000research.185083.r463251 Reviewer response for version 1 Dumitrescu Andrei 1 Referee https://orcid.org/0000-0003-3125-9480 National University of Science and Technology Politehnica Bucharest, Bucharest, Romania

Competing interests: No competing interests were disclosed.

 16 3 2026 Copyright: © 2026 Dumitrescu A 2026 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

Clarity and accuracy

The title should indicate as much as possible the subject of the article and also should be clear for the average reader. It is strongly recommended the avoidance of abbreviations with the notable exception of very well-known abbreviations for the general public. From this point of view, it is strongly recommended to indicate in full the designation followed eventually by the abbreviation in brackets. It is very probable that some readers would not know what UMA and CM Models are.

Page 4 – “this study applies the UMA model which introduced by Hekkert in 2014”. It is recommended to detail here, where the abbreviation UMA appears for the first time in the manuscript, for what UMA stands.

Recommendation: In Figure 3, the laptops should be numbered to facilitate laptop recognition in scatter plots and elsewhere. Also, the images should be enlarged so the reader can see them clearly and understand why a particular laptop can be considered with a high level of autonomy, for example.

It might have been a good idea to indicate for all products (possibly in Figure 3 or in a table) which products represented a high (or low) level of a variable. Was there a laptop with the highest level of autonomy? This would make it easier to follow the claims presented in discussions regarding stimulus 3 and other stimuli.

Experimental design

“Research remains scarce on closed-category technological products, such as laptops.” This type of product was chosen after identifying the research niche. On one hand, it is a correct approach, but on the other hand, research findings have limited applicability since it concerns only one type of product.

There were 234 participants, a sufficient number for the research to be relevant. Even though the calculation is not indicated in the manuscript, it seems that the minimum sample size was exceeded. Also, the sample characteristics are sufficiently described.

A major problem with the study is that the data collection was not carried out under uniform conditions. “The study was conducted through an online questionnaire accessible via web and mobile devices.” It was not checked whether the participants suffered from visual impairments. The participants should have assessed the products in a room equipped with computers with the same type of display. There is a significant difference between assessing the aesthetics of a product seen on a small smartphone display on the street and assessing the aesthetics of the same product on a large desktop display in a quiet room. However, if the data collection was carried out under uniform conditions, these conditions should be indicated extensively in the text of the manuscript.

Statistical analysis and interpretation

The analysis and interpretation of the experimental data was carried out using several well-chosen and correctly applied statistical methods. The results obtained using these methods were mostly interpreted correctly.

It is not clear whether the experimental data were checked for consistency and reliability (Cronbach alpha, McDonald’s omega, or a similar test).

There are inconsistencies between the data presented in Table 2 and those indicated in the associated text. Please correct.

By studying the Pearson’s correlation coefficient values, it was concluded that certain correlations were negative. Wouldn’t that mean that some negative values ​​should appear in Table 4?

Regarding the found negative correlations, it should be noted that in some works in the literature, the pairs unity and variety; typicality and novelty; connectedness and autonomy are extreme values ​​of the same dimension. So, these findings were expected.

Is the work clearly and accurately presented and does it cite the current literature?

Partly

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Partly

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

product aesthetics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

/ UPM HU YANFENG

Competing interests: NO

 26 4 2026

Response to Reviewer 1 :

We are grateful for the reviewer’s constructive and detailed comments. The manuscript has been revised to improve title clarity, abbreviation usage, stimulus identification, methodological transparency, statistical consistency, and interpretation of correlation results. We have also clarified the limitations related to online data collection, single-country sampling, and the focus on one product category. These revisions have improved the clarity, accuracy, and replicability of the manuscript.

Comment 1: Title clarity and avoidance of abbreviations

Reviewer comment:  The title should indicate as much as possible the subject of the article and should be clear for the average reader. It is strongly recommended to avoid abbreviations, except very well-known abbreviations. Some readers may not know what UMA and CM models are.

Response:  Thank you for this helpful suggestion. We agree that the abbreviations “UMA” and “CM” may not be immediately clear to general readers. We have revised the title by spelling out both model names in full.

Revision location:  Title page.

Revised text:  Explaining Aesthetic Judgement in Closed-Category Product Design through the Unified Model of Aesthetics and the Categorical-Motivation Model: A Laptop Product Study

Comment 2: First appearance of UMA should be defined in full

Reviewer comment : Page 4 “this study applies the UMA model which introduced by Hekkert in 2014”. It is recommended to detail here, where the abbreviation UMA appears for the first time in the manuscript, what UMA stands for.

Response:  Thank you for pointing this out. We have revised the first occurrence of UMA in the Introduction by spelling out the full name, “Unified Model of Aesthetics (UMA),” and by briefly explaining its three levels and six aesthetic variables.

Revision location:  Introduction, paragraph introducing the theoretical model.

Revised text:  To systematically examine how design factors influence aesthetic preferences, this study applies the Unified Model of Aesthetics (UMA), introduced by Hekkert (2014). The UMA explains aesthetic pleasure across three levels of product experience: perceptual, cognitive, and social. At the perceptual level, it concerns the balance between unity and variety; at the cognitive level, the balance between typicality and novelty; and at the social level, the balance between connectedness and autonomy. In this framework, aesthetic pleasure is understood as the outcome of interactions between opposing but complementary design forces.

Comment 3: Figure 3 should number the laptops and enlarge the images

Reviewer comment:  In Figure 3, the laptops should be numbered to facilitate laptop recognition in scatter plots and elsewhere. Also, the images should be enlarged so the reader can see them clearly and understand why a particular laptop can be considered with a high level of autonomy, for example.

Response:  Thank you for this useful recommendation. We revised the stimulus figure by numbering all laptop stimuli from S1 to S10. These labels are now used consistently in the Results section, including the estimated marginal mean figures and scatter plots. We also revised the figure caption to clarify that the numbering facilitates cross-reference across the analyses. The image layout has been adjusted to improve visual readability.

Revision location:  Section 3.3 Stimuli; Figure 3 caption.

Revised figure caption:  Figure 3. Visual stimuli of ten laptop designs used in the study. Each stimulus is numbered from S1 to S10 to facilitate identification in the estimated marginal mean plots and scatter plots.

Comment 4: Indicate which products represent high or low levels of variables

Reviewer comment:  It might have been a good idea to indicate for all products which products represented a high or low level of a variable. Was there a laptop with the highest level of autonomy? This would make it easier to follow the claims presented in the discussion regarding Stimulus 3 and other stimuli.

Response:  We agree with this suggestion. To make the stimulus interpretation clearer, we added a descriptive stimulus-classification table. This table summarizes the source type of each stimulus and its dominant aesthetic profile based on the stimulus intention and the estimated marginal mean pattern. The table helps readers identify which stimuli were associated with higher typicality, novelty, connectedness, autonomy, and aesthetic pleasure.

Revision location:  Section 3.3 Stimuli; Table 1.

Added text:  A descriptive classification of the ten stimuli is provided in Table 1.

Added table title:  Table 1. Descriptive classification of the ten laptop stimuli according to dominant aesthetic characteristics.

Comment 5: Limited applicability because only laptops were studied

Reviewer comment:  The product was chosen after identifying the research niche. On one hand, it is a correct approach, but on the other hand, research findings have limited applicability since it concerns only one type of product.

Response:  Thank you for this important point. We agree that the findings should not be generalized too broadly beyond the laptop category. We revised the Discussion and Conclusion to acknowledge that the study focuses on one closed-category technological product and that the proposed category-sensitive interpretation should be tested with other closed-category products.

Revision location:  Discussion, final paragraph; Conclusion, limitations paragraph.

Revised text:  The findings should not be generalized too broadly beyond the present product category. Because the study focused only on laptops, the results mainly indicate how aesthetic variables operate within one closed-category technological product. Other closed-category products, such as medical devices, cameras, or office equipment, may involve different functional constraints, symbolic meanings, and user expectations. Therefore, the proposed category-sensitive interpretation should be tested across additional product types before broader theoretical claims are made.

Comment 6: Sample size and absence of calculation

Reviewer comment:  There were 234 participants, a sufficient number for the research to be relevant. Even though the calculation is not indicated in the manuscript, it seems that the minimum sample size was exceeded.

Response:  Thank you for this observation. We added a clarification in the Participants section. We stated that the sample size was consistent with previous UMA-based product-aesthetics studies but also acknowledged that no formal a priori power analysis was conducted before data collection. This limitation is now explicitly noted.

Revision location : Section 3.2 Participants.

Revised text : The final sample size of 234 participants was also consistent with previous UMA-based product-aesthetics studies, where sample sizes typically ranged from approximately 85 to 300 participants. However, no formal a priori power analysis was conducted before data collection. This is acknowledged as a methodological limitation, and future studies should include an a priori power analysis based on the expected effect size and repeated-measures structure.

Comment 7: Non-uniform online data collection conditions

Reviewer comment:  A major problem with the study is that data collection was not carried out under uniform conditions. The study was conducted through an online questionnaire accessible via web and mobile devices. It was not checked whether participants suffered from visual impairments. Display size, lighting, and setting may influence aesthetic evaluation.

Response:  We agree that online visual evaluation may introduce uncontrolled variability. We revised the Procedures section to specify the instructions given to participants and to acknowledge that screen size, resolution, ambient lighting, viewing distance, and visual impairments were not experimentally controlled. We also added this issue to the limitations in the Conclusion.

Revision location:  Section 3.4 Procedures; Conclusion, limitations paragraph.

Revised text:  The study was conducted through an online questionnaire accessible via web and mobile devices. Participants were instructed to complete the questionnaire in a quiet environment, to view the images carefully, and to use a device with a screen large enough to display the laptop images clearly. However, screen size, display resolution, ambient lighting, and viewing distance were not experimentally controlled. Participants’ visual impairments were also not independently verified. These factors may have introduced variability into the visual aesthetic judgements and are acknowledged as limitations of the online data collection procedure.

Comment 8: Reliability and consistency checks

Reviewer comment:  It is not clear whether the experimental data were checked for consistency and reliability, such as Cronbach’s alpha, McDonald’s omega, or similar tests.

Response:  Thank you for raising this issue. We clarified the measurement structure in the Procedures section. Each UMA variable and the dependent variable were measured using one item per stimulus. Therefore, internal consistency indices such as Cronbach’s alpha or McDonald’s omega were not appropriate, as these are designed for multi-item scales. To strengthen the validity of the stimulus manipulation, we also added an independent manipulation check conducted before the main study.

Revision location:  Section 3.3 Stimuli; Section 3.4 Procedures; Section 3.5 Data analysis.

Revised text in Procedures:  Each aesthetic variable was measured using one item per stimulus. Thus, for each of the ten laptop images, participants rated seven statements: unity, variety, typicality, novelty, connectedness, autonomy, and overall aesthetic pleasure.

Added manipulation-check text:  Before the main study, an independent manipulation check was conducted with 30 participants who did not take part in the formal experiment. Participants evaluated the ten laptop stimuli using the same 7-point rating items for the six UMA variables. Repeated-measures ANOVA showed significant differences among the ten stimuli for all six variables. These results indicated that the stimulus set produced sufficient perceived variation across the intended UMA dimensions and supported the suitability of the stimuli for the main experiment.

Comment 9: Inconsistencies between Table 2 and associated text

Reviewer comment:  There are inconsistencies between the data presented in Table 2 and those indicated in the associated text. Please correct.

Response:  Thank you for identifying this error. We rechecked the SPSS output and corrected the values in both the text and Table 2. The revised table now reports the Greenhouse–Geisser corrected degrees of freedom, F values, p values, and partial eta squared values consistently (now is Table 3).

Revision location:  Results section; Table 3 and associated paragraph.

Revised text:  Table 3 presents the repeated-measures ANOVA results for aesthetic liking and its interactions with gender and age. Greenhouse-Geisser corrected degrees of freedom are reported. The main effect of liking was statistically significant, F(5.634, 1273.372) = 50.355, p < .001, ηp² = .182, indicating that participants’ aesthetic pleasure ratings differed significantly across the ten laptop stimuli. The interaction between liking and gender was also significant, F(5.634, 1273.372) = 3.680, p = .002, ηp² = .016. However, the interaction between liking and age was not significant, F(16.903, 1273.372) = 1.422, p = .118, ηp² = .019. The three-way interaction among liking, gender, and age was also not significant, F(16.903, 1273.372) = 1.354, p = .151, ηp² = .018.

Revised Table 3 :

Table 3

R epeated measures ANOVA result   for   liking , age, and gender.

Comment 10: Pearson correlations described as negative although Table 4 shows positive values

Reviewer comment:  By studying the Pearson’s correlation coefficient values, it was concluded that certain correlations were negative. Wouldn’t that mean that some negative values should appear in Table 4?

Response:  Thank you for pointing out this important inconsistency. We corrected the interpretation of the Pearson correlation results. All reported correlations in Table 5 are positive and statistically significant. We have removed the previous statements describing negative correlations and revised the text to distinguish theoretical opposition from empirical correlation.

Revision location : Results section, paragraph before Table 5; Discussion section.

Revised text:  Pearson correlation coefficients were computed to examine bivariate relationships among the six UMA variables and aesthetic pleasure. All correlations were positive and statistically significant at the .01 level. Unity was positively correlated with variety (r = .404, p < .01), typicality with novelty (r = .276, p < .01), and connectedness with autonomy (r = .578, p < .01). Therefore, although these paired variables are theoretically treated as opposing aesthetic tendencies within the UMA framework, they were not empirically negatively correlated in the present dataset.

Comment 11: Negative correlations were expected because UMA pairs can be viewed as extremes of the same dimension

Reviewer comment:  Regarding the found negative correlations, it should be noted that in some works in the literature, the pairs unity and variety, typicality and novelty, connectedness and autonomy are extreme values of the same dimension. So, these findings were expected.

Response:  Thank you for this theoretical clarification. We have revised the Discussion to distinguish theoretical opposition from empirical negative correlation. In the UMA framework, the paired variables represent opposing aesthetic tendencies, but our data showed that they were positively correlated. We now explain that asymmetry in the present study refers to differences in predictive strength rather than negative empirical relationships.

Revision location:  Discussion section, after interpretation of perceptual, cognitive, and social results.

Revised text:  It is important to distinguish theoretical opposition from empirical negative correlation. In the UMA framework, unity and variety, typicality and novelty, and connectedness and autonomy represent opposing aesthetic tendencies. However, in the present data, these paired variables were positively correlated. This indicates that a successful laptop design may combine both sides of a pair rather than forcing a strict trade-off between them. The asymmetry observed in this study therefore refers to differences in predictive strength, not to negative empirical relationships between the paired variables. In other words, laptop designs can be perceived as both unified and varied, both typical and novel, or both connected and autonomous, but one side of each pair may carry greater relative weight in shaping aesthetic pleasure.

Comment 12: Statistical analysis and interpretation

Reviewer comment:  The analysis and interpretation of the experimental data were carried out using several well-chosen and correctly applied statistical methods. The results obtained using these methods were mostly interpreted correctly.

Response:  Thank you for this positive evaluation. To further improve clarity and statistical transparency, we revised the Data analysis section to better explain the complementary roles of repeated-measures ANOVA, GEE, and LMM. We also added multicollinearity diagnostics before interpreting the regression-based models.

Revision location:  Section 3.5 Data analysis; Results section after Table 4.

Revised text:  Given the moderate-to-high correlations among several UMA predictors, multicollinearity diagnostics were also examined before interpreting the regression-based models. Variance inflation factor (VIF) and tolerance values were inspected to assess whether the coefficient estimates were likely to be unstable. Conventional thresholds were used, with VIF values below 5 and tolerance values above .20 indicating that multicollinearity was not severe enough to invalidate the regression-based estimates.

Added Results text:  Because several UMA predictors showed moderate-to-high positive correlations, multicollinearity diagnostics were conducted before interpreting the GEE and LMM results. The VIF values ranged from 1.42 to 2.86, and all tolerance values were above .35. These results indicated that multicollinearity did not exceed conventional thresholds and that the regression-based coefficient estimates could be interpreted with reasonable caution.