Design preferences for contemporary Chinese-style wooden furniture: Insights from conjoint analysi

Abstract

Chinese wooden furniture occupies a central role in the nation’s cultural and historical heritage, serving not only as practical household items for period classification, but also as symbols of social status and artistic achievement. Recently, a new wave of Chinese-style furniture that blends traditional design elements with modern aesthetics has gained considerable market attention and recognition. This paper utilizes Conjoint Analysis to thoroughly investigate and assess consumer preferences and the visual appeal of contemporary Chinese-style furniture, leveraging a combination of user experience surveys and eye-tracking technology. This study suggests a sustained social interest in the materiality of Chinese heritage, emphasizing its relevance in today’s culture. The findings show that in subjective evaluations, consumers prioritize material selection. Eye-tracking data reveals that “material,” particularly “redwood,” demands more intensive cognitive processing during the fixation stage. However, “decoration type” plays a dominant role in visual searches across multiple stages, indicating that consumers employ varied cognitive strategies when interacting with different product attributes. Additionally, consumers’ focus on backrests and pattern craftsmanship offers valuable insights into future market trends.

Full Article

Design Preferences for Contemporary Chinese-Style Wooden Furniture: Insights from Conjoint Analysis

Xiaolei Cui,^a Jindong Xu,^b and Huajun Dong ^a,*

Chinese wooden furniture occupies a central role in the nation’s cultural and historical heritage, serving not only as practical household items for period classification, but also as symbols of social status and artistic achievement. Recently, a new wave of Chinese-style furniture that blends traditional design elements with modern aesthetics has gained considerable market attention and recognition. This paper utilizes Conjoint Analysis to thoroughly investigate and assess consumer preferences and the visual appeal of contemporary Chinese-style furniture, leveraging a combination of user experience surveys and eye-tracking technology. This study suggests a sustained social interest in the materiality of Chinese heritage, emphasizing its relevance in today’s culture. The findings show that in subjective evaluations, consumers prioritize material selection. Eye-tracking data reveals that “material,” particularly “redwood,” demands more intensive cognitive processing during the fixation stage. However, “decoration type” plays a dominant role in visual searches across multiple stages, indicating that consumers employ varied cognitive strategies when interacting with different product attributes. Additionally, consumers’ focus on backrests and pattern craftsmanship offers valuable insights into future market trends.

DOI: 10.15376/biores.20.1.164-189

Keywords: Chinese wooden furniture; Eye-tracking; Conjoint analysis; User preferences

Contact information: a: School of Art, Qingdao Agriculture University, No. 700, Great Wall Road, Chengyang District, Qingdao 266109, China; b: School of Science and Information Science, Qingdao Agriculture University, No. 700, Great Wall Road, Chengyang District; *Corresponding author: 202101095@qau.edu.cn

INTRODUCTION

As society advances and living standards broaden across a more diversified array of user groups, consumer demand for furniture has shifted from mere functionality to a greater focus on aesthetic value and cultural significance. New Chinese-style furniture, which combines traditional Chinese design elements with modern aesthetic concepts, has gained widespread attention and recognition in the market in recent years (Cao and Hansen 2006; Xiong et al. 2017). Despite the production appeal, Chinese-style furniture still faces numerous challenges in promotion, inheriting and innovating traditional craftsmanship, consumer perception and acceptance, and the phenomenon of design homogenization

Additionally, wood as a material with thousands of years of history, holds a crucial role in the preservation of cultural heritage. From buildings to furniture, wooden objects carry the craftsmanship and cultural values of various periods in Chinese history, serving as key mediums for understanding our past (Yin et al. 2012; Liu et al. 2013). Chinese wooden furniture is not only an item of home decoration and daily use but also a symbol of status and identity. In ancient China, different materials and designs in furniture often corresponded to different social classes. High-quality wood, such as rosewood and huanghuali, was typically reserved for royal family and nobility (Liu et al. 2013; Jiang et al. 2020). The creation of Chinese wooden furniture embodies the essence of traditional Chinese craftsmanship, notably during the Ming and Qing dynasties, when woodworking techniques reached their peak in innovation and popularity. Techniques, such as mortise and tenon joints, carving, and inlaying, not only made the furniture durable but also imbued it with unique artistic value that is significant to its historical legacy (Xue and Chen 2024; Yan et al. 2024). The design of Chinese furniture emphasizes simplicity, symmetry, and harmony, reflecting Confucian practices of faith and aesthetic pursuits in Chinese culture (Xu and Zhang 2011; Chai et al. 2020).

As time has progressed, Chinese wooden furniture has continuously evolved, from ancient furniture with low shape to Song Dynasty furniture with simple and high furniture, and then to Ming and Qing furniture of a brilliant period, each period displaying unique styles and characteristics. In recent years, there has been a resurgence in the collection and study of Chinese furniture, driven by a renewed appreciation of traditional culture and heritage (Cao and Hansen 2006; Xiong et al. 2017). Many pieces of Chinese wooden furniture have been nationally recognized as culturally significant heritage pieces, receiving more systematic protection and study (Wu and Liu 2019; Wu et al. 2021; Xiong et al. 2021). Even today, the design principles and craftsmanship of Chinese wooden furniture have a profound influence on modern furniture design. Modern designers have fused traditional elements with contemporary aesthetics to create a new style of Chinese furniture, allowing the aesthetic concepts of Chinese wooden furniture to endure and develop (Niu and Huang 2022; Sun and Du 2022; Ye et al. 2022). Therefore, the protection and preservation of materials of cultural significance—in this instance, wood and wood production techniques—is not only about safeguarding historical records, but also about passing on the aesthetics and skills accumulated by previous generations (Wu 2022).

In recent years, as consumer demands have become more diverse and personalized, various analysis methods have been widely applied in the field of furniture design to ensure that products not only meet functional requirements but also provide a superior user experience. Common furniture design analysis methods include the Kano model, Quality Function Deployment (QFD), and Conjoint Analysis. These methods systematically identify and analyze user needs, offering effective tools for furniture design. The Kano model helps designers prioritize fundamental requirements and explore features that enhance user satisfaction by classifying customer needs (Matzler and Hinterhuber 1998). QFD translates consumer needs into actionable design parameters, ensuring that the design process closely aligns with user expectations (Akao 1990; Du et al. 2024). In addition, Conjoint Analysis uses a multi-attribute preference model to quantify consumer preferences for different design elements, such as materials, colors, and styles, helping designers make more precise design decisions (Green and Srinivasan 1990). The application of these methods in modern furniture design not only enhances the accuracy of design but also deepens designers’ understanding of consumer psychology and behavior (Wan et al. 2018).

To better understand modern consumers’ preferences for new Chinese-style wooden furniture and to create products that better align with market demands and cultural characteristics, it is essential to gain deeper insights into the materials, colors, and designs that consumers favor (Furst et al. 1996; Egbue and Long 2012). Traditional subjective measurement methods, which rely on surveys and questionnaires, are often limited by consumers’ self-awareness and abilities of expression, lacking objective behavioral data to support its suggestions (Feldmann and Hamm 2015; Osburg et al. 2016; Li et al. 2020). With technological advancements, current methods for researching consumer preferences typically combine quantitative and qualitative approaches to achieve more comprehensive results. Mao (2024) uses big data, such as consumer purchase records, browsing behavior, and social media data, and analyzes consumer preferences and behavioral patterns through data mining and machine learning techniques (Mao 2024). Eye-tracking technology is also widely used. Eye-tracking technology can record the eye movements and visual focus points of consumers when viewing furniture. Through analyzing this data, one can obtain consumers’ true visual reactions and preferences (Wan et al. 2018).

The principle of eye-tracking technology is based on monitoring eye movements, accurately capturing the time and location of gaze fixation, reflecting the consumer’s attention to different parts of the furniture during browsing (Anliker 1976). The selective attention theory suggests that people tend to focus on components of a product related to the current task or characteristics that interest them, revealing potential preferences for certain stimuli without conscious awareness (Ede and Nobre 2023). When viewing visual scenes, the human eye moves rapidly (known as “saccades”) to maximize the efficiency of information intake. The fixation points and saccade paths during viewing can reveal how people process and understand visual information (Jacob and Karn 2003). Through analyzing these fixation points, one can infer how different types of information are processed and preferred by the audience, which may differ from conscious choices (Carrington et al. 2014). During decision-making, people often switch attention between multiple options, and eye-tracking data can help researchers understand how people weigh different options before making a decision. This process reflects the dynamic formation of group preferences (Mao 2024).

This paper, based on Conjoint Analysis (CA), aims to conduct a comprehensive study and evaluation of consumer preferences and visual quality for new Chinese-style furniture and its relation to historical design references, using a combination of subjective questionnaires and objective eye-tracking data. Conjoint Analysis is mainly used to understand how consumers weigh different attributes of a product or service when making purchasing decisions (Baier et al. 2009). Through designing a series of virtual products or services with different combinations of attributes and levels and asking respondents to rate or choose between these combinations, researchers can use statistical models to infer the contribution of each attribute and its levels to consumer decision-making. Through this method, researchers can determine which attributes are most important to consumers and the degree of preference for different combinations of attributes. Therefore, in this work, Conjoint Analysis was employed first to identify the various attributes and attribute levels that consumers value when choosing newly manufactured Chinese-style furniture, create product cards, collect data through surveys, and analyze it using SPSS Statistics 26 software to reveal the key factors influencing consumer purchasing decisions. Secondly, based on eye-tracking technology, representative new Chinese-style furniture products were experimentally tested, and the eye movement data of consumers viewing these products was collected and analyzed to assess their visual perception quality. Through the analysis of data from these two methods, this paper will explore the correlations between subjective questionnaire data and eye-tracking data, further deepening the understanding of the relationship between consumer preferences and visual quality.

This study considered the design preferences for contemporary Chinese-style wooden furniture, revealing consumer preferences regarding attributes such as material selection, design style, wood color, and decoration type. By utilizing Conjoint Analysis, the study identified the weighting of each attribute in consumer decision-making, and, combined with eye-tracking technology, it analyzed consumers’ visual focus points. This multi-dimensional research approach provides valuable market insights for furniture designers and manufacturers, helping them create furniture that aligns with modern aesthetics and market demands while preserving traditional cultural elements. By gaining a deeper understanding of the consumer decision-making process, this study holds theoretical and practical implications for promoting the modern innovation and cultural inheritance of traditional Chinese furniture design.

To better reflect the goals of this study, the cultural context is based on modern minimalist interior design. The focus of the study is new Chinese-style furniture, which represents a fusion of traditional Chinese design elements with modern aesthetics. Modern minimalist interior design emphasizes functionality and simplicity, while conveying a sense of elegance and tranquility through refined design language. New Chinese-style furniture, within this design context, not only inherits the cultural essence of traditional Chinese furniture but also incorporates modern elements that align with contemporary tastes. Therefore, this study aims to explore the cultural compatibility and aesthetic value of new Chinese-style furniture in a modern minimalist design environment, helping to understand how such furniture is perceived and appreciated by consumers in real-life spaces. This context provides a realistic cultural background for the experimental design, allowing for a more comprehensive assessment of consumers’ emotional attraction and functional recognition of new Chinese-style furniture in modern living spaces.

EXPERIMENTAL

Overall Experimental Design

This paper utilized Conjoint Analysis (CA) and eye-tracking technology to conduct a comprehensive study and evaluation of consumer preferences and visual quality for new Chinese-style furniture, focusing on their relation to historical design references. Conjoint Analysis is a method used to understand how consumers weigh different attributes of a product when making purchasing decisions (Baier et al. 2009). By designing virtual products with varying combinations of attributes and levels, and having respondents rate or choose between them, the study employs statistical models to infer the contribution of each attribute to consumer decision-making. Using this method, the key attributes and preference structures for newly manufactured Chinese-style furniture are identified. The data are collected through surveys and analyzed using SPSS Statistics 26 to reveal the factors that most influence consumer choices. Additionally, eye-tracking technology is employed to experimentally test representative furniture designs, capturing consumer eye movement data to assess their visual perception quality. To further explore the role of cultural emotions, interviews were conducted to gain deeper insights into how consumers emotionally connect with the cultural significance of Chinese-style furniture, enriching the analysis of the factors driving their preferences. By analyzing both subjective questionnaire data and objective eye-tracking data, this study deepens the understanding of the relationship between consumer preferences and visual perception quality.

This study focuses on the Chinese round-backed armchairs, the most representative furniture products in Chinese wooden furniture. The participants were students and faculty from Qingdao Agricultural University. This study selected students and faculty from Qingdao Agricultural University as the primary sample group, based on several considerations: First, this group was easily accessible, ensuring the smooth conduct of the experiment. Additionally, the high educational background and strong design perception ability of this group make them advantageous in understanding and evaluating modern furniture design trends. Particularly for contemporary Chinese-style furniture, younger and more highly educated consumers are often among those leading modern design trends. As such, this sample group can accurately reflect the preferences of a segment of the market that values innovative design and cultural heritage. However, recognizing that the core consumers of contemporary Chinese-style furniture may also include middle-aged and older individuals, high-income earners, and those with a deeper appreciation of traditional culture. Thus, future studies should expand the sample to include a more diverse range of participants. By incorporating collectors, traditional culture enthusiasts, and higher-income individuals into the survey, a more comprehensive understanding of consumer preferences in the actual market can be achieved.

To explore consumer preferences for Chinese round-backed armchairs, the study was conducted in three stages. First, consumer preferences were compared among typical products, aiming to identify the product attribute structures that influence these preferences. In this stage, both subjective ratings and eye-tracking data were collected. Second, eye-tracking data were compared for different parts of the round-backed armchair to determine which components consumers perceived as especially enticing. Finally, it was analyzed how different decoration types on the same component affect participants’ preferences. The schematic diagram of experimental design is shown in Fig. 1.

Fig. 1. Schematic diagram of experimental design

Conjoint Analysis Approach

Conjoint analysis is a widely used method in product design research for studying consumer preferences (Moore et al. 1999). This method involves having participants rate the overall profiles of multiple products, which allows for the decomposition and calculation of the preference biases of the participant group toward various product features. The essential data for conjoint analysis includes multiple attributes of the study object and the different levels of each attribute. An orthogonal experimental design is then used to generate non-repetitive combinations of attributes and levels, resulting in the specific products that participate in the experiment. The application of conjoint analysis in this study is as follows. Through literature analysis and expert experience, 4 attributes (material, design style, wood color, and decoration type) were obtained, and each attribute has 3 levels, the attributes and levels are summarized in Table 1. “Rosewood” types are classified by color and wood species as follows: Black-Purple refers to Pterocarpus santalinus, Red-Brown corresponds to Dalbergia cochinchinensis, and Yellow-Brown refers to Dalbergia odorifera. Similarly, “Hardwood” are categorized by color and species: Black-Purple is Ebony, Red-Brown is Walnut, and Yellow-Brown is Teak. Additionally, the category “Mixed with Other Materials” combines wood with materials such as fabric, feather, and weaving for a more diverse design approach.

Table 1. Attributes and Levels Settings for Chinese Round-Backed Armchairs

An orthogonal experimental design is then used to generate non-repetitive combinations of attributes and levels, resulting in the specific products that participate in the experiment. Through combining the attributes and levels of the round-backed armchair mentioned above, a total of 3⁴(3*3*3*3) possible product combinations can be generated. To simplify the product design combinations, this study used the orthogonal design module in SPSS Statistics 26 to perform an orthogonal design, which combined the attributes and levels to generate 9 representative products, as shown in Table 2.

Table 2. Experimental Products of Chinese Round-backed Armchairs

After collecting the questionnaire and eye-tracking data, the authors utilized the conjoint analysis method to calculate the utility values for each attribute and its levels, as well as the relative importance of each attribute. Utility values represent the level of preference consumers have for a particular attribute level, with higher values indicating stronger preferences. Meanwhile, using the utility values, the relative importance of each attribute is calculated. This is done by dividing the range of utility values for a particular attribute by the total range of utility values for all attributes. This shows how much influence each attribute has on consumers’ overall choices. The sum of the relative importance values for all attributes is 100%, and the proportion for each attribute represents how strongly consumers weigh that attribute when making their decision. The data processing for the conjoint analysis was carried out in SPSS Statistics 26. In general, the Pearson correlation was used to evaluate the model fit of the conjoint analysis for subjective preference data and eye-tracking data. When the Pearson’s R statistic (R > 0.7, Sig < 0.05) is observed, it indicates a good fit between the data and the statistical model.

Eye Tracking Experiment

Eye tracking device

This study used the ERGONEERS DG3 eye-tracking glasses system for the eye-tracking experiment. The system has a sampling rate of 60 Hz and a tracking accuracy of 0.1 to 0.3°. The eye tracker will track and analyze the participants’ visual data, including various types of visual activities related to fixation and saccades. Participants were positioned approximately 60 cm away from the product images. Experimental data were collected, processed, and recorded using D-Lab software.

Eye-tracking metrics

Areas of Interest (AOI) are a crucial concept in eye-tracking experiments, referring to pre-determined regions within the visual scene. The AOIs are used to define the scope of data calculation in eye-tracking studies, and their delineation is closely related to the research objectives and subjects.

Fixation refers to the alignment of the eyes so that the image of the area of interest being focused on falls on the fovea for a certain period (ranging from 100 to 2000 ms). Eye-tracking metrics related to fixation can be used to characterize the participant’s level of engagement (Bylinskii et al. 2017). Some studies suggest that prolonged fixation indicates a higher level of cognitive alignment by the observer (Shojaeizadeh et al. 2016). Additionally, fixation-related metrics are also associated with the difficulty of visual tasks (Fitts 1950). The fixation-related metrics primarily used in this study are as follows:

• Mean Fixation Duration (ms): The length of time that a gaze is fixed on a particular AOI, measured in milliseconds (Salvucci and Goldberg 2000). A longer mean fixation duration typically indicates a more complex visual task that requires more processing time.
• Number of Fixations: Number of fixations on the AOI for the selected time interval.
• Saccade means brief, fast movement of the eyes that changes the point of fixation. These movements are crucial for shifting the line of sight from one area of interest to another, allowing the observer to quickly gather visual information from different parts of a scene. Eye-tracking metrics related to saccades can provide insights into the participant’s visual search patterns and cognitive processing during a task. The specific saccade-related metrics used in this study are:
• Mean Saccade Duration (ms): Sum of saccade durations in a selected time interval divided by several saccades in a selected time interval.
• Mean Saccade Angle (deg): The sum of saccade angles within a selected time interval divided by the number of saccades in that interval. Larger saccade angles often indicate that the observed object’s features are more distinct.
• Number of Saccades: The total number of saccades within the selected time interval. A higher number of saccades within a given time frame indicates more complex visual search behavior by the participants, suggesting that the features of the observed object are not distinctive enough.

Interviews

To more comprehensively assess the impact of cultural emotions on consumer evaluations, this study incorporated interviews focusing on the cultural significance of Chinese-style furniture into the experimental design. A selection of consumer participants were invited to take part in in-depth interviews regarding the cultural value and emotional connection of Chinese-style furniture. Through open-ended questions, participants were encouraged to express their perceptions of the cultural heritage of Chinese-style furniture and their emotional attitudes, while also exploring how these cultural emotions influenced their purchasing decisions.

Specifically, interviews covered the following aspects:

1. Cultural Emotional Connection: Participants were asked about their views on the cultural connotations of Chinese-style furniture and its significance in modern life. These questions aimed to understand how consumers perceive the cultural history and traditional values embodied in Chinese-style furniture and their emotional connection to this culture.
2. Cultural Identity and Purchase Decisions: It was explored whether participants’ cultural identity had a significant impact on their decision-making when selecting Chinese-style furniture. Through the interviews, participants expressed the importance of cultural values in their furniture choices and whether cultural emotions motivated them to purchase this style of furniture.
3. Emotions and Aesthetic Preferences: The interviews also examined participants’ aesthetic evaluations of the appearance, design, and functionality of Chinese-style furniture. The aim was to explore the relationship between cultural emotions and aesthetic preferences, and whether these emotions influenced the way consumers evaluated the furniture.

Methods

Conjoint analysis of overall product attributes

Based on the generated 9 representative product features in Table 2, the 9 virtual product profiles are presented visually according to the attributes and levels of each product by 3D modeling. The front elevation views of each product are shown in Fig. 2.

A preference survey and eye-tracking data collection were conducted for the nine products mentioned above. During the eye-tracking experiment, each product image was displayed for 8 s. After collecting and organizing the subjective rating data and eye-tracking data, the conjoint analysis method was applied to process the two sets of data separately. This analysis ultimately revealed the attribute-level structures of round-backed armchair products that influence consumer preferences under both subjective evaluation and eye-tracking conditions.

Fig. 2. Product images

Comparative Analysis of Product Components and Decorative Details

Comparison of product components

In this stage, the authors selected the product that received the highest subjective rating from the previous experiment. A detailed analysis was conducted of the decorative types of each component of this product. The AOIs were defined as shown in Fig. 3. AOI-1 represents the armrest, AOI-2 represents the backrest, AOI-3 represents the seat cushion, and AOI-4 represents the chair legs.

Fig. 3. Schematic diagram of AOI division for product components

The purpose of this phase of the experiment was to explore the differences in eye-tracking data across these components and to investigate which part of the round-backed armchair captures the most consumer attention.

Comparative Analysis of Decorative Types for Individual Components

Based on expert opinions, the authors experimented with a variation of design on three key decorative components of the round-backed armchair: the backrest, seat surface, and chair legs (in Table 3 and in Fig. 4). Specifically, the authors analyzed eye-tracking data for different decorative types on the backrest (leather, carving, plain wood, and inlay), seat surface (plain wood, fabric, leather, and weaving), and chair legs (plain wood, carving, and inlay). The aim was to explore which design elements at the component level of the round-backed armchair generate the most visual interest among participants.

Table 3. Decorative Types for Components of Round-backed Armchairs

Fig. 4. Four decorative types for backrest of round-backed armchairs

Fig. 5. Four decorative types for seat surfaces of round-backed armchairs

Fig. 6. Three decorative types for chair legs of round-backed armchairs

Participants’ Demographics

The basic demographic characteristics of the sample population participating in this study are given in Table 4 (N = 105, after excluding 16 problematic samples from the original 121).

The gender distribution in the sample was fairly balanced, with 48.6% male and 51.4% female participants. The majority of participants (80%) were aged between 18 and 35 years, which indicates a younger population in this survey.

In terms of household income, 68.6% of participants report an annual income between 100,000 and 200,000 RMB. A considerable portion of the sample consists of students or doctoral candidates (70.5%), reflecting the academic background of most respondents. Educational attainment was also high, with 53.3% holding a bachelor’s degree and 41.0% possessing a master’s or doctoral degree.

Table 4. Basic Information of Participants

RESULTS

Analysis Results for Subjective Preferences

The conjoint analysis of the subjective preference ratings data for the nine round-backed chair products resulted in the relative importance values of each attribute, as shown in Table 5.

Table 5. Utility Values for Product Attribute Levels Based on Subjective Preferences

The research results indicate that among the attributes, “Material” had the highest utility value, with “Rosewood” having the highest utility value (0.242). This was followed by the “Design Style” attribute, where “Elegant and Refined” had a utility value of 0.153.

Within the “Material” attribute, “Rosewood” stood out with the highest utility value (0.242), while “Hardwood” (-0.148) and “Mixed Materials” (-0.094) had lower utility values. For the “Design Style” attribute, “Elegant and Refined” showed a higher utility value (0.153), whereas “Heavy and Luxurious” had a somewhat lower utility value (-0.148).

Fig. 7. Heatmap generated from eye-tracking data

In terms of relative importance, the “Material” attribute had the most significant impact on choice decisions, accounting for 35.649% of the total importance, followed by the “Design Style” attribute with 27.5%, the “Wood Color” attribute with 21.8%, and the “Decoration Type” attribute with 15.1%.

Analysis Results from Eye-tracking Experiment

Figure 7 illustrates the original choice set along with the heatmap generated by the software D-Lab, which visualizes participants’’ areas of interest (red regions) as an example.

Fixation Data Results

Table 6 reports the utility values for each level of the attributes and the relative importance of each attribute with respect to Mean Fixation Duration. Fixation duration is generally associated with the significance of areas of interest to consumer attention. The Pearson’s R correlation coefficient from the conjoint analysis (R = 0.963, Sig. = 0.000) indicated a good fit between the data and the statistical model.

Notably, the attribute with the greatest impact on Mean Fixation Duration was “Material,” with a relative importance of 39.2%, followed by Wood Color (27.6%), Decoration Type (20.0%), and Design Style (13.3%). According to the utility values, the product attributes considered most interesting by participants were Rosewood (Mean Utility = 106.5), Black-Purple (Mean Utility = 91.0), Geometric Elements (Mean Utility = 70.0), and Elegant and Refined (Mean Utility = 51.5).

Table 6. Utility Values for Product Attribute Levels Based on Mean Fixation Duration

Table 7 reports the utility values for each level of the attributes and the relative importance of each attribute concerning the number of fixations. The number of fixations is related to information processing and the importance of information to the consumers. More fixations may indicate that participants are processing complex information or need more time to understand certain details, while fewer fixations might suggest quicker or simpler processing. The Pearson’s R correlation coefficient from the conjoint analysis (R = 0.925, Sig. = 0.000) indicates a good fit between the data and the statistical model.

In particular, the attribute with the greatest impact on the number of fixations was “Decoration Type,” with a relative importance of 39.6%, followed by Design Style (22.3%), Wood Color (18.9%), and Material (19.2%). According to the utility values, the product attributes with the highest number of fixations were No Decoration (Mean Utility = 0.478), Yellow-Brown (Mean Utility = 0.303), Heavy and Luxurious (Mean Utility = 0.200), and Wood with Other Materials (Mean Utility = 0.258).

Table 7. Utility Values for Product Attribute Levels Based on Number of Fixations