Topic modeling and visualization of user comments
We collected a total of 23,544 comments. After removing preset content, blank comments, and comments containing only one word, 7,730 comments were used for text analysis. Descriptive statistical analysis was conducted on the review sample. After tokenizing the sample, we removed irrelevant characters, stopwords, links, and special symbols, focusing on crucial nouns related to clothing to ensure consistency of vocabulary and reduce noise in the topic model. The average length of comments in the sample was approximately 53.5 characters. The most common comment length (mode) was 27 characters. The total number of comments was 7,730. The standard deviation was approximately 42.3 characters. The significant variation in review lengths could be attributed to JD.com’s method of collecting consumer feedback after product use to facilitate evaluations. After purchase, consumers are typically invited to review and rate specific aspects such as usage experience, product quality, merchant service, and delivery service. Based on this review model, shorter reviews tend to provide less description of the product, while longer comments focus more on the value and attributes of sustainable fashion. It is important to note that the length of a comment does not directly indicate a positive or negative review. Reviews exceeding 100 characters are usually based on detailed evaluations of each attribute so that individual sentences can contain positive and negative feedback, such as, “I am satisfied with the quality, but not with the price.”
To establish an appropriate number of topics, this study manually adjusted the number of topics and used two indicators: model perplexity and coherence score. Using the pyLDAvis toolkit for visualization analysis, we evaluated LDA models with topics ranging from 1 to 10. The results showed that the perplexity values of the text ranged from 65 to 80, increasing with the number of topics, indicating that too many topics may decrease the clarity of the model. According to the perplexity data, it was understood that the model has 70 possible choices for each word when processing a segment of text. Whether this is considered “good” depends on a comparison with other models or results from the same task. In context, these perplexity values are within the expected range for the text data. As Ma et al.47 indicate, whether it is “good” also depends on comparisons with other models or results from similar tasks in complex language processing tasks. Through such assessments, we can better understand the impact of model configurations on predictive capabilities and adjust the number of topics accordingly to optimize the model’s overall performance.
The coherence scores fluctuate between 0.4 and 0.6, reflecting the semantic similarity between high-scoring words within each topic. Scores within this range are generally considered moderate, suggesting that while the identified topics are meaningfully related, there is room for improvement regarding topic clarity and distinctiveness. In our preliminary evaluation, as shown in Chart 2, we followed the principle of minimizing perplexity and maximizing coherence scores and chose a relatively lower and higher coherence score. After carefully considering model complexity and interpretability, we decided to select three topics definitively. This choice represents an optimal balance where topics remain distinct and comprehensible without overfitting noise in the data. The evolution of perplexity and coherence scores for the LDA model is visualized in Fig. 2. Additionally, visual feedback from pyLDAvis further supported this decision, as it showed clearer topic clusters when using three topics, which helps intuitively understand the underlying topic structure, as shown in Fig. 3.
Confusion values and coherence scores for sustainable clothing discussions. Note: Perplexity measures the accuracy with which a probability model predicts a sample (the lower the score, the better), indicating the model’s simplicity and generalization ability. The coherence score assesses the degree to which kay words in each topic coherently convey meaningful themes (the higher the score, the better), reflecting the model’s interpretive usefulness.
The pyLDAvis results of the sustainable clothing discussion.
Topic model and labels
This study employs LDA to conduct topic modeling on online comments related to clothing products. The topic modeling results and the proportion of keyword weights for the top 10 keywords in each topic are presented below.
Table 2 displays the main constituent words for each topic. Each topic is sorted based on TF-IDF values, showing the top 10 words ranked by importance. Notably, certain words appear in multiple topics, indicating their significant contribution to each topic. Considering previous studies that used Latent Dirichlet Allocation (LDA) topic modeling where the same word appeared in multiple topics, it suggests that topics may influence each other. This indicates a good correlation between topics and provides insights into the importance of these terms. According to our analysis, six words appear simultaneously in the top ten list of topics: “quality” appears in topics 1 and 2. “fit” appears in topics 1 and 3. “comfortable” appears in topics 1 and 3. “size”: appears in topics 1 and 3. “material”: appears in topics 1 and 2. “softness”: appears in topics 1 and 3.
As indicated above, we have noted that six key terms—‘quality,’ ‘fit,’ ‘comfort,’ ‘size,’ ‘material,’ and ‘softness’—appear across multiple topics, highlighting their multifaceted importance. To better understand the significance of these terms in various discussions, we calculated the percentage of comments in which each term appeared. The term “quality” was mentioned in 72.33% of the comments, reflecting its primary importance in discussions related to consumers’ perceptions of sustainable fashion. Similarly, “fit” appeared in 20.74% of comments, where discussions typically focus on body fit and garment comfort. “Comfort” was mentioned in 14.13% of comments, again emphasizing its cross-topic importance, particularly in conversations about expressing subjective consumer feelings. “Size” appeared in 25.12% of comments, highlighting its relevance in garment fit and personal comfort discussions. “Softness” was mentioned in 14.21% of comments, indicating its significant but more focused role in specific aspects of product discussions. “Material” appeared in 5.31% of comments, further demonstrating its specific but essential role in discussions related to garment attributes. These percentages underscore the influence of these terms in shaping the dimensions within the topic model, with “quality” being an essential attribute of clothing consumers discuss. The extent to which these keywords appear across different topics proves their relevance and indicates topic overlap, suggesting that these terms play a crucial role in shaping consumer dialogue in a sustainable fashion.
Analysis of text topics
Topic 1: “Perceived Quality”
When analyzing the distribution of content in Topic 2, it is crucial to consider the TF-IDF of each keyword within the discussed topic. The keyword “quality” in Topic 1 has the highest TF-IDF value at 0.112, emphasizing its role as the primary descriptor for topic identification. This high TF-IDF value underscores the critical nature of measurable standards in assessing apparel attributes. Following “quality,” the terms “warmth,” with a TF-IDF score of 0.070, and “fit,” with a score of 0.040, also show significant representativeness, highlighting their relevance to consumers’ perceptions of comfort and fit, which are crucial for perceived quality. Lower-weighted terms like “softness” (0.029), “comfort” (0.023), and “size” (0.023) contribute to the subjective dialogue about factors influencing consumer satisfaction and preferences. These keywords are weighted to represent their frequency and relevance, illustrating a layered understanding of quality.
The actual result of quality consists of 2 dimensions, i.e. objective quality which refers to measurable and verifiable aspects of the garment according to predetermined quality standards, and perceived quality where the consumer’s subjective judgement of the product quality is influenced by the individual situation and personal assessment. Objective quality refers to product attributes that can be directly measured and verified by scientific methods according to predefined quality standards. Perceived quality, on the other hand, is based on the consumer’s personal situation and assessment, and is a subjective judgement that involves factors such as material, comfort, style, etc., reflecting the consumer’s overall perception of product quality. The boundary that distinguishes the two is that objective quality can be assessed quantitatively, whereas perceived quality is a combination of personal experiences and preferences48.
We found that the key words in Theme 1 are closely related to the two dimensions of quality. The keyword “quality” in Theme 1 explains objective quality as a core term due to the highest distribution rate in the theme. As a physical characteristic of clothing, “quality” can be quantitatively assessed by durability and manufacturing process. The term “material”, “thickness” and “detail” thus explains in more detail a range of physical properties of garments that can be measured quantitatively in objective quality. Examples include colour fastness, breaking strength and elongation, stretch and recovery of fabrics containing elastic fibres, seam slippage, pilling resistance and dimensional stability49. “soft” “fit” “comfort” “size” “warmth “Style” appears in the vocabulary, which is influenced by factors such as expectations, experience and personal preferences, while the perception of the quality of clothing is a subjective judgement made by the consumer, which may be different from the actual objective quality of the product, so we consider this part of the psychological and physiological perception of the clothing, which is part of the perceived quality. It belongs to perceived quality. For example, fabric construction, composition and surface treatment also play a role in the sensory and thermal comfort of a garment. Parameters such as fabric drape, coefficient of friction, heat and evaporation resistance affect the comfort of a garment. Thermoregulation is a key comfort factor that should be considered in the design of garments, with ‘warmth’ performance becoming a key consideration. Similarly, for products that come into frequent contact with the skin, ‘soft’ properties ensure greater comfort and a better user experience50). The correct ‘size’ is not only related to comfort, but sizing is a sign of attention to detail and has a direct impact on user satisfaction51. The overview of style, on the other hand, reflects a consumer’s expectation of the style and design of the garment, and attention to style is crucial in fashion analysis, as it provides a more nuanced understanding of clothing trends and preferences52. It is important to note that the themes ‘comfort’ and ‘fit’ are intuitively communicated by consumers to express satisfaction with quality attributes and do not appear as essential attributes of clothing quality.
Topic 2: “Perceived Value”
While exploring Topic 2, it is evident that the keyword “quality” holds a TF-IDF score of 0.140, making it the core vocabulary of Topic 2. Although it is the highest core keyword and overlaps with Topic 1, its meaning within this theme is redefined by other keywords in the topic. The sequential TF-IDF scores of “price,” “cheap,” and “discount” underscore the dominant presence of these weighty keywords, highlighting the distinction of Topic 2’s essence from Topic 1. The distribution of keyword weights explains that overlapping core keywords do not necessarily indicate overlapping dimensions.
Perceived value is consumers’ subjective evaluation of the value or benefits they expect from a product or service. It is a multidimensional process of overall consumer evaluation53. In clothing, consumers’ perceived value evaluation indicators consist of five dimensions. However, the perceived value of clothing typically covers multiple dimensions, generally summarized as functional value (such as durability, practicality), emotional value (such as personal preference, aesthetics), social value (such as brand image, social recognition), cognitive value (such as price, cost-effectiveness), and situational value (value in specific situations, such as wearing on special occasions). These dimensions collectively determine consumers’ overall perception of the value of clothing54. Notably, although the keyword “quality” has the highest distribution probability in Topic 2, the keywords related to price predominate in this topic, indicating that the quality factor describes price. Functional value can be defined as consumers’ consideration of high- and low-quality factors in the fluctuation of prices when making purchases55. One of the factors that influences functional value is consumers’ consideration of “quality” when making purchases. Cognitive value refers to consumers’ subjective attitudes after making judgments. In the market environment, consumers can intuitively identify the most favorable price, which is undoubtedly one of the most critical factors. Since price is the first factor consumers pay attention to, rather than product quality or model, low price becomes one of the best choices for consumers. Therefore, in selling products, price becomes one of the essential variables determining purchasing behavior56.
Emotional value can be achieved through various sensory experiences of consumers, such as visual, olfactory, and tactile experiences. This refers to clothing-related factors, such as color, fabric, smell, and touch57. Social value refers to how consumers can create an identity through clothing. Clothing can also enhance existing identities, making it essential to establish professional status. Clothing is crucial in establishing professional status and identity58. Situational value is the subjective clothing awareness of specific groups of people. Consumers will choose their local cultural clothing for activities on specific holidays and special occasions59. Based on the results of clothing topics, we conclude that the word “quality” in the topic corresponds to functional value. “Price,” “cost-effectiveness,” “cheap,” and “discount” may be related to cognitive value.
Additionally, “color” is associated with emotional value. It is noteworthy that words such as “expectation,” “feature,” “material,” and “color fading” seem to have influences from two to multiple dimensions. Therefore, the vocabulary in Topic 2 refers to the concepts of functional value, cognitive value, and emotional value.
Topic 3: “Sensory Comfort”
In our exploration of Topic 3, we quantify the importance of specific terms using their TF-IDF scores to illustrate their relevance in the discussion context. The term “fabric” scores a TF-IDF of 0.146, underscoring its significant role within the topic model. This high score reflects the term’s central position in this theme, especially in discussions that help specifically define the dimensions of Topic 3. Similarly, other terms like “breathability” (0.088) and “size” (0.079) also exhibit TF-IDF values, highlighting their importance in further clarifying the dimensions.
The dimensions of sensory comfort typically include thermal comfort (such as physical comfort), aesthetic comfort (such as confidence in wearing and aesthetic satisfaction), and tactile comfort (such as the suitability of clothing and freedom of movement). These three dimensions interact, collectively influencing individuals’ overall perception of clothing comfort60. Our research results in Topic 3 indicate that the vocabulary is closely related to the definition of sensory comfort.
Physical comfort usually involves clothing attributes that directly contact the human body, such as temperature regulation, breathability, humidity control, and fabric softness. These factors directly affect consumers’ bodily sensations when wearing clothes, which is crucial for enhancing individual comfort and overall wearing experience61. The breathability of clothing is related to its thermal comfort. Thermal protective performance indicates that with increased recycled cotton fiber content in clothing, fabrics’ conductivity, and radiation resistance also increase62. In Topic 3, breathability is an essential indicator for evaluating thermal comfort, especially in products that directly contact the skin.
Similarly, the clothing material is also one factor that directly affects sensory perception. For example, fabrics with poor breathability can cause discomfort, excessive sweating, and skin irritation. On the other hand, breathable fabrics allow moisture and heat to escape, keeping the wearer cool and dry and enhancing thermal comfort63. Words such as “fabric” and “pure cotton” reflect consumers’ attention to the primary materials used in clothing, which directly affect the product’s appearance, texture, and durability. As a fundamental attribute, the type of fabric (such as pure cotton, polyester, etc.) directly affects breathability and moisture absorption. This indicates that consumers expect clothing to have high quality and good performance64.
Tactile comfort refers to a garment fitting to a certain extent and matching the wearer by considering the user’s body size and the corresponding parts of the garment. As an essential dimension for evaluating clothing comfort, it directly affects wearing experience and satisfaction. Softness is the primary factor in measuring the tactile comfort of clothing. Soft fabrics can reduce skin irritation and friction, providing a more comfortable wearing experience. Correct sizing affects the appearance of clothing and directly relates to the comfort and functionality of wearing65.
Aesthetic comfort principles indicate that our clothes should be comfortable and reflect our style and identity. This concept goes beyond the physical attributes of clothing, such as softness, breathability, and fit, to include the psychological effects of clothing on self-awareness and social interaction. For example, wearing fashionable clothes suitable for specific occasions can significantly increase personal confidence and comfort66, where style becomes essential. This balance between form and function is crucial in determining individuals’ overall satisfaction and confidence in their chosen clothing.
Based on these three themes-perceived quality, perceived value, and sensory comfort-we can gain a more comprehensive understanding of consumers’ psychological and behavioral aspects in the clothing purchase decision process. Each theme reveals how consumers evaluate clothing based on different criteria and personal preferences, including but not limited to the physical characteristics of clothing, price, brand image, and subjective perceptions of comfort and aesthetics.
Perceived quality emphasizes the distinction between objective quality and perceived quality, where objective quality involves quantifiable product attributes such as durability and manufacturing processes, while perceived quality is based on consumers’ personal circumstances and evaluations, such as material, comfort, and style. This indicates that consumers rely not only on physical attributes when assessing clothing quality but also on personal emotions and preferences. Understanding this is crucial for clothing companies to design and market strategies effectively to meet consumer demands.
Perceived value reflects how consumers evaluate the overall value of clothing based on multiple dimensions, including functional, emotional, social, cognitive, and situational values. Consumer purchase decisions are influenced by price and quality and personal emotions, social recognition, and value in specific situations. This multidimensional evaluation reveals the importance of increasing product value in the clothing industry by meeting consumers’ needs and expectations.
Sensory comfort emphasizes the importance of thermal, aesthetic, and tactile comfort in clothing selection. These dimensions influence consumers’ overall perception of clothing comfort, including physical comfort, such as temperature regulation and breathability, and aesthetic comfort, such as confidence in wearing and personal style. Clothing companies need to consider these factors and design comfortable and aesthetically pleasing products to increase satisfaction and loyalty.
In summary, clothing companies need to consider consumers’ perceived quality, perceived value, and sensory comfort comprehensively in product development and marketing strategies. By gaining a deeper understanding of these dimensions, companies can more effectively meet consumer needs, thereby enhancing their products’ market competitiveness.
Development of a user-centric clothing design index system
Collecting online reviews of clothing products and utilizing LDA modeling to identify the themes of the review texts, we integrate the textual information to map it to user needs, and developing sustainable clothing design strategies based on user requirements. We invited four product design experts (Table 3), aged between 29 and 37, with an average industry experience exceeding eight years, to integrate and elaborate on these themes and related requirements.
In this study, we meticulously organized and presented the analysis of the LDA topic model. We provided a clear display of keywords and topic labels, accompanied by an explanation of the significance of each topic through a literature review approach. This method enhanced the accuracy and readability of the analysis results, providing experts and readers with an intuitive understanding and facilitating a deeper interpretation of the underlying connections within the analysis.
In the “Discussion” section, we delved into various aspects of the analysis, including examining the identified topic dimensions. These core questions served as fundamental guidelines, ensuring the discussion was comprehensively and profoundly identified and analyzed each topic.
Furthermore, we conducted in-depth interviews with four experts with extensive experience in fashion and clothing-related fields. Before the interviews, we shared the discussion topics and relevant background materials with the experts to ensure they were adequately prepared to participate effectively in the analysis and discussion process. Before engaging in discussions with the experts, we presented them with the results of the LDA analysis to provide them with a basic understanding. We asked them to integrate and translate these analysis results into specific product design requirements based on their professional knowledge and experience.
When synthesizing the discussion results into specific product requirements, we used keywords such as “fabric” and “quality” as examples to guide the experts in concretizing these abstract concepts. This step ensured precise categorization of each topic, laying the foundation for refining subsequent requirements. In the explanations provided by the experts for the requirements, we emphasized thoroughness, including detailed content, potential implementation strategies, and possible challenges associated with each requirement. This approach ensured that each requirement underwent comprehensive deliberation and discussion, providing detailed information for formulating design metrics.
Finally, based on the formulated strategy and leveraging the topic model and research background, we differentiated the strategic framework for sustainable clothing in manufacturing and environmental aspects (Table 4). We translated the previously abstracted requirements into specific, actionable indicators, providing clear direction for optimizing and improving products. For example, under the “perceived quality” indicator, we specified designing through environmentally friendly production processes and durable materials to reduce clothing waste.
Indeed, these strategies ensure that clothing meets fashion trends in terms of aesthetics and maintains sustainability while meeting users’ needs in functionality and comfort. In terms of perceived quality, objective quality requirements consider durability, manufacturing processes, and material selection to ensure environmentally friendly, durable clothing. Perceived quality demands focus on comfort, proper sizing, and design details to enhance the wearing experience and appearance. Regarding perceived value, functional value emphasizes design durability and practicality; emotional value focuses on personalized design and aesthetics, and social value emphasizes brand social responsibility and sustainable partnerships. Regarding sensory comfort, physical comfort focuses on breathability, temperature and humidity regulation, and material softness, while aesthetic comfort emphasizes the balance between fashion and functionality, identity expression, and social interaction. Sustainability is then addressed through insights derived from the research background and the overall dimensions. Overall, this design strategy emphasizes that businesses need to consider multiple factors such as quality, value, comfort, and social responsibility when designing and manufacturing clothing to meet the comprehensive needs of modern consumers.
Validation of sustainable clothing design strategy
At the current research stage, we are exploring whether the developed sustainability clothing design indicator system is consistent with consumer evaluations. To achieve this goal, based on the 12 sustainable clothing brand strategies previously proposed and referencing relevant studies, we invited an expert group (Table 5) to select the top 35 products from the JD.com website according to the sustainable clothing design strategies. To eliminate any bias that may arise from products of different levels, we selected a pair of products from these 35 that were products that were relatively close in price and functionality. One product (Sample 1 in Table 4) aligns with our developed sustainable clothing design strategy, while another (Sample 2 in Table 4) aligns with lower sustainability. Furthermore, to ensure fairness in the evaluation, we processed the product images and added some textual descriptions to help respondents focus on evaluating product features and usability while avoiding the interference of factors such as brand and color.
The Appendix A shows the proportion of items used for validation. The questionnaire covers four aspects: perceived quality, value, purchase behavior, and purchase intention. The questionnaire for this study was revised based on mature scales validated in previous studies67,68,69,70. The questionnaire format adopted the Likert five-point scale. Before the formal survey, we randomly selected 6 participants who met the research criteria for questionnaire pretesting. During the pre-survey, participants were asked to assess their understanding of the questionnaire items and provide feedback to optimize the wording for improved readability. In the formal survey stage, all respondents confirmed having relevant product purchasing experience and thoroughly understood the functionality and application of the two samples before filling out the questionnaire. The questionnaire survey was initiated in February 2024, and 657 questionnaires were collected. To ensure the effectiveness of respondent feedback, we set reverse questions and evaluated their attention level based on response time. After excluding invalid samples, this study finally obtained 475 valid questionnaires. The demographic characteristics of the respondents are presented in Table 6.
To validate the effectiveness of sustainable clothing design strategies, we conducted data analysis using SPSS 27 to assess whether there were significant differences in perceived quality, perceived value, purchase behavior, and intention to purchase sustainable clothing. In this study, Cronbach’s α test was used to evaluate the reliability of the questionnaire data. The results showed that the Cronbach’s α value for the dimension of perceived quality was 0.869, for perceived value was 0.855, for purchase behavior was 0.879, and for intention to continue using was 0.880. The Cronbach’s α values for each dimension were more significant than 0.85, and deleting any item within the scale did not increase the Cronbach’s α value, indicating that the data in this study were reliable and suitable for subsequent analysis. As Jansen et al.71 demonstrated, an alpha value greater than 0.7 is considered sufficient. Alpha values exceeding the 0.85 thresholds confirm that our questionnaire effectively captured the intended dimensions. This high reliability aligns with the findings from psychometric studies in other fields, thereby emphasizing the high standards for scale reliability, ensuring the scale consistently reflects the specific constructs it intends to measure, unaffected by random measurement errors or external variables. Furthermore, in the context of sustainability research, high alpha values indicate that the questionnaire reliably captured dimensions such as perceived quality and value, which are subjective and may vary among respondents. Establishing this level of reliability is crucial for laying a solid foundation for subsequent analyses and ensuring that the research results can effectively inform practice and policy.
Next, we prepared for multivariate analysis of variance (MANOVA). Before the analysis, we tested the assumptions of the method. Kline skewness and kurtosis displayed normal distributions, with skewness values ranging from 0.071 (CI) to 0.002 (FE) and kurtosis ranging from 1.035 (CH) to 0.917 (FE). Therefore, the total skewness of the data was less than 3.0, and the total kurtosis was less than 8.0, indicating univariate normality.
Then, we measured boxplots and identified any outliers. In multicollinearity, the Pearson correlation coefficients between the four dependent variables ranged from 0.28 to 0.45, indicating a mild correlation and no multicollinearity (|r| < 0.9). When conducting Mahalanobis distance analysis, since there were four dependent variables in this case, the corresponding critical value was 18.47. The maximum Mahalanobis distance in this case was 13.45685, which is less than 18.47, indicating the absence of multivariate outliers.
This study used Levene’s Test and Box’s M test to determine whether the data distribution met the assumptions of MANOVA. Table 7 shows that the p-values for both tests were more significant than the significance level (typically 0.05), indicating homogeneity in variance and covariance. Thus, the data met the prerequisites for MANOVA. Therefore, we proceeded with the multivariate analysis of variance using the data.
The analysis of variance results showed that the p-values for samples 1 and 2 in all four dimensions were less than 0.05, indicating that the effects of samples 1 and 2 on all dimensions were significant (Table 8). The analysis revealed differences between the samples in perceived quality (F = 82.642, p < 0.05), perceived value (F = 9.427, p < 0.05), purchase behavior (F = 9.808, p < 0.05), and purchase intention (F = 8.383, p < 0.05). The Partial Eta squared values were 0.149, 0.02, 0.02, and 0.017 for perceived quality, perceived value, purchase behavior, and purchase intention, respectively. These values indicate low to moderate effect sizes for the differences observed between the samples across the four dimensions. Perceived quality had the most significant impact on the samples (0.149), suggesting its significant role, possibly related to environmental awareness. Perceived value and purchase behavior had similar, albeit much lower, values than perceived quality (0.02), indicating some influence on the samples. Purchase intention had the lowest value, suggesting a more minor sample impact.
Based on the Partial Eta squared values mentioned, 0.149 for “perceived quality” indicates a moderate effect size, accounting for 14.9% of the sample variance. This suggests that perceived quality can significantly impact consumer reactions to sustainable fashion. The relatively high percentage indicates that factors such as garment durability, fabric quality, and overall structure are considered critical quality indicators, playing an essential role in shaping consumers’ attitudes and behaviors. This is particularly important in the context of sustainable fashion, as quality not only affects immediate satisfaction but also impacts perceptions of long-term value and sustainability. When Partial Eta squared values are at 0.02 for perceived value and purchasing behavior, these dimensions explain about 2% of the variance. Although small, these effects are still significant, indicating that while these factors influence consumer decisions, their impact is less pronounced than perceived quality. This suggests that in sustainable purchasing, consumers prioritize the product’s intrinsic quality over pricing or immediate monetary value. Similarly, the effect sizes for purchasing behavior suggest that factors such as purchase convenience, availability, and consumer support play a supporting rather than a primary role in influencing sustainable purchasing decisions.
The minimal Partial Eta squared value of 0.017 for purchase intentions indicates a relatively small impact, covering only 1.7% of the variance. This lower level of influence suggests that while the willingness to purchase sustainable fashion is affected by the factors studied, they are likely more significantly driven by external or other variables not captured in this study, such as marketing influences or personal environmental beliefs.
Table 9 shows differences between samples 1 and 2 in perceived quality, perceived value, purchase behavior, and purchase intention. For example, in perceived quality, sample 1 (marked as 1) was significantly higher than sample 2 (p < 0.05). The same trend was observed across other dimensions. Sample 1 was rated higher than sample 2 across all dimensions, consistent with the evaluation results based on sustainable clothing design strategies by experts.
The significant difference in perceived quality between Sample 1 and Sample 2 (t = 9.088, p < 0.05) suggests that consumers consider Sample 1 significantly superior. This could be due to Sample 1 using higher quality materials or communicating its sustainability attributes more effectively. For instance, if Sample 1 utilizes environmentally friendly materials known for durability and performance and highlights these features in its marketing campaigns, it could significantly enhance consumers’ perception of quality. This starkly contrasts Sample 2, which may have yet to use similarly perceived high-quality materials or effectively communicate its sustainability attributes, resulting in a lower perceived quality score.
Although smaller than the differences in perceived quality, the differences in perceived value are statistically significant (t = 3.073, p < 0.05). This discrepancy suggests that even if Sample 1 might be priced higher, the perceived benefits (such as durability and ethical production practices) are considered to justify the cost. Sample 2’s lower perceived value might not align with consumers’ expectations of cost versus benefits, thereby impacting its lower valuation.
Similarly, variations in purchasing behavior and intentions further highlight the differences between the two samples. Consumers are more likely to buy and intend to purchase Sample 1 due to its higher ratings in quality and value. Sample 1 may more clearly align with consumer values regarding sustainability and product quality compared to Sample 2. These differences indicate that Sample 1’s marketing strategy may highlight the long-term benefits of sustainable purchasing more effectively than Sample 2.
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