A Study on the Impact of the Consumption Value of Sustainable Fashion Products on Purchase Intention Based on the Theory of Planned Behavior

Abstract

This study is based on the Theory of Planned Behavior (TPB) and focuses on university students in South Korea and China. It explores how the consumption value of sustainable fashion products affects consumers’ purchase intentions. Additionally, it verifies the moderating effect of environmental concern on the relationship between consumption value and purchase intention. An empirical analysis is conducted through a questionnaire survey using SPSS 26.0 and AMOS 26.0. This study integrates the TPB model to examine the impact of consumption value on the purchase intentions of university students in both countries. It also investigates how environmental concern moderates the relationship between the sub-factors of consumption value—functional value, social value, emotional value, precious value, and ethical value—and purchase intention. To achieve the research objectives, a comprehensive review of relevant domestic and international literature is undertaken. This review establishes a theoretical foundation for the constructs of consumption value, subjective norms, attitudes, perceived behavioral control, purchase intention, and environmental concern. Based on this framework, empirical research is conducted to develop and validate the research model and associated hypotheses. The primary objective of this study is to evaluate the market response to sustainable fashion products in China and South Korea. It analyzes the relationship between consumption value and purchase intention regarding these products. Additionally, the research aims to provide insights for fashion enterprises on the appropriate positioning of sustainable fashion products. It also establishes a theoretical foundation to guide the future development of sustainable fashion initiatives.

1. Introduction

With the rapid development of the global economy, people’s consumption capacity continues to increase, and the unsustainable consumption concepts and development models in the textile economy have led to significant resource waste and environmental pollution. According to predictions by the United Nations, if the global population reaches 8.5 billion by 2030, clothing consumption will surge from 62 million tons to 102 million tons. By 2050, the carbon emissions generated by the fashion industry will account for one-quarter of the global total, which means that future environmental pressures will intensify significantly [1].

Currently, the sales of sustainable fashion products are on the rise and are increasingly favored by consumers; however, empirical research on this topic is still inadequate. In the field of sustainable fashion, the existing research primarily focuses on consumers’ perceived value, environmental concerns, and purchase intentions regarding sustainable fashion products. These studies often base their analysis on a single dimension, lacking a comprehensive perspective that considers multiple characteristics. For instance, Gupta and colleagues utilized the Theory of Planned Behavior (TPB) model to explore the relationships among young consumers’ attitudes, purchase intentions, and purchasing behaviors towards green products [2]. However, there is a scarcity of research that systematically integrates multiple characteristics, such as functional value, social value, emotional value, precious value, and ethical value. The uniqueness of this study lies in its application of the TPB model, which comprehensively considers the multidimensional values of sustainable fashion products and their impact on purchase intentions. By integrating these variables, we aim to gain a more comprehensive understanding of consumer purchasing behavior, thereby filling the gap in the existing literature.

Although the existing research provides a foundation for understanding consumer behavior regarding sustainable fashion products, most studies lack an in-depth exploration of the interactions between different value dimensions. For example, the study by Leclercq-Machado et al. focuses solely on how environmental concern influences purchase intention, neglecting the roles of social value and emotional value [3]. Furthermore, current research often fails to adequately consider the impact of subjective norms and perceived behavioral control on consumer decision making.

Integrating consumer value with the Theory of Planned Behavior (TPB) model is crucial, as consumers’ purchase intentions are influenced not only by attitudes and subjective norms but also by their intrinsic values [4]. Consumer value can profoundly affect consumers’ behavioral choices, particularly in areas involving ethical and environmental responsibilities. By incorporating consumer value theory into the framework of the TPB model, we can gain a deeper understanding of consumer motivations, thereby more effectively predicting their purchasing behavior.

This study investigates the influence of the value of sustainable fashion products on purchase intention through the lens of the Theory of Planned Behavior model. Initially, it presents the theoretical framework surrounding the consumption value of sustainable fashion products, which includes functional value, social value, emotional value, precious value, and ethical value, as well as the constructs of subjective norms, attitudes, perceived behavioral control, environmental concern, and purchase intention. Building on this theoretical foundation, the research establishes its model and hypotheses, defines the operational parameters of the variables, and outlines the content of the questionnaire necessary for the survey. In the Section 3, the study examines the validation of the hypotheses and analyzes the results derived from the collected survey data. The Section 5 summarizes the key findings of the research, offering a comprehensive overview of its significance while also addressing limitations and proposing directions for future research.

At the theoretical level, this study contributes to the extension of the application of the Theory of Planned Behavior (TPB) model in the field of sustainable fashion, providing a new perspective for understanding consumer behavior. By comprehensively considering multiple value dimensions, this research offers a new theoretical foundation for future studies and promotes academic development in the sustainable fashion domain.

At the practical level, the findings of this study will provide valuable insights for brands and marketers, helping them to better design and promote sustainable fashion products. Understanding the different value factors that consumers consider in their purchasing decisions can assist businesses in formulating more effective marketing strategies, thereby enhancing the market acceptance of sustainable products.

In summary, this study fills the gap in the existing literature by conducting a comprehensive analysis of the multiple values of sustainable fashion products, offering new theoretical perspectives and practical guidance, and emphasizing the importance of consumer value in decision making.

2. Theoretical Background

2.1. Sustainable Fashion Products

2.1.1. Concept of Sustainable Fashion Products

Sustainable fashion products refer to a comprehensive concept that encompasses the materials and production of products, energy consumption and recycling, pollution reduction, waste issues, consumer health, working conditions, and fair distribution from the perspectives of environmental protection and social responsibility [5]. This means considering future generations and striving to minimize ethical, environmental, and social impacts at all stages of product planning and design, raw material procurement, production, distribution, use, and disposal [6]. At the same time, sustainable fashion products adhere to labor ethics throughout the entire process of materials, production methods, distribution, consumption, and disposal, aiming to minimize environmental impact and uphold fair trade principles, thereby achieving sustainable development in the realm of fashion [7].

Sustainable fashion products can be described as a fashion philosophy that is both environmentally friendly and socially responsible for future generations [8], meaning that existing resources are not depleted for future generations during the production, use, and disposal processes of fashion products [9]. Sustainable fashion products involve recycling waste generated from certain products to create another type of product or converting it back into energy products for reuse, thus achieving sustainability in fashion [10]. This includes fashion made from recycled fabrics, upcycled fashion, vintage fashion, organic fashion, vegan fashion that protects animals, zero-waste fashion that minimizes waste, and fashion that reflects environmental and social equity. It can be said that this is a fashion approach that considers environmental protection, economic growth, social contribution, and cultural value in a harmonious and balanced manner for the peace and future development of humanity [11].

2.1.2. Current Status of Sustainable Fashion Products in China

China is the world’s largest clothing manufacturer, exporter, and consumer, possessing the largest and most comprehensive modern industrial system globally. According to the United Nations’ updated report “World Economic Situation and Prospects 2024” released in May 2024, from January to April of 2024, China’s textile and apparel export value reached $89.84 billion, accounting for approximately 52% of the global apparel export value, maintaining its position as the world’s largest exporter [12].

The China National Textile and Apparel Council (CNTAC) outlined in the “Action Outline for Building a Modern Textile Industry System (2022–2035)” its vision for China to emerge as a significant force in global textile technology, a key leader in the international fashion arena, and a robust advocate for sustainable development. Central to this initiative is the emphasis on new fiber materials, which are deemed essential for advancing the greening and branding innovation of regenerated cellulose fibers from various perspectives. This approach aims to establish a green and sustainable industrial chain [13].

In the field of sustainable fashion, discussions about the “naturalness” of products occupy 75% of the dialogue, indicating that Chinese netizens perceive “natural” and “sustainable” as parallel concepts. In popular discussions about sustainable fashion on Zhihu, 56% of netizens expressed positive views, embracing green fashion, while 12% expressed skepticism [14].

The current status of sustainable fashion in China can be viewed from multiple aspects. Firstly, an increasing number of fashion brands are beginning to focus on environmental protection and social responsibility, launching products that use eco-friendly materials and sustainable production methods [15]. Additionally, more consumers are becoming aware of environmental factors such as the production process and material sources, showing a greater willingness to choose products that align with sustainable principles. This has, to some extent, driven the development of sustainable fashion in the Chinese market [16]. As people’s emphasis on environmental protection and sustainable development continues to grow, the fashion industry is gradually shifting towards a more eco-friendly and sustainable direction.

Chinese consumers have demonstrated a stronger awareness of health and environmental issues following the pandemic. The 2024 “Sustainable Consumption Report” indicates that 68% of Generation Z prioritizes clothing with traceable QR codes, which verify supply chain transparency through blockchain technology [17]. Data from Alibaba’s “Green Channel” shows that sales of clothing made from recycled materials increased by 120% year-on-year in 2023. Digital platforms, such as Xiaohongshu and Dewu, further promote green consumption behaviors through a “carbon credit” reward mechanism [18].

In 2024, the Ministry of Industry and Information Technology released the “Action Plan for the Digital Transformation of the Textile Industry,” emphasizing that AI-driven dyeing and water-saving technologies have reduced industrial water consumption by 30%. Additionally, 3D virtual sample technology has decreased sample production waste by 15% [19]. Brands like Li Ning are utilizing digital twin technology to achieve full lifecycle carbon footprint tracking, aligning with the European Union’s cross-border compliance requirements for the “Digital Product Passport” [20].

2.1.3. Current Status of Sustainable Fashion Products Overseas

Internationally, various conferences have been actively held on the topic of “sustainability,” such as the United Nations Conference on the Human Environment (1972, Sweden) [21], the Intergovernmental Conference on Environmental Education (1977, Soviet Union) [22], and the Rio de Janeiro Conference (1992, Brazil) [23]. These international meetings have further promoted the maturation of the “sustainability” concept, which is now widely recognized and popularized globally, catering to the increasingly prominent environmental needs of consumers.

A sustainable fashion conference was held in Milan, Italy, where sustainable fashion experts and innovative international brands introduced and updated solutions, certifications, legislation, and market trends regarding the environmental and social impacts of fashion [24].

The 18th Annual Sustainable Business and Design Conference, titled “Reimagining Our Future,” took place at the Fashion Institute of Technology in New York. This conference concentrated on the development of innovative sustainable solutions within the fashion industry, aiming to align with the United Nations’ sustainable development goals and broader global sustainability objectives [25].

The Copenhagen Fashion Summit aims to provide solutions to the biggest sustainable development challenges in the fashion industry, discussing the transformative changes needed to elevate the impact of sustainable development in fashion to a new level [26].

A report on the sustainable fashion market states that the market value of sustainable fashion will reach $7.9152 billion in 2024, with a bright future for the global sustainable fashion market. It is expected that by 2030, the global sustainable fashion market will reach $24.26 billion, with a compound annual growth rate of 18.6% from 2024 to 2030. The main drivers of this market are the increasing awareness of consumers and the demand for environmentally friendly products, as well as technological innovations that enhance supply chain visibility and promote circularity [27].

The 2024 McKinsey study highlights the phenomenon of “environmental fatigue” among consumers in Europe and the United States. While 75% of respondents agree with the concept of sustainability, only 35% are willing to pay a premium, reflecting the attitude-behavior gap [28].

To address this contradiction, brands such as Gucci have introduced “modular design” services, allowing consumers to replace specific components to extend the product’s lifespan. This strategy has resulted in a 22% increase in customer retention [29].

The United Nations Environment Programme’s 2025 “Fashion Industry Net Zero Roadmap” indicates that the mutual recognition agreement on recycled polyester standards between China and Europe has reduced trade barriers by 23%. Additionally, the LVMH Group’s collaboration with JD.com on the “AI Material Matching Platform” utilizes machine learning to optimize inventory turnover, achieving an 18% reduction in carbon emissions from unsold inventory [30].

2.1.4. Global Interaction of Digitalization and Ethical Consumption

Kim So-ra (2025) [31] utilized text mining to analyze the cognitive changes resulting from the integration of artificial intelligence and fashion. The findings indicate that 2024 marks a new phase for the fashion industry, focusing on the expansion of sustainable development and globalization strategies. In this phase, the application of AI technology brings dual impacts: on one hand, AI is expected to drive innovation in the global market and enhance industry sustainability; on the other hand, its application also raises numerous challenges, including the establishment of ethical standards, bridging the technological divide, and redefining traditional concepts of creativity.

Jang Namkyung (2024) [32] conducted keyword analysis through big data text mining on the implementation of sustainable fashion and digitalization, resulting in the identification of four distinct groups: “Core Values of Sustainable + Digital Fashion,” “Directions of Sustainable + Digital Fashion Led by Digitalization,” “Methods of Sustainable + Digital Fashion Led by Sustainability,” and “Sustainable + Digital Fashion Systems”.

The digitalization differences between China and South Korea are evident, with South Korea’s K-fashion achieving a 40% circulation rate of second-hand luxury goods through the “K-REUSE” program (2024 data) [33], while China’s Xianyu platform focuses more on affordable clothing in a C2C cycle [34].

According to Son You Kyung (2025) [35], the integration of digital technology and sustainable fashion has become an important trend in current academic research. The study points out that topics such as the application of the metaverse in fashion innovation, the development of digital fashion technologies, the formulation of brand sustainability strategies, and consumer behavior analysis are receiving widespread attention in academia. This trend indicates that digital technology is playing an increasingly critical role in promoting the development of sustainable fashion, with metaverse-based research likely to become a key focus in the future of this field. The research also emphasizes that achieving this transformation requires not only innovative breakthroughs in industrial practices but also strong policy support to enhance the implementation effectiveness of sustainable development.

2.2. Consumer Value

Looking at past research on consumer value, it can be broadly divided into attempts at unidimensional research and attempts at multidimensional research. Studies that view consumer value as a single-dimensional structure adopt an economic perspective based on utility. That is, consumers evaluate consumer value by comparing the benefits they receive with the costs they incur [36]. However, this unidimensional approach is overly focused on economic benefits and does not accurately reflect the complex structure of consumer value. The multidimensional analysis of consumer value not only reflects economic utility but also emotional aspects, thus gaining support from many researchers. In particular, the consumer value theory proposed by Sheth et al. (1991) [37] provides a comprehensive classification of consumer value based on this multidimensional perspective, categorizing consumer value into five types: functional value, social value, emotional value, and precious value (cognitive value and conditional value). Many scholars typically modify the consumer value theory based on the characteristics of the products being studied, proposing different classification dimensions.

Duan et al. (2023) [38] utilized consumer value theory to investigate college students’ willingness to purchase carbon-labeled products and the factors influencing this willingness. The findings reveal that, despite a relatively low public awareness of the carbon labeling system, there exists a strong inclination to purchase carbon-labeled products. Furthermore, the study identifies that functional value, emotional value, and cognitive value all exert a positive influence on consumers’ willingness to buy these products, with notable age-related differences observed in purchasing willingness.

Aravindan et al. (2023) [39] investigated the potential driving factors behind green procurement and examined the mediating role of positive word-of-mouth in this context. The results underscored the significance of positive word-of-mouth, identifying emotional value and cognitive value as the primary determinants influencing green purchasing intention.

Shin Jun-ho (2023) [40] sought to analyze the factors that influence attitudes, purchase intentions, and the willingness to pay a premium for environmentally friendly baking products. The findings indicated that price functional value, social ethical value, and emotional value significantly and positively impact consumer attitudes toward these products. Additionally, the study revealed that individuals’ attitudes toward environmentally friendly baking products have a substantial positive effect on their purchase intentions.

Putri et al. (2023) [41] examined the influence of consumer value and the mediating role of transaction utility in shaping green purchasing intentions among consumers, particularly focusing on Generation Z. The findings revealed that social value, experiential value, and transaction utility significantly and positively affect green purchasing intentions. Additionally, functional value and experiential value were found to positively influence acquisition utility and transaction utility, with acquisition utility also having a positive effect on transaction utility. However, it was noted that functional value and acquisition utility do not significantly impact green purchasing intentions.

Lin and Dong (2023) [42] developed a comprehensive model integrating planned behavior theory, consumer value theory, and environmental concern theory. The findings indicated that consumers’ willingness to purchase energy-efficient appliances is positively influenced by their purchase attitudes. These attitudes are significantly affected by functional value, price value, environmental value, and environmental concern. In contrast, emotional value and social value were found to have no significant impact on consumers’ purchase attitudes.

Jang Bo-hyun (2024) [43] conducted empirical analysis to determine the impact of consumer value and satisfaction with vegan cosmetics on the likelihood of continued consumption. The order of consumer value from highest to lowest was: functional value, economic value, and ethical value. Consumers’ value perceptions significantly influence the likelihood of continued consumption of vegan cosmetics, and interest and satisfaction with vegan cosmetics also significantly affect the likelihood of continued consumption.

Lee Eun-ah (2024) [44] applied rational behavior theory to fashion products made from regenerated fibers, examining the relationships among consumer value, attitudes, subjective norms, brand satisfaction, and purchase intentions. The study found that the social value, ethical value, and hedonic value dimensions of consumer value have significant positive impacts on subjective norms and also significantly influence brand satisfaction and purchase intentions. Moreover, the study by Sheikh et al. (2025) [45] on “An Empirical Study of Consumers’ Perceived Value of Circular Fashion” also indicates that the two dimensions of consumer value, functional value and emotional value, significantly influence consumer attitudes.

In this study, due to the unique nature of sustainable fashion products, which differ from general products, the most prominent feature in the green field is social and ethical responsibility. However, the ethical value dimension has received little attention. Given the unique attributes of sustainable fashion products, this study incorporates ethical value into consumer value theory. This research utilizes Sheth’s consumer value theory to examine the influence of five dimensions—functional value, emotional value, social value, precious value, and ethical value—on the purchase intention of sustainable fashion products. Functional value is defined by consumers’ assessment of the product’s tangible attributes and is contingent upon the degree to which consumers’ utilitarian needs are satisfied. Social value is derived from the product or service’s capacity to enhance the customer’s social self-concept. Emotional value pertains to the emotional benefits gained from the product or service. Precious value emerges from behaviors that seek diversity, exploration, and novelty. Finally, ethical value encompasses the social and personal standards employed to evaluate behaviors and decisions, determining their alignment with ethical norms [46].

2.3. Theory of Planned Behavior

The Theory of Planned Behavior (TPB) asserts that behavioral intention is primarily influenced by three key variables: behavioral attitude, subjective norms, and perceived behavioral control [47]. According to this theory, an individual’s behavior is determined by their intention to engage in that behavior, which is shaped by their attitude toward the behavior, the subjective norms surrounding it, and their perceived control over the behavior [48]. Figure 1 illustrates this theory in a structural diagram.

Figure 1. Model of the Theory of Planned Behavior [44].

Subjective norms refer to the influence of external factors on an individual’s behavioral decisions [49]. When people see important or respected figures purchasing sustainable products, they are more likely to try buying such sustainable fashion products. This perceived social influence may come from family, friends, government, or social groups [50]. Attitude is an important factor in an individual’s positive or negative evaluation of the purchasing process for sustainable fashion products [51]. Consumers who hold a positive attitude toward sustainable fashion believe that this choice will bring personal satisfaction and actively promote environmental protection [52]. Perceived behavioral control refers to an individual’s assessment of the ease or difficulty associated with performing a particular behavior. Consumers’ awareness of the facilitators and barriers associated with the adoption of sustainable fashion practices plays a significant role in their decision-making processes. This includes the challenges they face in engaging with circular fashion practices, their willingness to explore and embrace slow fashion principles, and their ability to make sustainable choices. An individual’s confidence in navigating these obstacles is essential for evaluating their intentions to integrate sustainable fashion into their consumption strategies [53].

Jeong Da-un and Kim Young-sam (2022) [54] conducted a study utilizing the expanded Theory of Planned Behavior to investigate the factors that influence consumers’ intentions and behaviors related to the purchase of sustainable fashion products. The findings of this research reveal that consumers’ attitudes, subjective norms, and perceived behavioral control significantly and positively affect their willingness to purchase sustainable fashion items. Additionally, Gupta et al. (2023) [55] examined the clothing purchasing habits of Indian millennials, focusing on the relationship between the Theory of Planned Behavior and intentions to purchase sustainable fashion. The study’s findings indicate that the three components of the Theory of Planned Behavior—attitude, subjective norms, and perceived behavioral control—significantly influence consumers’ willingness to buy sustainable fashion products. Notably, the attitude toward sustainable clothing emerged as a critical factor, suggesting that consumers who hold a positive attitude toward sustainable fashion are more inclined to make purchases in this category. Prabhakar et al. (2024) [56] employed the Theory of Planned Behavior as a theoretical framework to investigate the factors that influence the purchasing intentions of green products among Chinese Generation Z consumers in the post-pandemic context. The findings of the study revealed that anticipated positive emotions serve as the most significant factor affecting the green product purchasing intentions of this demographic, followed by perceived behavioral control, personal norms, attitudes, and subjective norms.

The preliminary research indicates that the Theory of Planned Behavior is instrumental in elucidating the psychological factors that underpin consumer behavior. Within this framework, the components of attitude, subjective norms, and perceived behavioral control emerge as critical determinants influencing consumers’ purchasing intentions.

2.4. Purchase Intention

Purchase intention refers to the expected likelihood of a consumer’s purchase and is a form of behavioral intention [57]. Purchase willingness is the consumer’s inclination or tendency to buy a certain product or service, indicating the probability of purchasing a specific product. It is an important indicator that determines whether consumers ultimately make a purchase, used to predict potential sales opportunities and market demand [58]. On the other hand, purchase intention is defined as a measure of word-of-mouth information, an important variable influencing consumer decision making, and is viewed as a planned future action to purchase sustainable fashion products [59]. Consumers’ purchase willingness may vary due to individual preference differences, but overall, consumers’ purchase willingness reflects their intrinsic willingness to pay and inclination to buy products or services. Since products or services are traded based on consumers’ purchase inclinations, this is crucial for marketing sustainable fashion products.

Gold and Terner (2023) [60] utilized the extended Theory of Planned Behavior to examine sustainable fashion consumption in Japan, specifically assessing the significance of sociocultural barriers on consumers’ purchase intentions. The results confirmed the significance of attitude and perceived behavioral control in predicting sustainable fashion purchase intentions, while subjective norms had no significant impact. This was explained by the lack of sustainable fashion discourse in Japan, leading to a smaller influence of subjective norms on the younger generation.

Jeon Chan-ho (2023) [61] analyzed the impact of upgraded remanufactured products on college students’ environmental concern, environmental knowledge, and consumption values on purchase intention and behavior. The research findings indicate that environmental concern significantly and positively influences the intention to purchase upgraded remanufactured products. In contrast, environmental knowledge does not exert a direct effect on purchase intention. Furthermore, various consumption values—including functional value, social value, emotional value, novelty value, and situational value—positively impact the purchase intention for upgraded remanufactured products. Kim Eun-hye (2022) [62] employed a value theory model to investigate the perceived benefits and sacrifices associated with an eco-friendly fashion curation platform, focusing on their effects on perceived consumer value and purchase intention. The findings of the study confirmed that the perceived consumer value of the eco-friendly fashion curation platform significantly and positively influences purchase intention. Additionally, the sub-factors of perceived consumer value, specifically precious value and social consumption values, were found to have significant positive impacts on consumers’ willingness to make a purchase.

Yang Il-jeong (2024) [63] investigated the mediating role of perceived value in the relationship between the design characteristics of upgraded remanufactured products and consumer purchase intention. The results of the research indicated that both emotional value and social value positively influence purchase intention. Furthermore, the design characteristics of upgraded remanufactured products—specifically emotional, functional, ethical, and aesthetic attributes—were found to have a positive impact on purchase intention. Furthermore, Song Ji-an (2024) [64] conducted an analysis of the factors influencing consumers’ intentions to purchase upgraded, remanufactured fashion products through the lens of the Theory of Planned Behavior. The findings revealed that personal attitude, subjective norms, and perceived behavioral control all exert a positive influence on purchase intention. Additionally, environmental value was found to positively impact attitude, subjective norms, and perceived behavioral control.

2.5. Environmental Concern

Environmental concern is defined as the overall attitude or value orientation towards environmental protection, as well as the extent of an individual’s concern for the environment [65]. It is understood as the level of awareness and intention of individuals regarding their support for efforts to address environmental issues or their personal contributions to solving these problems. This encompasses core aspects of environmentally friendly consumer behavior, including both behavioral and mental efforts, where consumers can make eco-friendly purchasing decisions and actively participate in environmental protection activities [66]. Furthermore, environmental concern indicates the emotional investment of individuals in addressing environmental issues, demonstrating the willingness to alleviate these problems and support measures for environmental protection, thereby influencing environmentally friendly behaviors and consumption [67].

Hudayah et al. (2023) [68] investigated the relationship between the perceived value of green products and the purchase intention of Generation Z consumers in Indonesia. Additionally, the study examined the moderating role of environmental concern on the impact of perceived green value on these consumers’ intentions to purchase green products. The analysis revealed that both functional value and conditional value significantly and positively influence the intention to purchase green products. This finding suggests that Generation Z consumers are more likely to choose green products when they perceive these products to possess strong functional value and exhibit a high level of environmental concern. Furthermore, environmental concern moderates the effect of social value on the intention to purchase green products, indicating that consumers with heightened environmental awareness are more susceptible to the influence of social value benefits when making decisions regarding the purchase of green products. Meanwhile, Iqbal et al. (2023) [69] explored how to utilize green marketing strategies to develop the willingness to purchase environmentally friendly products, as well as how consumers’ overall consumption behavior shifts towards green consumption. The research findings suggest that green purchase intention acts as a mediating variable, whereas green concern is regarded as a moderating variable in the relationship between green value and purchase intention.

Smelt and Famke (2024) [70] investigated the factors that influence Dutch consumers’ willingness to purchase sustainable fashion, as well as the moderating role of environmental concern in the relationship between social influence and purchase intention. The findings revealed that perceived environmental knowledge does not exert a direct impact on consumers’ willingness to purchase sustainable fashion. Furthermore, environmental concern was found to have a significant negative moderating effect on the direct relationship between social influence and purchase intention, suggesting that a higher level of environmental concern may diminish the influence of social factors on the willingness to purchase sustainable fashion products.

The above preliminary research indicates that consumer environmental concern is not only a key term in academic research but also a crucial factor influencing consumers’ purchase intentions. Consumers who exhibit a higher level of environmental concern are more likely to opt for the purchase of sustainable fashion products. Therefore, this study aims to analyze the moderating relationship of environmental concern between consumption value and purchase intention and also considers environmental concern as an important factor in consumer responses.

3. Research Methodology

3.1. Research Subjects

This study targets university students in the Busan area of South Korea and the Guangzhou area of China, using a questionnaire survey for sample collection. The questionnaire survey in the Busan area was conducted from 7 September to 11 September 2024, with a total of 650 responses collected through direct surveys. The questionnaire for the Guangzhou area was distributed from 5 September to 10 September 2024, using Wenjuanxing (China Survey Network), and a total of 740 responses were received. Among these, there were 1308 valid questionnaires, after 58 were excluded from South Korea and 24 from China. The total number of valid questionnaires used for analysis from South Korea was 592, with 276 male and 316 female respondents; the total number of valid questionnaires used for analysis from China was 716, with 356 male and 360 female respondents.

The model of this study is shown in Figure 2.

Figure 2. Schematic diagram of the research model.

3.2. Research Hypotheses

H1. 

Consumer value will have a positive impact on consumers’ planned behavior (subjective norms, attitudes, perceived behavioral control).

H1-1. 

The functional value dimension of consumer value will have a positive impact on consumers’ planned behavior subjective norms.

H1-2. 

The social value dimension of consumer value will have a positive impact on consumers’ planned behavior subjective norms.

H1-3. 

The emotional value dimension of consumer value will have a positive impact on consumers’ planned behavior subjective norms.

H1-4. 

The precious value dimension of consumer value will have a positive impact on consumers’ planned behavior subjective norms.

H1-5. 

The ethical value dimension of consumer value will have a positive impact on consumers’ planned behavior subjective norms.

H1-6. 

The functional value dimension of consumer value will have a positive impact on consumers’ planned behavior attitudes.

H1-7. 

The social value dimension of consumer value will have a positive impact on consumers’ planned behavior attitudes.

H1-8. 

The emotional value dimension of consumer value will have a positive impact on consumers’ planned behavior attitudes.

H1-9. 

The precious value dimension of consumer value will have a positive impact on consumers’ planned behavior attitudes.

H1-10. 

The ethical value dimension of consumer value will have a positive impact on consumers’ planned behavior attitudes.

H1-11. 

The functional value dimension of consumer value will have a positive impact on consumers’ planned behavior perceived behavioral control.

H1-12. 

The social value of dimension consumer value will have a positive impact on consumers’ planned behavior perceived behavioral control.

H1-13. 

The emotional value dimension of consumer value will have a positive impact on consumers’ planned behavior perceived behavioral control.

H1-14. 

The precious value dimension of consumer value will have a positive impact on consumers’ planned behavior perceived behavioral control.

H1-15. 

The ethical value dimension of consumer value will have a positive impact on consumers’ planned behavior perceived behavioral control.

H2. 

Consumers’ planned behavior (subjective norms, attitudes, perceived behavioral control) will have a positive impact on the purchase intention of sustainable fashion products.

H2-1. 

The subjective norms of consumers’ planned behavior will have a positive impact on the purchase intention of sustainable fashion products.

H2-2. 

The attitudes of consumers’ planned behavior will have a positive impact on the purchase intention of sustainable fashion products.

H2-3. 

The perceived behavioral control of consumers’ planned behavior will have a positive impact on the purchase intention of sustainable fashion products.

H3. 

Consumer value will have a positive impact on the purchase intention of sustainable fashion products.

H3-1. 

The functional value dimension of consumer value will have a positive impact on the purchase intention of sustainable fashion products.

H3-2. 

The social value dimension of consumer value will have a positive impact on the purchase intention of sustainable fashion products.

H3-3. 

The emotional value dimension of consumer value will have a positive impact on the purchase intention of sustainable fashion products.

H3-4. 

The precious value dimension of consumer value will have a positive impact on the purchase intention of sustainable fashion products.

H3-5. 

The ethical value dimension of consumer value will have a positive impact on the purchase intention of sustainable fashion products.

H4. 

Environmental concern will play a positive moderating role between consumer value and purchase intention.

H4-1. 

Environmental concern will play a positive moderating role between the functional value dimension of consumer value and purchase intention.

H4-2. 

Environmental concern will play a positive moderating role between the social value dimension of consumer value and purchase intention.

H4-3. 

Environmental concern will play a positive moderating role between the emotional value dimension of consumer value and purchase intention.

H4-4. 

Environmental concern will play a positive moderating role between the precious value dimension of consumer value and purchase intention.

H4-5. 

Environmental concern will play a positive moderating role between the ethical value dimension of consumer value and purchase intention.

3.3. Questionnaire Design and Composition

This study investigates the impact of consumer value of sustainable fashion products on purchase intention. Therefore, the questionnaire was developed based on related research papers, which were modified and supplemented for use. The measured variables include consumer value, planned behavior, purchase intention, environmental concern, and demographic characteristics. All items, except for demographic characteristics, were measured using a 5-point Likert scale.

To determine the survey items for the consumer value of purchasing sustainable fashion products, references [35,36,37,38,39,40,41] were consulted, resulting in a total of 29 items; for the items related to planned behavior theory, references [50,51,52,53] were used, totaling 15 items; for the items related to purchase intention, references [55,56,57,58,59,60,61] were referenced, totaling 5 items; and for the items related to environmental concern, references [62,63,64,65,66] were consulted, resulting in 4 items. The specific measurement variables and survey items are shown in Table 1.

Table 1. Measurement variables and survey items.

Factors		Questions
I. Consumption Value	functional value	I-1	Sustainable fashion products perform well.
		I-2	Sustainable fashion products are durable.
		I-3	Sustainable fashion products are cost-effective.
		I-4	Sustainable fashion products are available at affordable prices.
		I-5	Sustainable fashion products are safe.
		I-6	Sustainable fashion products have a positive impact on physical health.
	social value	I-7	Sustainable fashion products are in line with my social status and taste.
		I-8	Buying sustainable fashion products can make a good impression on others.
		I-9	I think buying sustainable fashion would fit in well with group values (i.e., academic discipline, sports club, etc.).
		I-10	Buying sustainable fashion products can garner social recognition.
		I-11	Buying sustainable fashion products is a good reflection of my social identity.
		I-12	Buying sustainable fashion products increases my value.
	emotional value	I-13	Buying sustainable fashion brings me joy.
		I-14	I can achieve happiness by purchasing sustainable fashion products.
		I-15	There is satisfaction in buying sustainable fashion products.
		I-16	Buying sustainable fashion products satisfies the image I aspire to have.
		I-17	I feel good about buying sustainable fashion.
		I-18	Buying sustainable fashion makes me feel confident.
	precious value	I-19	Sustainable fashion products are what make me unique.
		I-20	Sustainable fashion products spark my curiosity.
		I-21	Sustainable fashion products are very unique in style and color.
		I-22	Sustainable fashion products can provide me with a sense of freshness.
		I-23	Buying sustainable fashion is a new experience.
	ethical value	I-24	Companies that produce sustainable fashion products refrain from exploiting labor.
		I-25	Sustainable fashion products can be ethically produced.
		I-26	The production of sustainable fashion products can address problems caused by the unfair distribution of services.
		I-27	Sustainable fashion products help improve environmental pollution.
		I-28	Sustainable fashion products can awaken corporate ethical responsibility.
		I-29	Buying sustainable fashion products can help society eliminate inequality.
II. Theory of Planned Behavior	Subjective norm	II-1	I think most people who are important to me would support me in buying sustainable fashion.
		II-2	Most people who are important to me would agree that I should buy sustainable fashion.
		II-3	Most of the people who are important to me think highly of me and influence me to buy sustainable fashion products.
		II-4	Most people who are important to me want me to buy sustainable fashion.
	Attitude	II-5	I think buying sustainable fashion is helpful.
		II-6	I think buying sustainable fashion is beneficial.
		II-7	I think it’s smart to buy sustainable fashion.
		II-8	I think buying sustainable fashion is a positive.
		II-9	I think it makes sense to buy sustainable fashion.
		II-10	I think buying sustainable fashion is a great idea.
	Perceived behavioral control	II-11	I think it’s totally my decision whether or not to buy sustainable fashion.
		II-12	I think I have complete control over the amount of sustainable fashion I want to buy.
		II-13	I think we have the resources, time, and inclination to buy sustainable fashion.
		II-14	I think I can afford to buy the sustainable fashion I need.
		II-15	I think it’s easy to buy sustainable fashion through effective channels.
III. Purchase Intention		III-1	I intentionally buy sustainable fashion whenever possible.
		III-2	I try to buy sustainable fashion as much as possible.
		III-3	I plan to buy sustainable fashion products in the near future.
		III-4	I plan to prioritize sustainable fashion when I buy fashion products.
		III-5	I will invest more time and energy into sustainable fashion products.
IV. Environmental Concern		IV-1	I feel like I know more about recycling than anyone else.
		IV-2	I know how to choose products and packaging that will reduce the amount of waste that goes to landfill.
		IV-3	I understand the environmental words and symbols on product packaging.
		IV-4	I understand the environmental issues of the fashion product manufacturing business, such as the impact of fashion clothing manufacturing on the environment.
V. General characteristics of sustainable fashion product purchases		V1. Through what channels do you mainly purchase sustainable fashion products? (Multiple choice)		V1-1. Online channels ① E-commerce official brand website ② Social media ③ Live streaming ④ Mobile application ⑤ Others
				V1-2. Offline channels ① Brand store ② Department store ③ Market ④ Others
		V2. What are your reasons for choosing to buy your sustainable fashion products? (Multiple choice) ① Environmental protection ② Personal health ③ Personal value pursuit ④ Human care and social recognition ⑤ Good wearing feeling ⑥ Functionality and practicality ⑦ Resource recycling ⑧ Others
		V3. How interested are you in sustainable fashion products? ① Don’t care at all ② Don’t care ③ Moderate ④ Care ⑤ Very concerned
		V4. Are you willing to buy sustainable fashion products in the future? ① Willing ② Unwilling ③ I don’t know
		V5. What are the items that you purchase sustainable fashion products for? (Multiple choice) ① Clothes (tops/pants/underwear, etc.) ② Bags and similar items ③ Sundries (accessories, scarves, hats, etc.) ④ Miscellaneous items
		V6. What factors have the greatest influence on your purchasing decisions for sustainable fashion products? (Multiple choice) ① Personal concern ② Recommendations from family and friends ③ Online and mobile phone ads ④ TV and radio ads ⑤ Print ads such as magazines, newspapers, and flyers ⑥ Worn by celebrities and others ⑦ Past purchase experience ⑧ Recommendations from store staff ⑨ Others
		V7. What factors do you consider most when purchasing sustainable fashion products? (Multiple choice) ① Design (color/style) ② Material ③ Popularity ④ Quality (feeling/functionality) ⑤ Price ⑥ Brand ⑦ Others
F. Demographic characteristics		F1. What is your gender? ① Female ② Male
		F2. What year are you in? ① 1st year ② 2nd year ③ 3rd year ④ 4th year
		F3. What is your major? ① Art and Design Series ② Humanities and Social Sciences Series ③ Teacher Education Series ④ Engineering Series ⑤ Natural Science Series ⑥ Business and Economics Series ⑦ Health Care Series ⑧ Others
		F4. What is your average monthly pocket money? (Currency unit: RMB) ① Below RMB 2000 ② RMB 2000–3000 ③ RMB 3000–4000 ④ RMB 4000–5000 ⑤ More than RMB 5000

3.4. Data Processing and Analysis Methods

This study employed SPSS 26.0 and AMOS 26.0 to conduct various analyses, including descriptive statistical analysis, exploratory factor analysis, confirmatory factor analysis, reliability analysis, and correlation analysis. Additionally, structural equation modeling was utilized to test the proposed hypotheses.

Initially, descriptive statistical analysis was performed to examine the demographic characteristics of the consumer subjects, specifically university students from the Busan area of South Korea and the Guangzhou area of China.

Secondly, to assess the validity and reliability of the measured variables in this study—including consumer value (functional value, social value, emotional value, precious value, and ethical value), subjective norms, attitudes, perceived behavioral control, environmental concern, and consumer purchase intention—exploratory factor analysis, reliability analysis, and confirmatory factor analysis (CFA) were employed. In this process, particular attention was paid to the differences that may arise from questionnaire design and data collection methods across different cultural contexts, and corresponding adjustments were made to ensure the comparability and validity of the data.

Finally, to investigate the relationships among the primary factors of consumer value (functional value, social value, emotional value, precious value, and ethical value), subjective norms, attitudes, perceived behavioral control, environmental concern, and purchase intention, correlation analysis was initially conducted to assess discriminant validity and identify potential multicollinearity issues among the variables. To address potential cultural and methodological differences, appropriate statistical methods were employed for correction. Subsequently, the relationships among the variables were validated using the AMOS 26.0 structural equation modeling (SEM). Finally, the moderation effects were tested using the PROCESS plugin in SPSS.

4. Research Results

4.1. Analysis of Sample Characteristics

4.1.1. Demographic Characteristics

To understand the demographic characteristics of the samples from South Korea and China, a frequency analysis was conducted. From the overall frequency analysis of demographic characteristics, a total of 1390 questionnaires were distributed, with 55 invalid questionnaires excluded, resulting in 1308 valid responses, yielding an effective rate of 94.10%. Among these, there were 716 responses from China and 592 from South Korea. Table 2 presents the descriptive statistical results of the sample.

Table 2. Demographic information.

Item	Category	Number of People (%)
		Overall (1308)	China (716)	South Korea (592)
Gender	Male	636 (48.62%)	356 (49.72%)	276 (46.62%)
	Female	672 (51.38%)	360 (50.28%)	316 (53.38%)
Grade	1st Year	353 (26.99%)	193 (26.96%)	160 (27.03%)
	2nd Year	349 (26.68%)	186 (25.98%)	163 (27.53%)
	3rd Year	316 (24.16%)	177 (24.72%)	139 (23.48%)
	4th Year	290 (22.17%)	160 (22.35%)	130 (21.96%)
Major	Art and Design Series	273 (20.87%)	93 (12.99%)	180 (30.41%)
	Humanities and Social Sciences Series	215 (16.44%)	101 (14.11%)	114 (19.26%)
	Teacher Training Series	122 (9.33%)	119 (16.62%)	3 (0.51%)
	Engineering Series	242 (18.5%)	142 (19.83%)	100 (16.89%)
	Natural Sciences Series	81 (6.19%)	74 (10.34%)	7 (1.18%)
	Business and Economics Series	118 (9.02%)	104 (14.53%)	14 (2.36%)
	Health Series	215 (16.44%)	67 (9.36%)	148 (25.00%)
	Other	42 (3.21%)	16 (2.23%)	26 (4.39%)
Average Monthly Pocket Money	Below RMB 2000	378 (28.9%)	191 (26.68%)	187 (31.59%)
	RMB 2000–3000	403 (30.81%)	208 (29.05%)	195 (32.94%)
	RMB 3000–4000	274 (20.95%)	155 (21.65%)	119 (20.10%)
	RMB 4000–5000	135 (10.32%)	102 (14.25%)	33 (5.57%)
	Above RMB 5000	118 (9.02%)	60 (8.38%)	58 (9.80%)

Note: Exchange rate situation in China (calculated based on the average value for December 2024); RMB 1 ≈ USD 0.137.

The data show that the numbers of male and female respondents are relatively balanced, with 636 males (48.62%) and 672 females (51.38%). In terms of grade distribution, the number of students decreases gradually from the first year to the fourth year, with 353 students (26.99%), 349 students (26.68%), 316 students (24.16%), and 290 students (22.17%), indicating a relatively balanced number across years, but with a slight decreasing trend overall.

Regarding major selection, the largest group consists of students in the art and design series, totaling 273 students (20.87%), followed by engineering, humanities and social sciences, and health series, with 242 students (18.5%), 215 students (16.44%), and 215 students (16.44%), respectively. The number of students in the natural sciences series is relatively low, with only 81 students (6.19%).

In terms of the distribution of average monthly pocket money, the largest group of students falls within the range of RMB 2000–3000, totaling 403 students (30.81%), followed by those with less than RMB 2000 (378 students, 28.9%) and those with RMB 3000–4000 (274 students, 20.95%). Students with pocket money of RMB 4000–5000 and those with more than RMB 5000 are relatively few, with 135 students (10.32%) and 118 students (9.02%), respectively.

4.1.2. General Characteristics

First, according to the frequency analysis of the general characteristics of the overall samples from China and South Korea, Table 3 shows that, regarding the main purchasing channels, the official brand websites of e-commerce are the most popular online channel (819 people, 62.61%), followed by mobile applications (631 people, 48.24%) and social media (515 people, 39.37%). In terms of offline channels, brand specialty stores (836 people, 63.91%) and department stores (606 people, 46.33%) are the main channels.

Table 3. General characteristics.

Item		Category	Number of People (%)
			Overall	China	South Korea
Where do you mainly purchase fashion products	Online Channels	Official Brand Websites	819 (62.61%)	456 (63.69%)	363 (61.32%)
		Social Media	515 (39.37%)	396 (55.31%)	119 (20.1%)
		Live Streaming	222 (16.97%)	197 (27.51%)	25 (4.22%)
		Mobile Applications	631 (48.24%)	374 (52.23%)	257 (43.41%)
		Others	95 (7.26%)	57 (7.96%)	38 (6.42%)
	Offline Channels	Brand Specialty Stores	836 (63.91%)	463 (64.66%)	373 (63.01%)
		Department Stores	606 (46.33%)	376 (52.51%)	230 (38.85%)
		Markets	215 (16.44%)	166 (23.18%)	49 (8.28%)
		Others	134 (10.24%)	70 (9.78%)	64 (10.81%)
Reasons for purchasing sustainable fashion products		Environmental Protection	630 (48.17%)	437 (61.03%)	193 (32.6%)
		Personal Health	345 (26.38%)	270 (37.71%)	75 (12.67%)
		Pursuit of Personal Values	368 (28.13%)	173 (24.16%)	195 (32.94%)
		Human Care and Social Recognition	171 (13.07%)	117 (16.34%)	54 (9.12%)
		Good Wearing Feeling	251 (19.19%)	146 (20.39%)	105 (17.74%)
		Functionality and Practicality	509 (38.91%)	299 (41.76%)	210 (35.47%)
		Resource Reuse	488 (37.31%)	394 (55.03%)	94 (15.88%)
		Others	66 (5.05%)	50 (6.98%)	16 (2.7%)
Interest in sustainable fashion products		Indifferent	74 (5.66%)	42 (5.87%)	32 (5.41%)
		Unconcerned	169 (12.92%)	76 (10.61%)	93 (15.71%)
		Average	341 (26.07%)	101 (14.11%)	240 (40.54%)
		Concerned	531 (40.6%)	361 (50.42%)	170 (28.72%)
		Very Concerned	193 (14.76%)	136 (18.99%)	57 (9.63%)
Willingness to purchase sustainable fashion products		Willing	864 (66.06%)	503 (70.25%)	361 (60.98%)
		Unwilling	110 (8.41%)	96 (13.41%)	14 (2.36%)
		Don’t Know	334 (25.54%)	117 (16.34%)	217 (36.66%)
Categories of purchased sustainable fashion products		Clothing (tops/pants/underwear, etc.)	912 (69.72%)	479 (66.9%)	433 (73.14%)
		Baggage Goods	464 (35.47%)	304 (42.46%)	160 (27.03%)
		Sundries (accessories, scarves, hats, etc.)	489 (37.39%)	377 (52.65%)	112 (18.92%)
		Others	124 (9.48%)	93 (12.99%)	31 (5.24%)
Most influential factors in purchasing decisions		Personal Attention	793 (60.63%)	435 (60.75%)	358 (60.47%)
		Family and Friends’ Recommendations	335 (25.61%)	198 (27.65%)	137 (23.14%)
		Online and Mobile Advertising	468 (35.78%)	295 (41.2%)	173 (29.22%)
		Television Advertising	375 (28.67%)	354 (49.44%)	21 (3.55%)
		Print Advertising (magazines, newspapers, flyers, etc.)	376 (28.75%)	344 (48.04%)	32 (5.41%)
		Celebrity and Others’ Wearing	461 (35.24%)	392 (54.75%)	69 (11.66%)
		Past Purchase Experience	472 (36.09%)	413 (57.68%)	59 (9.97%)
		Sales Staff’s Recommendations	202 (15.44%)	186 (25.98%)	16 (2.7%)
		Others	67 (5.12%)	32 (4.47%)	35 (5.91%)
Most important factors when purchasing sustainable fashion products		Design (color/style)	858 (65.60%)	493 (68.85%)	365 (61.66%)
		Material	540 (41.28%)	395 (55.17%)	145 (24.49%)
		Fashion	486 (37.16%)	410 (57.26%)	76 (12.84%)
		Quality (wear comfort/functionality)	542 (41.44%)	361 (50.42%)	181 (30.57%)
		Price	416 (31.8%)	289 (40.36%)	127 (21.45%)
		Brand	371 (28.36%)	311 (43.44%)	60 (10.14%)
		Others	58 (4.43%)	43 (6.01%)	15 (2.53%)

Regarding the reasons for purchasing sustainable fashion products, environmental protection (630 people, 48.17%) is the primary reason consumers choose sustainable products, along with concerns for functionality and practicality (509 people, 38.91%) and resource reuse (488 people, 37.31%). In terms of interest in sustainable fashion products, the majority of consumers show a high level of interest, with 66.06% willing to make a purchase, while only 8.41% explicitly state they are unwilling to buy.

As for the categories of sustainable fashion products, the most purchased category is clothing (912 people, 69.72%), followed by sundries (accessories, scarves, hats, etc.) (489 people, 37.39%) and baggage goods (464 people, 35.47%). In terms of influencing factors in purchasing decisions, personal attention (793 people, 60.63%) is the largest influencing factor for buying sustainable products. Additionally, past purchasing experience (472 people, 36.09%), online and mobile advertising (468 people, 35.78%), and celebrities and others wearing these products (461 people, 35.24%) also have significant effects on purchasing decisions.

Regarding the most important considerations when buying sustainable fashion products, design (858 people, 65.60%) is the most important factor, followed by quality (wear comfort/functionality) (542 people, 41.44%) and material (540 people, 41.28%).

4.2. Reliability and Validity Analysis

4.2.1. Reliability Analysis of Measurement

This study employed a total of 53 items in the questionnaire survey, comprising 29 items to measure consumer value, four items to assess subjective norms, six items to evaluate attitudes, five items to gauge perceived behavioral control, five items to determine purchase intention, and four items to measure environmental concern.

In this study, after conducting a reliability analysis, a feasibility analysis was performed. During the reliability analysis, when using multiple items to measure the same concept, one method to improve reliability is to consider internal consistency by identifying and excluding items that hinder the reliability of the measurement tool, using Cronbach’s α coefficient. If the α coefficient is greater than 0.5, it is considered that there are no issues with the reliability of the measurement tool; if it exceeds 0.8, the reliability is deemed very high.

The results of the reliability verification for the measurement variables are presented in Table 4. The Cronbach’s α values for functional value, social value, emotional value, precious value, ethical value, subjective norms, attitude, perceived behavioral control, purchase intention, and environmental concern were all found to exceed 0.9. This indicates that these variables are considered valid and possess high reliability.

Table 4. Reliability analysis.

Factors	Measurement Variables	Number of Items	Cronbach’s α
Consumption value	Functional value	6	0.964
	Social value	6	0.960
	Emotional value	6	0.955
	Precious value	5	0.971
	Ethical value	6	0.963
Theory of Planned Behavior	Subjective norms	4	0.972
	Attitude	6	0.956
	Perceived behavioral control	5	0.937
Purchase intention	Purchase intention	5	0.957
Environmental concern	Environmental concern	4	0.976

4.2.2. Feasibility Analysis of Variables

Feasibility refers to the accuracy with which researchers intend to measure a concept, typically classified into types such as content feasibility, criterion feasibility, and conceptual feasibility. Factor analysis is a technique that identifies the inherent systematic structure among many variables based on the interrelationships among them, simplifying a large amount of information into fewer factors.

In this study, prior to conducting factor analysis, the Kaiser–Meyer–Olkin (KMO) test and Bartlett’s test of sphericity were utilized to determine the suitability of the questionnaire for factor analysis. The KMO test assesses the partial and overall correlations among variables, with KMO values ranging from 0 to 1. A KMO value exceeding 0.8 indicates high feasibility, values between 0.7 and 0.8 suggest good feasibility, values between 0.6 and 0.7 are considered acceptable, and values below 0.6 indicate poor feasibility. Furthermore, factor analysis can only be conducted if the KMO value is greater than 0.5 and the p-value from Bartlett’s test is less than 0.05.

This study employed principal component analysis for factor extraction and utilized the Varimax method for factor rotation, which is an orthogonal rotation technique. Factors were extracted only if their eigenvalues exceeded 1.0. To assess the feasibility of the questionnaire variables, a factor analysis was conducted on five independent variables (functional value, social value, emotional value, precious value, and ethical value), three parameters (subjective norms, attitude, and perceived behavioral control), one moderating variable (environmental concern), and the dependent variable measuring purchase intention.

(1)

Factor Analysis of Independent Variables

Appropriateness analysis of the independent variable, consumer value, was conducted in this study. As shown in Table 5, the communalities for all research items were above 0.4, indicating that the information from the research items can be effectively extracted.

Table 5. Exploratory factor analysis of consumer value scale.

Item	Factor					Communality
	Functional Value	Ethical Value	Social Value	Emotional Value	Precious Value
I-1	0.966	0.054	0.103	0.034	0.071	0.953
I-2	0.936	0.067	0.105	0.038	0.075	0.899
I-3	0.871	0.034	0.054	0.038	0.068	0.769
I-4	0.912	0.040	0.077	0.009	0.058	0.843
I-5	0.873	0.027	0.132	0.041	0.044	0.784
I-6	0.931	0.054	0.103	0.026	0.061	0.885
I-7	0.109	0.061	0.920	0.084	0.072	0.874
I-8	0.095	0.053	0.926	0.068	0.063	0.878
I-9	0.085	0.055	0.877	0.053	0.080	0.789
I-10	0.095	0.053	0.911	0.092	0.062	0.854
I-11	0.075	0.068	0.868	0.094	0.065	0.777
I-12	0.117	0.061	0.919	0.094	0.085	0.878
I-13	0.016	0.090	0.095	0.923	0.101	0.880
I-14	0.054	0.087	0.077	0.905	0.094	0.844
I-15	0.041	0.083	0.064	0.851	0.103	0.747
I-16	0.012	0.092	0.082	0.888	0.091	0.812
I-17	0.011	0.067	0.081	0.860	0.087	0.758
I-18	0.054	0.112	0.080	0.931	0.111	0.901
I-19	0.077	0.104	0.086	0.126	0.966	0.973
I-20	0.073	0.088	0.084	0.125	0.957	0.952
I-21	0.052	0.091	0.080	0.114	0.883	0.810
I-22	0.076	0.094	0.093	0.119	0.958	0.955
I-23	0.090	0.108	0.071	0.092	0.892	0.829
I-24	0.048	0.919	0.069	0.079	0.087	0.865
I-25	0.055	0.926	0.055	0.098	0.072	0.878
I-26	0.035	0.857	0.055	0.081	0.096	0.754
I-27	0.034	0.933	0.069	0.095	0.075	0.891
I-28	0.068	0.872	0.046	0.081	0.081	0.780
I-29	0.035	0.955	0.058	0.098	0.086	0.934
Eigenvalues	5.135	5.109	5.054	4.953	4.495	—
Percentage %	17.709	17.618	17.429	17.079	15.501	—
Cumulative Percentage %	17.709	35.326	52.755	69.835	85.336	—
KMO		0.934
Bartlett’s Test of Sphericity		Approximate Chi-Square		49,853.289
		Degrees of Freedom		406
		Significance		0.000

The validation results for the fit of the factor analysis indicated that the KMO (Kaiser–Meyer–Olkin) sample adequacy was 0.934, which exceeds the threshold of 0.6. This suggests that the data can be effectively extracted and that the selection of variables for factor analysis is relatively appropriate. The Bartlett’s test of sphericity yielded a value of 49,853.289 with a p-value of 0.000, indicating the presence of common factors. A KMO standard fit value below 0.5 indicates that factor analysis is inappropriate, a value greater than 0.6 indicates moderate adequacy, and a value greater than 0.9 signifies that the factor analysis is ideal.

Furthermore, the variance explained by the five factors was 17.709%, 17.618%, 17.429%, 17.079%, and 15.501%, respectively, resulting in an explained cumulative variance of 85.336% after rotation, which exceeds the 50% threshold. This indicates that the information from the research items can be effectively extracted.

Therefore, the factor analysis of consumer value measurement items related to sustainable fashion products in this study is considered sound. The results of the factor analysis identified Factor 1 as “functional value,” Factor 2 as “ethical value,” Factor 3 as “social value,” Factor 4 as “emotional value,” and Factor 5 as “precious value.” The gray-highlighted sections in Table 5 correspond to the KMO values of the consumption value factors.

(2)

Factor Analysis of Parameters

This study conducted a sufficiency analysis of the Theory of Planned Behavior (TPB), as shown in Table 6. The communalities for all research items were found to be above 0.4, indicating that the information from these research items can be effectively extracted.

Table 6. Exploratory factor analysis of the Theory of Planned Behavior scale.

Item	Factor
	Attitude	Perceived Behavioral Control	Subjective Norm	Communality
II-1	0.095	0.082	0.978	0.972
II-2	0.098	0.063	0.971	0.956
II-3	0.076	0.061	0.906	0.830
II-4	0.094	0.070	0.969	0.953
II-5	0.892	0.115	0.104	0.820
II-6	0.911	0.108	0.074	0.847
II-7	0.860	0.097	0.050	0.752
II-8	0.910	0.091	0.070	0.841
II-9	0.870	0.119	0.084	0.778
II-10	0.956	0.112	0.080	0.933
II-11	0.123	0.908	0.072	0.845
II-12	0.111	0.901	0.046	0.826
II-13	0.099	0.838	0.059	0.716
II-14	0.091	0.923	0.068	0.865
II-15	0.128	0.868	0.053	0.773
Eigenvalues	4.958	4.033	3.715	—
Percentage %	33.055	26.884	24.767	—
Cumulative Percentage %	33.055	59.939	84.706	—
KMO		0.912
Bartlett’s Test of Sphericity	Approximate Chi-Square	23,996.000
	Degrees of Freedom	105
	Significance	0.000

From the validation results representing the fit of the factor analysis, the KMO (Kaiser–Meyer–Olkin) sample adequacy was found to be 0.912, which is greater than 0.6, suggesting that the data can be effectively extracted and that the selection of variables for factor analysis is relatively ideal. The Bartlett’s test of sphericity yielded a value of 23,996.000 with a p-value of 0.000, indicating the presence of common factors. A KMO standard fit value below 0.5 indicates that factor analysis is inappropriate, a value greater than 0.6 indicates moderate adequacy, and a value greater than 0.9 signifies that the factor analysis is ideal.

Furthermore, the variance explained by the three factors was 33.055%, 26.884%, and 24.767%, respectively, resulting in an explained cumulative variance of 84.706% after rotation, which exceeds the 50% threshold. This indicates that the information from the research items can be effectively extracted.

Consequently, the factor analysis of the measurement items associated with the Theory of Planned Behavior for sustainable fashion products in this study is deemed robust. The results of the factor analysis identified Factor 1 as “attitude,” Factor 2 as “perceived behavioral control,” and Factor 3 as “subjective norms.” The gray-highlighted sections in Table 6 correspond to the KMO values of the theory of planned behavior factors.

(3)

Factor Analysis of the Dependent Variable

This study conducted a sufficiency analysis of the dependent variable, purchase intention. As presented in Table 7, the communalities for all research items were above 0.4, indicating that the information from these research items can be effectively extracted.

Table 7. Results of the exploratory factor analysis of the purchase intention scale.

Item	Factor	Communality
	Purchase Intention
III-1	0.954	0.910
III-2	0.946	0.895
III-3	0.881	0.776
III-4	0.968	0.937
III-5	0.889	0.790
Eigenvalues	4.307	—
Percentage %	86.136	—
Cumulative Percentage %	86.136	—
KMO	0.909
Bartlett’s Test of Sphericity	Approximate Chi-Square	8082.670
	Degrees of Freedom	10
	Significance	0.000

The validation results for the fit of the factor analysis indicated that the KMO (Kaiser–Meyer–Olkin) sample adequacy was 0.909, which exceeds the threshold of 0.6. This suggests that the data can be effectively extracted and that the selection of variables for factor analysis is relatively appropriate. Additionally, Bartlett’s test of sphericity yielded a value of 8082.670 with a p-value of 0.000, confirming the presence of common factors. A KMO standard fit value below 0.5 indicates that factor analysis is inappropriate, a value greater than 0.6 indicates moderate adequacy, and a value greater than 0.9 signifies that the factor analysis is ideal.

Furthermore, the variance explained by the factor was 86.136%, which exceeds the 50% threshold. This indicates that the information from the research items can be effectively extracted.

Consequently, the factor analysis of the measurement items related to purchase intention utilized in this study is deemed robust. The results of the factor analysis validated the factor associated with purchase intention. The gray-highlighted sections in Table 7 correspond to the KMO values of the purchase intention factors.

(4)

Factor Analysis of the Moderating Variable

This study performed a sufficiency analysis of the moderating variable, environmental concern. As presented in Table 8, the communalities for all research items were above 0.4, indicating that the information from these research items can be effectively extracted.

Table 8. Results of the exploratory factor analysis of the environmental concern scale.

Item	Factor	Communality
	Environmental Concern
IV-1	0.988	0.976
IV-2	0.984	0.968
IV-3	0.919	0.845
IV-4	0.982	0.964
Eigenvalues	3.753	—
Percentage %	93.821	—
Cumulative Percentage %	93.821	—
KMO	0.864
Bartlett’s Test of Sphericity	Approximate Chi-Square	10,333.795
	Degrees of Freedom	6
	Significance	0.000

The validation results for the fit of the factor analysis indicated that the KMO (Kaiser–Meyer–Olkin) sample adequacy was 0.864, which exceeds the threshold of 0.6. This suggests that the data can be effectively extracted and that the selection of variables for factor analysis is relatively appropriate. Additionally, Bartlett’s test of sphericity yielded a value of 10,333.795 with a p-value of 0.000, confirming the presence of common factors. A KMO standard fit value below 0.5 indicates that factor analysis is inappropriate, a value greater than 0.6 indicates moderate adequacy, and a value greater than 0.9 signifies that the factor analysis is ideal.

Furthermore, the variance explained by the factor was 93.821%, which exceeds 50%. This means that the information from the research items can be effectively extracted.

Consequently, the factor analysis of the measurement items related to environmental concern utilized in this study is deemed robust. The results of the factor analysis validated the factor associated with environmental concern. The gray-highlighted sections in Table 8 correspond to the KMO values of the environmental concern factors.

4.3. Confirmatory Factor Analysis

Confirmatory factor analysis (CFA) refers to the process where researchers establish relationships between variables based on theoretical background to determine the unidimensionality of research units formed by multiple items. It verifies the underlying factors and hypotheses of that relationship. CFA is widely used in existing research as a means to validate model fit among various variables.

The goodness-of-fit indices for all variables are shown in Table 9. Although the overall applicability of the structural analysis results of the survey items was not perfect, it can be rated as acceptable. The results of the confirmatory factor analysis for the various scales indicated that the factor loadings for each scale item exceeded 0.8, the Average Variance Extracted (AVE) values were all greater than 0.5, and the Composite Reliability (CR) values were all above 0.7. These findings demonstrate good construct validity for each scale.

Table 9. Results of the confirmatory factor analysis for each scale.

Scale	Dimension	Item	Path Coefficient	Standardized Path Coefficient	C.R.	AVE	CR	Fit Indices
Consumption value	Functional value	I-1	1	0.988	—	0.827	0.966	CMIN/DF = 1.846, GFI = 0.966, AGFI = 0.960, NFI = 0.987, CFI = 0.994, RMR = 0.011, RMSEA = 0.025, IFI = 0.994, TLI = 0.993
		I-2	0.897	0.935	86.250 ***
		I-3	0.620	0.844	54.410 ***
		I-4	0.858	0.897	68.630 ***
		I-5	0.628	0.847	55.243 ***
		I-6	0.898	0.938	87.644 ***
	Social value	1–7	1	0.928	—	0.810	0.962
		I-8	0.930	0.927	61.574 ***
		I-9	0.685	0.857	48.779 ***
		I-10	0.916	0.909	57.786 ***
		I-11	0.671	0.846	47.260 ***
		I-12	0.939	0.930	62.265 ***
	Emotional value	I-13	1	0.932	—	0.790	0.957
		I-14	0.920	0.901	56.707 ***
		I-15	0.656	0.828	45.185 ***
		I-16	0.902	0.883	53.477 ***
		I-17	0.654	0.833	45.882 ***
		I-18	0.960	0.948	67.760 ***
	Precious value	I-19	1	0.997	—	0.882	0.974
		I-20	0.907	0.977	155.188 ***
		I-21	0.630	0.857	59.372 ***
		I-22	0.930	0.981	168.508 ***
		I-23	0.648	0.874	64.248 ***
	Ethical value	I-24	1	0.916	—	0.822	0.965
		I-25	0.958	0.930	60.240 ***
		I-26	0.680	0.829	44.346 ***
		I-27	0.974	0.935	61.248 ***
		I-28	0.677	0.847	46.579 ***
		I-29	0.987	0.973	70.811 ***
Theory of Planned Behavior	Subjective norms	II-1	1	0.992		0.907	0.975	CMIN/DF = 2.510, GFI = 0.978, AGFI = 0.970, NFI = 0.991, CFI = 0.995, RMR = 0.010, RMSEA = 0.034, IFI = 0.995, TLI = 0.993
		II-2	0.911	0.977	141.910 ***
		II-3	0.653	0.858	58.624 ***
		II-4	0.909	0.977	142.102 ***
	Attitude	II-5	1	0.887		0.795	0.959
		II-6	0.926	0.902	50.348 ***
		II-7	0.692	0.828	41.676 ***
		II-8	0.922	0.902	50.238 ***
		II-9	0.685	0.848	43.742 ***
		II-10	0.994	0.976	62.019 ***
	Perceived behavioral control	II-11	1	0.902		0.758	0.940
		II-12	0.908	0.889	48.948 ***
		II-13	0.673	0.789	38.112 ***
		II-14	0.947	0.924	53.802 ***
		II-15	0.692	0.842	43.305 ***
Purchase intention	Purchase intention	III-1	1	0.949		0.830	0.960	CMIN/DF = 4.541, GFI = 0.993, AGFI = 0.980, NFI = 0.997, CFI = 0.998, RMR = 0.004, RMSEA = 0.052, IFI = 0.998, TLI = 0.996,
		III-2	0.912	0.938	71.253 ***
		III-3	0.631	0.832	47.763 ***
		III-4	0.935	0.976	86.519 ***
		III-5	0.662	0.850	50.535 ***
Environmental concern	Environmental concern	IV-1	1	0.996		0.921	0.979	CMIN/DF = 1.576, GFI = 0.999, AGFI = 0.994, NFI = 1.000, CFI = 1.000, RMR = 0.001, RMSEA = 0.021, IFI = 1.000, TLI = 1.000
		IV-2	0.927	0.986	182.679 ***
		IV-3	0.632	0.866	61.674 ***
		IV-4	0.925	0.984	173.345 ***

Note: *** p < 0.001.

In the estimation of the measurement model, confirmatory factor analysis was conducted on each latent factor, and the results are presented in Table 8.

For the consumer value measurement model, the goodness-of-fit indices were GFI = 0.966, AGFI = 0.960, NFI = 0.987, and CFI = 0.994 (above 0.9 is excellent), and the RMR (Root Mean Square Residual) value was 0.011 (below 0.05 is excellent), indicating a satisfactory fit for the measurement model. Moreover, the CMIN/DF value for the model was 1.846 (the recommended maximum is below 5.0 for large samples, typically advised to be below 2–3), which is below the suggested level, confirming the excellence of the measurement model used in this study.

For the Theory of Planned Behavior measurement model, the goodness-of-fit indices were GFI = 0.978, AGFI = 0.970, NFI = 0.991, and CFI = 0.995 (above 0.9 is excellent), and the RMR value was 0.010 (below 0.05 is excellent), also indicating a satisfactory fit for the measurement model. The CMIN/DF value for the model was 2.510, which is below the recommended level, confirming the excellence of the measurement model used in this study.

For the purchase intention measurement model, the goodness-of-fit indices were GFI = 0.993, AGFI = 0.980, NFI = 0.997, and CFI = 0.998 (above 0.9 is excellent), and the RMR value was 0.004 (below 0.05 is excellent), indicating a satisfactory fit for the measurement model. The CMIN/DF value for the model was 4.541, which is below the recommended level, confirming the excellence of the measurement model used in this study.

For the environmental concern measurement model, the goodness-of-fit indices were GFI = 0.999, AGFI = 0.994, NFI = 1.000, and CFI = 1.000 (above 0.9 is excellent), and the RMR value was 0.001 (below 0.05 is excellent), indicating a satisfactory fit for the measurement model. The CMIN/DF value for the model was 1.576, which is below the recommended level, confirming the excellence of the measurement model used in this study.

The standardized factor coefficients for each variable’s measurement items were above 0.5, the AVE values were above 0.5, and the CR values (Composite Reliability) were above 0.7, indicating their appropriateness. Therefore, since the indicators representing factor loadings and composite reliability met the standard values, it can be concluded that the appropriateness of the survey conducted among university students in South Korea and China is adequate.

4.4. Discriminant Validity

The Fornell–Larcker criterion is primarily used to assess discriminant validity, which is a method for testing the differences between latent variables. In statistics and data analysis, discriminant validity refers to the degree to which different latent variables (or constructs) can be distinguished from one another, in order to determine whether these latent variables can be effectively differentiated. The Fornell–Larcker criterion evaluates the model’s discriminant validity by comparing the square root of the Average Variance Extracted (AVE) for each latent variable with the correlation coefficients between that variable and other latent variables. Specifically, if the square root of the AVE for each latent variable is greater than its correlation coefficients with other latent variables, the model can be considered to have good discriminant validity.

As shown in Table 10, first, the diagonal elements of the table represent the square root of the AVE, while the off-diagonal elements represent the correlation coefficients. Second, the square root of the AVE indicates the “convergent validity” of the factors, while the correlation coefficients indicate the relationships between them. If the “convergent validity” of a factor is strong (significantly greater than the absolute values of its correlation coefficients with other factors), it suggests that the factor possesses discriminant validity. Third, if the square root of the AVE for a factor is greater than the absolute values of its correlation coefficients with other factors, and this conclusion holds true for all factors, it indicates that the model has good discriminant validity. The gray highlighted portion in Table 10 represents the square root of AVE.

Table 10. Discriminant validity: Fornell–Larcker criterion.

	IA	IB	IC	ID	IE	IIA	IIB	IIC	III	IV
IA	0.924
IB	0.218	0.917
IC	0.089	0.192	0.907
ID	0.162	0.187	0.246	0.95
IE	0.115	0.146	0.209	0.21	0.922
IIA	0.153	0.283	0.202	0.195	0.231	0.963
IIB	0.248	0.23	0.276	0.261	0.266	0.186	0.91
IIC	0.311	0.272	0.275	0.296	0.237	0.149	0.244	0.897
III	0.445	0.504	0.418	0.442	0.457	0.343	0.387	0.389	0.928
IV	0.599	0.537	0.524	0.534	0.511	0.281	0.366	0.435	0.718	0.969

Discriminant validity is typically verified by comparing the square root of the Average Variance Extracted (AVE) with the sizes of the inter-factor correlation coefficients (Fornell–Larcker method). In addition to this, the HTMT (Heterotrait–Monotrait Ratio) method, as shown in Table 11, can also be used to validate discriminant validity. An analysis of the HTMT values indicates that all HTMT values are below 0.85, which suggests that there is good discriminability among the factors, and the research data demonstrate good discriminant validity.

Table 11. HTMT.

	IA	IB	IC	ID	IE	IIA	IIB	IIC	III	IV
IA
IB	0.225
IC	0.092	0.2
ID	0.167	0.193	0.254
IE	0.118	0.152	0.217	0.217
IIA	0.157	0.293	0.209	0.199	0.237
IIB	0.258	0.24	0.287	0.27	0.276	0.192
IIC	0.325	0.285	0.29	0.31	0.249	0.155	0.257
III	0.461	0.524	0.436	0.456	0.474	0.355	0.402	0.407
IV	0.616	0.554	0.541	0.547	0.527	0.287	0.378	0.453	0.74

As shown in Table 12, a cross-loadings table is commonly used in statistical analyses such as Structural Equation Modeling (SEM) to illustrate the relationships between observed variables and latent variables. The following is a relevant interpretation: The numbering of the row and column headers in the table (e.g., I-1, I-2, IA, IB, etc.) represents different variables, with rows typically indicating observed variables and columns representing latent variables or different categories of variables. The values in the table are loading coefficients, which generally range from 0 to 1, reflecting the degree of association between the variables. A loading coefficient closer to 1 indicates a stronger relationship between the corresponding observed variable and the latent variable, meaning that the observed variable effectively reflects the characteristics of the latent variable; conversely, a coefficient closer to 0 indicates a weaker relationship. For example, the loading coefficient corresponding to I-1 and IA is 0.977, indicating a close association between I-1 and IA.

Table 12. Cross-loadings table.

	IA	IB	IC	ID	IE	IIA	IIB	IIC	III	IV
I-1	0.977	0.215	0.089	0.163	0.117	0.154	0.244	0.314	0.438	0.593
I-2	0.949	0.215	0.094	0.166	0.128	0.148	0.23	0.294	0.437	0.587
I-3	0.874	0.157	0.082	0.146	0.091	0.127	0.224	0.277	0.379	0.504
I-4	0.917	0.18	0.059	0.14	0.095	0.13	0.214	0.277	0.396	0.54
I-5	0.884	0.228	0.089	0.131	0.087	0.137	0.226	0.278	0.401	0.518
I-6	0.941	0.21	0.079	0.15	0.113	0.151	0.236	0.284	0.413	0.575
I-7	0.214	0.936	0.182	0.176	0.139	0.262	0.214	0.268	0.472	0.513
I-8	0.199	0.936	0.164	0.164	0.129	0.259	0.205	0.248	0.477	0.489
I-9	0.186	0.887	0.148	0.173	0.127	0.243	0.206	0.218	0.432	0.468
I-10	0.199	0.924	0.186	0.164	0.129	0.268	0.213	0.254	0.465	0.492
I-11	0.176	0.881	0.185	0.164	0.141	0.255	0.199	0.224	0.446	0.476
I-12	0.222	0.937	0.192	0.189	0.141	0.273	0.23	0.281	0.482	0.515
I-13	0.068	0.19	0.938	0.23	0.193	0.204	0.267	0.251	0.391	0.488
I-14	0.101	0.174	0.919	0.222	0.189	0.164	0.249	0.244	0.381	0.49
I-15	0.085	0.157	0.865	0.221	0.179	0.175	0.234	0.259	0.361	0.451
I-16	0.062	0.174	0.901	0.215	0.19	0.188	0.242	0.229	0.378	0.473
I-17	0.058	0.168	0.868	0.205	0.164	0.173	0.234	0.231	0.352	0.429
I-18	0.105	0.183	0.95	0.244	0.217	0.195	0.272	0.279	0.412	0.517
I-19	0.161	0.186	0.249	0.987	0.211	0.198	0.266	0.292	0.438	0.531
I-20	0.154	0.181	0.245	0.975	0.194	0.182	0.246	0.287	0.432	0.517
I-21	0.129	0.168	0.225	0.899	0.188	0.181	0.226	0.277	0.399	0.472
I-22	0.159	0.19	0.241	0.977	0.2	0.194	0.257	0.289	0.43	0.524
I-23	0.165	0.164	0.208	0.911	0.205	0.169	0.244	0.261	0.4	0.49
I-24	0.109	0.146	0.186	0.199	0.93	0.236	0.24	0.221	0.439	0.475
I-25	0.115	0.133	0.201	0.187	0.936	0.222	0.255	0.233	0.434	0.48
I-26	0.093	0.127	0.181	0.198	0.869	0.181	0.235	0.212	0.388	0.447
I-27	0.096	0.145	0.2	0.189	0.943	0.223	0.253	0.193	0.418	0.479
I-28	0.123	0.122	0.181	0.188	0.884	0.192	0.228	0.213	0.407	0.463
I-29	0.098	0.137	0.205	0.202	0.966	0.221	0.259	0.238	0.442	0.484
II-1	0.168	0.278	0.206	0.207	0.241	0.986	0.187	0.158	0.348	0.294
II-2	0.133	0.283	0.196	0.188	0.23	0.978	0.187	0.14	0.326	0.264
II-3	0.145	0.247	0.17	0.173	0.18	0.911	0.16	0.13	0.313	0.252
II-4	0.142	0.282	0.204	0.181	0.236	0.976	0.183	0.145	0.334	0.27
II-5	0.224	0.212	0.264	0.247	0.246	0.194	0.906	0.229	0.352	0.344
II-6	0.228	0.212	0.255	0.243	0.259	0.167	0.921	0.224	0.374	0.341
II-7	0.227	0.203	0.222	0.214	0.196	0.139	0.866	0.205	0.325	0.315
II-8	0.212	0.206	0.268	0.233	0.247	0.162	0.916	0.207	0.341	0.328
II-9	0.223	0.202	0.224	0.229	0.239	0.174	0.882	0.23	0.341	0.319
II-10	0.237	0.222	0.267	0.258	0.261	0.177	0.965	0.234	0.376	0.349
II-11	0.304	0.263	0.24	0.28	0.223	0.148	0.234	0.922	0.379	0.42
II-12	0.286	0.249	0.255	0.254	0.217	0.122	0.22	0.91	0.343	0.404
II-13	0.224	0.23	0.246	0.241	0.204	0.128	0.202	0.841	0.305	0.346
II-14	0.293	0.251	0.246	0.284	0.218	0.142	0.205	0.93	0.37	0.407
II-15	0.281	0.225	0.246	0.267	0.2	0.128	0.232	0.878	0.342	0.37
III-1	0.44	0.487	0.414	0.439	0.449	0.325	0.373	0.382	0.955	0.71
III-2	0.428	0.481	0.389	0.422	0.424	0.325	0.374	0.365	0.946	0.676
III-3	0.369	0.434	0.354	0.352	0.374	0.296	0.303	0.308	0.877	0.592
III-4	0.437	0.502	0.411	0.443	0.455	0.342	0.39	0.39	0.968	0.708
III-5	0.383	0.431	0.369	0.387	0.415	0.302	0.347	0.351	0.89	0.636
IV-1	0.592	0.528	0.511	0.516	0.497	0.279	0.357	0.422	0.706	0.987
IV-2	0.583	0.515	0.51	0.509	0.497	0.266	0.359	0.418	0.706	0.983
IV-3	0.56	0.51	0.503	0.536	0.494	0.274	0.348	0.43	0.661	0.921
IV-4	0.586	0.529	0.507	0.506	0.493	0.269	0.353	0.417	0.706	0.981

The application and significance of this in Confirmatory Factor Analysis (CFA) lie in the ability to assess whether each observed variable is appropriately assigned to its latent variable by examining the cross-loadings. Specifically, it can be determined whether the observed variable has a high loading on the corresponding latent variable and a low loading on other latent variables, thereby evaluating the validity of the measurement model. In SEM, cross-loadings assist in assessing model fit and the relationships between variables, helping researchers determine the contribution of observed variables to latent variable and subsequently optimize the model structure. The gray-highlighted sections in Table 12 are intended to prominently display the higher values of the corresponding cross-factor loadings.

4.5. Correlation Analysis

Table 13 presents the results of the correlation analysis. It can be seen that functional value is significantly positively correlated with subjective norms (r = 0.153, p < 0.05), attitude (r = 0.247, p < 0.05), perceived behavioral control (r = 0.311, p < 0.05), and purchase intention (r = 0.445, p < 0.05). Similarly, social value is significantly positively correlated with subjective norms (r = 0.284, p < 0.05), attitude (r = 0.230, p < 0.05), perceived behavioral control (r = 0.273, p < 0.05), and purchase intention (r = 0.505, p < 0.05). Emotional value also shows significant positive correlations with subjective norms (r = 0.203, p < 0.05), attitude (r = 0.277, p < 0.05), perceived behavioral control (r = 0.273, p < 0.05), and purchase intention (r = 0.419, p < 0.05). Additionally, precious value is significantly positively correlated with subjective norms (r = 0.195, p < 0.05), attitude (r = 0.262, p < 0.05), perceived behavioral control (r = 0.296, p < 0.05), and purchase intention (r = 0.443, p < 0.05). Ethical value also shows significant positive correlations with subjective norms (r = 0.233, p < 0.05), attitude (r = 0.266, p < 0.05), perceived behavioral control (r = 0.237, p < 0.05), and purchase intention (r = 0.458, p < 0.05). Furthermore, subjective norms (r = 0.343, p < 0.05), attitude (r = 0.387, p < 0.05), and perceived behavioral control (r = 0.389, p < 0.05) are all significantly positively correlated with purchase intention.

Table 13. Correlation analysis.

	Functional Value	Social Value	Emotional Value	Precious Value	Ethical Value	Subjective Norms	Attitude	Perceived Behavioral Control	Purchase Intention	Environmental Concern
Functional value	1
Social value	0.218 ***	1
Emotional value	0.088 **	0.193 ***	1
Precious value	0.162 ***	0.187 ***	0.246 ***	1
Ethical value	0.114 ***	0.146 ***	0.209 ***	0.209 ***	1
Subjective norms	0.153 ***	0.284 ***	0.203 ***	0.195 ***	0.233 ***	1
Attitude	0.247 ***	0.230 ***	0.277 ***	0.262 ***	0.266 ***	0.187 ***	1
Perceived behavioral control	0.311 ***	0.273 ***	0.273 ***	0.296 ***	0.237 ***	0.149 ***	0.243 ***	1
Purchase intention	0.445 ***	0.505 ***	0.419 ***	0.443 ***	0.458 ***	0.343 ***	0.387 ***	0.389 ***	1
Environmental concern	0.599 ***	0.536 ***	0.523 ***	0.531 ***	0.509 ***	0.280 ***	0.365 ***	0.434 ***	0.718 ***	1

Note: ** p < 0.01, *** p < 0.001.

4.6. Hypothesis Testing

4.6.1. Overall Hypothesis Testing Results on the Impact of Consumer Values in China and South Korea on the Theory of Planned Behavior

To test the hypotheses, this study constructed a structural equation model using AMOS 26. Table 14 displays the hypothesis testing results for China and South Korea. It can be seen that the fit indices for each model met the required standards, and all hypotheses were confirmed.

Table 14. Overall hypothesis testing results on the impact of consumer values in China and South Korea on the Theory of Planned Behavior.

Hypothesis Result					Estimate	S.E.	Standardized Estimate	C.R.	p	Remarks
H1	H1-1	Functional value	→	Subjective norms	0.062	0.023	0.074	2.772	0.006	Established
	H1-2	Social value	→	Subjective norms	0.210	0.026	0.217	8.066	***	Established
	H1-3	Emotional value	→	Subjective norms	0.105	0.026	0.108	4.033	***	Established
	H1-4	Precious value	→	Subjective norms	0.085	0.023	0.096	3.626	***	Established
	H1-5	Ethical value	→	Subjective norms	0.160	0.025	0.169	6.303	***	Established
	H1-6	Functional value	→	Attitude	0.135	0.021	0.172	6.454	***	Established
	H1-7	Social value	→	Attitude	0.106	0.024	0.118	4.392	***	Established
	H1-8	Emotional value	→	Attitude	0.166	0.024	0.184	6.832	***	Established
	H1-9	Precious value	→	Attitude	0.124	0.022	0.150	5.696	***	Established
	H1-10	Ethical value	→	Attitude	0.159	0.024	0.180	6.727	***	Established
	H1-11	Functional value	→	Perceived behavioral control	0.187	0.020	0.250	9.377	***	Established
	H1-12	Social value	→	Perceived behavioral control	0.138	0.023	0.161	6.019	***	Established
	H1-13	Emotional value	→	Perceived behavioral control	0.147	0.023	0.171	6.376	***	Established
	H1-14	Precious value	→	Perceived behavioral control	0.141	0.021	0.179	6.781	***	Established
	H1-15	Ethical value	→	Perceived behavioral control	0.113	0.022	0.134	5.044	***	Established
CMIN/DF = 2.006, GFI = 0.940, AGFI = 0.933, NFI = 0.976, CFI = 0.988, RMR = 0.094, RMSEA = 0.028, IFI = 0.988, TLI = 0.987
H2	H2-1	Subjective norms	→	Purchase intention	0.246	0.023	0.266	10.802	***	Established
	H2-2	Attitude	→	Purchase intention	0.291	0.025	0.296	11.750	***	Established
	H2-3	Perceived behavioral control	→	Purchase intention	0.325	0.026	0.318	12.476	***	Established
CMIN/DF = 2.821, GFI = 0.964, AGFI = 0.955, NFI = 0.986, CFI = 0.991, RMR = 0.086, RMSEA = 0.037, IFI = 0.991, TLI = 0.989
H3	H3-1	Functional value	→	Purchase intention	0.248	0.015	0.340	16.475	***	Established
	H3-2	Social value	→	Purchase intention	0.319	0.018	0.382	18.020	***	Established
	H3-3	Emotional value	→	Purchase intention	0.215	0.017	0.257	12.361	***	Established
	H3-4	Precious value	→	Purchase intention	0.203	0.016	0.265	13.012	***	Established
	H3-5	Ethical value	→	Purchase intention	0.279	0.017	0.340	16.180	***	Established
CMIN/DF = 2.348, GFI = 0.945, AGFI = 0.937, NFI = 0.979, CFI = 0.988, RMR = 0.122,RMSEA = 0.032, IFI = 0.988, TLI = 0.987

Note: *** p < 0.001.

Consumer value, encompassing functional value, social value, emotional value, precious value, and ethical value, exerts a significant positive impact on subjective norms. Among these dimensions, social value and ethical value demonstrate the most substantial influence on subjective norms. Additionally, consumer value also significantly positively affects attitude, with emotional value having the greatest impact, followed by ethical value and functional value. In contrast, social value and precious value exhibit relatively smaller effects on attitude.

Furthermore, consumer value, which includes functional value, social value, emotional value, precious value, and ethical value, positively and significantly influences perceived behavioral control, with functional value exhibiting the most substantial effect.

The Theory of Planned Behavior, encompassing subjective norms, attitude, and perceived behavioral control, significantly positively influences purchase intention. Among these components, perceived behavioral control exerts the most substantial effect on purchase intention, followed by attitude.

Moreover, consumer value, which includes functional value, social value, emotional value, precious value, and ethical value, significantly positively impacts purchase intention. Among these dimensions, social value exerts the most substantial influence, followed by functional value and ethical value, while the effects of emotional value and precious value are relatively smaller.

4.6.2. Overall Results of Testing the Moderating Effect of Environmental Concern on Consumer Value and Purchase Intention

To verify whether environmental concern plays a positive moderating role between consumer value and purchase intention, this study used Model 1 from the Process plugin in SPSS for validation. The results are shown in Table 15. It can be observed that the coefficients for all interaction terms are greater than 0, and the p-values are less than 0.05, indicating that environmental concern positively moderates the relationship between consumer value (functional value, social value, emotional value, precious value, and ethical value) and purchase intention.

Table 15. Results of testing the moderating effect of environmental concern on consumer value and purchase intention (overall).

Model	Variable	B	se	t	p	Remarks
H4-1	Constant	3.101	0.017	181.103	0.000	Established
	Functional value	0.033	0.021	1.618	0.106
	Environmental concern	0.577	0.019	29.739	0.000
	Functional value × Environmental concern	0.085	0.017	4.913	0.000
H4-2	Constant	3.112	0.016	191.013	0.000	Established
	Social value	0.150	0.021	7.221	0.000
	Environmental concern	0.522	0.018	28.625	0.000
	Social value × Environmental concern	0.077	0.018	4.273	0.000
H4-3	Constant	3.103	0.017	187.730	0.000	Established
	Emotional value	0.044	0.021	2.096	0.036
	Environmental concern	0.573	0.018	31.273	0.000
	Emotional value × Environmental concern	0.102	0.019	5.373	0.000
H4-4	Constant	3.097	0.016	190.319	0.000	Established
	Precious value	0.078	0.020	3.903	0.000
	Environmental concern	0.564	0.018	30.830	0.000
	Precious value × Environmental concern	0.110	0.017	6.425	0.000
H4-5	Constant	3.124	0.016	191.157	0.000	Established
	Ethical value	0.109	0.020	5.503	0.000
	Environmental concern	0.539	0.018	29.815	0.000
	Ethical value × Environmental concern	0.049	0.018	2.799	0.005

4.6.3. Interpretation of Analysis Results

The overall hypothesis testing results of this study are summarized as shown in Table 16.

Table 16. Overall hypothesis testing results.

Hypothesis		Validation Result
H1	Consumer value will have a positive impact on consumers’ planned behavior (subjective norms, attitudes, perceived behavioral control).	Accepted
H1-1	The functional value dimension of consumer value will have a positive impact on consumers’ planned behavior subjective norms.	Accepted
H1-2	The social value dimension of consumer value will have a positive impact on consumers’ planned behavior subjective norms.	Accepted
H1-3	The emotional value dimension of consumer value will have a positive impact on consumers’ planned behavior subjective norms.	Accepted
H1-4	The precious value dimension of consumer value will have a positive impact on consumers’ planned behavior subjective norms.	Accepted
H1-5	The ethical value dimension of consumer value will have a positive impact on consumers’ planned behavior subjective norms.	Accepted
H1-6	The functional value dimension of consumer value will have a positive impact on consumers’ planned behavior attitudes.	Accepted
H1-7	The social value of consumer value will have a positive impact on consumers’ planned behavior attitudes.	Accepted
H1-8	The emotional value dimension of consumer value will have a positive impact on consumers’ planned behavior attitudes.	Accepted
H1-9	The precious value dimension of consumer value will have a positive impact on consumers’ planned behavior attitudes.	Accepted
H1-10	The ethical value dimension of consumer value will have a positive impact on consumers’ planned behavior attitudes.	Accepted
H1-11	The functional value dimension of consumer value will have a positive impact on consumers’ planned behavior perceived behavioral control.	Accepted
H1-12	The social value dimension of consumer value will have a positive impact on consumers’ planned behavior perceived behavioral control.	Accepted
H1-13	The emotional value dimension of consumer value will have a positive impact on consumers’ planned behavior perceived behavioral control.	Accepted
H1-14	The precious value dimension of consumer value will have a positive impact on consumers’ planned behavior perceived behavioral control.	Accepted
H1-15	The ethical value dimension of consumer value will have a positive impact on consumers’ planned behavior perceived behavioral control.	Accepted
H2	Consumers’ planned behavior (subjective norms, attitudes, perceived behavioral control) will have a positive impact on the purchase intention of sustainable fashion products.	Accepted
H2-1	The subjective norms of consumers’ planned behavior will have a positive impact on the purchase intention of sustainable fashion products.	Accepted
H2-2	The attitudes of consumers’ planned behavior will have a positive impact on the purchase intention of sustainable fashion products.	Accepted
H2-3	The perceived behavioral control of consumers’ planned behavior will have a positive impact on the purchase intention of sustainable fashion products.	Accepted
H3	Consumer value will have a positive impact on the purchase intention of sustainable fashion products.	Accepted
H3-1	The functional value dimension of consumer value will have a positive impact on the purchase intention of sustainable fashion products.	Accepted
H3-2	The social value dimension of consumer value will have a positive impact on the purchase intention of sustainable fashion products.	Accepted
H3-3	The emotional value dimension of consumer value will have a positive impact on the purchase intention of sustainable fashion products.	Accepted
H3-4	The precious value dimension of consumer value will have a positive impact on the purchase intention of sustainable fashion products.	Accepted
H3-5	The ethical value dimension of consumer value will have a positive impact on the purchase intention of sustainable fashion products.	Accepted
H4	Environmental concern will play a positive moderating role between consumer value and purchase intention.	Accepted
H4-1	Environmental concern will play a positive moderating role between the functional value dimension of consumer value and purchase intention.	Accepted
H4-2	Environmental concern will play a positive moderating role between the social value dimension of consumer value and purchase intention.	Accepted
H4-3	Environmental concern will play a positive moderating role between the emotional value dimension of consumer value and purchase intention.	Accepted
H4-4	Environmental concern will play a positive moderating role between the precious value dimension of consumer value and purchase intention.	Accepted
H4-5	Environmental concern will play a positive moderating role between the ethical value dimension of consumer value and purchase intention.	Accepted

To enhance the purchase intention of Chinese and South Korean consumers for sustainable fashion products, it is crucial to develop strategies based on the empirical analysis results. These strategies should focus on improving the consumer value associated with sustainable fashion products. This includes enhancing functional value, social value, emotional value, precious value, and ethical value. Additionally, efforts should be directed toward strengthening subjective norms, attitudes, and perceived behavioral control within the framework of consumers’ planned behavior. It is also important to reinforce the moderating role of environmental concern in the relationship between consumer value and purchase intention.

5. Conclusions

5.1. Research Summary and Implications

5.1.1. Research Summary

This study focused on university students in the Busan area of South Korea and the Guangzhou area of China. The objective was to investigate the relationships among consumer value, subjective norms, attitudes, perceived behavioral control, environmental concern, and purchase intention in the context of sustainable fashion products. The findings aim to provide insights for marketing strategies and new product development in the apparel industry in both countries.

An empirical analysis was conducted with university students in South Korea and China. A total of 1390 surveys were distributed. After excluding 82 incomplete or invalid questionnaires, 1308 valid samples were used for the final analysis (592 from Korea and 716 from China). The analysis was performed using SPSS 26.0 and AMOS 26.0.

Based on the empirical analysis results, the overall findings are summarized as follows.

First, hypothesis H1 indicates that consumer value (including functional value, social value, emotional value, precious value, and ethical value) has a significant positive impact on planned behavior. The study found that consumer value significantly influences the subjective norms of planned behavior. Among these, social value and ethical value have the largest effects. Consumer value also significantly affects the attitude of planned behavior. Emotional value has the most significant impact, followed by ethical value and functional value. In contrast, social value and precious value have relatively smaller effects. These results are consistent with the findings of Md Hasan et al. (2025) [45], which show that functional value and emotional value significantly influence consumer attitudes. Additionally, consumer value significantly influences perceived behavioral control in planned behavior, with functional value having the largest effect.

Second, for hypothesis H2, the findings indicate that the Theory of Planned Behavior significantly affects purchase intention. This theory includes subjective norms, attitude, and perceived behavioral control. Among these components, perceived behavioral control has the most substantial impact on purchase intention. Attitude follows, while subjective norms has the least effect. These results are consistent with the findings of Prabhakar et al. (2024) [56]. Their study shows that perceived behavioral control, personal norms, attitudes, and subjective norms significantly influence consumers’ willingness to purchase green products.

Third, for hypothesis H3, the results indicate that consumer value, which includes functional value, social value, emotional value, precious value, and ethical value, has a significant positive impact on purchase intention. Among these dimensions, social value exerts the most substantial effect on purchase intention, followed by functional value and ethical value, while emotional value and precious value demonstrate relatively smaller effects. The results of this study are consistent with the findings of Yang Il-jeong (2024) [63].

Fourth, hypothesis H4 shows that environmental concern positively moderates the relationship between consumer value (functional value, social value, emotional value, precious value, and ethical value) and purchase intention. The results of this study are consistent with the findings of Smelt and Famke (2024) [70].

5.1.2. Discussion of Research Results

This study focuses on university students in the Busan region of South Korea and the Guangzhou region of China. It explores the relationships among the consumption value of sustainable fashion products, subjective norms, attitudes, perceived behavioral control, environmental concern, and purchase intention. We conducted an empirical analysis of 1308 valid questionnaires and drew several important conclusions. Notably, there are significant differences between South Korean and Chinese respondents regarding perceived values and environmental concern. These differences may be closely related to the cultural, economic, and educational backgrounds of the two countries.

First, from the perspective of cultural factors, the cultural backgrounds of South Korea and China significantly impact consumers’ values and environmental concern. South Korean culture emphasizes collectivism and social responsibility. This may lead respondents to focus more on social and ethical values when choosing sustainable fashion products. In contrast, China’s rapid economic development and changes in consumer culture have influenced young consumers. They are gradually recognizing the importance of environmental protection while pursuing fashion and individuality. Therefore, although university students in both countries show concern for sustainable fashion, their areas of focus may differ.

Second, analyzing from the perspective of economic factors, the level of economic development may also influence consumers’ purchase intentions and environmental concern. In South Korea, the economy is relatively mature, and consumers have a higher acceptance of sustainable products and are willing to pay a premium for environmental and social responsibility. In China, despite rapid economic growth, a portion of consumers still have a low awareness and acceptance of sustainable fashion, possibly focusing more on the functional value and price of products. Thus, economic factors play an important role in the purchase intentions of university students in both countries.

Third, the level of education and the extent of environmental education may influence consumers’ environmental concern. In South Korea, environmental education is well developed. As a result, university students generally have a high level of environmental awareness and knowledge of sustainable development. This leads them to consider environmental impacts more in their purchasing decisions. In China, environmental education has been strengthened in recent years. However, some university students still lack understanding and concern for sustainable fashion. This gap may result in significant differences in their purchase intentions.

In summary, the differences between South Korean and Chinese university students regarding consumption value, subjective norms, attitudes, perceived behavioral control, and purchase intention for sustainable fashion products reflect various cultural, economic, and educational factors. These differences influence consumer behavior patterns and provide important insights for future marketing strategies and new product development in the clothing industry in both countries. Future research could further explore how these factors affect consumer decision-making processes in different cultural contexts. This would enrich the theoretical framework of cross-cultural studies.

5.1.3. Implications of Research Findings

Based on the results of hypothesis testing, this study analyzes the relationships among consumption value, subjective norms, attitudes, perceived behavioral control, environmental concern, and purchase intention. This analysis focuses on consumers in South Korea and China regarding sustainable fashion products. The study proposes the following strategic insights.

First, enhance the recognition of ethical values. The dimensions of consumption value—functional value, social value, emotional value, precious value, and ethical value—positively and significantly impact the subjective norms of planned behavior. Among these, social value and ethical value have a particularly strong influence. To improve societal recognition of the ethical value of sustainable fashion products, education and promotion efforts can be made. For example, in South Korea, fashion brands can cultivate students’ sense of social responsibility and moral awareness through courses and activities. This approach can emphasize the ethical value of sustainable fashion products. Additionally, leveraging social norms can inspire others to adopt positive behaviors through role models.

Second, emphasize emotional experiences. The dimensions of consumption value positively and significantly impact attitudes toward planned behavior. Among these, emotional value has the greatest influence. In marketing activities, companies should highlight the emotional experiences consumers can gain from using sustainable fashion products. In South Korea, brands can use storytelling and emotional marketing. This approach can demonstrate how products enhance quality of life, boost confidence, and promote social responsibility. Such strategies can increase emotional resonance with consumers. In China, brands may focus more on the functionality and social value of products. They should emphasize practicality and social recognition in daily life.

Third, clearly communicate functional advantages. The dimensions of consumption value positively and significantly influence perceived behavioral control. Among these, functional value has the greatest impact. Companies should clearly convey the functional advantages and unique selling points of sustainable fashion products. In China, brands can enhance consumers’ perceived behavioral control by providing detailed product descriptions, demonstrations, and case studies. This helps consumers understand how products meet their needs. In South Korea, it is equally important to emphasize the emotional value and social responsibility of the products.

Fourth, enhance purchase confidence. The theory of planned behavior shows that subjective norms, attitudes, and perceived behavioral control significantly impact purchase intention. Among these, perceived behavioral control has the greatest influence. Companies should boost consumers’ confidence and ability to purchase sustainable fashion products. In China, providing multiple purchasing channels and convenient payment options can improve purchase convenience. In South Korea, fostering a positive attitude can also enhance purchase intention. To achieve this, companies can provide information about sustainable materials, production processes, and environmental impacts. This will help enhance consumer awareness.

Fifth, emphasize social value. The dimensions of consumption value have a positive and significant impact on purchase intention, with social value having the greatest influence. Therefore, companies can increase consumer awareness and understanding of sustainable fashion products through proactive promotion and marketing activities. In China, brands can emphasize the social recognition and personal esteem that come from purchasing sustainable fashion products, while in South Korea, it is equally important to showcase the environmental and social responsibility advantages of the products.

Sixth, environmental concern plays a positive moderating role between consumption value and purchase intention. This includes functional value, social value, emotional value, precious value, and ethical value. Consumers with a stronger awareness of environmental protection are more likely to be influenced by social value benefits when deciding to purchase sustainable fashion products. Therefore, brands should develop environmental protection promotion strategies tailored to consumers in different markets. This approach will help enhance their environmental concern.

Finally, regarding the operationalization of variables, this study systematically measured each variable using scales and questionnaire surveys. Each dimension of consumption value—functional, social, emotional, precious, and ethical—was quantified through specific indicators. This approach ensured the reliability and validity of the data. The measurements of subjective norms, attitudes, and perceived behavioral control were also based on established theoretical frameworks. This ensures the scientific rigor and reproducibility of the research results. Thus, the study reveals the relationships among the variables and provides an actionable framework for future research.

5.2. Limitations of the Study and Future Research Directions

This study presents the following limitations and suggestions for future research.

First, there is a lack of sample diversity. The survey conducted for data collection in this study was limited to university student consumers in Busan, South Korea, and Guangzhou, China. While university students play a significant role in the consumption of sustainable fashion products, the limited diversity of the sample poses challenges. The study did not fully address temporal constraints, geographic limitations, or the impact of technological advancements on consumer behavior. These limitations may affect the generalizability of the research conclusions. They also restrict the depth and breadth of the theoretical model. Therefore, the results of this study should not be viewed as universally reflective of the opinions of university student consumers in both countries. Caution is needed when applying the analysis results, especially when considering consumers from other age groups and socioeconomic strata.

Second, there are limitations related to the young consumer group. This study focuses on university student consumers in South Korea and China. While university students are often seen as typical representatives of the sustainable fashion product consumer group, they generally lack purchasing power and experience. This can lead to discrepancies in survey results among different consumer groups. This limitation restricts the applicability of the research findings. Therefore, future research should investigate consumers of sustainable fashion products across all age groups in South Korea and China. It should particularly focus on different age brackets and socioeconomic classes. By increasing comparative studies, researchers can achieve a more comprehensive understanding of the differences in consumption value, subjective norms, attitudes, perceived behavioral control, environmental concern, and purchase intention among various groups. This will help refine the relevant structural models.

Third, there are geographic limitations. This study derives its findings from a comparative analysis, but the national samples are limited to South Korea and China, where consumer cultures are similar. Significant cultural differences in consumer behavior exist in other countries around the world. This highlights the need for comparative research. In the future, we hope to continue our research by selecting appropriate variables. We aim to conduct comparative studies on sustainable fashion products in other countries, such as the United States and Europe. This will help us gain a more comprehensive understanding of consumer behavior patterns in different cultural contexts. Fourth, future research should expand the scope and subjects of the survey, conducting comparative studies across various age groups and consumer classes to enhance the generalizability of the research. This approach can provide empirical evidence for understanding the differences between various strata and assist in formulating more accurate and effective marketing strategies for sustainable fashion products. When considering consumers from different socioeconomic backgrounds, the research findings will be more representative and practical.