Exploring the Key Factors Affecting Customer Satisfaction in China’s Sustainable Second-Hand Clothing Market: A Mixed Methods Approach

Abstract

Driven by the increasing awareness of environmental protection and the demand for personalized fashion, China’s second-hand clothing market is developing rapidly. Chinese consumers have begun to accept second-hand clothing, and online platforms such as Xianyu and Zhier have promoted the widespread trading of second-hand clothing. This study explored the key factors influencing customer satisfaction in China’s sustainable second-hand clothing market. Using a mixed research approach, factors such as pricing strategy, product quality, brand image, customer service, market environment and promotions were identified. The conclusion of grounded theory is that price, product quality, brand reputation, customer service quality, economic environment and platform promotions have a strong impact on customer satisfaction. The Kano model highlights the sensitivity of customer service quality, economic environment and promotions in improving satisfaction. Price is crucial, confirming the price sensitivity of customers. Brand reputation and product quality significantly increase satisfaction. Customer satisfaction significantly affects the amount of sustainable recycling. This study improves the theoretical framework and research hypotheses, provides valuable insights for future research and practical applications and contributes to the sustainable development of the second-hand clothing market.

1. Introduction

The reuse of second-hand clothing has emerged as a prominent trend in the 21st century, driven by evolving consumer needs and the pressing demand for sustainability in the clothing industry [1]. With growing consumption levels, customer needs are constantly evolving towards nationalization, diversification and stratification [2]. Severely homogenized goods can no longer attract customers’ attention, so the second-hand clothing market came into being [3]. China has a huge clothing market, and clothing is a necessity for mass consumption [4]. With higher standards of consumption in the country, the Chinese clothing industry is bound to face new changes [5]. Although second-hand clothing has a history of a hundred years in other countries, it is still in its early stages in China’s commercial environment [6]. By purchasing or using second-hand clothing, customers can not only protect the environment but also save costs [7]. As early as the beginning of the 20th century, second-hand clothing stores appeared in Japan, engaging in the import, export and processing of second-hand clothing [8]. Countries with second-hand clothing markets have established industry associations and sound second-hand market trading orders and systems [9]. From raw material production to clothing sales, each link in the clothing industry consumes a large amount of resources. Therefore, the circulation of second-hand clothing is an effective way to avoid the consumption of additional resources [10]. Developed countries, having major second-hand clothing markets, export a large amount of second-hand clothing to other countries through different channels based on factors such as brand popularity or economics [11]. The global second-hand clothing trade has increased tenfold since the 1990s, reaching USD 1 billion per year, accounting for 0.5% of the total global textile and clothing trade [12]. The 2021 China Idle Second-hand Transaction Carbon Emission Reduction Report shows that in 2020, the scale of China’s second-hand e-commerce transactions reached CNY 374.55 billion, a year-on-year increase of 44% [13]. Moreover, the number of online second-hand transactions has exceeded 180 million. Overall, consumers’ acceptance of second-hand goods continues to increase, and platforms entering this market will have huge opportunities to attract new customers [14].

Customer satisfaction is an important concept in economic psychology [15]. To measure it, a model must be established to link customer satisfaction with relevant variables such as value, quality, complaint behavior and loyalty [16,17,18]. In the field of customer satisfaction, there are few studies on the second-hand clothing market [19]. Most of the research on customer satisfaction in China’s second-hand clothing market aims to not only improve customer satisfaction, but also to better leverage the advantages of the second-hand clothing market.

Understanding the experience and satisfaction of customers in China’s second-hand clothing market is crucial to promoting the long-term growth of the market and attracting potential consumers by recognizing the factors involved in customer satisfaction in the second-hand clothing market [20]. Previous research investigated the motivations of consumers to choose second-hand clothing, covering economic factors, quality and price performance, as well as the desire to support the circular economy [20]. The differences among consumer groups were evaluated, and it was found that although reasonable prices are a common purchase motivation, environmental protection and sustainability factors have also become important motivators for some consumers.

The main objective of this research is to explore the key factors that influence customer satisfaction among Chinese consumers with sustainable second-hand clothing. Despite the breadth of this research area, studies that comprehensively explore customer satisfaction with sustainable second-hand clothing in China are rare. Therefore, this study aims to fill this gap by conducting an in-depth analysis of customer satisfaction with sustainable second-hand clothing using grounded theory and the Kano model. In particular, it aims to reveal the key factors affecting customer satisfaction on second-hand clothing platforms and comprehensively analyze the complexity of customer experience and its impact on overall satisfaction.

The rest of this paper is organized as follows: Section 2 summarizes the relevant literature on the second-hand clothing market, Section 3 introduces the evaluation model, Section 4 outlines the evaluation results, Section 5 describes the analysis and discussion of the findings and Section 6 presents the theoretical and practical implications of this research. Finally, the research concludes with a discussion of the study’s limitations and future research directions.

2. Literature Review

2.1. Sustainable Second-Hand Clothing Consumption

Sustainable second-hand clothing consumption has become an important research topic in academia and practice, covering three core levels: economic, environmental and social [21]. From an economic perspective, second-hand clothing has become an important choice for price-sensitive consumers due to its lower price and higher cost performance [22]. Existing research shows that by extending the life cycle of clothing products, second-hand clothing effectively reduces repeat purchase costs while satisfying consumers’ needs for personalization and uniqueness [23]. For example, the unique value of personalized goods gives second-hand clothing a competitive advantage in the fashion field, especially among young consumer groups who pursue differentiation [24]. From an environmental perspective, sustainable clothing and second-hand clothing consumption provide an effective way to solve the problem of resource waste and environmental pollution in the clothing industry [25]. The apparel industry is a major source of resource consumption and pollution, consuming hundreds of tons of water and emitting large amounts of carbon dioxide for each ton of fabric produced [26]. Second-hand clothing significantly reduces carbon emissions, water use and waste generation by reducing the need for new clothing [27]. From a social perspective, second-hand clothing consumption reflects consumers’ emphasis on ethical consumption and social responsibility [28]. Consumers choose second-hand clothing not only due to economic and environmental considerations, but they are also driven by recognition of the concept of sustainable development [29]. In the Chinese market, the rapid growth of second-hand clothing consumption reflects the rise of the sharing economy and environmental awareness [30].

The second-hand clothing market has a relatively complete system in developed countries [31]. Developed countries are not only the main consumer markets for second-hand clothing, but they also export a large amount of second-hand clothing to developing countries through various channels to meet the latter’s demand for low-priced clothing [32]. Research points out that although this kind of cross-border trade promotes resource reuse to a certain extent, it may also cause competitive pressure on the local clothing industry in developing countries [33]. In recent years, as consumers have become more concerned about sustainable development and environmental protection policies have been strengthened, the global second-hand clothing market is developing in a more standardized and efficient direction [34].

2.2. Customer Satisfaction

Customer satisfaction refers to the degree to which consumers’ needs, expectations and desires are satisfied after using a product or service [35]. It reflects the overall satisfaction of consumers with products or services and is an important indicator for enterprises to measure customer relationship and service quality [36]. Customer satisfaction not only affects the willingness of customers to repeat purchases and their loyalty, but also affects whether they will recommend the product or service to others [37]. Improving customer satisfaction helps enterprises gain long-term competitive market advantages and good reputations [38].

Customer satisfaction in the second-hand clothing market is crucial and is often influenced by perceived value, which can act as a substitute for satisfaction in business markets [39]. Customers prefer buying second-hand clothes due to factors such as greater affordability and higher quality compared to local brands [40]. Additionally, a generational cohort analysis revealed that consumers from different age groups exhibit varying behaviors towards second-hand clothing [41]. In terms of customer preferences, the emergence of second-hand clothing markets has not only provided affordable options but has also contributed to local job creation and improved access to quality clothing [42]. As environmental awareness increases, consumers are paying more and more attention to the impact of clothing production on the environment and society. They are beginning to realize that choosing second-hand clothing can not only reduce resource waste and environmental pollution, but also support social welfare and help those who need cheap clothing. This sense of social responsibility prompts consumers to give priority to second-hand clothing in their purchasing decisions. Factors influencing purchase intentions in markets like India indicate a diverse set of motivators for second-hand clothing shoppers [43]. Moreover, the comparison between second-hand clothing consumption in countries like China and Korea highlights the substantial growth expected in the global sales of second-hand items [44,45].

By researching and analyzing customer satisfaction, we can discover the factors that affect it and gain greater understanding of the relationship between customer satisfaction and customer consumption behavior. This knowledge would allow service providers to effectively improve the key factors that affect customer satisfaction through optimizing costs to change consumer behavior, establish and enhance customer loyalty and reduce customer complaints and customer churn. This in turn would increase repetitive purchasing behavior, create a good reputation for the company and enhance the company’s competitiveness and profitability.

2.3. Grounded Theory

Grounded theory was proposed by sociologists as an important qualitative methodological research framework [46]. Its core conceit is to build theories based on the data itself rather than verifying pre-set hypotheses [47]. This inductive method emphasizes the dynamic interaction between data collection and analysis [48]. Under this framework, researchers go deep into the field to discover patterns, themes and connections from the data [49]. Through an iterative coding and comparison process, researchers gradually build bottom-up theoretical propositions that closely fit the phenomenon being studied [50]. Grounded theory has strong interdisciplinary applicability and is widely used in many fields such as psychology, education and health care to explore complex social phenomena [51]. By emphasizing reflexivity and the simultaneous development of data and analysis, grounded theory provides researchers with a tool with which to gain a deep understanding of the lived experiences of individuals and communities, thereby generating insights with depth and detail [52].

2.4. Kano Model

The Kano model is a customer satisfaction model proposed by Professor Noriaki Kano of the Tokyo Institute of Technology in Japan in 1910 [53]. The model helps companies understand and classify customer needs for products or services (in our case, sustainable second-hand clothing) to improve product design and customer experience [54]. Furthermore, it enables the prediction of customer satisfaction and, therefore, behavioral changes when product or service attributes are changed [55]. For example, it was tested whether sustainable products (reusable packaging, participation in eco-initiatives) can improve customer satisfaction in an online store selling organic products and, thus, increase sustainable consumption [56]. Another investigation looked at which new services and technologies could convince fast fashion consumers to try on clothes in physical stores in order to reduce returns resulting from ordering clothes that do not fit [57]. The Kano model divides customer needs into five categories: basic needs, expected needs, attractive needs, undifferentiated needs and reverse needs [58].

3. Materials and Methods

3.1. Research Design

This research combines in-depth interviews and questionnaire surveys to collect qualitative and quantitative insights. This research uses grounded theory to identify key variables of sustainable second-hand clothing and then applies the Kano model to analyze the sensitivity of customer satisfaction to different factors. A qualitative research method is adopted to collect information on issues related to the sustainable second-hand clothing market, focusing on the views and opinions of different research participants, and deeply understanding and interpreting the phenomena and events they have experienced. However, since the results of qualitative research are usually difficult to generalize, this research also collected questionnaire survey data to conduct a comprehensive analysis of the factors affecting customer satisfaction in the sustainable second-hand clothing market through quantitative research. The research process is shown in Figure 1.

3.2. Qualitative Analysis

3.2.1. Participants

In this qualitative research, six industry experts on sustainable second-hand clothing and three market analysis experts were selected, resulting in a total of nine participants.

3.2.2. Measure

In-depth interviews are an effective method for collecting data in field research through conversations with individuals or multiple research participants [59,60].

In this research, 9 participants were asked to describe their satisfactory or unsatisfactory experiences in the sustainable second-hand clothing market. A semi-structured interview method was used, and an interview guide was designed to efficiently guide the interview process. The interview guide included questions covering topics such as current trends in the second-hand clothing market, what factors most influence customers’ purchasing decisions, key factors for improving customer satisfaction and factors to consider when further developing the market. The guide ensured that the interview content was focused on the research goals and direction, which influenced the generation of expected results [61]. Based on grounded theory, the researcher thoroughly examined the information in the interviews to extract relevant information and identify key themes and patterns [62]. This detailed analysis provided rich and nuanced insights into the research topic, capturing complexities that structured methods may miss [63]. The flexibility of semi-structured interviews allowed the researchers to discover deeper understandings and perspectives, making qualitative analysis a key part of the research process [64]. This research used NVivo software. This research analyzed the interview transcripts and questionnaire descriptions according to the following coding procedures: (1) open coding, (2) axial coding and (3) selective coding. Figure 2 presents the data collection and analysis process.

After collecting the qualitative interview data, the interview data were coded. In the open coding stage, the transcripts were conceptualized and then clustered into subcategories in the coding stage. In the axial coding stage, we analyzed the subcategories and explored their relationships to form main categories. In the selective coding stage, we further interpreted the interrelationships between the main categories to form core categories. To ensure that the core categories can explain all the main categories, they should be closely related. QSR NVivo 11.0 software was used to analyze the transcripts and synthesize the descriptions [65].

3.2.3. Saturation Test

A saturation test is essential to verify the completion of the theoretical model [66]. Expert interviews and expert verification were used to test the theoretical saturation to ensure the integrity and reliability of the results [67]. First, in this research, we interviewed three experts related to the topic and then coded the transcripts [68]. After coding, no new codes appeared compared to the previous codes. Secondly, the raw data, codes and results were submitted to three experts for review, and the theoretical model was considered saturated without any objection from the experts.

3.3. Quantitative Analysis

3.3.1. Participants

Based on the semi-structured interviews with nine participants in the qualitative analysis, a questionnaire for the quantitative analysis was developed through an online platform called Wenjuanxing (https://www.wjx.cn/). The questionnaire respondents were merchants in the sustainable second-hand clothing industry in China, and the electronic questionnaire was sent using WeChat (Version 8.0.56) (an instant messaging tool widely used in the workplace) and online shopping platforms. Based on the research of Krejcie and Morgan (1970), the target sample size set for this study was 384 people [69]. The sampling strategy of this research was to select sellers on the mainstream second-hand clothing platforms in China (Xianyu, Zhier, etc.). These platforms were selected because of their market dominance and broad seller base, representing a variety of sales behaviors from small-scale individual resellers to large-scale professional sellers, in order to improve the generalizability of the results. The sample is diverse, covering gender, age, education level and geographic distribution; it includes male and female sellers, the age span covers multiple generations, education levels range from high school to advanced degrees and the geographical distribution covers major urban centers in China as well as smaller cities and rural areas. These characteristics provide a solid foundation for analyzing the relationship between customer satisfaction and sales volume and provide a reference for the promotion and verification of future research.

3.3.2. Measure

In order to identify the key attributes that affect customer satisfaction in the sustainable second-hand clothing market, this research used the Kano evaluation table to classify each attribute to determine the best characteristics of each attribute (

Table 1. Classification of Kano evaluation results.

Factors Affecting Customer Satisfaction in Second-hand Market (Positive Question)		Satisfied	Should Be So	Indifferent	Accept Reluctantly	Dissatisfied
	Satisfied	Q	A	A	A	O
	Should be so	R	I	I	I	M
	Indifferent	R	I	I	I	M
	Accept reluctantly	R	I	I	I	M
	Dissatisfied	R	R	R	R	Q

). Secondly, the factors that affect customer satisfaction were determined through CSC, as shown in Equations (1) and (2), using the quality attributes classified through the Kano model. The main idea of our application of the Kano model is to classify the investigated persistent products according to the relationship between their presence and the customer questionnaire using the following Kano categories [70]:

The must-be (M) category consists of attributes considered to be the basic expectations of customers. Their presence does not increase customer satisfaction, but their absence causes customer dissatisfaction.

The one-dimensional (O) category has a positive relationship with customer satisfaction, and the presence of these attributes increases satisfaction, while their absence causes dissatisfaction.

The attractive (A) category comprises attributes that customers did not expect. When they are present, attractive attributes significantly increase customer satisfaction, but their absence does not cause dissatisfaction. However, their impact is often short-lived, as they may gradually become popular or eventually become basic expectations (must-have products).

Indifferent (I) attributes have no effect on customer satisfaction or dissatisfaction. From the customer’s point of view, their presence or absence is irrelevant.

Reverse (R) offerings cause dissatisfaction when they are present and satisfaction when they are absent.

If an offering did not clearly fall into any of these categories, or its classification could not be determined, it was marked as questionable (Q).

Customer satisfaction index (Better, Worse):

$${\displaystyle \frac{Better}{SI}}={\displaystyle \frac{A+O}{A+O+M+I}}$$

(1)

$${\displaystyle \frac{\mathrm{W}\mathrm{o}\mathrm{r}\mathrm{s}\mathrm{e}}{\mathrm{D}\mathrm{S}\mathrm{I}}}={\displaystyle \frac{O+M}{A+O+M+I}}$$

(2)

A: Attractive

O: One-dimensional

M: Must-be

I: Indifferent

4. Results and Analysis

4.1. Qualitative Research Results

4.1.1. Semi-Structured Interview

Semi-structured interviews are a qualitative research method combining predetermined questions with the flexibility to explore topics in depth [71]. This approach is particularly suitable for researching customer satisfaction in the second-hand clothing market. By using a structured yet adaptable framework, researchers can ensure that core themes are addressed while allowing interviewees to elaborate on their unique perspectives.

Prior to conducting the interviews, participants were provided with an overview of the research objectives. This was supplemented by insights from industry experts and second-hand store operators, which contextualized the interview questions within the realities of the current market.

Table 2. Semi-structured interview questions.

Number	Question
1	How do you evaluate the current development trend of the second-hand clothing market?
2	In the second-hand clothing market, what factors can most influence customers’ purchasing decisions?
3	What do you think are the key factors to improve customer satisfaction?
4	What role does customer satisfaction play in second-hand clothing sales?
5	How important do you think effective sales strategies are for second-hand clothing sales?
6	How would you suggest sellers adjust their sales strategies based on customer satisfaction?
7	How do you view the impact of technological progress on second-hand clothing sales?
8	What challenges and opportunities do you think the second-hand clothing industry will face in the future?

outlines the key questions that guided the interviews, covering aspects such as market trends, customer satisfaction and future opportunities in the second-hand clothing industry.

To analyze the data, thematic coding was used. The process began with open coding to identify initial themes, followed by axial coding to link related concepts and identify overarching categories [72]. Representative quotes from the interviews are presented to substantiate the findings.

The analysis of semi-structured interviews revealed several critical factors influencing customer purchasing decisions in the second-hand clothing market, including price, product quality and customer service. Respondent 3 highlighted that “product quality, price, and uniqueness play critical roles”, while Respondent 1 emphasized the importance of a seamless shopping experience, stating that “ease of return, customer service quality, and environmental responsibility during the purchase process are essential considerations”. Trends in the market also point to rapid expansion, with Respondent 2 noting that “the second-hand market continues to grow globally, especially among millennials and Gen Z, who are highly concerned with sustainability”. Respondent 7 further highlighted the role of technology, stating, “technological progress, including digital platforms and AI-driven personalization, has significantly expanded the market’s reach”. Additionally, customer satisfaction emerged as a central theme, with Respondent 4 observing that “satisfied customers are not just repeat buyers but also brand advocates who promote the store through word-of-mouth”. Effective sales strategies were also discussed, with Respondent 6 recommending that “sellers need to adopt flexible pricing strategies and create a sustainable brand image to align with customer values”.

Table 3. Sorted semi-structured interview content (sample number N = 9).

Respondent (Number)	Factors Affecting the Purchase of Customers in the Second-Hand Clothing Market	Trends in the Second-Hand Clothing Market
1	The quality and condition of the product, the price, the reputation of the store and the shopping experience, the speed of logistics, the ease of return and exchange, the quality of customer service and the recognition of environmental and social responsibility during the purchase process.	A significant upward trend, acceptance and demand in the second-hand clothing market is expanding rapidly
2	Price and quality.	It continues to grow globally and is particularly popular with young people such as millennials and Gen Z
3	Product quality, price and uniqueness. In addition, the purchase experience (such as a convenient shopping process and good customer service) and social recognition (such as friend recommendations or positive reviews on social media) also play an important role.	Consumers are increasingly focusing on sustainability and reducing waste, which has made the second-hand clothing market popular
4	Price, customer service, quality and variety.	Strong growth momentum, the scale will continue to expand
5	Pricing strategies, marketing campaigns, customer service, product diversity, market positioning and sustainable practices. Reasonable prices, attractive marketing campaigns, excellent customer service, diverse product selection, clear market positioning and environmentally friendly store image.	Significant growth stage, mainly due to environmental awareness
6	Reasonable price, attractive marketing strategies, quality service, diversified product selection, clear market positioning.	Increase
7	Efficient customer service, accurate market positioning.	Further expanded because of technological advances
8	Efficient customer service, personalized product recommendation function.	Expansion, and the emergence of digital and intelligent trends

summarizes the aggregated insights from the nine respondents, highlighting recurring themes and individual perspectives.

4.1.2. Open Coding

Open coding is a qualitative analysis method used to define phenomena by breaking down collected data and information, and then conceptualizing and categorizing them. The goal is to extract concepts and categories that accurately reflect the studied phenomenon. In this process, Nvivo qualitative analysis software is often employed to manage and analyze data efficiently (Klarin, 2024 [45]). Through multiple rounds of sorting and coding, researchers can refine and categorize the data into meaningful themes. The iterative process ensures that the final summary accurately represents the core concepts and categories derived from the data. The summary results of the open coding process, after extensive analysis using Nvivo, are presented in

Table 4. Summary of open coding results.

Number	Concept	Category
1	Product quality and condition	Product quality
2	Price	Pricing strategy
3	Store reputation	Market positioning
4	Shopping experience, ease of returns, quality of customer service	Strong growth momentum, the scale will continue to increase, customer service
5	Environmental protection and social responsibility	Significant growth stage, mainly due to environmental awareness
6	Reasonable price, attractive marketing means, quality service, diversified product selection, clear market positioning	Sustainable practice
7	Marketing activity	Promotion

, showcasing the key themes and insights uncovered during the research. One theme is to strengthen AI personalization and use artificial intelligence technology to provide users with customized content, products and services based on user behavior, preferences, needs and other data. AI personalization can realize accurate construction of user portraits, enabling personalized recommendations, intelligent interactions and other functions, improving user experience and satisfaction and thus bringing higher user loyalty and commercial value to digital platforms. In the development of personalized services, companies need to pay more attention to sustainable development and social responsibility; pay attention to data privacy protection, ethics and other issues; and ensure the healthy development of personalized services and benign social interaction.

4.1.3. Spindle Coding

The second stage of data processing is spindle coding, which aims to clarify the generic relationships between categories [73]. This involves re-encoding the results from the open coding stage to further distinguish between main categories and preliminary categories. Through spindle coding, researchers can refine and organize the data into a more structured hierarchy. In this research, spindle coding resulted in the identification of six main categories. Additionally, ten preliminary categories were initially identified during open coding and were further clarified and refined through the spindle coding process. This structured approach ensures a comprehensive understanding of the relationships and hierarchies within the data. The detailed results of this coding process are presented in

Table 5. Main categories and preliminary categories.

Principal Category	Preliminary Category
Pricing strategy	Price
Product quality	Product quality, commodity quality
Brand image	Brand reputation
Customer service	Customer service, customer service quality
Market environment	Economic environment
Promotion	The promotion of the platform

, illustrating the main and preliminary categories derived from the analysis.

4.1.4. Selective Coding

Selective coding is the final stage in the coding process, aiming to determine the core category [74]. This core category must exhibit strong stability, explanatory power, frequent occurrence and concentrated relevance. By analyzing the six main categories obtained through spindle coding, researchers can identify the core category that best encapsulates the primary theme of the research. In this research, the core category for customer satisfaction in the second-hand clothing market was determined based on these criteria. This core category provides a comprehensive and cohesive understanding of customer satisfaction, synthesizing the most critical elements identified in the earlier coding stages. The result is a focused and robust framework that effectively represents the key drivers of customer satisfaction in this market.

4.1.5. Saturation Detection

This research reserved two interview data sets for theoretical saturation detection. The results demonstrate that the concepts in these two data sets were already encompassed within the concepts and categories identified in the previous coding stages. This indicates that no new concepts emerged from the additional data, confirming that the coding process in this research has achieved theoretical saturation. Therefore, the existing categories and concepts are sufficient to explain the phenomena being studied, ensuring the reliability and completeness of the analysis.

4.1.6. Establishment of Evaluation System

Through the three steps of open coding, spindle coding and selective coding, the core category of customer satisfaction in the second-hand clothing market has been determined. The core category is defined by six main categories: Pricing Strategy, Product Quality, Brand Image, Customer Service, Market Environment and Promotion. These main categories encompass the essential index elements of customer satisfaction, thereby establishing a comprehensive evaluation index system for this market. Each category plays a crucial role in influencing customer perceptions and satisfaction levels. Pricing Strategy addresses affordability and perceived value; Product Quality focuses on the condition and durability of items; Brand Image reflects reputation and customer trust; Customer Service highlights the importance of support and engagement; Market Environment pertains to the shopping experience; and Promotion covers the effectiveness of marketing efforts. The detailed evaluation index system is presented in

Table 6. Evaluation index system of customer satisfaction in second-hand clothing market.

Criteria Layer	Indicator Layer
Pricing strategy	Price
Product quality	Product quality
	Commodity quality
Brand image	Brand reputation
Customer service	Customer service
	Customer service quality
Market environment	Economic environment
Promotion	The promotion of the platform

.

To better understand the key factors highlighted in Table 6, the following definitions in

Table 7. Definitions of the indicator system.

Criteria Layer	Indicator Layer	Detailed Description	References
Pricing strategy	Price	The cost customers pay for products or services.	[75,76]
Product quality	Product quality	The overall standard of a product, including durability and appearance.	[77,78]
	Commodity quality	The intrinsic features of a product, such as material and craftsmanship.	[79]
Brand image	Brand reputation	The public perception and trustworthiness of a brand.	[80]
Customer service	Customer service	Assistance provided to customers during and after purchase.	[81,82]
	Customer service quality	The effectiveness and experience of customer support.	[83]
Market environment	Economic environment	Economic factors like income levels and consumer spending.	[84]
Promotion	The promotion of the platform	Marketing efforts to attract customers and boost visibility.	[85]

provide clarification on terms frequently mentioned in the analysis.

4.2. Quantitative Research Results

4.2.1. Questionnaire Survey and Sample Characteristics

The questionnaire survey aims to systematically collect data from different groups of sellers in China’s second-hand market. The questionnaire consists of two parts: the first part collects basic demographic and platform-related information of respondents, such as gender, age, education level, geographic location and sales platform, to ensure the representativeness and diversity of the sample; the second part includes 10 paired items designed to investigate the key factors affecting customer satisfaction and explore the potential impact of customer satisfaction on sales volume. In order to obtain more accurate customer satisfaction information, this survey also included users of other platforms in addition to Xianyu and Zhier platform users, based on the actual app usage, so as to improve the accuracy of the questionnaire survey. The detailed questionnaire design is shown in Appendix A. The questionnaire was distributed through WeChat (Version 8.0.56) (an instant messaging tool widely used in the workplace) and online shopping networks to ensure that different types of sellers were reached. At the same time, the questionnaire was distributed through the online survey platform WJX (Version 8.0.56) to improve the efficiency and coverage of the questionnaire distribution. This research received 213 questionnaires, and after excluding invalid responses, a total of 191 valid questionnaires were collected.

The sampling strategy of this research targets sellers on China’s second-hand clothing platforms, which were selected because of their market dominance and broad seller base; the sample thus represents a variety of sales behaviors from small-scale individual resellers to larger-scale professional sellers. The sample selection aimed to capture as wide a range of views as possible to improve the generalizability of the results. The diversity of the sample is reflected in the aspects of gender, age, education level and geographic distribution: gender includes both male and female sellers, providing gender-balanced insights; the age span covers multiple generations of sellers, reflecting the participation of different generations in the second-hand market; education levels range from high school graduates to advanced degrees, providing a diverse cognitive perspective for analysis; geographical distribution covers most cities in China receiving free shipping from these reselling platforms (including but not limited to major first- and second-tier cities and urban–rural fringe areas) as well as smaller cities or rural areas (such as remote mountainous areas in various ethnic minority autonomous regions), ensuring that the results are not biased towards specific economic regions or seller types. Through these diverse sample characteristics, this research provides a solid foundation for the exploration of the relationship between customer satisfaction and sales volume, also providing a reference for future promotion and verification on a larger scale.

4.2.2. Basic Information

Table 8. Sample characteristics of the surveyed second-hand clothing market sellers (Sample number N = 191).

Item	Classification	Percent (%)
Gender	Male	56.2
	Female	43.8
Age	20 and below	1.5
	21–34 years old	23.4
	35-–years old	46.9
	51 and above	28.2
Sales Platform	Zhier	67.5
	Xianyu	28.3
	Others	4.2
Education Level	Junior high school and below	5.8
	High school/technical secondary school	20.9
	Bachelor’s degree/College degree	70.7
	Master’s degree or above	2.6

provides four sets of data: gender and age distribution, sales platform and education level. In terms of gender, men account for 56.2% of the population and women for 43.8%. Regarding age, 46.9% of respondents are aged 35–50, and 28.2% are aged 51 and above. When it comes to educational attainment, 70.7% have undergraduate or tertiary education, 20.9% have senior high school or secondary school education, 5.8% have junior high school education or below and 2.6% have a Master’s degree or above. In terms of sales platforms, 67.5% use Zhier, 28.3% use Xianyu and 4.2% use other platforms. According to Table 3, Table 4, Table 5 and Table 6, most of the interviewees sell second-hand clothing on Zhier and Xianyu, and they are primarily aged between 21 and 50 years old. These data provide a comprehensive overview of the demographics and educational background of sellers in the second-hand clothing market, as well as the platforms they utilize for sales.

4.2.3. Reliability and Validity Analysis

The reliability test assesses the consistency of the data results using Cronbach’s Alpha reliability test. The validity test evaluates the accuracy of the data collected by the questionnaire, specifically through a structural validity test. The collected data were imported into SPSS 26.0 for analysis. The results indicated that Cronbach’s Alpha value was 0.863, which is greater than the threshold of 0.65, signifying the high accuracy and reliability of the data. Additionally, the KMO (Kaiser–Meyer–Olkin) value was 0.904, exceeding the benchmark of 0.8, confirming that the data passed the structural validity test. To ensure the authenticity and validity of the questionnaire survey, both the reliability and validity of the scale questionnaire were analyzed. The detailed results are presented in Table 8, demonstrating that the questionnaire is both reliable and valid for this research.

4.2.4. Better–Worse Coefficient Analysis

By calculating the Better–Worse coefficient, we can determine the impact of various factors on user satisfaction. The Better coefficient is typically a positive number, indicating that the provision or improvement of this factor will increase user satisfaction. This metric allows us to identify which elements are most influential in enhancing the overall customer experience. By focusing on factors with high Better coefficients, businesses can strategically prioritize improvements that will have the greatest positive effect on user satisfaction. The Worse factor is generally a negative number, indicating that without this factor, the user’s satisfaction will decrease; the greater the negative value, the more dissatisfied the user is. The calculation for determining the Better–Worse coefficient is shown in Equations (1) and (2).

According to the answers from the questionnaire, factors affecting customer satisfaction and the influence of customer satisfaction on the sustainable recycling volume of the second-hand clothing market were statistically analyzed by sellers in 191 second-hand clothing markets. The total quantity was calculated, and the results of the Better–Worse coefficient and the classification of demand were determined. The results obtained after thorough analysis are summarized in

Table 9. Testing the reliability and validity of the questionnaire data.

KMO and Bartlett’s Test				Reliability Statistics
Kaiser–Meyer–Olkin Measure of Sampling Adequacy	Bartlett’s Test of Sphericity			Cronbach’s Alpha	Number of Items
0.904	Approx. Chi-Square	df	Sig.	0.863	9
	581.684	36	0.000

, which displays the Better–Worse coefficients for each factor, highlighting their impact on customer satisfaction. This table provides a clear classification of demand, indicating which factors are most crucial for enhancing satisfaction and ensuring sustainable recycling practices in the second-hand clothing market.

According to

Table 10. The relationship between customer satisfaction and demographic characteristics in the second-hand clothing market.

Item	Factor	A	O	M	I	R	Total
Gender	Male	36	41	11	5	8	52.9%
	Female	40	28	12	5	5	47.1%
Education	Junior high school and below	3	7	1	0	0	5.8%
	High school/technical secondary school	11	17	2	4	6	20.9%
	Bachelor’s degree/College	58	44	20	6	7	70.7%
	Master’s degree or above	4	1	0	0	0	2.6%
Age	20 and below	10	13	6	4	1	17.8%
	21–35	37	23	9	1	3	38.2%
	35–50	19	25	4	3	6	29.8%
	51 and above	10	8	4	2	3	14.1%
Platform	Zhier	53	47	18	5	6	67.5%
	Xianyu	21	16	5	5	7	28.3%
	Others	2	6	0	0	0	4.2%

, the satisfaction rate of men is 52.9%, and that of women is 47.1%. It can be seen that male customers are slightly more satisfied with the product than female customers. There are large differences in satisfaction among groups with different educational levels. The satisfaction rate of consumers with education up to junior high school and below is 5.8%, that of consumers with a high school/technical secondary school education is 20.9%, that of consumers with undergraduate/technical college degrees is 70.7%, and that of consumers with Master’s degrees and above is only 2.6%. It can be seen that the higher the education level, the higher the satisfaction of customers with the product, especially those with undergraduate and technical college education. In addition, customer satisfaction varies among different age groups. The satisfaction rate of the group aged 20 and below is 17.8%, that of the group aged 21–35 is 38.2%, that of the group aged 35–50 is 29.8% and that of the group aged 51 and above is 14.1%. The satisfaction rate of the youngest group is lower, while that of the middle-aged group and the elderly group is relatively higher. Finally, the differences in customer satisfaction among different platforms are also very obvious. The customer satisfaction rate among users of the Zhier platform is the highest, reaching 67.5%, while satisfaction among users of the Xianyu platform is 28.3% and that among users of other platforms is only 4.2%. Therefore, it can be inferred that customers on the Zhier platform are more satisfied with the products or services offered, while customer satisfaction on the Xianyu platform is relatively low.

According to

Table 11. Customer satisfaction with second-hand clothing market and the relationship between customer satisfaction and sustainable recycling volume.

Item	Influence Factor	A	O	M	I	R	Mean	Better	Worse
PQ	Product quality	67	80	28	12	4	A	0.79	−0.58
CQ	Commodity quality	70	83	23	10	5	A	0.82	−0.57
Price	Price	65	79	36	9	2	O	0.76	−0.61
BR	Brand reputation	72	71	33	7	8	A	0.78	−0.57
CS	Customer service	62	73	35	16	5	O	0.73	−0.58
CSQ	Customer service quality	68	61	38	12	12	A	0.72	−0.55
EE	Economic environment	62	71	31	15	12	O	0.74	−0.57
Promotion	The promotion of the platform	67	71	27	19	7	O	0.75	−0.53
Relationship	Customer satisfaction and sales in the second-hand clothing market	76	69	23	10	13	A	0.81	−0.52

, customers in the second-hand clothing market have the corresponding demand types for goods:

One-Dimensional Quality (O): Price, CS (Customer Service), EE (Economic Environment), Promotion (the promotion of the platform);

Attractive Quality (A): PQ (Product Quality), CQ (Commodity Quality), BR (Brand Reputation), CSQ (Customer Service Quality), Relationship (Relationship between customer satisfaction and sustainable recycling volume in the second-hand clothing market).

In Figure 3, the impacts of the Better and Worse coefficients of each factor on customer satisfaction are presented. The higher the Better coefficient, the greater the impact of the presence or improvement of the factor on customer satisfaction; the lower the Worse coefficient, the greater the impact of the absence of the factor on customer dissatisfaction. The Better coefficient of price is low, but the Worse coefficient is the lowest, which means that reasonable prices have a limited effect on improving customer satisfaction while unreasonable prices will greatly reduce satisfaction. The Worse coefficient of customer service (CS) and its quality (customer service quality, CSQ) is low, indicating that poor service will significantly reduce satisfaction, while its improvement effect is relatively limited. The economic environment (EE) has a medium positioning on both coefficients, meaning that it has moderate positive and negative effects on satisfaction. Brand reputation (BR) and product quality (PQ) have higher Better coefficients, but also higher Worse coefficients, indicating that their improvement can effectively increase satisfaction, but their absence will not lead to obvious dissatisfaction. Commodity quality (CQ) is similar: its improvement has a significant effect on customer satisfaction, but the negative impact of its absence is small. The promotion of the platform (Promotion) on satisfaction is slightly higher for the Better coefficient, but the effect of its absence is relatively small. The relationship between customer satisfaction and second-hand market sales (Relationship) has the highest Better coefficient, which shows that when customers perceive a relationship between satisfaction and sales, satisfaction is significantly improved, but its absence will not significantly affect dissatisfaction. Overall, price and customer service have a greater negative impact on satisfaction, while improvements in brand reputation, product quality and customer relationships can effectively increase satisfaction.

According to

Table 12. Significance test.

ANOVA
Causes	Sum of Squares	df	Mean Square	F
PQ	23.019	4	5.755	6.864
CQ	25.160	4	6.290	7.693
Price	22.957	4	5.739	8.093
BR	37.590	4	9.389	10.441
CS	29.297	4	7.324	7.802
CSQ	81.425	4	20.359	21.534
EE	87.057	4	21.764	24.164
Promotion	78.252	4	19.563	23.804

, the following conclusions can be drawn. It shows that all variables (product quality, merchandise quality, price, brand reputation, customer service, customer service quality, economic environment and promotion) had significant differences in their means across groups. This finding shows that these factors have a significant impact on customer satisfaction. In addition, customer satisfaction can affect the sustainable recycling volume of the second-hand clothing market.

Table 13. KMO and Bartlett’s test.

KMO and Bartlett’s Test
Kaiser–Meyer–Olkin Measure of Sampling Adequacy.		0.89
Bartlett’s Test of Sphericity	Approx. Chi-Square	486.073
	df	28
	Sig.	0.000

evaluates the overall suitability of the data for factor analysis. The KMO value is 0.890, which indicates that the sample data are highly adequate for factor analysis. A KMO value above 0.7 is considered excellent for factor analysis. Additionally, Bartlett’s test of sphericity shows a Chi-Square value of 486.073 with a significance level (Sig.) of 0.000. This result confirms that the variables exhibit significant correlations and are suitable for factor analysis.

Table 14. Total variance explained.

Total Variance Explained
Component	Initial Eigenvalues			Extraction Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	3.89	48.623	48.623	3.89	48.623	48.623
2	0.841	10.508	59.131
3	0.732	9.154	68.286
4	0.612	7.653	75.939
5	0.59	7.372	83.311
6	0.531	6.637	89.948
7	0.432	5.404	95.352
8	0.372	4.648	100

provides the proportion of each variable’s variance that is explained by the extracted factors.

Table 15. Communalities.

Communalities
	Initial	Extraction
PQ	1	0.402
CQ	1	0.42
Price	1	0.423
BR	1	0.433
CS	1	0.494
CSQ	1	0.612
EE	1	0.574
Promotion	1	0.531

highlights the variance explained by each extracted component. The first principal component has an eigenvalue of 3.890, which accounts for 48.623% of the total variance, making it the dominant contributing factor. The cumulative variance explained by the second component increases to 59.131%, meaning these two components together explain over half of the variance in the data set. Components beyond the second one contribute only marginally, as evidenced by their eigenvalues falling below 1, which implies their limited explanatory power. Thus, the first component is the most critical in capturing the relationships among variables. For example, customer service quality (CSQ) has the highest extraction value of 0.612, followed by economic environment (EE) at 0.574 and promotion of the platform at 0.531. These high extraction values suggest that these variables are strongly represented in the extracted principal components. In contrast, product quality (PQ) has a relatively lower extraction value of 0.402, indicating it is less strongly captured by the principal component. The analysis reveals that the first principal component explains the largest proportion of the variance (48.623%), as shown in Table 14. Further analysis, based on individual contributions of variables, indicates that customer service quality (CSQ) contributes the most (14.08%), followed by economic environment (EE) at 13.63% and promotion of the platform at 13.10%. Other variables, including customer service (CS), brand reputation (BR), price, commodity quality (CQ) and product quality (PQ), have slightly lower contributions, ranging from 12.64% to 11.40%. These results, supported by Table 15, underscore the critical role of customer service quality and economic environment in shaping customer satisfaction in China’s sustainable second-hand clothing market.

Table 16. Multifactor analysis of variance (ANOVA) results.

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
Gender	PQ	0.245	1	0.245	0.259	0.611
	CQ	1.574	1	1.574	1.693	0.195
	Price	0.003	1	0.003	0.003	0.955
	BR	0.045	1	0.045	0.042	0.838
	CS	0.123	1	0.123	0.115	0.735
	CSQ	0.073	1	0.073	0.054	0.817
	EE	0.886	1	0.886	0.66	0.418
	Promotion	0.328	1	0.328	0.269	0.605
Age	PQ	7.985	3	2.662	2.911	0.036
	CQ	5.786	3	1.929	2.104	0.101
	Price	3.081	3	1.027	1.265	0.288
	BR	15.688	3	5.229	5.166	0.002
	CS	1.259	3	0.42	0.387	0.762
	CSQ	6.215	3	2.072	1.543	0.205
	EE	10.314	3	3.438	2.632	0.051
	Promotion	8.504	3	2.835	2.381	0.071
Platform	PQ	0.535	2	0.267	0.282	0.755
	CQ	2.782	2	1.391	1.499	0.226
	Price	3.979	2	1.99	2.479	0.087
	BR	1.208	2	0.604	0.557	0.574
	CS	1.96	2	0.98	0.912	0.403
	CSQ	4.253	2	2.127	1.58	0.209
	EE	1.831	2	0.915	0.681	0.507
	Promotion	4.781	2	2.39	1.985	0.14
Educational Level	PQ	1.402	3	0.467	0.492	0.688
	CQ	2.377	3	0.792	0.847	0.47
	Price	2.401	3	0.8	0.981	0.403
	BR	1.029	3	0.343	0.315	0.815
	CS	1.252	3	0.417	0.385	0.764
	CSQ	7.009	3	2.336	1.746	0.159
	EE	9.696	3	3.232	2.468	0.063
	Promotion	4.173	3	1.391	1.146	0.332

shows the impact of different factors (gender, age, platform, education level) on multiple variables. From the analysis results of gender, gender has no significant effect on these variables. Specifically, the p value of PQ (product quality) is 0.611, the p value of CQ (customer satisfaction) is 0.195, the p value of Price (price) is 0.955 and the p value of BR (brand reputation) is 0.838. These results all show that gender has no significant effect on these factors. In addition, the p values of CS (customer service), CSQ (customer satisfaction), EE (economic environment), Promotion and Relationship are 0.735, 0.817, 0.418, 0.605 and 0.468, respectively, which are also greater than 0.05, so gender has no significant effect on these variables. In the analysis of age (Age), some variables show significant effects. The p value of PQ (product quality) is 0.036, which is less than 0.05, indicating that age has a significant impact on product quality. The p value of BR (brand reputation) is 0.002, which is much lower than 0.05, further supporting that age plays a significant role in brand perception. However, the p values of other variables such as CQ (customer satisfaction), Price (price), CS (customer service), CSQ (customer satisfaction) and Promotion (promotion) are all greater than 0.05, indicating that age has no significant effect on these variables. For platform factors, the p values of all variables are greater than 0.05, indicating that some effects may not exist. For example, the p values of Price (price), PQ (product quality), CQ (customer satisfaction), BR (brand reputation), CS (customer service), CSQ (customer satisfaction), EE (economic environment) and Promotion (promotion) are all greater than 0.05, indicating that the platform has no significant effect on these variables. Education level has no effect on the variables, and the p values of the variables are all greater than 0.05, indicating that education level has no significant effect on these variables. From the analysis results, gender has no significant effect on all variables, and all relevant p values are greater than 0.05. Age has a significant effect on product quality and brand perception. The impact of platform factors and education level on all variables was also not significant, with p values all greater than 0.05.

According to

Table 17. Tukey HSD post hoc test.

Dependent Variable	(I) Age	(J) Age	Mean Difference (I-J)	Std. Error	Sig.	95% Confidence Interval
PQ	20 and below	21–35	0.029	0.199	0.999	−0.49	0.54
		35–50	−0.405	0.207	0.21	−0.94	0.13
		51 and above	−0.362	0.246	0.459	−1	0.28
	21–35	20 and below	−0.029	0.199	0.999	−0.54	0.49
		35–50	−0.434	0.169	0.054	−0.87	0
		51 and above	−0.391	0.215	0.27	−0.95	0.17
	35–50	20 and below	0.405	0.207	0.21	−0.13	0.94
		21–35	0.434	0.169	0.054	0	0.87
		51 and above	0.043	0.223	0.997	−0.54	0.62
	51 and above	20 and below	0.362	0.246	0.459	−0.28	1
		21–35	0.391	0.215	0.27	−0.17	0.95
		35–50	−0.043	0.223	0.997	−0.62	0.54
CQ	20 and below	21–35	0.245	0.199	0.609	−0.27	0.76
		35–50	−0.117	0.207	0.942	−0.66	0.42
		51 and above	−0.178	0.247	0.889	−0.82	0.46
	21–35	20 and below	−0.245	0.199	0.609	−0.76	0.27
		35–50	−0.362	0.169	0.145	−0.8	0.08
		51 and above	−0.422	0.216	0.208	−0.98	0.14
	35–50	20 and below	0.117	0.207	0.942	−0.42	0.66
		21–35	0.362	0.169	0.145	−0.08	0.8
		51 and above	−0.06	0.224	0.993	−0.64	0.52
	51 and above	20 and below	0.178	0.247	0.889	−0.46	0.82
		21–35	0.422	0.216	0.208	−0.14	0.98
		35–50	0.06	0.224	0.993	−0.52	0.64
Price	20 and below	21–35	0.178	0.187	0.777	−0.31	0.66
		35–50	−0.123	0.195	0.923	−0.63	0.38
		51 and above	−0.037	0.232	0.999	−0.64	0.57
	21–35	20 and below	−0.178	0.187	0.777	−0.66	0.31
		35–50	−0.301	0.159	0.236	−0.71	0.11
		51 and above	−0.215	0.203	0.714	−0.74	0.31
	35–50	20 and below	0.123	0.195	0.923	−0.38	0.63
		21–35	0.301	0.159	0.236	−0.11	0.71
		51 and above	0.086	0.21	0.977	−0.46	0.63
	51 and above	20 and below	0.037	0.232	0.999	−0.57	0.64
		21–35	0.215	0.203	0.714	−0.31	0.74
		35–50	−0.086	0.21	0.977	−0.63	0.46
BR	20 and below	21–35	0.358	0.209	0.319	−0.18	0.9
		35–50	−0.339	0.218	0.407	−0.9	0.23
		51 and above	−0.008	0.259	1	−0.68	0.66
	21–35	20 and below	−0.358	0.209	0.319	−0.9	0.18
		35–50	−0.697 *	0.178	0.001	−1.16	−0.24
		51 and above	−0.366	0.227	0.373	−0.95	0.22
	35–50	20 and below	0.339	0.218	0.407	−0.23	0.9
		21–35	0.697 *	0.178	0.001	0.24	1.16
		51 and above	0.331	0.235	0.495	−0.28	0.94
	51 and above	20 and below	0.008	0.259	1	−0.66	0.68
		21–35	0.366	0.227	0.373	−0.22	0.95
		35–50	−0.331	0.235	0.495	−0.94	0.28
CS	20 and below	21–35	0.12	0.216	0.945	−0.44	0.68
		35–50	−0.063	0.226	0.992	−0.65	0.52
		51 and above	0.11	0.268	0.977	−0.59	0.81
	21–35	20 and below	−0.12	0.216	0.945	−0.68	0.44
		35–50	−0.183	0.184	0.752	−0.66	0.29
		51 and above	−0.01	0.234	1	−0.62	0.6
	35–50	20 and below	0.063	0.226	0.992	−0.52	0.65
		21–35	0.183	0.184	0.752	−0.29	0.66
		51 and above	0.173	0.243	0.892	−0.46	0.8
	51 and above	20 and below	−0.11	0.268	0.977	−0.81	0.59
		21–35	0.01	0.234	1	−0.6	0.62
		35–50	−0.173	0.243	0.892	−0.8	0.46
CSQ	20 and below	21–35	0.118	0.241	0.961	−0.51	0.74
		35–50	−0.075	0.251	0.991	−0.73	0.58
		51 and above	−0.438	0.299	0.46	−1.21	0.34
	21–35	20 and below	−0.118	0.241	0.961	−0.74	0.51
		35–50	−0.193	0.205	0.782	−0.72	0.34
		51 and above	−0.556	0.261	0.148	−1.23	0.12
	35–50	20 and below	0.075	0.251	0.991	−0.58	0.73
		21–35	0.193	0.205	0.782	−0.34	0.72
		51 and above	−0.363	0.271	0.539	−1.06	0.34
	51 and above	20 and below	0.438	0.299	0.46	−0.34	1.21
		21–35	0.556	0.261	0.148	−0.12	1.23
		35–50	0.363	0.271	0.539	−0.34	1.06
EE	20 and below	21–35	0.361	0.237	0.428	−0.25	0.98
		35–50	−0.191	0.248	0.867	−0.83	0.45
		51 and above	0.005	0.295	1	−0.76	0.77
	21–35	20 and below	−0.361	0.237	0.428	−0.98	0.25
		35–50	−0.552 *	0.202	0.034	−1.08	-0.03
		51 and above	−0.355	0.257	0.514	−1.02	0.31
	35–50	20 and below	0.191	0.248	0.867	−0.45	0.83
		21–35	0.552 *	0.202	0.034	0.03	1.08
		51 and above	0.197	0.267	0.882	−0.5	0.89
	51 and above	20 and below	−0.005	0.295	1	−0.77	0.76
		21–35	0.355	0.257	0.514	−0.31	1.02
		35–50	−0.197	0.267	0.882	−0.89	0.5
Promotion	20 and below	21–35	0.335	0.227	0.452	−0.25	0.92
		35–50	0.329	0.236	0.506	−0.28	0.94
		51 and above	−0.224	0.281	0.855	−0.95	0.5
	21–35	20 and below	−0.335	0.227	0.452	−0.92	0.25
		35–50	−0.006	0.193	1	−0.51	0.49
		51 and above	−0.56	0.246	0.107	−1.2	0.08
	35–50	20 and below	−0.329	0.236	0.506	−0.94	0.28
		21–35	0.006	0.193	1	−0.49	0.51
		51 and above	−0.554	0.255	0.135	−1.21	0.11
	51 and above	20 and below	0.224	0.281	0.855	−0.5	0.95
		21–35	0.56	0.246	0.107	−0.08	1.2
		35–50	0.554	0.255	0.135	−0.11	1.21

* The mean difference is significant at the 0.05 level.

, the results of the Tukey HSD post hoc test showed that only a few differences between specific age groups were significant. The comparison results between different age groups for most variables (such as PQ, CQ, Price) showed that the p-values were greater than 0.05, indicating that the differences between these age groups were not statistically significant. However, on some variables, the differences between specific age groups were significant. For example, in the BR (brand reputation) variable, the difference between the 35–50 age group and the 21–35 age group was statistically significant (p = 0.001). In addition, the difference between the 21–35 age group and the 35–50 age group in the EE (economic environment) variable was also significant (p = 0.034).

4.3. Result Integration

Contrastive Analysis

When conducting research on the second-hand clothing market, qualitative and quantitative methods provide different but complementary perspectives. By comparing the themes and data trends found in both approaches, a fuller understanding of customer needs and market dynamics can be gained.

Our qualitative research highlights key areas of customer demand in the second-hand apparel market, including pricing strategies, product quality, brand image, customer service, market environment and promotion strategies. Specifically, customers focus on price, product quality, merchandise quality, brand reputation, customer service quality, economic environment and platform promotion. The quantitative research further refines these requirements using Kano model analysis. The results categorize price, customer service, economic environment and promotions as one-dimensional quality factors, meaning their satisfaction directly affects customer satisfaction. In contrast, product quality, merchandise quality, brand reputation and customer service quality are classified as attractive quality factors, as they significantly increase customer satisfaction when met, but do not significantly decrease satisfaction when unmet.

Scatter plot analysis reveals that customer service quality, customer service, economic environment and promotion are particularly sensitive to improving customer satisfaction. This aligns with themes found in the qualitative data, emphasizing the importance of customer service and the economic environment. Price is also identified as a key factor, validating the qualitative findings on customer price sensitivity. While brand reputation and product quality are classified as attractive quality factors in the quantitative analysis, they still play an important role in enhancing customer satisfaction, consistent with the qualitative emphasis on these aspects.

The consistency between qualitative and quantitative research indicates that improving customer service and service quality should be a top priority. Ensuring an effective economic environment and promotional strategies, along with maintaining reasonable price levels, is equally important. Although brand reputation and product quality rank lower in priority, they remain critical for long-term market competitiveness. These insights provide clear guidance for marketing strategies and design improvements. Moreover, customer satisfaction significantly impacts sustainable recycling volume in the second-hand clothing market. This integrated analysis underscores the importance of a balanced approach, addressing immediate customer service needs while also fostering a strong brand reputation and high product quality for sustainable success in the second-hand clothing market.

5. Discussion and Implications

5.1. Discussion

In the global context of sustainable consumption and the circular economy, the sustainable second-hand clothing market is becoming increasingly important. There are many risk factors threatening global economic growth, such as debt crises and financial market fluctuations. As the debt scale of many countries continues to expand, debt risk increases and geopolitical tensions intensify, a decline in the consumption capacity of most people can be observed. This trend can help explain the growing demand for the second-hand market economy. The sustainable second-hand clothing industry not only provides an environmentally friendly approach to consumption but also provides consumers with an affordable and sustainable choice by promoting reuse. Further research shows that the sustainable second-hand clothing market is not only a collection of commodity transactions, but also a strategic area where the economy, environmental awareness and consumer behavior intersect. The purpose of this discussion is to explore the factors that affect customer satisfaction in the sustainable second-hand clothing market and the role of these factors in improving consumer experience and promoting market development. From the qualitative and quantitative analysis conducted in this work, price and customer service are shown to play an important role in customer satisfaction. In addition, analysis of the quantitative research results through the Kano model verifies the core position of price and customer service as one-dimensional quality factors, indicating that their importance in customer satisfaction cannot be ignored.

The economic environment and promotion of the platform are classified in the quantitative analysis as one-dimensional quality factors that have a direct impact on customer satisfaction, reflecting their significant role in customer purchase decisions. In agreement with Friedline et al.’s (2020) research on promotion strategies in marketing, the present study found that effective promotion and the stability of the economic environment are crucial to customers’ purchase intentions and satisfaction [84]. In particular, in the sustainable second-hand clothing market, consumers’ perception of the effectiveness of the promotion of the platform and the economic environment significantly affects their purchase intentions and satisfaction. The development of the second-hand clothing market has had a positive impact on the economy in many ways, including creating jobs, stimulating the development of related industries, changing consumption patterns, promoting economic growth and social welfare [31]. With the enhancement of environmental awareness and the advancement of technology, the second-hand clothing market will continue to maintain rapid growth and become an important force in economic and social development [27,59]. Advertising directly influences consumers’ perceptions and attitudes by conveying product information and showcasing product benefits and features [24,43]. Consumers are highly reliant on the information provided by advertising in their purchasing decision-making process [86]. For example, product features, scarcity and promotional messages in advertisements can significantly influence a consumer’s purchase intention. In addition, through emotional appeal and storytelling, advertising can elicit emotional resonance among consumers, thereby enhancing their trust and loyalty to the brand. Social media advertising significantly influences consumer buying behavior by enhancing brand awareness, providing personalized recommendations and driving user engagement [4,38].

When evaluating the factors affecting customer satisfaction and their priorities, two methods can be combined for a more comprehensive evaluation. In this combined approach, qualitative interviews should be used first to gain an in-depth understanding of customers’ specific views and emotional responses to key factors such as brand reputation and product quality. Targeted questionnaires and models can be designed to verify and quantify the importance of these factors through quantitative analysis. For example, in qualitative interviews, if it is found that customers are very satisfied with the after-sales service of a certain brand and believe that this is an important part of the brand reputation, then questions specifically about after-sales service satisfaction can be set up in the quantitative questionnaire, allowing its impact on overall customer satisfaction to be analyzed. The mixed research method employed in this research combines the advantages of qualitative and quantitative research to more comprehensively evaluate the factors affecting customer satisfaction and their priorities. For example, when studying customer satisfaction with a new product, researchers can first collect preliminary feedback and suggestions from customers through qualitative interviews, then design questionnaires based on this information, collect a large amount of data through these quantitative surveys and finally combine the obtained qualitative and quantitative results for comprehensive analysis, allowing more accurate conclusions to be drawn. When analyzing the factors affecting customer satisfaction and their priorities, particular attention must be paid to the differences between different customer groups. Qualitative interviews can help identify the specific needs and preferences of different consumer groups, while quantitative analysis can verify the universality and significance of these differences. For example, young customers may pay more attention to the appearance, design and innovation of the product, while middle-aged and elderly customers may pay more attention to the quality and after-sales service of the product. Enterprises can formulate targeted marketing strategies based on these differences. It is necessary to continuously track customer satisfaction and make dynamic adjustments as needed. The factors affecting customer satisfaction and their priorities may change with changes in the market environment, customer needs and corporate strategies. Therefore, enterprises need to continue to conduct qualitative and quantitative surveys and research, timely and continuously track customer satisfaction and dynamically adjust the direction and priorities of improving their products and services.

In the interviews, brand reputation and product quality were considered important factors in building long-term competitive advantages, which is consistent with Sharma’s (2023) emphasis on the long-term impact of brand image on consumer loyalty [87]. However, the quantitative results show that these factors have a relatively low priority and are classified as attractive quality factors; that is, they are additional factors to customer satisfaction, but their absence does not significantly reduce customer satisfaction. The reason for this difference may be due to different research backgrounds—Sharma’s research mainly focused on the brand-led market, while this study focused on the sustainable second-hand clothing market, which pays more attention to price sensitivity and whose consumers are more concerned about immediate price and product quality. This study found that Pricing strategy, Product quality, Market environment and Promotion are the most important factors affecting customer satisfaction. However, compared with these factors, Brand image has a lesser short-term impact on customer satisfaction and its impact is more reflected in the construction of long-term customer loyalty and market competitiveness. The relationship between customer satisfaction and second-hand market sales (Relationship) has the highest Better coefficient, which shows that, when customers perceive the relationship between satisfaction and sales, satisfaction is significantly improved, but its absence will not significantly affect satisfaction.

5.2. Implications

5.2.1. Theoretical Implications

The results of this research contribute to a deeper understanding of the key factors affecting customer satisfaction in the sustainable second-hand clothing market, especially in the context of sustainable consumption. Therefore, based on grounded theory and the Kano model, this research integrates qualitative and quantitative analysis to gain a deeper understanding of customer satisfaction and its influencing factors in the second-hand clothing market. The qualitative analysis reveals some important findings that can serve as a theoretical basis for future research. It is important to acknowledge that while the Kano model provided valuable insights into the second-hand clothing sector in this study, its applicability to other industries or markets may require further consideration. The second-hand clothing industry has unique characteristics, such as the unpredictability of consumer preferences, the role of sustainability concerns and the emotional aspect of purchasing second-hand goods, which may not be present or as prominent in other markets. Therefore, while the model’s framework offers a useful approach for understanding customer satisfaction in this context, its generalization to different sectors may be limited.

Grounded theory is the basis of the qualitative research in this study, which helps to identify and analyze the multidimensional factors affecting customer satisfaction. In the process of semi-structured interviews, through in-depth analysis of the opinions of the respondents, this research confirms the importance of factors such as price, customer service, brand reputation and platform promotion in customer satisfaction. This approach not only reveals the shortcomings in existing theories, but also provides a new perspective and research framework for future research on customer satisfaction.

The quantitative analysis further classifies and verifies the influencing factors of customer satisfaction through the Kano model. The results of this research show that price, customer service, economic environment and platform promotion are the main factors affecting customer satisfaction in the sustainable second-hand clothing market. In addition, the importance of effective promotion strategies mentioned in customer purchase decisions was also verified in this research, indicating that promotional activities play an important role in enhancing customer perception and satisfaction.

However, certain differences in the importance of brand reputation and product quality were observed between the quantitative and qualitative analyses. In the qualitative interviews, respondents generally indicated that brand reputation and product quality play an important role in customer satisfaction and long-term competitiveness, which is consistent. However, in quantitative analysis, these factors have a lower priority and are classified as attractive quality factors; that is, their presence can significantly improve customer satisfaction, but their absence will not lead to extreme customer dissatisfaction. This difference may be due to the price-sensitive nature of the second-hand clothing market, as opposed to the traditional brand-dominated market. In the second-hand clothing market, consumers pay more attention to immediate price and service experience rather than the long-term impact of brand image and product quality.

By combining qualitative and quantitative analysis, this research sheds light on the multidimensional influencing factors of customer satisfaction and confirms the relative priority of each factor, providing a new theoretical basis for how companies can optimize customer experience in the short term and enhance brand image in the long term. For example, by improving product quality, optimizing service experience, innovating products and services and actively fulfilling social responsibilities, companies can gradually improve their brand image, enhance brand awareness, reputation and loyalty, and thus gain a long-term and effective advantage in the fiercely competitive market. These theoretical contributions provide a scientific basis for enterprises to formulate more targeted customer satisfaction strategies in different market environments.

5.2.2. Managerial Implications

The findings of this study have several managerial implications. First, it needs to be emphasized that in the second-hand clothing market, price and customer service play a more direct and significant role in affecting customer satisfaction than long-term purchase drivers such as brand reputation and product quality. This research shows that reasonable prices and efficient customer service are crucial to improving customer satisfaction, which ultimately leads to increased sales and customer loyalty. In this research, the Kano model was used to conduct a detailed classification of the driving factors of customer satisfaction, verifying the important status of price and customer service as one-dimensional quality factors. These factors directly affect customers’ purchasing decisions, and companies should prioritize resources in these areas to ensure an improved customer experience in the short term.

In addition, platform promotion and the economic environment have also been shown to have a significant impact on customer satisfaction. Promotional activities also play an important role in increasing customer perception and satisfaction with products, so businesses should focus on effective promotional activities when developing market strategies, such as well-targeted advertising on social media platforms or personalized promotional offers through online shopping. Not only will this attract more potential customers, but it will also significantly enhance customers’ recognition and trust in the platform. At the same time, maintaining a good economic environment is also crucial for customers’ purchasing confidence, which requires the platform to provide a stable and transparent trading environment when prices fluctuate or markets change to enhance customer trust.

In terms of brand reputation and product quality, although their impact on customer satisfaction is relatively small, they are still important in building long-term customer loyalty and brand image. Therefore, companies should pay more attention to the optimization of price and service experience in the short term, but in the long-term strategy, they still need to continuously improve their brand image and product quality to ensure continued market competitiveness. For example, a long-term relationship with an emotional connection with customers can be built through brand storytelling or product quality improvements, thereby increasing customer loyalty while reducing price sensitivity.

All in all, enterprises should adopt a dual-track management strategy, that is, improve customer satisfaction in the short term by optimizing price and customer service, and at the same time commit to improving brand reputation and product quality in the long term to achieve comprehensive customer satisfaction and improve market competitiveness. This research shows that combining short-term benefits with long-term strategies requires direct investment in customer service and price on the one hand and long-term customer loyalty and market share through brand building and product optimization on the other.

5.3. Limitations and Future Studies

This research has some limitations that need to be considered when interpreting the findings. First, the data for this research were derived from second-hand clothing platforms in China (such as Xianyu and Zhier), so there may be limitations in terms of product types and market environments. Future research can consider expanding the data collection to more platforms to verify the impact of these factors on customer satisfaction in different product types and market environments. In addition, the indicators for quantitative analysis are mainly based on the opinions of experts in the sustainable second-hand clothing market in China, which ensures that the analysis results are highly practical and targeted. However, the opinions of these experts reflect the specific experience of the Chinese market and may not fully cover the diverse needs of consumers in other product categories. Therefore, if this research is replicated in other sustainable products in the future, it is recommended to introduce opinions from other sustainable product experts and market experts to ensure the wide applicability of the analysis results in different cultural and market environments. Finally, future research could explore how the Kano model might need to be adapted or complemented with other tools to account for the specific factors influencing consumer behavior in various industries. This would ensure a more robust and comprehensive understanding of customer needs and expectations across diverse markets. As a classic tool for analyzing customer needs, the Kano model helps companies understand how different product or service attributes affect customer satisfaction. However, as market competition intensifies and customer behavior evolves, its limitations are becoming increasingly apparent. The model has difficulty explaining the dynamic nature of customer expectations, which continue to change as industries and consumer trends evolve. In addition, the Kano model itself cannot cover all dimensions of customer satisfaction. To overcome limitations, future research should integrate multiple analytical methods. Empirical data-driven models can enhance adaptability by constantly adjusting analysis based on real-time feedback. Machine learning algorithms can identify hidden patterns and trends in customer behavior, providing deeper insights into changing preferences. Advanced statistical methods can further improve prediction accuracy, leading to a more accurate understanding of customer needs. By combining these techniques, the Kano model can be adapted to different industries, providing a more comprehensive and dynamic framework for analyzing customer satisfaction.

6. Conclusions

In order to better understand the factors that affect customer satisfaction in China’s sustainable second-hand clothing market, this research explored which factors drive or inhibit consumer satisfaction in this sector. To this end, this research combined grounded theory and the Kano model to conduct qualitative and quantitative analysis. Typical consumers (n = 191) participated in the survey to evaluate the impact of factors such as price, customer service, brand reputation and platform promotion on satisfaction. The results showed that price and customer service were the main drivers of customer satisfaction, while brand reputation and product quality had an additional impact on satisfaction. It is hoped that this research will serve as a reference for relevant practitioners to formulate effective market strategies, not only in the second-hand clothing industry, but also in other sustainable consumption fields, as well as contribute to promoting the large-scale adoption of sustainable consumption methods.