Exploring Sustainable Fashion Consumption Behavior in the Post-Pandemic Era: Changes in the Antecedents of Second-Hand Clothing-Sharing in China

Abstract

Second-hand consumption of clothing plays a vital role in promoting the overall global trend of low-carbon transition; however, the COVID-19 outbreak put this consumption model into a development dilemma. Cultivating consumers’ sustainable behavior will be an effective way to promote the sustainable development of the apparel industry. Based on the unified theory of acceptance and use of technology (UTAUT), this study starts with fashion-sharing behavior and investigates the antecedents that influence consumers’ use of second-hand clothing-sharing platforms in the post-pandemic era. The research background involves the Chinese clothing-sharing market in the growing period. The findings revealed that the pandemic raised people’s awareness of health and hygiene protection. In addition, the cleaning problem of platform clothing has become the primary reason for curbing consumers’ choice of sharing. High-cost performance, high efficiency, and convenience can stimulate consumers to use shared services. Considering that the pandemic has driven consumer economic fluctuations, perceived economic risks could widen the gap between willingness and behavior. In conclusion, platforms must fully realize the transparency of the clothing cleaning and maintenance process, improve their own construction level such as ease of use, convenience, and safety, and incorporate functional clothing-sharing to refine people’s sustainable consumption habits.

1. Introduction

Shared consumption, also known as cooperative consumption, means that consumers obtain the right to use and own goods through renting, exchange, gift, and other ways, which can attain efficient use of resources and have a strong economic mutual benefit [1,2]. Various industries have initiated the sharing model with their own differences. The sharing model of the clothing fashion industry is primarily a “shared wardrobe” based on second-hand clothing.

Owing to the open consumption concept of European people and the pursuit of diversified life, since 2009, several clothing-sharing platforms for different groups of people and covering different categories have appeared in the European market. Most businesses are mostly based on the provision of clothing rental services (CRS), supplemented by the resale of second-hand clothing. Compared with the mature management system in Europe, owing to cultural differences and different life concepts, the overall second-hand clothing-sharing market in Asian countries remains in the exploratory stage. For example, in China, influenced by the traditional social hierarchy thinking, Chinese people focus more on identity and status, and their views on second-hand items could be more rigid [3,4]. For instance, in China, people are generally conservative and care more about privacy, and they might exhibit a certain degree of resistance to items from strangers. But now, the practice of sustainable development has become a global consensus. Due to its ability to tap excess capacity and reduce resource costs, the sharing economy in the modern sustainable process has become increasingly prominent. At the same time, under the guidance of the government and the state, the awareness of sustainable consumption among social groups has been further enhanced [5]. Consumers with stereotyped views on the sharing and trading of second-hand products may gradually change their attitudes. The 2021 “China Sustainable Consumption Report” showed that second-hand idle trading platforms will become a holy place for most consumers to buy good goods [6]. China has a particularly large population and is the world’s largest clothing consumer. As an emerging form of consumption, clothing-sharing may be an effective solution to reduce its textile waste and environmental pollution caused by clothing production [7].

For a growing consumer platform, combining current events to precisely detect the factors affecting its own development will help it get closer to consumers [8]. The global pandemic of COVID-19 has severely hit the sharing economy system [9]. According to the WHO [10], COVID-19 primarily spreads through respiratory droplets and close contact with the source of infection. Thus, most people believe that the use of shared objects amplifies their risk of being infected by the virus, especially in close-fitting clothing categories. Even if the virus can only survive on the fabric for a short time, sharing frequency should be minimized [11]. Accordingly, the cleanliness and hygiene of second-hand clothing might become an aspect that a platform will focus on in the post-pandemic era. Affected by the continuation of the pandemic, consumers will choose products from a more comprehensive perspective [12]. Thus, platforms need to assess the current consumer considerations and determine the antecedents that affect consumption so that when the pandemic eases, and the sharing economy picks up, they can grasp the appropriate development trend and precisely fit the sustainable development path.

Thus, this study demonstrates how a growth clothing-sharing platform determines the entry point of development in the post-pandemic era. Measuring the weight of factors and determining the key influencing variables are the focus of this research. Taking the Chinese clothing-sharing market as a representative of growth, the basic research framework adopts the unified theory of acceptance and use of technology (UTAUT) [13]. From the standpoint of the individual, society, cleanliness, health, the economy, and other dimensions, theoretical expansion is performed to investigate the overall growth variables of China’s fashion-sharing economy before and after the pandemic. At this stage, few studies have investigated consumers’ clothing-sharing tendency in the Chinese market; hence, this study will delve into its current situation and make up for the lack of this aspect. Meanwhile, it can also provide some development inspiration for other countries in Asia that are still in the initial stage of the “shared wardrobe” application model. For European and even global clothing brands, it is expected that the sustainable development strategy of a sharing market and platforms will be of guiding significance.

The remainder of this paper is structured as follows: Section 2 summarizes the related work on clothing-sharing; Section 3 presents the corresponding assumptions and evaluation models; Section 4 provides an overview of sample characteristics, measurements, and a structural model evaluation using a two-step analysis method of the partial least squares structural equation modeling algorithm (PLS-SEM); Section 5 analyzes and discusses the results; Section 6 presents the theoretical and practical implications of the research. Finally, the full text is summarized by providing limitations and future research directions.

2. Literature Review, Theoretical Model, and Hypotheses Development

2.1. “Shared Wardrobe” Model under Sustainable Development

In the production process of clothing, a large amount of waste liquid, waste gas, and solid waste are often discharged, including serious environmental pollution problems [14]. Unsustainable consumption behaviors, such as excessive or one-off clothing consumption, exacerbate global environmental pollution. China is the largest textile manufacturing country globally, and its textile and garment production waste has exceeded 100 million tons [15]. Moreover, about 26 million tons of used clothes are discarded every year in China, which is expected to reach around 50 million tons by 2030; however, the recycling rate is <1% [16].

To alleviate the adverse impact of the clothing industry in all aspects, it is imperative for clothing manufacturers and distributors to effectively upgrade their business models to attain sustainable development of fashion and ecology [17]. As one of the measures to match this viewpoint, the “shared wardrobe” model endows clothing with the value of multiple reuse in the form of second-hand clothing resale and leasing, decreasing the “dormancy rate” of clothing [18]. It not only fulfills the wearing needs of consumers within a specific period but also decreases their excessive clothing consumption rate and waste rate, which helps to promote the circular development of the clothing economy [19].

China started a fashion-sharing boom in 2015. In recent years, many online clothing rental platforms have emerged, such as “MSPARIS”, “Le Tote”, “YCLOSET”, and so on. However, because of the short-term nature of the trend and the peculiarity of the Chinese market, the adaptation speed of shared clothing is slightly slow. Most sharing platforms in China are short-lived or operate poorly. Among them, the largest sharing platform, “YCLOSET”, completely shut down its services on 15 August 2021. The impact of the pandemic is one aspect of the failure, and other reasons can be roughly attributed to the neglect of consumers’ real clothing needs, small user scale, and inappropriate establishment of a membership system. If growing fashion-sharing platforms do not have a precise perception of consumer attitude changes and market demand positioning, then the long-term popularity of the clothing-sharing economy in China will become a false proposition. “YCLOSET” is a lesson from the past.

2.2. Summary of the Antecedents of Second-Hand Clothing Consumption before the Pandemic

Within the scope of traditional perception, second-hand consumption is more common among low-economic consumer groups [20]. However, with the surge of the new generation of consumer forces, the definition of poverty in second-hand consumption has been rewritten [21]. The second-hand clothing market is gaining momentum because of the mounting popularity of the collaborative economy and sustainable fashion development models, and it has also garnered widespread academic attention. Considering the relationship between second-hand clothing consumption and consumers’ personal factors, social environment, and ethics, a lot of studies have explored this aspect.

In Sweden, to draw attention to the residual value and environmental status of clothing, some clothing stores have set up a recycling system, followed by professionally reorganizing and reselling the collected clothing [22]. Thus, some studies claimed that people’s willingness to consume second-hand clothing could be driven by environmental protection awareness. Yan et al. [23] surveyed students who consumed second-hand clothing and found that they generally believed that shopping for vintage is a better way to express their own personality and fashion. In addition, high-frequency research perspectives included economic interests and hedonism. When consumers think that the clothing product has high practical value and a reasonable price, they are willing to share it. The innovation and convenience involved in product search can attain the purpose of pleasing consumption [24]. Among the hindering factors of second-hand consumption, Fisher et al. [25] claimed that the psychological factors of consumers occupy a sufficiently large position of influence, such as rejection because of the unclear source of clothing. As mentioned earlier, many Chinese people have expressed their willingness to acquire second-hand clothing from acquaintances out of trust; however, if the clothing comes from an unfamiliar group, they might hesitate and worry about the quality and contamination of the clothing, which eventually leads to rejection. Nevertheless, many sharing platforms have stated that they have professional cleaning and disinfection processes to ensure the hygiene and safety of clothing, thereby decreasing consumers’ concerns about this issue [26].

This study divides the significant factors included in the previous studies into two parts: personal factors and external factors (

Table 1. A sub-summary of the influencing factors of used clothing-sharing behavior in previous studies.

Paper	Personal Factors				External Factors
	Hedonism	Fashion Sense	Environmental Awareness	Hygiene Protection Awareness	Expense	Convenient Condition	Bad Quality	Cleaning Issue
Park and Armstrong [18]				This factor has not been studied in these surveys on the clothing rental behavior.	✓	✓	✓ *
Becker-Leifhold [28]	✓	✓
Lang and Joyner Armstrong [29]		✓			✓
Tu and Hu [30]	✓					✓
Yuan and Shen [31]		✓			✓
Lang et al. [32]	✓				✓	✓	✓ *
Lee and Chow [33]		✓	✓		✓	✓
Kim [34]		✓
Shrivastava et al. [19]	✓	✓				✓
Hur [35]	✓	✓	✓		✓		✓ *	✓ *
Morais et al. [36]		✓	✓
Park and Lin [37]			✓		✓
Lee et al. [38]	✓				✓	✓
Zhang and Dong [39]		✓	✓

Note: “✓” represents a significant influencing factor derived from previous research. The additional “*” represents that this factor exerts a negative regulatory impact on the purchase and renting of second-hand clothing.

). Before the pandemic and the beginning of the pandemic, people’s weak awareness of health protection was not included in the scope of research. Similarly, in terms of external factors, although product cleanliness has been established to exert a negative regulating impact on clothing-sharing behavior, the frequency of consideration is low, which also reflects that people’s hygiene consciousness is not usually believed to hinder the generation of sharing intentions. However, in Rekhter and Ermasova’s [27] survey on people’s health perception after the pandemic, 64% of 315 respondents stated that their health pressure has increased, and they will be cautious owing to the likelihood of contamination. Hence, we believe that the current framework of factors affecting consumer fashion-sharing will change, and health awareness and cleanliness will become two critical parts of the two sectors. Furthermore, we conjecture that the influence indicators of other factors will be changed by the interference of hotspots of the times. To validate the conjectures mentioned above, we examined the Chinese market and explored the changes in the antecedents of sharing behavior in the post-pandemic era.

2.3. Theoretical Background

The selection and birth of emerging technology is performed per understanding the users’ needs, but how to find the exact direction of improvement of this technology still needs to be user-centric. Accordingly, many studies have proposed theories and models to explore user technology acceptance, such as the theory of planned behavior (TPB) [40], theory of reasoned action (TRA) [41], and technology acceptance model (TAM) [42]. UTAUT is integrated and evolved from the previous theoretical models, and it covers four core variables: performance expectancy, effort expectancy, social influence, and facilitating conditions (Figure 1). The model can better reflect the influence of an individual’s unique knowledge, experience, and autonomy on behavior from a comprehensive perspective. Compared with previous theoretical models such as TPB, TRA, TAM, etc., its explanatory power for individual intention has been proven to reach 70%, which makes it regarded as a relatively superior predictive analysis tool [13]. UTAUT provides effective help for promoters to understand the reasons for people’s acceptance of new things and new technologies so that they can rationally design interventions [43].

Based on the open structure of UTAUT, many studies expanded the model to enrich the research system. By reasonably introducing new elements related to the research object in the original model, the behavioral laws of individuals can be better grasped [44]. Curtale et al. [8] explained consumers’ willingness to accept car-sharing by introducing the psychological dimension into the UTAUT model, accurately obtaining the degree of influence of psychological awareness. Researching consumers’ online shopping behavior, Çelik [45] claimed that the ability of the model ontology to explain behavioral intentions has reached the limit; thus, he integrated a new element component, an emotional component, into the original framework, and he drew the conclusion that anxiety negatively affects online shopping behavior. Similarly, Jeon et al. [46] took consumers’ individual innovation and perceived risk level as an additional branch of the UTAUT model so that consumers’ willingness to accept information service technology could be investigated more comprehensively. Based on the above studies, it is concluded that a reasonable extension of UTAUT is feasible and meaningful for a comprehensive understanding of consumers’ behavioral motivations. Inspired by this, our study compounded appropriate new variables on the basis of the original model. The antecedents that influence second-hand clothing-sharing behavior will be explored from multiple dimensions. Figure 2 provides an overview of the research ideas of this paper.

2.4. Research Framework and Hypothesis

To investigate the overall changes in the influencing factors of consumers’ second-hand clothing-sharing behavior in the post-pandemic era, we selected the UTAUT model with better comprehensive performance and superior explanatory ability as the basis for the construction of a research theory. Facilitating conditions were expanded according to a sub-aggregation of factors from Section 2.2. We added the external factors section, product cost performance, and cleaning issues, and expanded personal affective factors among the antecedent variables affecting intention generation—health awareness, hedonism, and environmental awareness.

As personal fashion style and orientation are aesthetic tastes formed after long-term exploration and precipitation, they are not easily affected by social events [47], so we did not include fashion perception in the research scope of influencing factors. In reality, some intentions might not be transformed into actual behaviors after being disturbed by certain factors. Considering this intention-behavior gap [48], we added an additional dimension of perceived risk—perceived financial risk and perceived information risk—as moderators between willingness and behavior.

In addition, we chose the Chinese clothing-sharing market as the research object to identify the development issues that this type of growth platform needs to pay attention to at present, so as to draw practical improvement strategies. Compared with the research on platforms with mature systems, this is more meaningful and valuable. As the main business of platforms (second-hand clothing renting and resale) is highly similar to the affected factors, and it has been verified that the weight of rental services on a platform is usually higher than that of resale business, this study finally researched around CRS.

2.4.1. Basic Core Variables: Performance Expectancy, Effort Expectancy, and Social Influence

In previous academic studies, positive performance expectations can improve people’s willingness to accept and use an information application or technical service [49]. In this study, the original intention was extended to the users’ expectation that CRS would bring benefits and value to them. In the “P2P” sharing platform Tulerie, clothing grades range from affordable to luxury. Users can choose fashion items for different occasions and functional needs to obtain an extremely rich product value experience. Meanwhile, as the product is a direct connection between members, friendship could be formed after repeated reciprocation, helping to broaden their social circles and extend the interest chain. Notably, the benefits obtained by consumers after choosing CRS should not be underestimated.

Effort expectancy comprises perceived usefulness and perceived ease of use, the latter of which is considered to be a critical determinant of individual satisfaction with technology [50]. People’s physical interactions have been reduced due to the pandemic, which has made consumers prefer online channels when shopping [51]. Su et al. [52] assessed users’ perceptions of the e-commerce interface and demonstrated that users are more accustomed to attaining their goals through simple and easy-to-understand operations. To effectively decrease the cumbersome steps that consumers have to take when purchasing products, more and more e-commerce operation systems are constantly improving toward convenience and diversification [53]. Hence, for online clothing rental platforms, having a minimalist clothing rental system could be a key point to increase users’ acceptance of CRS. Besides, realizing users’ quick reservation, receipt, and return of clothing products around perceived ease of use is an essential means to fulfill user needs.

Social influence denotes that when an individual communicates information and emotionally interacts with the surrounding people, his/her thoughts, attitudes, and behaviors are influenced by others and, thus, change [54]. Luo [55] reported that the ideas and practices of peers and family members would affect personal consumption desire to some extent; this is because they play a vital role in consumers’ lives, and consumers consider their opinions to be acceptable. For example, when trying on clothes, consumers might have the impulse to spend because of their approval. For online consumption, consumers not only listen to the opinions of acquaintances but also consider the overall online reputation of the product to measure the consumption value [56].

Based on the above, the following hypotheses are proposed:

Hypothesis 1 (H1).

Performance expectancy positively correlates with behavioral intention.

Hypothesis 2 (H2).

Effort expectancy positively correlates with behavioral intention.

Hypothesis 3 (H3).

Social influence is positively correlates with behavioral intention.

2.4.2. External Factor: Product Cost Performance

The COVID-19 pandemic has disrupted the global economy. Per a survey of 500 Chinese consumers by Cotton Incorporated [57], the overall clothing expenditure ratio declined by 69% after the pandemic, but 96% of them stated that the comfort of clothing is still the top priority when purchasing. A reduction in spending does not mean a decline in the pursuit of quality. From a long-term perspective, the ultimate cost-effectiveness will become the long-term pursuit of consumers in the post-pandemic era. Hence, we hypothesize the following:

Hypothesis 4 (H4).

Product cost performance exerts a positive impact on behavioral intention.

2.4.3. Personal Factors: Health and Hygiene Awareness, Hedonism, Environmental Awareness

In the context of the pandemic, people’s views on health, consumption, and the environment have all changed. In terms of personal health protection, COVID-19 has brought an unprecedented psychological burden to people. According to the actual situation, a virus-infected person will have multiple close contacts, which will cause a chain reaction [58]. As a densely populated country, China attaches great importance to this type of vicious virus. After this high-scale and persistent virus attack, people have profoundly realized the necessity of preventive medicine, public health, and the significance of health management. In this state, consumers will become increasingly cautious about their access to public goods. Yang and Lee pointed out that perceived physical risks will affect consumer trust and adoption of shared services [59]. For direct-to-skin services like clothing-sharing, consumers will inevitably deepen their fear and resistance.

From the perspective of consumers’ needs, they will desire to obtain happiness from the service and then create a sense of accomplishment, which is called hedonism [60]. The hedonism in this study denotes the interesting dressing experience that consumers enjoy by renting clothes. In the fashion subscription service, most consumers unpack the subscription package with excitement and enjoy it when trying on new clothes. This hedonic atmosphere plays a positive role in the later subscription intention [61]. In addition, driven by the pandemic, “pleasant” consumption is on the rise, and “living in the moment and having fun in time” has become the main life attitude of consumers. Hence, we believe that under the pandemic, hedonism is still an essential factor affecting the willingness to rent clothing.

Focusing on the environmental level, studies have demonstrated that a poor ecological environment will aggravate the threat of COVID-19 to people’s health. For example, in areas with severe air pollution, the transmission rate of COVID-19 will increase, resulting in increased human mortality [62]. Affected by the pandemic, people have to re-evaluate and deeply reflect on the mode of living with nature, and environmental protection will attract higher attention. Under this opportunity, people will be more willing to adopt a green lifestyle, such as taking a more positive attitude toward clothing renting, which is environment friendly.

These subjective consciousnesses of consumers inseparably correlate with the consumption content they pay attention to, and they are considered to play a crucial role in influencing consumption activities [63]. Hence, the following hypotheses are proposed:

Hypothesis 5 (H5).

Health and hygiene awareness negatively affect behavioral intention.

Hypothesis 6 (H6).

Hedonism positively affects behavioral intention.

Hypothesis 7 (H7).

Environmental awareness positively affects behavioral intention.

2.4.4. External Factor: Cleaning Issue

As the identity of the original user of the second-hand product is unknown, some consumers believe that the product has been contaminated, and the invisible dirty feeling makes them feel bad [64]. Thus, it is essential for second-hand commodity operators to have professional cleaning and disinfection measures. Clube and Tennant [65] analyzed 500 consumer reviews of two clothing rental companies and revealed that the hygiene and safety of clothing is a key concern of consumers. If the cleaning of the platform is not in place, causing consumers to learn that the rented clothing has serious contamination issues, then the rental business will also be negatively affected. Hence, we hypothesize the following:

Hypothesis 8 (H8).

Cleaning issue significantly negatively affects actual behavior.

2.4.5. Intention-Behavior Gap: Perceived Risk

As the conversion of intentions into behaviors encounters some hindering factors, consumers might not be able to act according to their initial intentions [48]. To narrow the gap between the intention and behavior of second-hand clothing-sharing, we combined actual cases and included two moderating factors, perceived financial risk and perceived information risk, for research.

Owing to the collaborative nature of the sharing economy, mutual trust between merchants and users serves as the basis for promoting sharing activities [66]. However, in the current sharing economy, an increasing number of incidents related to trust risks have been reported (such as users suffering financial losses, users’ private lives being disturbed). Regarding the financial risks in shared electronic products, the China Consumers Association stated that unreasonable billing acts as a trigger for consumers to complain. Given the same shared nature, if consumers perceive noticeable financial disadvantages in the clothing-leasing process, dissatisfaction and complaints about the business will also increase.

Similarly, violation of consumer privacy has not been unheard of in some of Airbnb’s rental disputes. For example, when a consumer leaves negative feedback on a product, a lessor can contact the consumer through his/her private account, which is disclosed by the platform to persuade him/her to modify the comment, thereby disrupting the consumer’s life. Hence, the platform’s poor protection of consumer information is also a major failure that results in decreasing consumers’ trust.

Finally, we propose the following hypotheses regarding the perceived risk in the intention-behavior gap and the magnitude of the effect of intention on behavior:

Hypothesis 9 (H9).

Perceived financial risk exerts a negative impact on the relationship.

Hypothesis 10 (H10).

Perceived information risk exerts a negative impact on the relationship.

Hypothesis 11 (H11).

Behavioral intention positively correlates with actual behavior.

Considering the assumptions mentioned above, Figure 3 shows the final proposed theoretical model framework.

3. Methods

The content design of this questionnaire mainly comprised two parts. The first part was the interviewees’ personal information, and the second part used a 5-level Likert scale (1–5: strongly disagree to strongly agree) to index each conceptual variable.

In the scale, the measurement items of the three original dimensions (performance expectancy, effort expectancy, and social influence) were adapted from previous studies by Venkatesh et al. [13] and Shrivastava et al. [19] applying UTAUT. The items for production cost performance were inspired by the research of Rothenberg and Matthews [67]. We set up measurement items for the cleanliness dimension (personal health and hygiene awareness and platform cleanliness) based on the opinions of Lee et al. [68] and De Liberato et al. [69] on the hygiene status of fashion-sharing platforms and second-hand clothing. A measure of hedonism was formed by adapting the theory of Lo et al. [24]. According to research by Yan et al. [23] and Borusiak et al. [70] on consumers’ perception of environmental issues, we have adapted the content of environmental awareness. Drawing on the experience of Rausch and Kopplin [71] and James et al. [72], the items of perceived financial risk and perceived information risk were integrated. The pre-test of the questionnaire was used to check the possible semantic and logical problems of each item. Appendix A 

Table A1. Measurement items.

Construct	Item	Description
Performance expectancy	PE1	Renting second-hand clothing is what I need for certain occasions.
	PE2	I can try more different styles and styles by renting second-hand clothing.
	PE3	The sharing of second-hand clothing may broaden my social circle.
Effort expectancy	EE1	Online clothing rental is easy and the rental process is simple.
	EE2	The interface of online clothing rental platforms is clear and concise, and the operation is simple, allowing me to quickly select the clothing I want.
	EE3	Online clothing rental is convenient to order, and delivery is fast.
Social influence	SI1	Positive consumer reviews online motivate me to try CRS.
	SI2	People who are important to me (family, friends) think I can use CRS.
	SI3	Someone who can give me an important opinion would prefer me to choose clothing rental.
Product cost performance	PCP1	Platforms strictly control the quality of second-hand clothing.
	PCP2	Second-hand clothing is more cost-effective.
	PCP3	The rental platforms can meet my requirement of garment cost performance.
Health and hygiene awareness	HHA1	I think second-hand clothing that is not cleaned properly can affect health later.
	HHA2	I don’t think platforms do enough cleaning for the second-hand clothing it takes back.
	HHA3	Shared items generally lack good disinfection measures.
Hedonism	HED1	Renting second-hand clothes can give me a different dressing experience and happiness.
	HED2	Renting different styles of second-hand clothing can add color to my life.
	HED3	Using CRS allows me to enjoy more novel fashion elements.
Environmental awareness	EA1	I understand the current level of pollution to the environment by the clothing industry.
	EA2	I am very concerned about the impact of the clothing industry on environmental development.
	EA3	I think renting second-hand clothing can reduce the production and consumption of clothing, thereby reducing environmental pollution.
Cleaning issue	CI1	Platforms don’t have high hygiene and cleanliness standards for second-hand clothing.
	CI2	The hygiene condition of the second-hand clothing is not good.
	CI3	Rental clothing makes me worry about needing a second cleaning.
Perceived financial risk	PFR1	I’m worried that there will be malicious deductions on clothing rental platforms.
	PFR2	I’m worried that I won’t be able to cover the cost of compensation if the rental clothing is damaged.
	PFR3	I am concerned that the product value of the rental clothing is not proportional to the fee paid.
Perceived information risk	PIR1	Platforms may lack a mature integrity system and supervision system.
	PIR2	I am concerned that my personal information will be leaked.
	PIR3	I do not upload personal real information lightly.
Behavioral intention	BI1	I am willing to rent second-hand clothing if needed.
	BI2	I would like to recommend friends to choose CRS.
Actual behavior	AB1	I will choose some clothing rental platforms for clothing rental.
	AB2	I may become a member of the clothing rental community in the future.

shows the final measurement items.

In this survey, we separately carried out the following work. First of all, the target population is identified. Due to the current development of e-commerce, the new generation of young people has become the main force of consumers, so the sample group was expected to be dominated by the younger generation. Secondly, regarding the delivery method of questionnaires, more convenient online channels were considered to distribute questionnaires to obtain sample data. Finally, WeChat, a multi-functional communication program, was selected as our questionnaire distribution and collection tool. At the same time, in order to expand the sample size, we contacted college students and young colleagues in other cities (Beijing, Tianjin, Suzhou, Shanghai) online to assist in distributing the questionnaires, mainly by posting questionnaire links in their social groups and “WeChat Moments”, to get more respondents. A total of 587 questionnaires were collected. To ensure the objectivity and validity of the total data, 54 invalid questionnaires with inconsistent answers, extreme values, and questionnaire filling time <90 s were excluded. Finally, 533 valid questionnaire samples were recovered. From the descriptive statistics of the sample population (

Table 2. Descriptive statistics (n = 533).

Sample Statistics	Specifications	Counts	Percentage
Gender	Female	392	73.5
	Male	133	25.5
	Reluctance to disclose	8	1.5
Age (in years)	<18	2	0.4
	18–28	504	94.6
	29–35	14	2.6
	36–45	10	1.9
	46–60	3	0.6
Education	Undergraduate	246	46.2
	Postgraduate and above	266	49.9
	Higher vocational and other skills education	10	1.9
	Other	11	2.1
Frequency of buying clothes	Weekly	9	1.7
	Monthly	229	43.
	Quarterly	237	44.5
	Half-yearly	39	7.3
	Yearly	19	3.6
Quantity of clothing per purchase	1–3 garments	418	78.4
	4–6 garments	102	19.1
	7–10 garments	11	2.1
	≥10 garments	2	0.4
Clothing rental experience	Yes	129	24.2
	No	339	63.6
	Thought but didn’t try	65	12.2
Types of clothing that have been rented (n = 129)	Business wear	60	46.5
	Costume	47	36.4
	Fast fashion category	9	7.0
	Luxury/Designer category	10	7.8
	Other	3	2.3
Are personal attitudes toward shared goods affected by the pandemic?	Very affected	240	45
	More affected	77	14.4
	Moderately affected	112	21.0
	Less affected	61	11.4
	Unaffected	43	8.1

), 73.5% of all participants were female (n = 392), and most of them were aged 18–28 years (n = 504, 94.6%). The obtained sample data were mostly from young consumers, which matched our previous expectations. The rental channels were not limited to online channels or offline stores. About 25% of them had experience in clothing rental, and the clothing types were mostly concentrated in business wear and costume. Another 12.2% had thought about trying to rent clothes. The remaining 63.6% of the respondents had never rented clothing. Based on the above data, the popularity of clothing sharing was not high. We could not deny that respondents without rental experience do not have any views on the sharing of second-hand clothing. Likewise, those respondents who refused to rent because of their own prejudice against shared goods could not be excluded. It was expected that various comments and insights about clothing-sharing platforms from people with different experiences and attitudes could be collected. In addition, more than half of the participants stated that the pandemic affected their attitudes toward shared goods to a high degree.

4. Results

4.1. Measurement Model Evaluation

We used the two-step analysis method in the PLS-SEM [73], combined with PLS program (Smart-PLS) version 3.0 for analysis and measurement. PLS is an optimized new parameter estimation method. It can fully mine data information and obtain the best matching data by measuring the minimum sum of squares of errors. This approach does not require a high sample size and overcomes the potential limitations of the normal distribution [74]. PLS can effectively deal with the problem of collinearity between variables and is suitable for robust prediction of complex causal models [75]. The first step of the analysis was to assess the measurement model, that is, to test the reliability and validity of each measurement variable and indexed measurement items. The indicators involved primarily included Cronbach’s α, composite reliability (CR), average variance extraction (AVE), and cross-loadings. The second step was the evaluation of the structural model, which is mainly based on the R² explanatory ability, variance inflation factor (VIF), path coefficients, confidence interval, t-statistics, p-value, and other evaluation indicators to judge the predictive ability of the model and the establishment of various assumptions.

The PLS algorithm was used to calculate this time. In the basic settings of the algorithm, the path weighting scheme was selected, the maximum number of iterations was 300 times, and the end criterion is 107. To avoid the error of the results, the calculation was repeated multiple times.

Table 3. Evaluation of data reliability and convergence validity.

Latent Variable	Indicators	Mean (SD)	Cronbach’s α (>0.7)	CR (>0.7)	AVE (>0.5)
PE	3	3.380 (0.693)	0.786	0.875	0.701
EE	3	3.729 (0.690)	0.793	0.878	0.707
SI	3	2.824 (0.719)	0.806	0.885	0.720
PCP	3	3.845 (0.718)	0.846	0.907	0.764
HHA	3	3.832 (0.622)	0.784	0.874	0.679
HED	3	3.364 (0.662)	0.861	0.912	0.777
EA	3	3.438 (0.678)	0.827	0.896	0.742
CI	3	3.879 (0.832)	0.875	0.923	0.800
PFR	3	3.682 (0.637)	0.829	0.898	0.746
PIR	3	3.175 (0.645)	0.831	0.899	0.747
BI	2	3.639 (0.818)	0.867	0.938	0.882
AB	2	3.343 (0.697)	0.814	0.915	0.843

Note: PE = performance expectancy, EE = effort expectancy, SI = social influence, PCP = product cost performance, HHA = Health and hygiene awareness, HED = hedonism, EA = environmental awareness, CI = cleaning issue, PFR = perceived financial risk, PIR = perceived information risk, BI = behavioral intention, AB = actual behavior.

shows the final analysis results. The Cronbach’s α of each variable was 0.784–0.867, and the CR and AVE values were higher than the standard thresholds. Thus, the questionnaire passed the internal consistency test and has good introverted validity.

Regarding discriminant validity, three methods are usually used for comprehensive evaluation, which are based on cross-loadings, the Fornell-Larcker criterion, and the heterotrait–monotrait ratio (HTMT). Appendix A 

Table A2. Data discriminant validity assessment method 3-Cross-loadings.

	PE	EE	SI	PCP	HHA	HED	EA	CI	PFR	PIR	BI	AB
PE1	0.854	0.216	0.277	0.178	0.061	0.231	0.236	0.005	−0.163	−0.076	0.384	0.264
PE2	0.849	0.192	0.213	0.188	0.083	0.205	0.219	−0.021	−0.111	−0.012	0.376	0.273
PE3	0.808	0.203	0.224	0.227	0.078	0.191	0.192	−0.050	−0.135	−0.100	0.359	0.312
EE1	0.257	0.814	0.171	0.179	0.071	0.241	0.189	−0.066	−0.166	−0.090	0.248	0.269
EE2	0.210	0.848	0.224	0.165	0.041	0.292	0.235	−0.145	−0.129	−0.122	0.281	0.299
EE3	0.202	0.860	0.216	0.177	0.071	0.204	0.257	−0.091	−0.159	−0.092	0.290	0.288
SI1	0.279	0.204	0.846	0.217	0.097	0.270	0.260	−0.042	−0.155	−0.023	0.179	0.264
SI2	0.227	0.225	0.858	0.204	0.099	0.205	0.143	0.015	−0.087	−0.055	0.188	0.204
SI3	0.219	0.190	0.842	0.179	0.083	0.146	0.178	0.047	−0.104	−0.015	0.170	0.201
PCP1	0.228	0.195	0.206	0.903	0.055	0.217	0.203	−0.106	−0.121	−0.016	0.206	0.316
PCP2	0.215	0.194	0.228	0.885	0.002	0.243	0.209	−0.114	−0.122	−0.070	0.230	0.286
PCP3	0.169	0.145	0.179	0.832	0.111	0.226	0.193	−0.051	−0.123	0.005	0.172	0.270
HHA1	0.058	0.035	0.095	0.039	0.796	0.076	0.074	−0.005	−0.066	−0.061	−0.152	0.077
HHA2	0.043	0.037	0.051	0.007	0.860	−0.013	0.059	0.028	−0.099	0.024	−0.192	0.062
HHA3	0.120	0.109	0.134	0.101	0.848	0.083	0.159	0.016	−0.131	0.029	−0.177	0.065
HED1	0.231	0.188	0.212	0.227	0.040	0.807	0.240	−0.063	−0.217	−0.037	0.128	0.269
HED2	0.230	0.269	0.221	0.247	0.052	0.924	0.276	−0.025	−0.221	−0.084	0.233	0.250
HED3	0.213	0.291	0.220	0.224	0.050	0.909	0.288	−0.047	−0.159	−0.105	0.231	0.254
EA1	0.257	0.226	0.193	0.199	0.120	0.263	0.847	−0.116	−0.160	−0.065	0.163	0.275
EA2	0.210	0.256	0.188	0.183	0.083	0.286	0.863	−0.085	−0.157	−0.123	0.175	0.229
EA3	0.202	0.221	0.208	0.214	0.098	0.241	0.875	−0.078	−0.173	−0.087	0.176	0.257
CI1	0.030	−0.069	−0.008	−0.081	0.005	−0.062	−0.116	0.871	0.056	−0.017	−0.082	−0.379
CI2	−0.042	−0.11	0.006	−0.098	0.039	−0.049	−0.087	0.920	0.051	0.001	−0.125	−0.463
CI3	−0.049	−0.143	0.021	−0.106	−0.004	−0.017	−0.087	0.892	0.050	−0.005	−0.099	−0.414
PFR1	−0.137	−0.165	−0.121	−0.105	−0.141	−0.204	−0.147	0.069	0.822	−0.014	−0.077	−0.226
PFR2	−0.107	−0.161	−0.131	−0.106	−0.088	−0.177	−0.189	0.037	0.870	0.103	−0.092	−0.244
PFR3	−0.173	−0.142	−0.102	−0.146	−0.087	−0.190	−0.156	0.048	0.897	0.078	−0.140	−0.281
PIR1	−0.090	−0.098	−0.028	−0.031	0.035	−0.077	−0.078	0.013	0.015	0.865	−0.085	−0.087
PIR2	−0.024	−0.110	−0.021	−0.029	−0.019	−0.107	−0.105	0.009	0.087	0.889	−0.070	−0.098
PIR3	−0.081	−0.106	−0.047	−0.030	−0.010	−0.054	−0.093	−0.038	0.068	0.839	−0.071	−0.097
BI1	0.446	0.319	0.232	0.235	−0.174	0.221	0.220	−0.123	−0.110	−0.086	0.944	0.395
BI2	0.389	0.293	0.162	0.204	−0.221	0.220	0.151	−0.093	−0.119	−0.077	0.935	0.365
AB1	0.306	0.292	0.257	0.299	0.082	0.277	0.275	−0.423	−0.241	−0.082	0.364	0.912
AB2	0.313	0.330	0.226	0.312	0.067	0.250	0.264	−0.440	−0.292	−0.116	0.380	0.924

Note: PE = performance expectancy, EE = effort expectancy, SI = social influence, PCP = product cost performance, HHA = Health and hygiene awareness, HED = hedonism, EA = environmental awareness, CI = cleaning issue, PFR = perceived financial risk, PIR = perceived information risk, BI = behavioral intention, AB = actual behavior. The factor loadings for each variable are shown in bold.

shows the cross-loadings. The Fornell-Larcker criterion requires that the square root of the AVE to which a variable belongs should be greater than its correlation coefficient with other latent variables.

Table 4. Data discriminant validity assessment method 1: the Fornell-Larcker criterion.

	PE	EE	SI	PCP	HHA	HED	EA	CI	PFR	PIR	BI	AB
PE	0.837
EE	0.243	0.841
SI	0.285	0.244	0.848
PCP	0.235	0.206	0.236	0.874
HHA	0.088	0.073	0.110	0.058	0.835
HED	0.250	0.291	0.245	0.262	0.054	0.882
EA	0.258	0.272	0.228	0.231	0.116	0.306	0.862
CI	−0.025	−0.121	0.007	−0.106	0.017	−0.047	−0.107	0.895
PFR	−0.163	−0.179	−0.135	−0.139	−0.120	−0.219	−0.189	0.058	0.863
PIR	−0.074	−0.121	−0.037	−0.035	0.001	−0.092	−0.107	−0.007	0.067	0.864
BI	0.446	0.326	0.211	0.235	−0.209	0.235	0.199	−0.115	−0.122	−0.087	0.939
AB	0.337	0.339	0.263	0.333	0.081	0.286	0.293	−0.471	−0.291	−0.109	0.405	0.918

Note: PE = performance expectancy, EE = effort expectancy, SI = social influence, PCP = product cost performance, HHA = Health and hygiene awareness, HED = hedonism, EA = environmental awareness, CI = cleaning issue, PFR = perceived financial risk, PIR = perceived information risk, BI = behavioral intention, AB = actual behavior.

shows that the square roots of all AVEs on the main diagonal are much higher than the rest of the values in the column where they are located, meeting the criteria for judgment.

Table 5. Data discriminant validity assessment method 2: HTMT.

	PE	EE	SI	PCP	HHA	HED	EA	CI	PFR	PIR	BI	AB
PE
EE	0.308
SI	0.357	0.302
PCP	0.287	0.249	0.283
HHA	0.113	0.094	0.14	0.096
HED	0.308	0.342	0.294	0.308	0.083
EA	0.321	0.333	0.28	0.276	0.145	0.359
CI	0.056	0.142	0.049	0.119	0.034	0.06	0.128
PFR	0.200	0.224	0.168	0.165	0.149	0.267	0.229	0.07
PIR	0.109	0.149	0.045	0.044	0.056	0.103	0.128	0.028	0.095
BI	0.538	0.391	0.251	0.27	0.253	0.258	0.233	0.13	0.141	0.103
AB	0.422	0.421	0.325	0.4	0.103	0.349	0.359	0.554	0.351	0.131	0.481

Note: PE = performance expectancy, EE = effort expectancy, SI = social influence, PCP = product cost performance, HHA = Health and hygiene awareness, HED = hedonism, EA = environmental awareness, CI = cleaning issue, PFR = perceived financial risk, PIR = perceived information risk, BI = behavioral intention, AB = actual behavior.

presents the analysis results of HTMT. The value between each of the two facets was less than the conservative threshold of 0.85. Meanwhile, 5000 subsamples were set up by the bootstrapping method, which excluded the possibility that the confidence interval of all facets contained 1. The discriminant validity test results were good, and there was no reliability and validity problem in general.

4.2. Structural Model Evaluation

First, the multicollinearity test was conducted on the proposed model. The VIF of each latent variable was 1.523–2.675 and did not exceed the threshold of 3.3, indicating that there was no collinearity problem between the variables [76]. The multiple determination coefficient R² is often used to assess the predictive ability of structural models [77]. In this study, the R² of behavioral intention was 0.436 (0.430 after adjustment), and the R² of actual behavior was 0.336 (0.327 after adjustment), showing that the explanatory power of the model proposed in this study was moderate. Finally, we derived all hypothesized path coefficients, 95% confidence intervals, and t-statistics by bootstrapping the 5000-subsample configuration (

Table 6. Hypothesis test results.

Hypothesis				Path Coefficients	Confidence Interval (Bias Correction, 95%)	T-Statistics (p-Value)
H1	PE	→	BI (+)	0.364	[0.286, 0.431]	9.835 (p < 0.001)
H2	EE	→	BI (+)	0.203	[0.127, 0.282]	5.079 (p < 0.001)
H3	SI	→	BI (+)	0.046	[−0.035, 0.130]	1.096 (p = 0.273)
H4	PCP	→	BI (+)	0.090	[0.020, 0.161]	2.507 (p < 0.05)
H5	HHA	→	BI (−)	−0.273	[−0.339, −0.198]	7.552 (p < 0.001)
H6	HED	→	BI (+)	0.054	[−0.023, 0.133]	1.355 (p = 0.176)
H7	EA	→	BI (+)	0.034	[−0.047, 0.106]	0.875 (p = 0.381)
H8	CI	→	AB (−)	−0.428	[−0.484, −0.369]	14.815 (p < 0.001)
H9	PFR *	→	BI → AB (−)	−0.193	[−0.258, −0.128]	5.812 (p < 0.001)
H10	PIR *	→	BI → AB (−)	−0.017	[−0.075, 0.038]	0.592 (p = 0.554)
H11	BI	→	AB (+)	0.342	[0.274, 0.406]	10.108 (p < 0.001)

Note: PE = performance expectancy, EE = effort expectancy, SI = social influence, PCP = product cost performance, HHA = Health and hygiene awareness, HED = hedonism, EA = environmental awareness, CI = cleaning issue, PFR = perceived financial risk, PIR = perceived information risk, BI = behavioral intention, AB = actual behavior. “*” indicates that the variable has moderating effect.

).

In the structural model evaluation, path analysis is the principal part, which primarily measures the degree of influence of variables according to path coefficients [78]. Combining the p-values of the hypotheses, except for the four hypotheses H3, H6, H7, and H10, all the other hypotheses are valid. Figure 4 summarizes the final structural model evaluation results. The path coefficient of performance expectancy was 0.364, and its positive correlation exerted the greatest impact, showing that for young groups, the ability to fulfill the wearing needs of specific places and attain their expected interest value is a crucial factor for them to choose CRS. Among all the negative impact variables, the cleaning problem of the platform exerted the largest negative value. Its path coefficient reached 0.428, which was also greater than all positive impact indicators; this proves that in the post-pandemic era, the sanitary conditions of the outside world might become the primary factor influencing people to rent second-hand clothing.

5. Discussion

5.1. Theoretical Implications

This study investigates the changes in the influencing factors of China’s clothing-sharing economy before and after the pandemic. Aiming at the growth platform, based on the UTAUT, we expanded multiple dimension variables, such as cleanliness, health, and economy. Finally, an evaluation model of fashion-sharing influencing factors based on the PLS-SEM was constructed, filling the gap in the research on the sharing tendency of second-hand clothing in China after the pandemic.

Affected by COVID-19, people’s awareness of cleaning has deepened gradually; thus, we must consider whether people’s attitude toward the cleaning of second-hand platforms has changed. Figure 4 shows that the clothing cleaning problems and the awareness of personal hygiene protection have become the main negative influencing variables in the two sectors of external factors and personal factors. People’s attention to the pandemic has led to a continued increase in health and hygiene concerns. People now prefer buying unstained first-hand clothing rather than renting or buying second-hand clothing from unclear sources. Poor hygiene in clothing can deter consumers who would otherwise be willing to share.

Among several original basic variables of UTAUT, only social influence did not contribute to behavioral intention, which could be because second-hand clothing-sharing belongs to a niche circle in China, and consumers’ surrounding groups have a low level of understanding of fashion-sharing. In addition, most respondents in this survey are young people, who have independent ideas. Such people will have their own unique views and tendencies in the face of the new era product of “shared wardrobe”. Furthermore, supported by Medalla et al. [79] research on the antecedents of young consumers buying second-hand clothing, high quality and affordable prices are, indeed, the main reasons for the attention of used products. Online consumption can reduce the possibility of physical contact with others, and simultaneously meet the different needs of consumers. Compared with going to offline stores, online platforms that save time and effort will attract consumers more.

Unexpectedly, in the personal factors section, the assumptions of hedonism and environmental awareness did not hold. Combining the current stage of China’s clothing-sharing market and the results of the questionnaire, clothing rental is not a rigid demand. Most clothes that consumers rent are usually business wear and costumes, and few consumer groups choose CRS for the pursuit of wearing clothing for fun. Considering the frequency of wearing daily clothing, consumers are more willing to choose to obtain the independent ownership of clothing; thus, the failure of H6 also becomes reasonable. The meaninglessness of H7 might imply that consumers still have a superficial view of pollution in the clothing production process, and they cannot correctly recognize the environmental impact brought by the clothing industry. Fashion-sharing could be just simple product recycling for them, and they cannot autonomously link it to green sustainable development.

For moderator variables in the intention-behavior gap, only the negative moderator of perceived financial risk was validated. One of the reasons why China’s clothing rental platform “YCLOSET” failed is owing to its unreasonable formulation of the user payment system and the existence of arbitrary fees (such as “malicious deductions”, “member automatic renewal”, “difficult to refund after sales”), which has led to a large loss of platform customers. The failure case of “YCLOSET” can be supplemented by verification that the financial risk perceived by users will, indeed, inhibit the final formation of second-hand clothing-sharing behavior. The negative adjustment of another variable’s perceived information risk has not been verified. Although it is counterintuitive, it might also be that consumers attach less importance to personally identifiable information than financial information, and they cannot face up to the illegality of personal data and security risks caused by loss.

Combining the above conclusions, this study can provide a strong theoretical basis for how the growing Asian fashion-sharing platforms can iteratively update the current market development trend. For European investment merchants trying to enter the Chinese clothing-sharing market, it can give unique insights from a relatively relevant standpoint.

5.2. Practical Implications

This study also contributes to the actual development of the growing second-hand clothing-sharing platforms. To promote their own upgrading and innovation, platforms must face up to the factors that will adversely affect their development, such as the primary cleaning issue. To convince consumers with increasing awareness of hygiene protection in the professionalism and safety of clothing cleaning, platforms can clearly convey each cleaning process to consumers in the form of live broadcasts. The establishment of a professional qualification check portal for a cooperative cleaning platform on the webpage can help to trace the source of cleaning. Furthermore, it is necessary to focus on the financial risks that consumers think might arise during the shopping process. Reportedly, when users shop online, they worry about shopping because of some operational obstacles (such as bank card binding and cumbersome consumption checkout ports), which eventually lead to resistance and the choice to refuse consumption [80]. Growing second-hand clothing-sharing platforms need to attach great importance to the economic interests of consumers and fortify payment security technology. In addition, users’ rights and interests should be protected. Maliciously charging membership fees or automatically renewing membership fees without notice will seriously damage users’ interests and the platforms. Furthermore, a reasonable return and exchange policy is also a major measure to ensure the financial rights and interests of users. From the standpoint of morality and ethics, the protection of users’ personal information is the basic principle.

Regarding promoting adjustment factors, consumers attach great importance to the performance expectancy and effort expectancy that platforms can give themselves, as well as the high-cost performance of products. Thus, growing platforms should earnestly explore clothing-sharing categories and adjust the rental category of second-hand clothing in a timely manner, which can effectively satisfy the expectations of a broad range of users for the supply of main clothing. For the resale categories of second-hand clothing, it is possible to focus on daily clothing with a high new rate and formulate a reasonable price concession strategy. Owing to the temporary and sudden nature of work requirements and occasion attendance, it is essential to ensure that users can receive apparel products in a short period. Hence, a simple system design and an efficient transportation system can enhance ease of use and convenience, thereby decreasing user waiting time.

We should fully awaken consumers’ awareness of environmental protection. Although this study does not prove that this factor exerts a positive impact on the willingness to rent second-hand clothing, it could correlate with the fact that clothing-sharing is a non-rigid demand for Chinese consumers. For non-rigid-needed commodities, consumers care more about the short-term return ratio it brings to individuals, rather than looking at its impact from a macro-level. For example, clothing-sharing can promote the sustainability of the environment and the clothing industry. Platforms should follow the trend of green development and actively launch sustainable clothing response activities to deepen consumers’ positive views on second-hand clothing, especially in a clothing-producing country like China.

5.3. Limitations and Future Research

Due to limited platform transparency, we did not conduct research on specific platforms. In order to consolidate and expand our research, focusing on a specific group with rental experience is considered as a future research concern. To broaden the development depth of platforms, it is a good way to combine with medical, protection, and other fields. It is conceivable that the sharing of functional clothing (such as breathing monitoring, heart rate monitoring) might become a hotspot as health concerns continue to rise. This shared nature will give a deeper connotation to the consumption behavior of second-hand clothing. China’s huge clothing market space can provide fertile development soil for fashion-sharing. The exploration of China’s sharing platforms can bring positive significance to the sustainable development of the country. Considering the influence of the star effect in China and the trend-following power of young groups, it can be included in the social influence level in the future, and stronger insights into the Chinese clothing-sharing market have been obtained.

6. Conclusions

In the cultural migration from consumerism to a sustainable lifestyle, fashion-sharing has become an active force leading sustainable consumption in the new century owing to its value continuity, category diversity, and model novelty. Influenced by changes in current events, there are many variables in the development of growth platforms. A precise analysis of the category of variables will help the sustainable progress of platforms. This study investigates the motivation of consumers to share second-hand clothing after the pandemic. This study reveals that due to the impact of pandemic, cleaning and washing have become a rigid need. Meanwhile, hygienic conditions for shared clothing have become a major concern for consumers. The standard and transparency of the platform cleaning process will facilitate the final formation and continuation of consumer-sharing behavior. It is a vital measure to continuously optimize the usability of the sharing system to deepen consumers’ tendency to share clothing, as well as protect users’ property rights and interests. Under the appeal of value expectations, high-quality second-hand clothing and reasonable price strategies are indispensable. Briefly, we should effectively use the sharing system of clothing to promote consumers’ recognition and respect for sustainable consumption behavior.