What Affects Garment Lifespans? International Clothing Practices Based on a Wardrobe Survey in China, Germany, Japan, the UK, and the USA

Abstract

Increasing the length of clothing lifespans is crucial for reducing the total environmental impacts. This article discusses which factors contribute to the length of garment lifespans by studying how long garments are used, how many times they are worn, and by how many users. The analysis is based on quantitative wardrobe survey data from China, Germany, Japan, the UK, and the USA. Variables were divided into four blocks related respectively to the garment, user, garment use, and clothing practices, and used in two hierarchical multiple regressions and two binary logistic regressions. The models explain between 11% and 43% of the variation in clothing lifespans. The garment use block was most indicative for the number of wears, while garment related properties contribute most to variation in the number of users. For lifespans measured in years, all four aspects were almost equally important. Some aspects that affect the lifespans of clothing cannot be easily changed (e.g., the consumer’s income, nationality, and age) but they can be used to identify where different measures can have the largest benefits. Several of the other conditions that affect lifespans can be changed (e.g., garment price and attitudes towards fashion) through quality management, marketing strategies, information, and improved consumer policies.

1. Introduction

The clothing industry is one of the highest producers of pollution in the world and causes severe damage to the environment [1,2,3]. While a lot of effort is focused on improving the production phase, there is little done to improve consumer practices (use phase). Existing life cycle assessments (LCAs) of clothing show that decisions made in the use phase contribute significantly to overall environmental impacts [4,5,6,7]. Increasing clothing lifespans is one of the most efficient ways of reducing the environmental impacts of clothing, through reductions in replacement frequency which prevents waste and also reduces production and transport. This has been documented and understood for a long time; for example, the waste hierarchy lists prevention as its top priority [8]. However, there is only limited research that has focused on clothing lifespans and which specific factors affect the length and active wear of a garment, and where potential gains exist.

A better understanding of the various aspects of the use phase of clothing is needed for environmental accounting tools such as LCAs [9], and for the work towards targeted environmental improvements based on relevant policy instruments. There is a lack of systematic, empirical knowledge about the lifetime of garments considering their different characteristics. There is a crucial need to understand how garment lifespans can best be measured, and even predicted, and which properties of clothing influence the garment use, and the related environmental impacts.

The research question in this article is: Which factors contribute to garments lifespans? Initially, we introduce a more precise understanding of lifespans and build on a taxonomy based on previous consumer research on the aspects that impact garment lifespans. This is followed by a methodology section, where data from the international wardrobe survey is introduced, conducted in China, Germany, Japan, the United Kingdom, and the United States. In the results section, we present survey findings related to various ways of measuring clothing lifespans, and which factors affect the length of the lifespan. We conclude by answering the articles main research propositions, and by identifying which of the areas/impacts identified would benefit from further research.

2. Theoretical Background

We build on the theory of clothing lifespans developed by Klepp et al. [10]. In this theory, lifespans are understood as a combination of three different aspects, all three of which have independent value. The three can be measured in the number of years, the number of times used (wears), and the number of users, and will be referred to as lifespan in years, wears, and users throughout the paper. We will further develop a taxonomy of aspects that affect the lifespans based on literature that has studied at least one of the three different aspects of lifespans.

To date, garment lifespans have most commonly been measured by age in years, which is understood as the time a garment is owned, from when it was acquired until it goes out of use or is disposed of [10].

Figure 1 gives a summary of previous literature reviews, from studies that report the lifespan length in years of use [4]. These studies are mainly based on consumer surveys [11,12,13,14,15,16,17] but include some wardrobe and inventory studies [18,19]. Average lifespan including all the garment categories was four years, but it varied greatly between different garments types.

More recent studies have started to use a different unit of measurement to indicate lifespan, which relates to active use of items and the number of times the garment is worn, but there are significantly less of these than those which measure years of ownership. Cooper et al. [14] estimated the total number of wears of five different garment types, estimating that knitwear is worn 111 times, shirts 58 times, jeans 233 times, socks 90 times and t-shirts 83 times. This study was based on estimations and did not include various aspects that impact lifespans. The data indicated that if consumers were asked directly how many times a specific garment was worn (including previous and future wears), the average result was 76 wears. When calculating the number of wears based on reported wearing frequency, the figure was higher; 105 wears.

The number of times a garment is worn, and the related environmental impacts are also linked strongly to garment care, including choices related to laundering, dry-cleaning, drying, and ironing, and their requirements for energy, water, and detergent consumption. Since most research on clothing use is based on consumer studies where the current user reports their personal garment use, it is difficult to get information on the total lifespan of garments which have more than one user. Therefore, the knowledge gained about the number of users is very important for estimating total garment lifespan and impacts [10].

While product type is an important indicator in the lifespan of garments, significant variation exists because of garment characteristics and different user groups. A consumer survey in the UK showed that while the type of clothing was significant, some user groups are more likely than others to keep their clothes in active use longer [13]. These include men, older people, people with lower income, higher socio-economic groups, those with more garments in their wardrobes, and those who purchase longer-lasting clothing intentionally. Consumers’ values were also important, as lower active use is correlated strongly with consumers who place higher importance on fashion and brands, while longer active use phases of clothing correlated with consumers who purchase garments that are better quality and better value for money. Active use also differed depending on the purpose for which clothes are purchased. Formal clothing bought for specific occasions outside of work (such as weddings or funerals) had the longest lifespans measured in years, while casual wear had shorter lifespans.

Gwozdz et al. [16] analysed clothing consumption behaviours in Germany, Poland, Sweden, and the U.S. They found that when differentiating by purchasing behaviour there were five distinct consumer segments. In addition to demographic differences (nationality, gender, and income), other important aspects were consumers’ general lifestyle, values and attitudes towards brands and sustainability, as well as actual reported behaviour related to the acquisition, wearing frequency, maintenance, and disposal. The majority of consumers were those who have lower incomes, acquire most of their clothing from low budget brands and buy few garments in total (e.g., “low-consumption–budget brands”). This segment kept their clothes longer and wore them more times between washes. However, they were also less likely to send their garments for reuse for a second life. On the other end of the scale, consumers in the “high premium” segment purchased more garments, kept them for less time and wore them fewer times between washes. However, their end-of-life (EOL) practices were more likely to be environmentally responsible, as they were more likely to send them for reuse or recycling.

Two similar studies, one conducted in Canada [21] and another in South Korea [22], showed that several indicators influence whether consumers donate or sell clothing for reuse, and these were similar for both countries. The likelihood of reuse decreases if clothing is severely damaged or is a cheap fast-fashion garment instead of a more expensive, designer brand garment, or if the garment is identified as casual wear (jeans or t-shirt) instead of formal wear (dress). Consumers were more likely to mend garments if they are only slightly damaged.

Wardrobe studies of clothing disposed in Norwegian households (including children’s clothing, which was not included in any of the above-mentioned studies) showed that several aspects impacted the average lifespans [12,18]. Children and teens used their clothing for about two years less than adults, and those above the age of 51 used their clothing for the longest, and men kept their clothing longer than women. The way a garment was acquired was important for its lifespan, as items received as gifts were used for less time. Garments intended for reuse were used for less time than those to be put into the rubbish bin. The reason for disposal also influenced the length of the garment lifespan, as those disposed of due to changes in fashion trends or taste were generally kept longer than garments disposed of due to wear and tear. Fibre content was also significant, which was confirmed by a later study where we showed that woollens were used longer, on average, than similar garments made from other fibre types [4].

Research has shown that wear and tear is an important reason for the clothing disposal [23,24,25,26] and that an increase in durability will also extend the potential length of a garment’s lifespan. However, other properties of the products are also significant. Their social value varies, for example, some products have a greater aesthetic value when they are new, while others may remain the same or increase over time. The logic in fashion is that new clothing has a high value that quickly diminishes as they get outdated. Some clothing, like vintage items or folk costumes, can increase in value over time [27,28]. By researching “reasons for disposal”, the impact of such properties on clothing lifespans can be evaluated.

Consumer demographics have a noteworthy impact on the use period of clothing. Children and adolescents have a greater need for new clothes because they are still growing physically, and the situation of their life is often subject to change [29]. Expectations regarding the need for women to vary their clothing between different settings increase the amount of clothing they purchase compared to men [30]. Poor fit is one of the main reasons for the disposal of women’s clothing [23]. Clothing with a flexible fit can be used longer than other clothing [31,32]. This may apply, for example, to knitted versus woven clothing, and other clothing which is stretchy or clothing with inbuilt resizing features.

People with low income report longer clothing lifespans, probably because of the financial restrain on their purchasing decisions. Relationship and employment status can also impact clothing needs. Working life is an area where clothing decisions are important [26], and in many jobs, uniforms are used, which reduces the need for private clothing [33,34]. An important factor for clothing selection is garments for any occasion which requires a particular dress code at a specified place and time (e.g., work or a function such as a wedding) [35]. This includes categories such as sportswear, casual, business-casual, business, and formal [36].

The occasions consumers take part in influences the number of times a garment can be worn. Cooper et al. [31] include examples such as bridal wear, dinner jackets, evening wear, party dresses, and high-quality suits, which are all worn infrequently and stored, unworn, for long periods. The climate of the country users live in also influences the length of the garment lifespan. Seasonal clothing, such as a warm sweater, will have more use opportunities to be worn in colder climates.

Other aspects that can impact garments lifespans include users’ values, mending skills (e.g., repair and alteration) [37], and dressing habits (e.g., active-wear in everyday life). Laundering practices also impact lifespan length, as frequent or inappropriate laundering can cause wear and tear [38] which reduces lifespans [39].

This literature has shown that many aspects impact garment lifespans, but there is a lack of knowledge on how much each factor contributes, and if there is a difference in the importance between them for the different ways of measuring lifespans. We have combined these various factors that influence lifespans (years, wears, and users) to construct a Taxonomy. We will use this to study the factors’ relative importance for significance for longevity. The aspects have been divided into four categories: consumer/user demographics (user), use, garment attributes, and consumer behaviour related to clothing practices (Figure 2). This structuring gives a theoretical starting point for our analysis that is presented in the next section.

3. Method

This paper is based on a consumer survey conducted online from December 2018 to January 2019. The survey was administrated by AC Nielsen’s Australian office in cooperation with local branch offices. It focuses on five countries with large clothing markets: China, Germany, Japan, the UK, and the USA, with over two hundred respondents from each country answering a comprehensive, web-based survey on their wardrobe contents. Questions included the number of items owned in specific categories, and for a selection of these items, details such as clothing lifespan, length of active use, wear occasions, materials and laundering practices were also requested. The questionnaire is attached in Appendix A.

The participants were between 18 and 64 years old (working age), representing the different demographics and genders within the surveyed countries based on a pre-stratified sample. The respondent demographics for each country and the average/total are given in

Table 1. Respondents’ background variables/demographics.

		China	Germany	Japan	UK	USA	Total
Number of respondents	N	230	224	224	213	220	1111
Number of garments	N	10,595	11,705	12,022	9384	9755	53,461
Gender distribution	Men (%)	48.3%	49.6%	50.0%	46.5%	47.7%	48.4%
	Women (%)	51.7%	50.4%	50.0%	53.5%	52.3%	51.6%
Age group	18–29 years (%)	43.5%	20.1%	20.1%	21.6%	19.5%	25.1%
	30–49 years (%)	48.7%	44.2%	48.2%	51.6%	36.4%	45.8%
	50–64 years (%)	7.8%	35.7%	31.7%	26.8%	44.1%	29.1%
Marital status	Single (%)	22.6%	33.0%	44.6%	26.8%	27.3%	30.9%
	Married or living with a partner (%)	76.5%	54.9%	48.7%	64.8%	58.6%	60.8%
	Divorced, separated, or widowed (%)	0.4%	11.2%	6.3%	8.0%	14.1%	7.9%
Average household size	Number of people	3.5	2.3	3.0	2.8	2.7	2.8
Employment status	Full time (%)	88.3%	53.6%	50.4%	47.9%	44.1%	57.2%
	Part time/casual (%)	1.3%	20.5%	23.2%	24.4%	15.0%	16.7%
	Seeking work (%)	0.0%	4.5%	2.2%	4.2%	5.9%	3.3%
	Home duties/not seeking work (%)	1.7%	4.0%	13.8%	14.6%	17.3%	10.2%
	Student (%)	6.5%	7.1%	7.6%	3.3%	3.2%	5.6%
	Retired (%)	2.2%	10.3%	2.2%	4.7%	14.1%	6.7%

. The data was sorted in two ways, by “per respondent” (N = 1111), and “per garment” (N = 53,461).

The data in China was collected differently to that of the other countries to enable comparison of more similar consumer groups, in terms of living standards, in the five countries, while also using the same design as at the 2012 study [4,11,40]. With this in mind, the data from China was collected from Tier 1 and 2 cities (Beijing, Shanghai, Guangzhou, Shenyang, Taiyuan, Nanjing, Hefei, Fuzhou, Changsha, Chengdu, and Xi’an) instead of the whole country. This led to a lower average age for Chinese respondents. China also has a higher proportion of respondents who work full time and less who are divorced, separated, or widowed.

Yearly income and monthly expenditure on clothing were reported in the local currency, and in the final analysis, they were grouped into three categories (low, medium, and high) based on country trisects. Garment prices were converted to US dollars in the analysis and divided into four categories.

The number of clothing categories assessed in detail in the survey was limited to a list of “focus categories” for males and females. These are listed in

Table 2. Garments in focus.

Accessories	Formal Wear	Casual Wear	Casual Next-to-Skin	Underwear	Sportswear
Scarfs, shawls, pashminas and stoles	suits (2pc) pants and trousers skirts (F) dresses (F) jackets and blazers overcoats and raincoats	jumpers, sweaters and cardigans pants and trousers skirts (F) dresses (F) jackets and blazers overcoats and raincoats	t-shirts and polo shirts singlets and tanks (F)	socks and stockings (F) thermal tops undershirts thermal leggings and underpants	sports t-shirts and tops sports singlets and tanks

(F)—this category only includes female respondents.

. Item-specific questions were also limited to a maximum of 10 items per focus categories (meaning a respondent only had to answer for ten items per category regardless of how many they actually owned). For selecting which items to answer for, they were asked to try to evaluate a wide variety of items of different materials, or for different occasions. The analysis is based on these garments in focus categories.

The analysis was conducted with SPSS statistical software. Statistical tests for significance given as follows: * p < 0.050; ** p < 0.010; and *** p < 0.001.

3.1. Building Models for Regression Analysis

3.1.1. Dependent Variables

Based on our theoretical approach (Section 2), we constructed dependent variables for measuring lengths of clothing lifespans by number of years, the number of wears and number of users [10]. The first variable, “DEP1-Years”, indicates the total garment age measured in years, and is based on the survey question, “When did you buy or acquire this clothes item or accessory?”. Answers varied from, “in the last 6 months” to “more than 30 years ago” (coded as 3 months, and 35 years, respectively). For estimating the total lifespan, the average current age is multiplied by two. This way of estimating the possession span will not be correct for the individual garments, but it will be a close estimate for the average of the total number of garments.

The second variable, which measures the number of wears “DEP2-Wears”, is based on two survey questions: “How many times have you worn this item?” and “How many times do you expect to wear this item in the future?” The highest response category, “more than 200 times” was coded as 250 wears. When the current and planned number of wears were combined, the values of this variable vary from between 0 and 500 wears. These two ways of measuring lifespans were analysed with a hierarchical (sequential enter method) multiple regression.

The third variable is based on the number of users, including garments that have had a previous owner, or that owners plan to deliver for reuse. These two dependent variables are thus “DEP3a-Preowned” and “DEP3b-Planned reuse”. These are based on questions about the method of acquirement and the planned disposal method (sell or donate). As these variables are binary, we chose to use a binary logistic regression to analyse the impact of various predictors.

3.1.2. Independent Variables

Independent variables are introduced in 4 blocks based on the theoretical background. The first block represents garment specific properties (variables 1–3), the second is the demographics of the user (variables 4–9), followed by garment use (variables 10–15), and finally the general clothing practices of the user (variables 16–22). For categorical variables, dummy coding was used to be able to compare each level of a variable to the omitted (reference) level. The reference category was chosen based on it being the most common value, or in cases where this was not applicable, as the one with lowest lifespan values. For simplicity, ordinal variables with more than five categories were treated as numeric (nominal) variables. This applies to variables 11, 19, and 22. Appendix B includes descriptive statistics for these variables.

In the linear regression analysis in models 1 and 2 the predictors were coded and entered as follows:

• Type of garment (suits, trousers/pants, skirts/dresses, t-shirts/polos/singlets, jumpers/pullovers, blazers, overcoats, scarfs/pashminas, thermal underwear, or sportswear. Socks and stockings were used as the reference group).
• Fibre content (wool and wool blends, silk, synthetics, regenerated cellulose, or unknown fibre content. Cotton and cotton blends were used as the reference group).
• Garment price (10–39 USD, 40–99 USD, and over 100 USD. Reference group are those which cost below 10 USD).
• Gender (0 = male, 1 = female).
• Age group (30–49 years and 50–64 years compared to reference group 18–29 years).
• Nationality (German, Japanese, UK, and USA. China were used as the reference).
• Employment status (0 = non-working, student or part time, 1 = working full time).
• Marital status (0 = single, divorced, separated, 1 = married/living with partner).
• Income in three groups, trisects of each country (middle and high compared to low income).
• Wearing frequency (number of wears per year) (based on how often the garment was worn either whole year-round or seasonally).
• Laundering frequency (number of wears before laundering) (0 = after every wear, 1 = after every 2–3 wears, 2 = after every 4–5 wears, 3 = after every 6–10 wears, 4 = after every 11–19 wears, 5 = after every 20–29 wears, and 6 = after every 30 or more wears).
• Occasion used (work, formal social occasion, casual social occasion, training/doing sports, religious, sleeping, gardening/painting/other dirty household chores, not in active use, or other/unknown occasion. Everyday use and around the home used as the reference group).
• Preowned (0 = no previous owner, 1 = garment had previous owner)
• Assumed disposal reason (the style is not in fashion anymore, it doesn’t fit me properly, I don’t like the colour or style anymore, not enough space in my wardrobe for new items, I don’t need the garment anymore, other/unknown reason. General wear and tear were used as a reference group).
• Planned disposal method (donate to charity, give to friends/family, recycle at home, sell, other/unknown. Put in the rubbish bin was used for reference).
• Monthly expenditure on clothing (broken into three groups—middle and high, where low expenditure is the reference).
• The number of new clothing items purchased last year (from less than 5 to over 50, coded as from 2.5 to 70).
• Wardrobe size (number of garments, varied from 35 to 663).
• Fashion interest (measured based on the level of agreement to the statement “I change fashion by season”—from 0 = completely disagree up to 4 = completely agree).
• Has repaired or sewn clothing last 12 months (0 = no, 1 = yes).
• Sewing skills (measured in two ways: “Can use sewing machine”—0 = no, 1 = yes, or “Can sew by hand”—0 = no, 1 = yes).
• Purchase priorities (respondents had to evaluate 11 garment attributes when buying smart casual or formal wear—rated from completely unimportant (0) to most important. The highest grade (6) applies if the factor was chosen as most important for both garment categories. The aspects included were fashion, price, fit, fabric quality, fibre content, country of manufacture, design/style, sustainable/environmentally friendly production, ethical production, colour, and designer brand).

This gives 71 variables in total. Due to this high number, we used the adjusted R² to estimate the model fit.

The same order is used in the logistic regressions for DEP3a and DEP3b with some exceptions. The variable “preowned” was not included in “DEP3a-Preowned”, and the variable “planned disposal method” is not included in “DEP3b-Planned reuse”, as the dependent and independent variables are based on the same question. Garment age in years and the total number of wears were introduced as predictors in the third block instead.

During the development of models, variables that did not contribute significantly to any of the regressions, or had high collinearity, were excluded. The robustness of the models was tested with model variation by adding, removing, and changing some of the regressors. Examples of these changes included removing variables that were no longer significant in the final block of the models, adding variables that were initially excluded due to their small contribution, changing nominal variables to categorical variables and vice versa (such as wardrobe size in trisects for small/average/large instead of number of garments), and using different reference groups for categorical variables (such as fashion as disposal reason instead of wear and tear). These changes did not impact largely to the core regressions coefficients that retained their direction. The adjusted R² of models 1 and 2, and pseudo R² values (Cox and Snell and Nagelkerke values) of models 3a and 3b changed on average 1.3 percentage points, and 1.7 percentage points at most. This indicates sufficient structural validity.

4. Results

4.1. Garment Lifespans Measured in Years

The average current garment age (including all focus categories) was 2.6 years, indicating that total expected lifespan is likely to be double, 5.2 years (N = 46,857 garments). This estimation includes only the current user (not previous owners if the garment was preowned or would be reused).

A significant regression model was found (F (71, 22,525) = 91.941, p < 0.001), with an adjusted R² of 0.222, which indicates the model explains 22.2% of the variation in garment lifespans measured in years. The contribution of each block is presented in

Table 3. Summary of four blocks that build the first Model DEP1 total garment age measured in years—* p < 0.050; ** p < 0.010; *** p < 0.001, ns = not significant, p ≥ 0.05.

	Model 1: Garment			Model 2: User			Model 3: Use			Model 4: Practices
	B	SE B	Beta	B	SE B	Beta	B	SE B	Beta	B	SE B	Beta
(Constant) Garment age in years	2.181	0.140	***	−0.102	0.239	(ns)	0.783	0.269	**	3.078	0.465	***
Garment type
Suits vs. socks/stockings	3.525	0.320	0.087 ***	3.372	0.310	0.083 ***	1.850	0.307	0.046 ***	1.6	0.299	0.038 ***
Trousers, pants vs. socks/stockings	2.401	0.223	0.092 ***	2.185	0.216	0.084 ***	1.200	0.216	0.046 ***	1.0	0.211	0.037 ***
Skirts, dresses vs. socks/stockings	2.711	0.227	0.100 ***	2.863	0.228	0.106 ***	1.564	0.229	0.058 ***	1.3	0.223	0.049 ***
T-shirts, polos, singlets vs. socks/stockings	2.239	0.185	0.105 ***	1.924	0.180	0.090 ***	1.168	0.179	0.055 ***	1.1	0.173	0.050 ***
Jumpers, pullovers vs. socks/stockings	2.803	0.230	0.102 ***	2.373	0.224	0.086 ***	0.963	0.227	0.035 ***	0.7	0.221	0.025 **
Blazers vs. socks/stockings	3.121	0.260	0.097 ***	2.887	0.252	0.089 ***	0.831	0.259	0.026 **	0.7	0.252	0.022 **
Overcoats vs. socks/stockings	3.222	0.271	0.097 ***	3.301	0.263	0.100 ***	0.935	0.275	0.028 **	0.7	0.269	0.021 **
Scarfs, pashminas vs. socks/stockings	3.645	0.281	0.097 ***	3.352	0.273	0.089 ***	0.708	0.289	0.019 *	0.7	0.282	0.019 *
Thermal underwear vs. socks/stockings	1.057	0.283	0.027 ***	1.238	0.274	0.032 ***	0.096	0.268	0.002 (ns)	0.3	0.262	0.007 (ns)
Sportswear vs. socks/stockings	1.293	0.252	0.039 ***	1.556	0.243	0.047 ***	0.268	0.259	0.008 (ns)	0.4	0.253	0.011 (ns)
Main fibre category
Wool and wool blends vs. cotton and cotton blends	1.569	0.160	0.072 ***	1.814	0.156	0.083 ***	1.202	0.153	0.055 ***	1.234	0.149	0.056 ***
Silk vs. cotton and cotton blends	1.750	0.486	0.024 ***	2.601	0.470	0.035 ***	2.170	0.456	0.030 ***	2.189	0.443	0.030 ***
Synthetics vs. cotton and cotton blends	1.282	0.160	0.055 ***	0.983	0.156	0.042 ***	0.618	0.152	0.026 ***	0.458	0.148	0.020 **
Regenerated cellulose vs. cotton and cotton blends	0.715	0.321	0.015 *	1.416	0.312	0.029 ***	0.858	0.302	0.018 **	0.811	0.293	0.017 **
Unknown/other fibre vs. cotton and cotton blends	2.692	0.198	0.091 ***	1.896	0.194	0.064 ***	0.380	0.230	0.013 (ns)	0.133	0.225	0.005 (ns)
Garment price
10–39 USD vs. <10 USD	0.070	0.163	0.004 (ns)	0.372	0.159	0.022 *	0.195	0.160	0.012 (ns)	0.409	0.156	0.025 **
40–99 USD vs. <10 USD	−0.026	0.187	−0.001 (ns)	0.685	0.185	0.036 ***	0.361	0.191	0.019 (ns)	0.871	0.188	0.046 ***
Over 100 USD vs. <10 USD	1.661	0.219	0.075 ***	2.143	0.221	0.097 ***	1.595	0.230	0.072 ***	2.039	0.229	0.093 ***
Gender (0 = male, 1 = female)				−0.146	0.117	−0.009 (ns)	−0.181	0.114	−0.011 (ns)	−0.361	0.118	−0.022 **
Age group
30–49 years vs. ≤29 years				0.747	0.131	0.046 ***	0.645	0.128	0.040 ***	0.103	0.127	0.006 (ns)
50–64 years vs. ≤29 years				2.576	0.150	0.143 ***	2.379	0.148	0.132 ***	1.238	0.151	0.069 ***
Nationality
Germany vs. China				3.024	0.171	0.154 ***	2.894	0.172	0.147 ***	2.233	0.184	0.114 ***
Japan vs. China				3.389	0.172	0.174 ***	3.098	0.181	0.159 ***	2.677	0.197	0.138 ***
UK vs. China				2.443	0.179	0.114 ***	2.494	0.181	0.117 ***	1.802	0.191	0.084 ***
USA vs. China				3.419	0.181	0.163 ***	3.249	0.183	0.155 ***	2.446	0.196	0.116 ***
Employed (0 = No, 1 = Yes)				−0.976	0.122	−0.059 ***	−0.796	0.120	−0.048 ***	−0.299	0.120	−0.018 *
Married (0 = No, 1 = Yes)				0.114	0.114	0.007 (ns)	0.271	0.110	0.016 *	0.628	0.109	0.037 ***
Income
Middle vs. low				−1.455	0.138	−0.087 ***	−1.302	0.134	−0.078 ***	−0.938	0.133	−0.056 ***
High vs. low				−0.969	0.148	−0.058 ***	−0.856	0.144	−0.051 ***	−0.318	0.145	−0.019 *
Wearing frequency Number of wears per year							−0.022	0.001	−0.115 ***	−0.018	0.001	−0.094 ***
Number of wears before laundering							0.840	0.043	0.153 ***	0.792	0.042	0.144 ***
Main use occasion
Work vs. home							−0.115	0.149	−0.006 (ns)	0.0	0.145	−0.001 (ns)
Formal social occasion vs. home							0.185	0.175	0.007 (ns)	0.5	0.171	0.019 **
Casual social occasion vs. home							0.040	0.150	0.002 (ns)	0.1	0.146	0.005(ns)
Sport/training vs. home							1.316	0.267	0.035 ***	1.3	0.259	0.035 ***
Religious occasion vs. home							4.440	0.540	0.051 ***	4.2	0.525	0.048 ***
Sleeping vs. home							3.561	0.496	0.044 ***	3.7	0.482	0.045 ***
Dirty household chores vs. home							5.668	0.582	0.059 ***	5.6	0.565	0.059 ***
Not in active use vs. home							5.703	0.343	0.106 ***	5.4	0.333	0.100 ***
Other or unknown occasion vs. home							1.677	0.280	0.050 ***	1.6	0.274	0.047 ***
Preowned (0 = no, 1 = yes)							0.723	0.199	0.023 ***	0.633	0.195	0.020 **
Likely disposal reason
Fashion vs. wear and tear							−0.497	0.179	−0.019 **	0.24	0.177	0.009 (ns)
Poor fit vs. wear and tear							0.536	0.165	0.022 **	0.66	0.161	0.027 ***
Dislike of colour/style vs. wear and tear							0.150	0.186	0.006 (ns)	0.52	0.182	0.019 **
Lack of space vs. wear and tear							0.513	0.274	0.012 (ns)	0.78	0.267	0.018 **
Don’t need it anymore vs. wear and tear							1.817	0.183	0.065 ***	2.05	0.178	0.073 ***
Unknown vs. wear and tear							−0.740	0.258	−0.025 **	−0.56	0.253	−0.019 *
Planned disposal route
Donate to charity vs. bin							−0.538	0.144	−0.031 ***	−0.488	0.141	−0.029 **
Give/donate to family/friends vs. bin							−0.981	0.194	−0.038 ***	−0.790	0.189	−0.030 ***
Recycle at home vs. bin							0.412	0.197	0.014 *	0.287	0.192	0.010 (ns)
Sell vs. bin							−0.001	0.253	0.000 (ns)	0.008	0.247	0.000 (ns)
Other/don’t know vs. bin							0.752	0.222	0.030 **	0.540	0.217	0.021 *
Monthly spending on clothing
Middle vs. low										−0.502	0.130	−0.030 ***
High vs. low										−1.234	0.157	−0.071 ***
Number of new clothing items last 12 months										−0.044	0.003	−0.094 ***
Wardrobe size as number of garments										0.009	0.001	0.103 ***
Fashion interest (0 = completely disagree, 4 = completely agree)										−0.651	0.049	−0.092 ***
Repaired or sewn clothing last 12 months (0 = no, 1 = yes)										0.171	0.129	0.009 (ns)
Can use a sewing machine (0 = no, 1 = yes)										0.255	0.116	0.015 *
Can sew by hand (0 = no, 1 = yes)										0.054	0.113	0.003 (ns)
Clothing purchase priorities from 0 to 6. Important:
Designer brand										−0.199	0.041	−0.037 ***
Price										0.262	0.041	0.042 ***
Fabric quality										−0.064	0.050	−0.009 (ns)
Fibre content										0.074	0.046	0.012 (ns)
Country of manufacture										0.261	0.043	0.047 ***
Important: Design/Style										−0.039	0.048	−0.006 (ns)
Sustainable production										−0.057	0.049	−0.010 (ns)
Ethically produced										−0.336	0.049	−0.059 ***
Fit										0.074	0.045	0.011 (ns)
Colour										−0.077	0.062	−0.008 (ns)
In fashion										−0.327	0.044	−0.057 ***
R2		0.051			0.117			0.176			0.225
Adjusted R2		0.050			0.116			0.174			0.222
Delta R2		0.051			0.066			0.059			0.049
Delta F		67.456 ***			153.199 ***			70.221 ***			74.440 ***

. The first model (garment information only) explains 5% of the variation. By adding demographic information about the user, the rate increases to 11.6% (ΔR² = 0.066), and by adding use related variables, the rate increases to 17.4% (ΔR² = 0.059). Finally, by including user’s clothing practices, the rate increases to 22.2% (ΔR² = 0.049). Therefore, all four blocks contribute significantly and add between 4.9 and 6.6 percentage points to the final model. The demographics of the user come out as the largest contributor mainly due to the importance of national differences, but the disparities between the four blocks are not that large indicating that all four aspects are important.

The individual variables’ contribution to the final, complete model (Block 4) is evaluated based on part correlations and standardised beta values. The model shows that the ten most important single predictors are:

• Number of wears before laundering: Garments that are washed less frequently (worn more times before washing) have longer lifespans. Those that are washed less often than after every 30 wears have lifespans of up to 4.8 years longer than those washed after every wear.
• Nationality (country): Clothing is kept for a shorter time in China than in the other included countries. The difference is largest when compared to Japan where clothing is kept longest (2.7 years longer), followed by the US (2.4 years), Germany (2.2 years) and finally the UK (1.8 years). However, Chinese respondents include only those living in large cities and do not represent the average Chinese citizens.
• Wardrobe size as the number of garments: For every ten garments increase in garments owned, the total average clothing lifespan increases by one month.
• The number of new clothing items purchased in the last 12 months: For every ten additional items purchased per year, the expected lifespan is reduced by five months, which is the opposite to those with large wardrobes, indicating greater purchases being associated with higher turnover.
• The number of times worn during an active year of wearing: Increase in wearing frequency decreases the lifespan measured in years, on average 10 more wears reduces the garment lifespan by 2 months.
• Fashion interest: The more strongly the respondent reacted to the statement “I change fashion by season” (0 = completely disagree, 4 = completely agree), the shorter the clothing lifespan (each step reduced lifespan by 7.8 months). Those that completely agree have garment lifespans 2.6 years shorter than those that disagree with the statement completely.
• Garment price: Garments at low mid-range pricing (10–39 USD) are used almost 5 months longer than the lowest groups (below 10 USD), those at higher mid-range (40–99 USD) 10.5 months longer, and the most expensive garment group (over 100 USD) are used 2 years longer than the cheapest garments.
• Occasion: The most common garment use category was the reference group of garments used as “every day and around the home”. Garments for all other occasions have longer lifespans, besides workwear, but the difference between workwear and everyday clothing is not significant after controlling for other variables. Garments used for dirty household chores were the oldest reported, followed by those no longer in active use.
• Likely disposal reason: When the users were asked to indicate the most likely reason for disposal of specific garments, wear and tear was the most common (about half). Therefore, this category was used as a reference. Garments that are no longer needed are kept the longest (difference 2.1 years longer), followed by those that are to be disposed of due to lack of space (nine months longer). Garments that do not fit are about 8 months older and those which the user dislikes are about six months older. Garments to be disposed of due to fashion are about three months older, but this difference is not significant in the final model.
• Monthly spending on clothing: The more money spent on clothing the shorter the garment lifespan (lowest trisect is 1.2 years longer than the highest, and 6 months longer than the middle trisect).

Many other variables also contribute significantly. Older consumers generally have older garments, with those aged over 50 keeping 1 year 3 months longer (on average) than garments belonging to the youngest age group (18–29).

The fibre content of garments also contributes significantly. Cotton was used as the reference, as the highest portion of garments in the study were made of cotton, and they have the shortest average lifespan. Silk garments have the longest lifespans (2.2 years more than cotton), followed by wool (1.2 years longer than cotton), and lastly, man-made fibres (synthetics 5.5 months more and regenerated cellulose fibres 9.7 months more than cotton).

These are not average descriptive statistics for the groups compared, but part of a regression where all the other reported variables are included and controlled for. They will change based on which variables are included in the final model.

Variables that do not contribute significantly to the final model are retained for comparison of the variables between the different models. This can be justified, as we wish to control for background variables such as gender, avoid omitted variables bias, and because there are no high correlations between the variables in the model (all Pearson’s correlations below 0.7). The level of collinearity is low (highest VIF 3.1), largest Cooks’ distance of 0.005, and only 0.8% of cases have standard residual above 3.

4.2. Garment Lifespans Measured in Number of Wears

We asked the respondents to estimate the number of times they had worn the garment, and how many times they assumed they would continue wearing it. On average, the items had been worn 34.8 times and were assumed to be worn 46.3 times more in the future, giving a total of 80 wears.

A significant regression equation was found (F (71, 20,002) = 94.330, p < 0.001), with an adjusted R² of 0.248, which indicates that the model explains 24.8% of the variation. The first model (garment properties) explains only 3% of the variation in the number of wears of garments. By adding demographic information about the user, the explanation rate increases to 6.1% (ΔR² 0.035). Further, by adding information about the use, the explanation increases significantly to 21% (ΔR² 0.150). Finally, by including user’s clothing practices, the explanation rate increases to 24.8% (ΔR² 0.039). Therefore, block 3 (garment use) is by far the most significant contributor, while other blocks explain about 3–4% of the variation in garments wears.

As this model includes the same independent variables as the first model, the collinearity statistics are the same. However, this model has a slightly higher share of residuals, as 1.9% of cases have a standard residual above three. Cooks distance is only 0.002, so we assume that these cases do not cause problems to the model.

When looking at the contributions of the individual variables in the final model (block 4) where all variables are included and controlled for, the most important single predictors are:

• Wearing frequency per year: This is the most important predictor of the total number of wears, as expected.
• The number of wears before laundering: The estimated lifespan reported as the number of wears increases by 16 for each higher bracket reported. Garments that are washed after each wear are used 94 times less than those that are washed less often than after every 30 wears.
• High garment price: Garments that cost over 100 USD are estimated to be worn 31 times more than those that cost under 10 USD.
• Clothing purchase priorities: Largest contributions can be seen from those that evaluate fabric quality as important, as the difference in the number of wears between those that evaluate it from completely unimportant to most important is up to 38 wears. Other priorities that contribute positively to a larger number of wears are price and garment fit, which both contribute 33 additional wears, followed by prioritising of design/style (19 wears). The opposite effect can be seen on those that report that brand is important, and they wear garments 33 wears less than those that respond that brand is completely unimportant to them. Other priorities that contribute negatively to the number of wears are importance of fashion (30) and fibre content (10).
• Nationality: Compared to Chinese respondents, those from Germany and the UK use their garments 22 times more, US 8 more wears, and Japanese 6 more wears, after having controlled for other variables.
• Occasion: Garments used at formal social occasions are used 24 fewer times than those worn at home. Garments used the most times are those for sleeping (16 times more than home wear), followed by home wear. Other occasions that contribute significantly to the model are garments used at religious occasions (−16), not in active use (−12), casual social occasions (−12), and workwear (−7). Clothing used for dirty household chores or clothing for sports do not contribute significantly.
• Garment type: Socks and stockings are reported to be worn most times, and in comparison, all other included garment groups are worn fewer times; suits—34, blazers—29, skirts/dresses—20, jumpers/overcoats—20, scarves—16, trousers—14, thermal underwear—11, sportswear—7. The difference in the number of wears between socks and t-shirts/polos/singlets is no longer significant when other variables were included in the model.
• User’s age: This is a significant contributor, indicating that and 50–64 years old use their clothing 14 times less than those below 29.
• Sewing skills: Those that report knowing how to sew use their clothing 12 more times than those who do not know how. Surprisingly, the impact is opposite for those that say they have repaired or sewn clothing the past 12 months, as they have on average 12 wears less.
• Disposal reasons: Garments that are assumed to be disposed of due to wear and tear are worn the most times. Garments to be disposed of due to changes in fashion are worn at least times (−17 wears). This is followed by lack of space (−15 wears), dislike (−12 wears), and those no longer needed (−6 wears).

Other variables also contribute significantly (

Table 4. Summary of four blocks that build the Model DEP2- Total number of wears by current user. * p < 0.050; ** p < 0.010; *** p < 0.001, ns = not significant, p ≥ 0.05.

	Model 1: Garment			Model 2: User			Model 3: Use			Model 4: Practices
	B	SE B	Beta	B	SE B	Beta	B	SE B	Beta	B	SE B	Beta
(Constant) Number of wears	95.7	1.902	***	91.6	3.310	***	57.2	3.540	***	23.3	6.149	***
Garment type
Suits vs. socks/stockings	−38.5	4.362	−0.075 ***	−39.5	4.302	−0.077 ***	−35.8	4.034	−0.070 ***	−34.1	3.953	−0.066 ***
Trousers, pants vs. socks/stockings	−5.7	3.037	−0.017 (ns)	−6.6	2.988	−0.020 *	−15.6	2.843	−0.047 ***	−14.0	2.787	−0.042 ***
Skirts, dresses vs. socks/stockings	−45.2	3.086	−0.132 ***	−38.0	3.153	−0.111 ***	−19.1	3.012	−0.056 ***	−19.8	2.950	−0.058 ***
T-shirts, polos, singlets vs. socks/stockings	−10.2	2.525	−0.038 ***	−10.9	2.489	−0.040 ***	−3.9	2.349	−0.015 (ns)	−3.6	2.293	−0.013 (ns)
Jumpers, pullovers vs. socks/stockings	−14.3	3.129	−0.041 ***	−18.3	3.098	−0.052 ***	−17.8	2.980	−0.051 ***	−19.7	2.915	−0.056 ***
Blazers vs. socks/stockings	−22.1	3.540	−0.054 ***	−24.2	3.491	−0.059 ***	−31.5	3.400	−0.077 ***	−29.4	3.333	−0.072 ***
Overcoats vs. socks/stockings	−2.6	3.684	−0.006 (ns)	−0.6	3.651	−0.001 (ns)	−20.2	3.623	−0.048 ***	−20.2	3.554	−0.048 ***
Scarfs, pashminas vs. socks/stockings	2.4	3.827	0.005 (ns)	2.3	3.778	0.005 (ns)	−17.0	3.802	−0.036 ***	−16.2	3.726	−0.034 ***
Thermal underwear vs. socks/stockings	−23.4	3.851	−0.047 ***	−18.5	3.795	−0.038 ***	−14.9	3.530	−0.030 ***	−10.5	3.464	−0.021 **
Sportswear vs. socks/stockings	−16.3	3.432	−0.039 ***	−14.1	3.373	−0.034 ***	−12.1	3.407	−0.029 ***	−6.6	3.341	−0.016 *
Main fibre category
Wool and wool blends vs. cotton and cotton blends	−16.5	2.180	−0.060 ***	−15.5	2.155	−0.056 ***	−10.7	2.011	−0.039 ***	−7.8	1.967	−0.028 ***
Silk vs. cotton and cotton blends	−20.2	6.622	−0.022 **	−11.1	6.520	−0.012 (ns)	3.1	6.000	0.003 (ns)	4.7	5.854	0.005 (ns)
Synthetics vs. cotton and cotton blends	9.7	2.179	0.033 ***	8.4	2.167	0.028 ***	9.4	2.002	0.032 ***	6.3	1.961	0.021 **
Regenerated cellulose vs. cotton and cotton blends	−7.5	4.376	−0.012 (ns)	0.1	4.321	0.000 (ns)	4.7	3.972	0.008 (ns)	2.4	3.878	0.004 (ns)
Unknown/other fibre vs. cotton and cotton blends	7.9	2.694	0.021 **	0.6	2.686	0.002 (ns)	5.1	3.031	0.014 (ns)	7.4	2.978	0.020 *
Garment price
10–39 USD vs. <10 USD	−5.7	2.222	−0.027 *	−4.6	2.202	−0.022 *	7.8	2.106	0.037 ***	9.1	2.060	0.043 ***
40–99 USD vs. <10 USD	−4.1	2.542	−0.017 (ns)	1.5	2.568	0.006 (ns)	15.1	2.515	0.063 ***	19.3	2.486	0.080 ***
Over 100 USD vs. <10 USD	13.5	2.985	0.048 ***	18.4	3.057	0.066 ***	26.7	3.028	0.096 ***	30.7	3.030	0.110 ***
Gender (0 = male, 1 = female)				−15.5	1.616	−0.074 ***	−6.2	1.503	−0.030 ***	−10.8	1.556	−0.052 ***
Age group
30–49 years vs. ≤29 years				1.2	1.816	0.006 (ns)	−0.5	1.679	−0.002 (ns)	−4.2	1.676	−0.021 *
50–64 years vs. ≤29 years				3.2	2.078	0.014 (ns)	−3.3	1.947	−0.015 (ns)	−13.8	1.995	−0.060 ***
Nationality
Germany vs. China				37.0	2.366	0.149 ***	24.5	2.268	0.099 ***	21.7	2.429	0.087 ***
Japan vs. China				27.4	2.381	0.111 ***	11.5	2.381	0.047 ***	5.7	2.601	0.023 *
UK vs. China				43.4	2.482	0.160 ***	29.2	2.377	0.108 ***	21.7	2.525	0.080 ***
USA vs. China				15.8	2.514	0.059 ***	13.3	2.405	0.050 ***	8.3	2.588	0.031 **
Employed (0 = No, 1 = Yes)				−5.1	1.697	−0.025 **	−4.3	1.575	−0.021 **	2.0	1.591	0.010 (ns)
Married (0 = No, 1 = Yes)				−3.9	1.577	−0.018 *	−3.5	1.453	−0.016 *	−2.2	1.439	−0.010 (ns)
Income
Middle vs. low				−12.4	1.913	−0.059 ***	−7.9	1.765	−0.037 ***	−6.0	1.758	−0.028 ***
High vs. low				−15.6	2.047	−0.073 ***	−9.9	1.889	−0.047 ***	−7.3	1.923	−0.034 ***
Wearing frequency Number of wears per year							0.7	0.017	0.281 ***	0.7	0.017	0.294 ***
Number of wears before laundering							16.5	0.560	0.237 ***	15.6	0.550	0.224 ***
Main use occasion
Work vs. home							−10.3	1.955	−0.040 ***	−7.4	1.913	−0.028 ***
Formal social occasion vs. home							−30.1	2.300	−0.095 ***	−24.5	2.254	−0.077 ***
Casual social occasion vs. home							−13.2	1.972	−0.048 ***	−11.7	1.927	−0.043 ***
Sport/training vs. home							2.3	3.509	0.005 (ns)	0.9	3.426	0.002 (ns)
Religious occasion vs. home							−19.5	7.104	−0.018 **	−15.5	6.943	−0.014 *
Sleeping vs. home							11.5	6.526	0.011 (ns)	15.9	6.371	0.015 *
Dirty household chores vs. home							1.9	7.653	0.002 (ns)	4.6	7.471	0.004 (ns)
Not in active use vs. home							−8.6	4.506	−0.013 (ns)	−11.9	4.400	−0.017 **
Other/unknown occasion vs. home							−16.7	3.680	−0.039 ***	−6.9	3.626	−0.016 (ns)
Preowned (0 = no, 1 = yes)							−11.8	2.617	−0.030 ***	−7.4	2.576	−0.019 **
Likely disposal reason
Fashion vs. wear and tear							−28.6	2.360	−0.087 ***	−17.2	2.342	−0.053 ***
Poor fit vs. wear and tear							−8.8	2.168	−0.028 ***	−2.5	2.134	−0.008 (ns)
Dislike of colour/style vs. wear and tear							−18.7	2.450	−0.055 ***	−12.3	2.404	−0.036 ***
Lack of space vs. wear and tear							−21.6	3.605	−0.039 ***	−15.0	3.533	−0.027 ***
Don’t need it anymore vs. wear and tear							−8.9	2.402	−0.025 ***	−5.8	2.352	−0.016 *
Unknown vs. wear and tear							−5.2	3.393	−0.014 (ns)	3.6	3.343	0.010 (ns)
Planned disposal route
Donate to charity vs. rubbish bin							−5.8	1.895	−0.027 **	−7.4	1.859	−0.034 ***
Give/donate to family/friends vs. rubbish bin							−12.3	2.546	−0.037 ***	−9.3	2.496	−0.028 ***
Recycle at home vs. rubbish bin							10.4	2.593	0.028 ***	8.7	2.539	0.023 ***
Sell vs. rubbish bin							9.6	3.330	0.020 **	5.2	3.266	0.011 (ns)
Other/don’t know vs. rubbish bin							15.6	2.923	0.049 ***	15.6	2.866	0.048 ***
Monthly spending on clothing
Middle vs. low										−7.5	1.717	−0.036 ***
High vs. low										−6.9	2.081	−0.031 ***
Number of new clothing items last 12 months										0.0	0.042	0.005 (ns)
Wardrobe size as number of garments										−0.01	0.008	−0.012 (ns)
Fashion interest 0 = completely disagree, 4 = completely agree)										−2.0	0.649	−0.022 **
Repaired or sewn clothing last 12 months (0 = no, 1 = yes)										−12.3	1.707	−0.051 ***
Can use a sewing machine (0 = no, 1 = yes)										12.1	1.529	0.055 ***
Can sew by hand (0 = no, 1 = yes)										11.8	1.497	0.057 ***
Clothing purchase priorities from 0 to 6. Important:
Designer brand										−5.6	0.536	−0.083 ***
Price										5.5	0.542	0.070 ***
Fabric quality										6.3	0.656	0.069 ***
Fibre content										−2.2	0.605	−0.028 ***
Country of manufacture										−0.4	0.567	−0.006 (ns)
Design/Style										3.1	0.633	0.036 ***
Sustainable production										−1.0	0.647	−0.013 (ns)
Ethically produced										−0.6	0.650	−0.009 (ns)
Fit										5.4	0.590	0.062 ***
Colour										−1.0	0.826	−0.009 (ns)
In fashion										−4.9	0.576	−0.068 ***
R2		0.027			0.062			0.212			0.251
Adjusted R2		0.026			0.061			0.210			0.248
Delta R2		0.027			0.035			0.150			0.039
Delta F		30.472	***		68.904	***		165.211	***		55.007	***

). Women wear garments 10.8 times less than men. This difference was larger before the introduction of the other predictors to the model. Those that spend more money on clothing per month wear it fewer times.

4.3. Number of Users

We do not have complete data for the number of users for each garment, as we only asked how it was acquired and how it is planned to be disposed of. We do not know if garments have had more than one previous owner, and whether the items delivered to reuse will be actually reused, and by how many more users.

Most garments were purchased new (75%), followed by those received as presents (10%). There were 3.7% that were received from someone else who no longer wanted them, and another 3.7% were bought second-hand. Less than 2% were self-made/tailored. This indicates that 7.4% of garments were pre-owned.

The most common disposal choice was planning to donate to charity (34%), followed by disposal to the rubbish bin (30%), give to family/friends (11%), recycle at home (8.4%), and only 4.8% were planned to be sold. This indicates that half of the garments are planned to have subsequent users. Combining these results, 4.3% of garments are likely to have three users.

4.3.1. Preowned Garments

A binary logistic regression model was built for evaluating which variables contribute most to the model that predicts whether garments are preowned or not (

Table 5. Summary of Model Dep3a: Logistic regression analysis for variables predicting whether the garment has had a previous owner (new garment = 0, preowned = 1).

	β	S.E. β	Wald’s χ	df	p	eβOdds Ratio
Garment type: Pairs of socks, stockings			168.760	10	0.000
Suits—Jacket + Trousers/Skirt	1.663	0.275	36.663	1	0.000	5.274
Pants, trousers	1.899	0.212	80.062	1	0.000	6.679
Skirts, dresses	2.196	0.216	103.488	1	0.000	8.989
T-shirts, polo shirts, singlets, tanks	1.264	0.192	43.360	1	0.000	3.540
Jumpers, pullovers, sweaters, cardigans	2.269	0.216	110.343	1	0.000	9.666
Jackets, blazers	2.438	0.235	107.168	1	0.000	11.445
Overcoats, coats, raincoats	2.691	0.249	117.139	1	0.000	14.747
Thermal underwear	1.265	0.247	26.162	1	0.000	3.543
Sports T-shirts, tops, singlets, tanks	1.701	0.232	53.590	1	0.000	5.478
Scarfs, shawls, pashmina’s, stoles	1.392	0.270	26.611	1	0.000	4.023
Fibre content Cotton and blends			42.017	5	0.000
Wool and blends	0.514	0.109	22.363	1	0.000	1.672
Silk	−0.223	0.487	0.210	1	0.646	0.800
Synthetic	−0.384	0.132	8.435	1	0.004	0.681
Regenerated cellulose	0.414	0.240	2.986	1	0.084	1.513
Other/Unknown	0.302	0.342	0.777	1	0.378	1.352
Garment price Cheap (<9.9 USD)			555.847	3	0.000
Medium–low (10–39 USD)	−2.079	0.109	360.841	1	0.000	0.125
Medium–high (40–99 USD)	−3.205	0.155	427.524	1	0.000	0.041
Expensive (>100 USD)	−3.791	0.204	346.696	1	0.000	0.023
Gender (0 = male, 1 = female)	−0.189	0.107	3.112	1	0.078	0.828
Age group 18–29 years			86.738	2	0.000
30–49 years	−0.703	0.097	52.852	1	0.000	0.495
50–64 years	−1.079	0.128	71.486	1	0.000	0.340
Country China			330.626	4	0.000
Germany	1.460	0.192	57.702	1	0.000	4.306
Japan	1.859	0.209	79.239	1	0.000	6.420
UK	2.515	0.184	186.215	1	0.000	12.364
USA	2.988	0.182	269.946	1	0.000	19.836
Employed (0 = no, 1 = yes)	0.061	0.103	0.352	1	0.553	1.063
Married (0 = no, 1 = yes)	0.093	0.093	0.999	1	0.318	1.098
Income group Low			5.980	2	0.050
Medium	−0.100	0.115	0.763	1	0.382	0.905
High	−0.314	0.136	5.302	1	0.021	0.730
Wearing frequency Number of wears per year	−0.008	0.001	34.409	1	0.000	0.992
Number of wears before laundering 0 = After every wear			51.293	6	0.000
1 = 2–3 wears	0.718	0.112	41.342	1	0.000	2.050
2 = 4–5 wears	0.725	0.132	30.358	1	0.000	2.065
3 = 6–10 wears	0.553	0.186	8.815	1	0.003	1.739
4 = 11–19 wears	0.628	0.264	5.649	1	0.017	1.875
5 = 20–29 wears	0.971	0.312	9.696	1	0.002	2.641
6 = Every 30 wears or less often	0.982	0.262	14.102	1	0.000	2.670
Wear occasion: Every day and around the home			21.805	9	0.010
Work occasion	−0.044	0.124	0.129	1	0.720	0.957
Formal social occasion	0.130	0.128	1.033	1	0.310	1.139
Casual social occasion	−0.001	0.118	0.000	1	0.990	0.999
Sport/training occasion	−0.131	0.233	0.318	1	0.573	0.877
Religious occasion	0.731	0.304	5.776	1	0.016	2.077
Sleeping	0.234	0.387	0.365	1	0.546	1.264
Dirty household chores	0.996	0.358	7.757	1	0.005	2.707
Not in active use	0.727	0.420	2.992	1	0.084	2.069
Other/Unknown occasion	−0.636	0.384	2.737	1	0.098	0.530
Likely disposal reason: Wear and tear			54.365	6	0.000
Not in fashion any more	0.767	0.123	38.853	1	0.000	2.153
Poor fit	0.482	0.119	16.278	1	0.000	1.619
Dislike the colour or style	0.348	0.151	5.342	1	0.021	1.416
Lack of space	−0.448	0.273	2.696	1	0.101	0.639
Don’t need it any more	0.168	0.160	1.107	1	0.293	1.183
Other/unknown	−0.392	0.378	1.079	1	0.299	0.675
Disposal route Rubbish bin			54.759	5	0.000
Donate to charity	−0.067	0.122	0.298	1	0.585	0.935
Give/donate to family/friends	0.341	0.137	6.217	1	0.013	1.406
Recycle at home	0.214	0.168	1.621	1	0.203	1.239
Sell	0.941	0.168	31.314	1	0.000	2.564
Other/Don’t know	−0.611	0.311	3.854	1	0.050	0.543
Lifespan in years	0.016	0.007	4.748	1	0.029	1.016
Total number of wears	−0.003	0.001	23.868	1	0.000	0.997
Monthly spending on clothing: Low			2.136	2	0.344
Medium	0.167	0.126	1.765	1	0.184	1.182
High	0.209	0.152	1.907	1	0.167	1.233
Number of new clothing items purchased last 12 months	−0.003	0.003	1.187	1	0.276	0.997
Wardrobe size	0.001	0.001	3.591	1	0.058	1.001
Fashion follower (0 = completely disagree, 4 = completely agree)	0.134	0.042	10.212	1	0.001	1.144
Has repaired or sewn clothing last 12 months (0 = no, 1 = yes)	0.752	0.136	30.805	1	0.000	2.122
Can use sewing machine (0 = no, 1 = yes)	−0.031	0.089	0.123	1	0.726	0.969
Can sew by hand (0 = no, 1 = yes)	0.005	0.093	0.003	1	0.955	1.005
Priorities when buying clothing: Designer brand	0.103	0.034	9.357	1	0.002	1.108
Price	−0.131	0.036	13.012	1	0.000	0.877
Fabric quality	−0.186	0.042	19.420	1	0.000	0.830
Fibre content	0.109	0.039	8.011	1	0.005	1.116
Country of manufacture	−0.100	0.037	7.247	1	0.007	0.905
Design/Style	0.023	0.043	0.301	1	0.583	1.024
Sustainable production	0.189	0.041	21.309	1	0.000	1.208
Ethically produced	−0.054	0.040	1.838	1	0.175	0.948
Fit	−0.094	0.038	6.285	1	0.012	0.910
Colour	−0.057	0.048	1.421	1	0.233	0.945
In fashion	−0.248	0.035	50.114	1	0.000	0.781
Constant (Preowned)	−4.025	0.434	86.080	1	0.000	0.018

). The overall model indicates a good fit (chi-square value 2266.01 with 77 degrees of freedom, p < 0.001, N = 18764), which was confirmed with the Hosmer and Lemeshow Test (p > 0.05). The model explains between 11.4% and 36.0% of the variations when estimated with pseudo R² values (Cox and Snell and Nagelkerke values). The model identifies 95.5% correctly, which is a minor improvement from the zero block which identified 95.3%. However, the true positives identified by the model increased from 0 to 58%.

The single most significant predictor for garments having previous owners is the price. That is, garments that cost over 100 USD are 46 times less likely to be preowned than those that cost less than 10 USD. This is understandable, as most second-hand garments are cheaper than new ones, and about half of the preowned garments in the study were hand-me-downs from friends and family, which are often given for free.

After that, nationality is the second most important predictor. The Chinese have the lowest rates of preowned clothing, and the odds of a garment being pre-owned are 20 times higher for Americans than the Chinese, followed by 12 times for Brits, 6 times for Japanese, and 4 times for Germans.

The third most important predictor is the type of garment. Coats are most likely to be second-hand (15 times more likely than socks/stockings), followed by jackets, jumpers, skirts/dresses, pants/trousers, suits, sportswear, scarfs, t-shirts, and finally thermal underwear which is still 3.5 times more likely to be preowned than socks.

When it comes to fibre content, there is a significant difference between cotton and wool products, and cotton and synthetics. Woollen garments are the most likely to have been preowned (1.7 times more likely than cotton), while synthetics are 1.5 times less likely to be purchased second-hand than cotton garments.

User’s age is also a significant predictor. Those above 50 are about 3 times less likely to have second-hand clothing than those below 30.

The difference between the lowest and highest income groups is also significant. Those with the highest income are 1.4 times less likely to have preowned clothing than those in the lowest income group. Differences between the middle and lowest group are not significant.

As expected, garments that are washed less often are more likely to be preowned. This is likely to be related to the types of garments, as outerwear is reused more often than next-to-skin products.

Most of the use occasions do not contribute significantly when compared to home wear, but clothing for dirty household chores are about 2.7 times more likely preowned.

Furthermore, the reason garments are assumed to be disposed of contributes significantly to the model. Preowned clothes are 2.2 times more likely to be disposed of due to fashion changes, 1.6 times more likely due to poor fit, and 1.4 times more likely due to dislike of style/colour than due to wear and tear.

The likely disposal route contributes significantly to the model, and preowned clothes are 2.6 times more likely to be sold again, and 1.4 times more likely to be given to friends/family than to be put in the rubbish bin at home.

Users that change fashion by the season are more likely to have preowned clothing.

Having repaired or made clothing in the last 12 months increases the chances of having preowned clothing by 2.1. This seems logical, as having older clothes increases the chances of them having to be repaired.

Users’ priorities in clothing acquisition have a significant but minor impact on the model. Those that give high priority to fashionable clothing are 1.3 times less likely to have preowned garments, whereas those that prioritize sustainable production are 1.2 times more likely to have second-hand clothing.

Garment age in years, number of times worn and wearing frequency all contribute significantly to the model, but the impact is minor (odds ratio close to one).

Predictors that do not contribute significantly to the final model include gender, employment and marital status, monthly spending on clothing, sewing skills, and size of the wardrobe.

4.3.2. Planned Reuse

A binary logistic regression model was built for evaluating which predictors contribute to the likelihood of garments being delivered for reuse (

Table 6. Summary of Model 3b: Logistic regression analysis for variables predicting whether a garment is planned to be sent for reuse (0 = to rubbish bin or unknown destination, 1 = to sold or donated to reuse).

	β	S.E. β	Wald’s χ	df	p	eβ Odds Ratio
Garment type: Pairs of socks, stockings			655.253	10	0.000
Suits—Jacket + trouser/skirt	2.034	0.116	305.257	1	0.000	7.647
Pants, trousers	1.933	0.091	452.168	1	0.000	6.909
Skirts, dresses	2.084	0.102	414.520	1	0.000	8.035
T-shirts, polo shirts, singlets, tanks	1.485	0.082	327.360	1	0.000	4.417
Jumpers, pullovers, sweaters, cardigans	2.229	0.104	462.804	1	0.000	9.290
Jackets, blazers	2.140	0.111	372.558	1	0.000	8.499
Overcoats, coats, raincoats	2.300	0.119	370.821	1	0.000	9.969
Thermal underwear	1.165	0.103	126.792	1	0.000	3.205
Sports T-shirts, tops, singlets, tanks	1.368	0.101	184.265	1	0.000	3.928
Scarfs, shawls, pashmina’s, stoles	2.041	0.125	264.730	1	0.000	7.702
Fibre content Cotton and blends			9.883	5	0.079
Wool and blends	0.140	0.057	6.060	1	0.014	1.151
Silk	0.046	0.171	0.073	1	0.787	1.047
Synthetic	−0.015	0.054	0.080	1	0.777	0.985
Regenerated cellulose	−0.010	0.100	0.009	1	0.924	0.990
Other/Unknown	−0.283	0.178	2.517	1	0.113	0.753
Garment price Cheap (<9.9 USD)			228.830	3	0.000
Medium–low (10–39 USD)	0.619	0.063	96.333	1	0.000	1.857
Medium–high (40–99 USD)	0.991	0.076	167.752	1	0.000	2.693
Expensive (>100 USD)	1.378	0.095	212.332	1	0.000	3.966
Gender (0 = male, 1 = female)	−0.208	0.046	20.450	1	0.000	0.812
Age group 18–29 years			79.146	2	0.000
30–49 years	−0.305	0.047	42.038	1	0.000	0.737
50–64 years	−0.515	0.059	75.484	1	0.000	0.597
Country China			1474.812	4	0.000
Germany	0.792	0.069	133.222	1	0.000	2.209
Japan	−1.591	0.075	452.057	1	0.000	0.204
UK	0.729	0.073	98.549	1	0.000	2.074
USA	0.836	0.073	129.597	1	0.000	2.307
Employed (0 = No, 1 = Yes)	−0.162	0.050	10.459	1	0.001	0.851
Married (0 = No, 1 = Yes)	0.028	0.044	0.397	1	0.529	1.028
Income group Low			12.292	2	0.002
Medium	0.127	0.054	5.517	1	0.019	1.135
High	−0.016	0.058	0.073	1	0.786	0.984
Wearing frequency Number of wears per year	−0.001	0.001	2.532	1	0.112	0.999
Number of wears before laundering 0 = After every wear			18.209	6	0.006
1 = 2–3 wears	0.173	0.048	12.889	1	0.000	1.189
2 = 4–5 wears	0.054	0.059	0.831	1	0.362	1.056
3 = 6–10 wears	0.215	0.081	7.126	1	0.008	1.240
4 = 11–19 wears	0.228	0.118	3.759	1	0.053	1.257
5 = 20–29 wears	0.148	0.140	1.118	1	0.290	1.160
6 = Every 30 wears or less often	0.066	0.119	0.306	1	0.580	1.068
Wear occasion: Every day and around the home			111.033	9	0.000
Work occasion	0.301	0.053	32.798	1	0.000	1.351
Formal social occasion	0.349	0.064	29.390	1	0.000	1.418
Casual social occasion	0.037	0.055	0.446	1	0.504	1.038
Sport/training occasion	−0.111	0.089	1.561	1	0.212	0.895
Religious occasion	0.797	0.282	8.002	1	0.005	2.220
Sleeping	−0.845	0.192	19.445	1	0.000	0.430
Dirty household chores	−0.891	0.213	17.540	1	0.000	0.410
Not in active use	0.029	0.217	0.018	1	0.893	1.030
Other/unknown occasion	−0.023	0.169	0.018	1	0.892	0.977
Likely disposal reason: Wear and tear			471.186	6	0.000
Not in fashion any more	0.988	0.064	240.047	1	0.000	2.686
Poor fit	0.716	0.062	135.212	1	0.000	2.046
Dislike the colour or style	0.729	0.065	125.424	1	0.000	2.072
Lack of space	0.840	0.099	71.831	1	0.000	2.316
Don’t need it any more	0.591	0.069	73.910	1	0.000	1.805
Other/unknown	−0.934	0.128	52.876	1	0.000	0.393
Preowned (0 = no, 1 = yes)	0.284	0.095	8.935	1	0.003	1.328
Lifespan in years	−0.011	0.004	10.447	1	0.001	0.989
Total number of wears	−0.002	0.000	50.690	1	0.000	0.998
Monthly spending on clothing: Low			17.427	2	0.000
Medium	0.118	0.053	4.993	1	0.025	1.125
High	0.258	0.063	16.959	1	0.000	1.294
Number of new clothing items purchased last 12 months	0.000	0.001	0.123	1	0.726	1.000
Wardrobe Size	0.000	0.000	0.813	1	0.367	1.000
Fashion follower (0 = completely disagree, 4 = completely agree)	−0.010	0.020	0.234	1	0.629	0.990
Has repaired or sewn clothing last 12 months (0 = no, 1 = yes)	−0.085	0.053	2.630	1	0.105	0.918
Can use sewing machine (0 = no, 1 = yes)	0.180	0.045	16.062	1	0.000	1.197
Can sew by hand (0 = no, 1 = yes)	−0.089	0.043	4.395	1	0.036	0.915
Priorities when buying clothing: Designer brand	0.064	0.016	16.420	1	0.000	1.066
Price	0.040	0.018	5.208	1	0.022	1.041
Fabric quality	0.079	0.021	14.159	1	0.000	1.082
Fibre content	−0.106	0.018	35.031	1	0.000	0.899
Country of manufacture	0.046	0.017	7.571	1	0.006	1.047
Design/Style	−0.159	0.021	58.271	1	0.000	0.853
Sustainable production	0.011	0.019	0.362	1	0.547	1.012
Ethically produced	0.003	0.019	0.034	1	0.854	1.003
Fit	0.025	0.019	1.867	1	0.172	1.026
Colour	0.027	0.026	1.114	1	0.291	1.028
In fashion	−0.017	0.018	0.986	1	0.321	0.983
Constant (planned reuse)	−2.157	0.218	97.872	1	0.000	0.116

). Overall model indicates a good fit (chi-square value 7440.89 with 73 degrees of freedom, p < 0.001, N = 18764). The model explains between 32.7% and 43.9% of variations when estimated with pseudo R2 values (Cox and Snell and Nagelkerke values). The model is 77% correct, which is a significant improvement from the zero block that identified 56.6% correctly.

The Hosmer and Lemeshow test showed a poor model fit (sig < 0.05), but as it has been discussed by several authors [41,42] that the test is less suitable for large data sets and may reject model incorrectly. Therefore, the logistic regression was repeated with five random subsets of the cases, and the Hosmer and Lemeshow Test showed a good model fit in all five cases (p > 0.05).

The most important predictor in the model for reuse is the type of garment. Socks/stockings are least likely to be delivered for reuse, while coats are most likely (10 times higher odds). These are followed by jumpers, jackets, skirts/dresses, scarfs, suits, trousers, t-shirts/polos, sportswear, and finally thermal underwear.

Clothing price is another of the most important predictors for reuse. Garments that have cost over 100 USD are almost four times more likely to be intended for reuse than garments that cost less than 10 USD.

Nationality is also a significant predictor for reuse. Clothes in Japan are least likely to be delivered for reuse (almost five times less likely than in China), while clothing in Germany, UK, and the US are about twice as likely to be sent for reuse than in China.

Concerning garments’ fibre content, the only significant difference can be seen between cotton and wool, as woollen garments are slightly more likely to be sent for reuse (1.2 times more).

Based on the assumed disposal reason for garments, their condition contributes significantly to whether they will be passed on to the next user. Garments disposed of due to wear and tear are the least likely to be sent for reuse (except for group the that had unknown disposal reasons). Garments disposed of due to changes in fashion trends are most likely to be sent for reuse, followed by garments disposed of due to lack of space, dislike of colour or style, or poor fit. The odds of reuse are about twice as high for disposal reasons other than wear and tear.

User’s age group is also a significant predictor. Clothing owned by those over 50 is 1.7 times less likely to be sent for reuse than clothing from those below 30.

Gender is also significant, but with only a minor contribution. In the final model after other variables were controlled for, men’s clothing is 1.2 times more likely to be sent for reuse than women’s clothing.

Those working full time are slightly less likely to send clothing for reuse than students, part-time workers, or other non-working people. The effect of income is minor, and the difference between the lowest and highest income groups is not significant.

Compared to home wear, clothing for dirty household chores or sleeping are less likely to be sent for reuse, while clothing for religious occasions is most likely to be reused, although this is a very small clothing group. This is followed by clothing for formal social occasions and workwear.

If a garment was preowned it increases the odds of it also being sent for reuse by 1.3.

Garment age in years and number of wears are also significant, but the contributions are minor, with slightly less likelihood for reuse for older and more worn garments.

The amount of money spent on clothing is a significant predictor. Those in the highest spending group are 1.3 times more likely to send clothing for reuse than those in the lowest spending group.

Sewing skills have only a minor contribution, but interestingly in two different directions. Knowing how to use a sewing machine contributes positively, while hand sewing skills contribute negatively, although both impacts are minor.

Most of the acquisition priorities did not contribute significantly to the model, except for prioritising designs/style or specific fibres, which contributed negatively to reuse intention, and designer brand, price, fabric quality, and country of manufacture, which contributed positively.

Variables that are not significant in the final model were marital status, wearing frequency, laundering frequency, wardrobe size, number of clothing purchases, how interested the user is in following fashion, and if the user has repaired clothing.

5. Conclusions

This article shows it is possible to study garment lifespans through a wardrobe survey and gain understanding into the relative importance of factors affecting lifespans. Clothing lifespans have been measured in three different ways, how long (years), how many times (wears) and by how many consumers (users) the garments were used. Independent contributing variables were divided into four blocks related to the garment, user, garment use and clothing practices. The four blocks differ in importance depending on the way clothing lifespan is measured (

Table 7. Summary that indicates how important the different blocks of predictors are in contributing to the four models as Δr².

Model	Model 1: Years	Model 2: Wears	Model 3a: Preowned Garments		Model 3b: Planned Reuse
	R2	R2	Cox and Snell R2	Nagelkerke R2	Cox and Snell R2	Nagelkerke R2
Model fit as r2 or pseudo r2	22.2%	24.8%	10.9%	34.6%	32.1%	43.0%
1 Garment	5.1%	2.7%	5.2%	16.6%	17.2%	23.0%
2 User	6.6%	3.5%	3.1%	9.7%	11.6%	15.6%
3 Garment use	5.9%	15.0%	2.1%	6.6%	3.2%	4.4%
4 Clothing practices	4.9%	3.9%	1.0%	3.1%	0.7%	0.9%

). Garment related properties are the most significant for predicting the number of users, while garment use was most indicative of the number of wears. For the number of years, all four aspects were important, but user demographics gave a slightly higher contribution to the model than the three other blocks.

We identified several factors affecting lifespans. The most important predictors are nationality, use occasions, laundering frequency, garment price, disposal reason, garment type, user’s age, fibre content, wearing frequency, and monthly spending on clothing. However, there are several other significant variables, and the importance of these varies between the models.

Some of the aspects that affect the lifespans of clothing are conditions that cannot be easily changed, such as the characteristics of the owner (e.g., age, nationality, and income). Knowledge of these conditions can nevertheless be useful because they indicate which changes give the largest impacts. Chinese respondents reported wearing clothing fewer times, kept it for shorter periods and were less likely to use second-hand garments than respondents from Germany, Japan, UK, or USA. This was partly explained by the sampling, where only large cities were included; thus, they were a younger population with a larger portion working full time (both attributes which significantly impact results). Additionally, previous research has indicated that Chinese consumers, in general, have a strong resistance towards second-hand clothing consumption [43]. Japanese respondents had the oldest garments but were least likely to donate for reuse, while Americans were most likely to purchase preowned clothing. This is likely to be related to the infrastructure available. Having clothing collection systems readily available makes it easier to donate clothing for reuse, and likewise having well-functioning markets for used clothing or established practices for private reuse (such as receiving hand-me-downs from friends or relatives), makes it easier to acquire used items. Ensuring a long lifetime through reuse has long been an important environmental strategy, and several circular business models are using this approach [44]. Our results are in line with past research and point out the importance of not overlooking the potential of reuse within family and friends.

Several aspects of the clothing itself affect their lifetime. Socks and stockings are worn the most times but have the shortest lifespans in years and are least likely to be preowned or donated for reuse. This is the opposite for outerwear like coats, which are among the oldest garments in the wardrobe and the most likely to be reused. The fibre content of the garments contributed to differences in lifespans. The oldest garments in the wardrobe were most often made of silk and wool, and a larger share of wool garments were preowned, and woollen garments were more likely to be sent for reuse.

Price is also important, and higher prices predict longer lifespan because of longer use, more wears and more users. Garment prices vary by garment category, but also within categories because of factors like the materials that are used, the brand, the quality and production costs, such as country of manufacture. Price is a simple way to compare products that has the potential to impact durability, but additionally the price can also play a role in how consumers value and take care of garments. Price is an important economic incentive that can be influenced by the industry through adjusting the costs of production and the profit levels, but also through politics, such as subsidies and taxes.

Disposal reasons indicated that the physical strength of garments is important and can also be increased through a commitment to quality in the industry and/or through measures that strengthen consumers right to complain. Another significant indicator for lifespans that the industry can work to improve was poor garment fit, where improvements in pattern grading and size labelling are key issues [32,45].

Garments with lower washing frequencies have many environmental benefits [46]. In addition to environmental savings from reduced laundering, they are worn longer and more times, while also being more likely to be preowned. Results suggest that frequent laundering causes wear and tear, giving additional motivation to increase efforts to reduce washing frequency and promote gentler cleaning methods.

5.1. Limitations

The survey targeted five key consumer markets: China, Germany, Japan, the UK, and the USA. The wardrobe and consumer habits identified by the data are representative of these markets only and are not representative of consumers worldwide.

Using quantitative survey methods has its limitations. Many responses could only be given in specific categories, such as the number of wears where the highest category was “over 200 wears” for past and future wears. To calculate the average number of wears, this category was coded to 250 times; thus, the maximum total value became 500 wears. This is likely to be too low for garments that are in active use for many years, but only about 2% of the garments were in this category.

Another limitation concerns the questions of future use, as it is likely difficult for the respondents to foresee how long they will keep the garments, or why they are going to dispose of them. Therefore, the uncertainty related to variables is likely higher than those concerning past use.

The survey was extraordinary long, and despite measures taken to avoid respondent wear out, it is possible that some respondents did not consider their answers as carefully at the end of the survey as at the beginning. Some careless responses were excluded during the quality control of the data.

5.2. Future Research Directions

Some of our results seem to be a bit contradictory, such as the relationship between repairing clothes and being able to sew. Increased knowledge about the importance of consumers’ habits, knowledge and skills for longevity is likely to explain these ambiguities and should be studied further.

The models explain between 11% and 43% of the variation in clothing lifespans. This shows that there are still other aspects that are important and were not assessed in the survey. These are likely to include aspects such as whether the user likes the garment and if it is flattering when worn. There is also likely to be a high degree of randomness related to the entirety of wardrobe (e.g., what other alternatives the user has for various occasions). Future research should aim to identify which other factors are important.

5.3. Implications

Several of the conditions that affect lifespans can be changed through policy instruments such as improved consumer rights and financial incentives, as well as work with consumer attitudes and education. This applies particularly to attitudes towards fashion which seem to be an especially important factor for the length of garments’ lifespans. Attitudes can be changed and created, and the industry itself is an important driver, through advertising and marketing. Increasing clothing durability and intrinsic value over time will therefore be an important aim for the industry to counteract unnecessary replacement of clothing.