ArticlePDF Available

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

Authors:

Abstract and Figures

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.
Content may be subject to copyright.
Sustainability 2020, 12, 9151; doi:10.3390/su12219151 www.mdpi.com/journal/sustainability
Article
What Affects Garment Lifespans? International
Clothing Practices Based on a Wardrobe Survey in
China, Germany, Japan, the UK, and the USA
Kirsi Laitala * and Ingun Grimstad Klepp
Consumption Research Norway (SIFO), Oslo Metropolitan University; 0130 Oslo, Norway;
ingunk@oslomet.no
* Correspondence: kirsil@oslomet.no; Tel.: +47-672-356-32
Received: 24 September 2020; Accepted: 30 October 2020; Published: 3 November 2020
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.
Keywords: clothing lifespans; sustainability; use phase; consumer behavior; wardrobe survey;
fashion consumption
1. Introduction
The clothing industry is one of the highest producers of pollution in the world and causes severe
damage to the environment [1–3]. 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 [4–7]. 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
Sustainability 2020, 12, 9151 2 of 47
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 [11–17] 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.
Figure 1. Summary of garment lifespans from various studies, given in years. Average and range
values are given, except for suits which had only one source. Lifespan here is not the total age of
preowned clothes, but only the time they were kept by the individual owners surveyed. Prepared
based on [20].
Sustainability 2020, 12, 9151 3 of 47
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
Sustainability 2020, 12, 9151 4 of 47
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 [23–26]
and that an increase in durability will also extend the potential length of a garments 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.
Sustainability 2020, 12, 9151 5 of 47
Figure 2. Taxonomy of factors that contribute to clothing lifespans.
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. The data was
sorted in two ways, by “per respondent” (N = 1111), and “per garment” (N = 53 461).
Sustainability 2020, 12, 9151 6 of 47
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 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. 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.
Sustainability 2020, 12, 9151 7 of 47
Table 2. Garments in focus.
Accessories Formal Wear Casual Wear
Casual
Next-to-
Skin
Underwear Sportswear
Scarfs,
shawls,
pashminas
and stoles
suits (2pc)
pant 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
single
ts 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.
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.
Sustainability 2020, 12, 9151 8 of 47
In the linear regression analysis in models 1 and 2 the predictors were coded and entered as
follows:
1. 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).
2. Fibre content (wool and wool blends, silk, synthetics, regenerated cellulose, or unknown fibre
content. Cotton and cotton blends were used as the reference group).
3. Garment price (10–39 USD, 40–99 USD, and over 100 USD. Reference group are those which cost
below 10 USD).
4. Gender (0 = male, 1 = female).
5. Age group (30–49 years and 50–64 years compared to reference group 18–29 years).
6. Nationality (German, Japanese, UK, and USA. China were used as the reference).
7. Employment status (0 = non-working, student or part time, 1 = working full time).
8. Marital status (0 = single, divorced, separated, 1 = married/living with partner).
9. Income in three groups, trisects of each country (middle and high compared to low income).
10. Wearing frequency (number of wears per year) (based on how often the garment was worn
either whole year-round or seasonally).
11. 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).
12. 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).
13. Preowned (0 = no previous owner, 1 = garment had previous owner)
14. 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).
15. 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).
16. Monthly expenditure on clothing (broken into three groups—middle and high, where low
expenditure is the reference).
17. The number of new clothing items purchased last year (from less than 5 to over 50, coded as
from 2.5 to 70).
18. Wardrobe size (number of garments, varied from 35 to 663).
19. 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).
20. Has repaired or sewn clothing last 12 months (0 = no, 1 = yes).
21. Sewing skills (measured in two ways: “Can use sewing machine”—0 = no, 1 = yes, or “Can sew
by hand”—0 = no, 1 = yes).
22. 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
Sustainability 2020, 12, 9151 9 of 47
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. 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.
Sustainability 2020, 12, 9151 10 of 47
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 ***
Sustainability 2020, 12, 9151 11 of 47
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 *
Sustainability 2020, 12, 9151 12 of 47
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 ***
Sustainability 2020, 12, 9151 13 of 47
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:
1. 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.
2. 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.
3. Wardrobe size as the number of garments: For every ten garments increase in garments owned,
the total average clothing lifespan increases by one month.
4. 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.
5. 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.
6. 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.
7. 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.
8. 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.
9. 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.
10. 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).
Sustainability 2020, 12, 9151 14 of 47
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:
1. Wearing frequency per year: This is the most important predictor of the total number of wears,
as expected.
2. 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.
3. High garment price: Garments that cost over 100 USD are estimated to be worn 31 times more
than those that cost under 10 USD.
4. 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).
5. 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.
6. 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
Sustainability 2020, 12, 9151 15 of 47
workwear (−7). Clothing used for dirty household chores or clothing for sports do not contribute
significantly.
7. 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.
8. 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.
9. 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.
10. 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). 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.
Sustainability 2020, 12, 9151 16 of 47
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 *
Sustainability 2020, 12, 9151 17 of 47
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 ***
Sustainability 2020, 12, 9151 18 of 47
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
***
Sustainability 2020, 12, 9151 19 of 47
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). 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.
Sustainability 2020, 12, 9151 20 of 47
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.
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
еβ
Odds
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
Sustainability 2020, 12, 9151 21 of 47
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
Sustainability 2020, 12, 9151 22 of 47
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
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). 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.
Sustainability 2020, 12, 9151 23 of 47
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.
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 еβ 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
Sustainability 2020, 12, 9151 24 of 47
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
Sustainability 2020, 12, 9151 25 of 47
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 las
t
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
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). 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.
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%
Sustainability 2020, 12, 9151 26 of 47
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
Sustainability 2020, 12, 9151 27 of 47
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.
Author Contributions: Conceptualization, K.L. and I.G.K.; formal analysis, K.L.; investigation, K.L.;
methodology, K.L.; writing—original draft, K.L. and I.G.K.; writing—review and editing, K.L. and I.G.K. All
authors have read and agreed to the published version of the manuscript.
Funding: This work was supported by Australian wool growers and the Australian government through
Australian Wool Innovation Limited (AWI), contract number 4500012208, and the Research Council of Norway,
project number 303080.
Acknowledgments: We would like to thank Nielsen AG for conducting the survey, and Stephen Wiedemann
from Integrity Ag & Environment, Angus Ireland and Sam Ropert from AWI, Roy Kettlewell from Kettlewell
Consulting, and Torvald Tangeland from SIFO, OsloMet, for commenting on the article draft.
Conflicts of Interest: The founding sponsor AWI financed and approved the publication but did not influence
the representation or interpretation of the reported research results. The Norwegian Research Council had no
role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the
manuscript, or in the decision to publish the results.
Appendix A. Questionnaire
Only questions used in this paper are included.
WARDROBE AUDIT
SCREENING
Sustainability 2020, 12, 9151 28 of 47
Q1
In which country do you live? [Single Answer] Code
Route
China 1
Germany 2
Japan 3
UK 4
USA 5
None of these 6 Termina
te
Q2
Which of the following age groups do you belong to? (Single Answer] Code
Route
Under 18 years 1 CLOSE
18–24 years 2
25–29 years 3
30–34 years 4
35–39 years 5
40–44 years 6
45–49 years 7
50–54 years 8
55–59 years 9
60–64 years 10
65 and over 11 CLOSE
Prefer not to say 12 CLOSE
Q3
Are you…? [Single Answer] Code
Route
Male 1
Female 2
Q7
Are you involved or responsible for washing and dry cleaning your clothes?
[Single Answer] Code
Route
Yes, I am solely responsible 1
Yes, I share this household chore with someone else at home 2
No, I am not involved/responsible 3 CLOSE
Q8
On average, approximately how much do you spend per month on clothing
including workwear, sportswear, underwear and accessories e.g., ties, scarfs
and gloves, but excluding shoes?
If you have in mind a yearly expenditure please calculate the monthly
expenditure by dividing the yearly amount by 12. [Single Answer]
PROBE Please select the option which is closest if you are unsure
(Options given in local currencies)
Code
(154) Route
INSTRUCTION
Please read this instruction carefully before you start. You will be asked about all the clothes
items and accessories that you own and have in your wardrobe or cupboard. You can disregard
clothes items and accessories that may be in your wardrobe/cupboard but do not belong to you.
Please take this survey with you to where you keep your clothes so that you can complete the survey
as you are answering the questions about your clothes items.
Please bear in mind to cover all clothes items and accessories you own and have, including items
that may be elsewhere than in your wardrobe (i.e., laundry, drying rack, dry cleaner, storage, or
stored at someone else’s home etc) at the time you are completing this survey.
Thank you. Your participation in this study is highly appreciated.
Sustainability 2020, 12, 9151 29 of 47
Q10a
MALE RESPONDENTS
Which of the following clothes items and accessories do you have? We assume you have
underwear and socks. Please select all the items that you have in the list below.
Please select all that apply. [Multiple Answer]
Deep dive categories are highlighted in green
Q10a
Q10b
Nos.
Q10b
For each clothing item and accessory you have, can you please indicate how many of each do
you have?
SuitsJacket + Trouser 01
Ties 02
Shirts (Work/Formal) 03
Shirts (Casual/Everyday) 04
Pants/Trousers (Work/Formal) 05
Jeans 07
Other Casual Pants/Trousers 06
Shorts 08
T-shirts/Polo shirts 09
Jumpers/Pullovers/Sweaters/Cardigans 10
Jackets/Blazers (Work/Formal) 11
Jackets/Blazers (Casual/Everyday) 12
Overcoats/Coats/Raincoats (Work/Formal) 13
Overcoats/Coats/Raincoats (Casual/Everyday) 14
Robes/Cloaks 15
Pyjama Sets 16
Pyjama Tops 17
Pyjama Pants/Shorts/Boxers 18
Ski/Snowboard Pants 19
Sports Tracksuits 20
Sports Track Pants/Tights/Shorts 21
Sports T-shirts/Tops 22
Sports Singlets/Vests 23
Sports Sweatshirts/Hoodies 24
Scarfs/Shawls 25
Hats/Beanies/Berets/Caps 26
Pairs of Socks 27
Pairs of Gloves/Mittens 28
Thermal Tops and warm undershirts 29
Thermal Leggings and warm long underpants 30
Underwear Briefs/Trunks/Boxers 31
Sustainability 2020, 12, 9151 30 of 47
Underwear Vests/Singlets 32
Ethnic Clothing/Ethnic Wear (e.g., kurta, hakama, jeogori, paji, uwagi etc) 33
Q27a
FEMALE RESPONDENTS
Which of the following clothes items and accessories do you have? We assume you have
underwear and socks. Please select all the items that you have in the list below. [Multiple
Answer] Q27a
Q27b
Nos.
Deep dive categories are highlighted in GREEN
Q27b
For each clothes item and accessory you have, can you please indicate how many of each do
you have?
Suits − Jacket + Trouser/Skirt 01
Blouses/Shirts/Tops (Work/Formal) 02
Blouses/Shirts/Tops (Casual/Everyday) 03
Pants/Trousers (Work/Formal) 04
Jeans 10
Shorts 11
Other Casual Pants/Trousers 05
Skirts (Work/Formal) 06
Skirts (Casual/Everyday) 07
Dresses (Work/Formal) 08
Dresses (Casual/Everyday) 09
T-shirts/Polo shirts 12
Singlets/Tanks 13
Jumpers/Pullovers/Sweaters/Cardigans 14
Jackets/Blazers (Work/Formal) 15
Jackets/Blazers (Casual/Everyday) 16
Overcoats/Coats/Raincoats (Work/Formal) 17
Overcoats/Coats/Raincoats (Casual/Everyday) 18
Robes/Cloaks 19
Pyjama Sets 20
Pyjama Tops 21
Pyjama Pants/Shorts 22
Chemises/Baby dolls/Night dresses 23
Ski/Snowboard Pants 24
Sports Tracksuits 25
Sports Track Pants/Tights/Shorts 26
Sports T-shirts/Tops 27
Sports Singlets/Tanks 28
Sustainability 2020, 12, 9151 31 of 47
Sports Sweatshirts/Hoodies 29
Scarfs/Shawls/Pashmina’s/Stoles 30
Hats/Beanies/Berets/Caps 31
Pairs of Socks/Stockings 32
Pairs of Gloves or mittens 33
Thermal Tops and warm undershirts 34
Thermal Leggings and warm long underpants 35
Underwear Briefs 36
Underwear Bras 37
Maternity Dresses 38
Maternity Skirts 39
Maternity Pants/Shorts 40
Maternity Jumpers/Pullovers/Sweaters/Cardigans 41
Maternity T-shirts/Polo shirts/Tops/Singlets 42
Ethnic Clothing/Ethnic Wear (e.g., kimono, hanbok, chima jeogori, sari etc) 43
You will now be asked about items of clothing you own in the different categories you indicated
previously. If you own less than 10 items in the category you will be asked for each of the items you
own. If you own more than 10 items in the category you will be asked to evaluate 10 items—please
try to evaluate a wide variety of items that are made of different materials or may be used for different
occasions.
Q11
FOR EACH GARMENT IN Q10b/Q27b
What fabric is this clothes item or accessory? Please refer to the garment care label for your
answer. [Single Answer]
Code
100% Cotton 1
Cotton Blends (i.e., cottons mixed with other synthetics or natural fibres) 2
100% Wool 3
Merino Wool 4
Wool Blends (i.e., Wool mixed with other synthetic or natural fibres) 5
Cashmere 6
Silk 7
Polyester/nylon/acrylic/polypropylene/polyamide 12
Viscose/rayon/modal/lyocell/acetate 13
Denim/Jeans fabric 09
Other 10
Don’t know 11
Q12
FOR EACH GARMENT IN Q10b/Q27b
When did you buy or acquire this clothes item or accessory? [Single Answer]
Code
In the last 6 months 1
Sustainability 2020, 12, 9151 32 of 47
7–11 months ago 2
1 year ago 3
2 years ago 4
3–4 years ago 5
5–10 years ago 6
11–15 years ago 7
16–20 years ago 8
21–25 years ago 9
26–30 years ago 10
More than 30 years ago 11
Don’t know/Cannot remember 12
Q13
FOR EACH GARMENT IN Q10b/Q27b. ONLY ASK FOR ITEMS 1–5
Which of the following best describes how this item was acquired? [Single Answer]
Code
Bought new 1
Bought second hand 2
It was given to me as a present 3
It was given to me as the other person no longer wanted it 4
I made it myself 5
Someone else made it for me 6
Other 7
Don’t know 8
Q14
FOR EACH GARMENT IN Q10b/Q27b. ONLY ASK FOR ITEMS 1–5
ONLY ASK IF CODE 1 OR CODE 2 AT Q13
How much did you pay for this item? Please insert amount in local currency.
An approximate amount is OK
(Respondent to enter approximate amount) [Single Answer]
Code
Don’t know/Can’t remember 8
Q15
FOR EACH GARMENT IN Q10b/Q27b. ONLY ASK FOR ITEMS 1–5
On what occasion do you wear this clothes item or accessory most often? If it is used for
multiple occasions, please give the most frequent occasion only.
[Single Answer]
Code
Work occasion 1
Formal social occasion 2
Everyday and around the home 3
Casual social occasion 4
Religious occasion 6
For Sleeping 9
When training/doing sports 10
For gardening/painting/other dirty household chores 11
Other occasion 7
The item is not in active use 12
Don’t know 8
Q16
FOR EACH GARMENT IN Q10b/Q27b. Do not show question if code 8,12 at Q15. ONLY ASK
FOR ITEMS 1–5
On what other occasions do you wear this clothes item or accessory? Please select all that apply.
[Multiple Answer]
Code
Work occasion 1
Formal social occasion 2
Sustainability 2020, 12, 9151 33 of 47
Everyday and around the home 3
Casual social occasion 4
Religious occasion 6
For Sleeping 9
When training/doing sports 10
For gardening/painting/other dirty household chores 11
None 13
Q17
FOR EACH GARMENT IN Q10b/Q27b. ONLY ASK FOR ITEMS 1–5
Which of the following best describes what time of year you wear this item?
[Single Answer]
Code
I wear it mostly in summer 1
I wear it mostly in winter 2
I wear it all year round 3
The item is not in active use 4 Q19
Q18
FOR EACH GARMENT IN Q10b/Q27b. DO NOT ASK IF CODE 4 AT Q17.
ONLY ASK FOR ITEMS 1–5.
How often do you typically wear this item PIPE IN RESPONSE FROM Q17 in Summer/in
Winter/all year round? [Single Answer]
Code
Several times a week 1
Once a week 2
Once every 2 weeks 3
Once every 3–4 weeks 4
Once every 3 months 5
Once every 6 months 6
Less often than once every 6 months 7
Don’t know 8
Q19
FOR EACH GARMENT IN Q10b/Q27b.
How many times have you worn this item? [Single Answer]
Code
Never 1
1–2 times 2
3–4 times 3
5–9 times 4
10–19 times 5
20–49 times 6
50–99 times 7
100–199 times 8
More than 200 times 9
Don’t know 10
Q20
FOR EACH GARMENT IN Q10b/Q27b. ONLY ASK FOR ITEMS 1–5 Code
Sustainability 2020, 12, 9151 34 of 47
How often do you expect to wear this item in the future? [Single Answer]
Several times a week 1
Once a week 2
Once every 2 weeks 3
Once every 3–4 weeks 4
Once every 3 months 5
Once every 6 months 6
Less often than once every 6 months 7
Don’t know 8
Q21
FOR EACH GARMENT IN Q10b/Q27b
How many times do you expect to wear this item in the future?
An approximate amount is OK. [Single Answer]
Code
Never 1
1–2 times 2
3–4 times 3
5–9 times 4
10–19 times 5
20–49 times 6
50–99 times 7
100–199 times 8
More than 200 times 9
Don’t know 10
Q22
FOR EACH GARMENT IN Q10b/Q27b
How often do you or someone else typically wash or dry clean this item? For seasonal items
please report frequency of washing or dry cleaning item when in use. [Single Answer]
Code
After every wear 1
After every 2 wears 2
After every 3 wears 3
After every 4 wears 4
After every 5 wears 5
After every 6–10 wears 6
After every 11–19 wears 7
After every 20–29 wears 8
After every 30 wears or less often 9
Never 10
Don’t know 11
Q23
FOR EACH GARMENT IN Q10b/Q27b (Do not ask if CODE 10 or CODE 11 at Q22)
Now regarding washing of this clothes item or accessory, do you…? [Single Answer]
Code
Sustainability 2020, 12, 9151 35 of 47
Hand wash it 1
Wash it in the washing machine 2
Send it for dry cleaning 3
Sometimes hand wash and sometimes machine wash 4
Sometimes hand wash and sometimes dry clean 5
Sometimes machine wash and sometimes dry clean 6
Don’t know 7
Q24
FOR EACH GARMENT IN Q10b/Q27b. ONLY ASK FOR ITEMS 1–5
Which of the following would you expect to do in order to dispose of this clothes item or
accessory when you no longer want it? [Single Answer]
Code
Donate to charity or clothes recycling collection 1
Donate/give to family/friends 2
Put in the Rubbish Bin at home 3
Recycle at home (e.g., use as cleaning cloth) 4
Sell (e.g., garage sale, eBay) 5
Other 6
Don’t know 7
Q25
FOR EACH GARMENT IN Q10b/Q27b. ONLY ASK FOR ITEMS 1–5
Here are some reasons people have said for disposing of garments. Which of the following do
you think is likely to be the main reason you would dispose of this particular item? [Single
Answer]
Code
General wear and tear 1
The style is not in fashion any more 2
Doesn’t fit me properly 3
I don’t like the colour or style any more 4
Not enough space in my wardrobe for new items 5
I don’t need the garment any more 6
Other 7
Don’t know 8
ATTITUDES—ASK ALL
Q44
ASK ALL. RANDOMISE
ONE ANSWER PER ROW—LIMIT COLUMN 1 TO ONE ANSWER
How important to you are each of the following aspects when you are buying Formal wear?
Code
Route
Most
important
aspect
One of the key
aspects
One I look
for but not
very
important
Not at all
important
Designer brand 1 2 3 4
Price 1 2 3 4
Fabric quality 1 2 3 4
Sustainability 2020, 12, 9151 36 of 47
Fibre content 1 2 3 4
Country of manufacture 1 2 3 4
Colour 1 2 3 4
In fashion 1 2 3 4
Design/Style 1 2 3 4
Sustainable/environmentally
friendly production 1 2 3 4
Ethically produced 1 2 3 4
Fit 1 2 3 4
Q45
ASK ALL. RANDOMISE.
ONE ANSWER PER ROW—LIMIT COLUMN 1 TO ONE ANSWER
How important to you are each of the following aspects when you are buying Smart Casual
wear?
Code
Route
Most
important
aspect
One of the key
aspects
One I look
for but not
very
important
Not at all
important
Designer brand 1 2 3 4
Price 1 2 3 4
Fabric quality 1 2 3 4
Fibre content 1 2 3 4
Country of manufacture 1 2 3 4
Colour 1 2 3 4
In fashion 1 2 3 4
Design/Style 1 2 3 4
Sustainable/environmentally
friendly production 1 2 3 4
Ethically produced 1 2 3 4
Fit 1 2 3 4
Q46
Here is a list of statements people have made. Thinking about yourself, please select how
much you agree or disagree with each statement.
Code
Completely
agree
Somewhat
agree
Neither
agree nor
disagree
Somewhat
disagree
Completely
disagree
I change fashion with the
season 1 2 3 4 5
Q47
In the last 12 months approximately how many new items of clothing have you bought,
including pairs of socks and underwear? [Single Answer]
Code
Less than 5 1
6–10 2
11–15 3
16–20 4
21–50 5
Sustainability 2020, 12, 9151 37 of 47
More than 50 7
Don’t know 6
Q53
ASK ALL. RANDOMISE CODES 1–4.
Here are some statements people have made about their skills in making/repairing/altering
clothes. Which of the following best applies to you? [Multiple Answer]
Code
I can use a sewing machine 1
I can sew by hand 2
I can knit 3
I can crochet 4
None of these 5
Don’t know 6
Q54
ASK ALL. RANDOMISE.
Which of the following have you done in the last 12 months? [Multiple Answer]
Probe: Please tick all that apply
Code
Sewn a button 1
Fixed an unravelled seam 2
Patched clothing 3
Darned clothing 4
Fixed a trouser length 5
Adjusted the size of an item of clothing 6
Changed a zipper 7
Made something new out of old clothing 8
Knitted or crocheted 9
Sewn new clothing 10
None of these 11
Don’t know 12
ASK ALL. DEMOGRAPHICS SECTION
And finally, a few details about yourself to ensure that we are covering a cross section of the
population.
Q72
What is your current marital status? [Single Answer] Code
Route
Single 1
Married/living with a partner 2
Divorced/Separated 3
Widowed 4
Prefer not to say 5
Q73
How many people live in your household including yourself?
Q76
What is your employment status? [Single Answer] Code
Route
Sustainability 2020, 12, 9151 38 of 47
Q72
What is your current marital status? [Single Answer] Code
Route
Part time 1
Full time 2
Casual 3
Seeking work 4
Not seeking work 5
Home duties 6
Student 7
Retired 8
Prefer not to say 9
Q77
Can you please indicate which of the following categories best describes your total/gross
household income per year before tax? [SA]
PROBE Please select all one.
(Options given in local currencies)
Code
Route
Appendix B. Descriptive Statistics
Table A1. Answer distributions as percentage of each country per garment. Significance tested with
Pearson chi-square and indicated for every variable. (N from 23498 to 53461. Weighted data).
China Germany Japan UK USA Total
Garment type
p < 0.001
Pairs of socks, stockings 17.8% 18.6% 16.9% 20.8% 19.8% 18.8%
Suits—Jacket +
Trouser/Skirt 5.2% 2.6% 6.4% 2.8% 3.4% 4.2%
Pants, trousers 12.2% 9.6% 10.8% 10.1% 11.2% 11.0%
Skirts, dresses 10.7% 7.8% 9.9% 11.3% 8.4% 9.3%
T-shirts, polo shirts,
singlets, tanks 12.4% 20.0% 17.1% 17.4% 21.1% 18.2%
Jumpers, pullovers,
sweaters, cardigans 7.3% 13.0% 8.8% 11.6% 7.5% 8.7%
Jackets, blazers 7.1% 8.4% 7.1% 5.4% 6.6% 6.9%
Overcoats, coats, raincoats 8.6% 3.9% 6.1% 7.0% 6.7% 6.6%
Thermal underwear 5.9% 2.6% 6.4% 3.0% 5.1% 5.0%
Sports T-shirts, tops,
singlets, tanks 8.4% 7.3% 5.8% 5.7% 6.8% 6.9%
Scarfs, shawls,
pashmina’s, stoles 4.3% 6.1% 4.7% 4.9% 3.4% 4.3%
Main fibre
category
p < 0.001
Cotton and blends 59.8% 70.3% 51.3% 65.1% 74.1% 65.8%
Wool and blends 22.1% 13.8% 22.0% 17.8% 12.6% 16.8%
Synthetics 12.2% 12.7% 25.3% 14.2% 11.4% 14.5%
Regenerated cellulose 5.9% 3.2% 1.4% 2.9% 1.9% 2.9%
When did
you buy or
acquire this
clothes item?
p < 0.001
In the last 6 months 32.5% 15.2% 16.7% 17.8% 20.2% 21.2%
7–11 months ago 23.1% 12.0% 8.7% 12.4% 13.9% 14.4%
1 year ago 20.3% 15.2% 15.3% 15.9% 13.6% 15.6%
2 years ago 12.0% 15.1% 12.6% 15.8% 13.8% 13.5%
3–4 years ago 6.7% 14.8% 13.7% 11.4% 12.4% 11.7%
5–10 years ago 2.4% 9.5% 11.3% 6.4% 8.1% 7.6%
11–15 years ago 0.5% 2.7% 3.2% 2.0% 2.7% 2.3%
Sustainability 2020, 12, 9151 39 of 47
China Germany Japan UK USA Total
16–20 years ago 0.2% 1.3% 1.5% 0.9% 1.0% 0.9%
21–25 years ago 0.0% 0.4% 0.6% 0.4% 0.3% 0.3%
26–30 years ago 0.0% 0.2% 0.3% 0.3% 0.2% 0.2%
More than 30 years ago 0.0% 0.2% 0.2% 0.3% 0.6% 0.3%
Don’t know/Cannot
remember 2.2% 13.4% 16.0% 16.4% 13.2% 11.8%
Acquisition
method
p < 0.001
Bought new 80.9% 74.5% 79.3% 70.7% 67.5% 73.8%
Bought second hand 0.7% 2.5% 3.1% 6.0% 8.1% 4.7%
It was given to me as a
present 11.1% 9.8% 8.3% 11.8% 9.5% 9.8%
It was given to me as the
other person no longer
wanted it
3.0% 3.7% 2.7%