See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/325403762
Key factors influencing the purchase intention of activewear: an empirical
study of US consumers
Article in International Journal of Fashion Design Technology and Education · May 2018
DOI: 10.1080/17543266.2018.1477995
CITATIONS
READS
2
3,147
2 authors, including:
Ting Chi
Washington State University
54 PUBLICATIONS 581 CITATIONS
SEE PROFILE
Some of the authors of this publication are also working on these related projects:
Apparel Mobile Commerce View project
Why U.S. Consumers Buy Sustainable Cotton Made Collegiate Apparel View project
All content following this page was uploaded by Ting Chi on 03 September 2018.
The user has requested enhancement of the downloaded file.
INTERNATIONAL JOURNAL OF FASHION DESIGN, TECHNOLOGY AND EDUCATION
https://doi.org/10.1080/17543266.2018.1477995
Key factors influencing the purchase intention of activewear: an empirical study of
US consumers
Lauren Watts and Ting Chi
Department of Apparel, Merchandising, Design and Textiles, Washington State University, Pullman, WA, USA
ABSTRACT
ARTICLE HISTORY
In recent years, activewear is no longer contained to the gym. It is visible in places such as grocery
store, movie theatre, and classroom. To better understand this phenomenon of activewear
purchased to be worn as casualwear, based on an expanded theory of planned behaviour
model, using the primary survey data, this study aimed to determine the key factors influencing
the US consumers’ intention towards purchasing activewear for casual use. Multiple regression
method was utilised for hypothesis testing. Attitude and perceived behavioural control showed
significantly positive effects on intent to purchase activewear. Past activewear purchase
behaviour and lifestyle orientation towards health and wellness help US consumers gain positive
attitude towards shopping activewear for casual purpose. The proposed model exhibits a high
explanatory power for US consumer purchase intention towards activewear for casual use,
accounting for 52.4% of variance in the purchase intention.
Received 1 May 2017
Accepted 12 May 2018
1. Introduction
In recent years, the activewear industry has become a
lucrative avenue of business worldwide (Euromonitor
International, 2014). Growing consumer interest in
healthy lifestyles has heralded an increased demand for
apparel to meet their needs in style, performance, and
functionality. Regardless of one’s intent to play sports
or simply look sporty, activewear has become a staple
in consumers’ wardrobes that extends beyond the gym.
From yoga pants in the supermarket to golf shirts in
the movie theatre, activewear is accepted in many facets
of life not necessarily associated with participation in
physical activity (Friedman, 2015). More styles and innovations coupled with increasing consumer demand have
generated considerable growth for the activewear
industry.
According to the research firm NPD group, the sales
of activewear in the US was $44 billion in 2015, a 16%
increase from 2014, while the sales of other apparel products experienced a 2% decrease in 2015 (Banjo, 2016).
This gap between these two categories is predicted to
widen in the future (Molina, 2016). Activewear was
once a term for apparel worn with the intent to perform
athletic activities. However, as casual, comfort-focused
clothing gains popularity, consumers adopt elements of
activewear into their everyday wardrobe (Wray &
Hodges, 2008). In order to look more fashionable, the
KEYWORDS
US consumer; purchase
intention; activewear
fashion-minded athlete conversely accepts casual style
elements into their collection of activewear (Lau,
Chang, Moon, & Liu, 2006). As a result, the line between
activewear and casualwear has blurred.
Activewear is apparel made for participating in physical activities or sports, although it is increasingly observed
worn in daily environments such as shopping, transporting to work, and coffee shops, due to the fusion of fashion
and fitness (Fromm, 2016). Chi and Kilduff (2011)
explains that some popular activewear include leggings,
tracksuits, golf shirts, tennis shirts, bike shorts, and unitards. These apparels are defined as activewear because
they are specifically engineered to deliver a pre-defined
performance or functionality to users, over and above
their normal functions (Gupta, 2011). Horton, FerreroRegis, and Payne (2016) argue that activewear is a form
of contemporary fashionable dress that signals a healthy
body and a leisured lifestyle devoted to the improvement
of the self. Activewear worn casually signifies the casualisation of dress and the idolisation of a healthy and physically fit body as a fashion aesthetic (Horton et al., 2016).
Consumers want apparel to be able to transition from
activity to activity, without the need to go home and
change. The athleisure trend, which refers to apparel
designed to be worn both for exercising and for general
use (Athleisure, 2015), is a socially acceptable way to
accomplish the consumers’ desire for both apparel
CONTACT Ting Chi
tchi@wsu.edu
Department of Apparel, Merchandising, Design & Textiles, Washington State University, Johnson Hall Annex C23, PO
Box 646406, Pullman, WA 99164-6406, USA
© The Textile Institute and Informa UK Ltd 2018
2
L. WATTS AND T. CHI
functionality and versatility (Green, 2017). As activewear
is accepted into the everyday wardrobe of Americans, consumer demand has led to an increase in products, product
variety, and retail locations made available for them.
Many retailers have responded to this growing demand
including notable brands such as Nike, Adidas, Under
Armour, Athleta, and Lululemon.
Despite this substantial growth, little research has been
conducted concerning the casual consumption of activewear. As suggested by Chi and Kilduff (2011), future
research is needed to explore purchase intentions for activewear. This lack of consumer identification and understanding within the activewear market is a potential
pitfall for apparel retailers and manufacturers. Based on
an expanded theory of planned behaviour (TPB) model
proposed in this study (Ajzen, 1985, 1991), using the primary consumer data gathered by an online survey, we
aimed to determine the key factors influencing the US
consumers’ intent to purchase activewear for casual use.
Findings from this study can be used by activewear retailers and brands to align marketing strategies to better service the target consumers, reinforce and encourage repeat
purchase behaviours (PBs), and strengthen the relationship between brands and their customers.
2. Literature review and hypothesis
development
2.1. Theory of planned behaviour
This study utilises one of the most widely used social psychology theories, the TPB (Ajzen, 1985, 1991), to examine the determinants of consumers’ purchase intentions
towards activewear for casual use. TPB suggests that a
person’s behavioural intention depends on the person’s
attitude about the behaviour, subjective norms, and perceived behavioural control (PBC). This theoretical framework and its myriad of variations are exceedingly
popular amongst researchers in numerous fields seeking
to predict behaviour through the identification of motivating factors of a reasoned process (Knowles, Hyde, &
White, 2012; Lac, Crano, Berger, & Alvaro, 2013; Norman, 2011; Yoon, 2011). Among these research fields,
apparel consumer studies find relevancy in the model
to examine purchase intention (Chi & Zheng, 2016;
Cowan & Kinley, 2014; Kim & Karpova, 2010; Xu,
Chen, Burman, & Zhao, 2014). From intention, we
learn the degree of motivation individual possesses to
perform a behaviour (Zheng & Chi, 2015).
2.1.1. Attitude
Ajzen (1985) describes attitude towards the behaviour as
a positive or negative response to an object or behaviour
which is the result of the evaluation of outcomes. Attitude is strongly related to behaviour as supported by
studies that focused on purchase intention of apparel
and footwear through the use of TPB (Kim & Karpova,
2010; Wang, 2014; Yan, Ogle, & Hyllegard, 2010;
Zheng & Chi, 2015). The research of Kim and Karpova
(2010) concerning consumers’ purchase intention of
counterfeit goods primarily focused on attitudes as a
determinant, which proved to be positively related to
purchase intent. Yan et al. (2010) investigated the purchase intentions and attitudes of Generation Y consumers towards American Apparel as influenced by
message appeal and sources. They discovered that participants held positive attitudes and an increased intention to purchase American Apparel products when
exposed to messages containing fair labour appeals. In
a study of environmentally friendly apparel purchase
intention of US consumers, Zheng and Chi (2015)
found that attitude within the TPB framework showed
the most significant impact on consumer purchase intention. In an empirical study of sportswear brand equity,
Tong and Hawley (2009) indicated brand equity could
affect consumer attitude that sequentially influenced
purchase intention. Thus, the following hypothesis is
proposed.
H1: There is a significantly positive relationship
between US consumer attitude towards activewear consumption and their intention to purchase activewear for
casual use.
2.1.2. Subjective norm
Subjective norm reflects an individual’s perception of
how other important figures in their lives, such as family,
friends, and peers, would want them to participate in the
behaviour (Ajzen, 1985). With the growing popularity of
the athleisure trend in mainstream fashion, it is reasonable to assume that consumers will feel the need to conform to the current fashion trends through the purchase
of activewear for casual purposes. Georg Simmel argued
that fashion is a form of a social relationship that allows
those who wish to conform to the demands of a group to
do so (Simmel, 1957). Prior empirical studies have found
that subjective norm can significantly affect purchase
intention of apparel (Chi, 2013; Jin & Kang, 2011; Yan,
Hyllegard, & Blaesi, 2012; Zheng & Chi, 2015). In
addition to following the rise of the athleisure trend, consumers may feel pressured by society’s positive perceptions of individuals who are physically active and
subsequently purchase and wear activewear to project
an athletic image to others (Martin, Sinden, & Fleming,
2000; Tong & Hawley, 2009). As found in Martin et al.’s
(2000) study, people who identify as regular participators
INTERNATIONAL JOURNAL OF FASHION DESIGN, TECHNOLOGY AND EDUCATION
in physical activities are regarded more favourably by
Canadian college students. Their findings hold relevance
to the present study as it is probable that consumers seek
approval from important people and groups in their lives
through the projection of an athletic image. Thus, we
propose hypothesis 2 below.
H2: There is a significantly positive relationship
between US consumer subjective norm and their intention to purchase activewear for casual use.
2.1.3. Perceived behavioural control
PBC accounts for the perceived barriers or facilitators to
performing the behaviour, whether they are internal or
external factors (Ajzen, 1991). If individuals lack the
resources (e.g. money, time, information, etc.) or the
self-confidence to perform a behaviour, their intentions
are largely valueless. In prior empirical studies, purchase
intention has been found to be positively affected by PBC
(Chi & Zheng, 2016; Jin & Kang, 2010; Shim, Eastlick,
Lotz, & Warrington, 2001). We may observe consumers
who hold a positive attitude towards activewear, but feel
that they lack the money to afford such products or they
feel that their lifestyle does not support the use of the
products effectively dissuading their intention to purchase. Additionally, consumers’ lack of self-confidence
as a result negative body-image may inhibit their intention to purchase activewear if they are unwilling to wear
apparel that is close-fitting to the body or does not
adhere to their self-image. Wasilenko, Kulik, and
Wanic (2007) observed similar avoidance behaviour in
women with low body-satisfaction who exercised less
often than women with high body-satisfaction. The
amount of social distance one feels between those who
dress in athletic styles and themselves may also be a significant influencer in the observation of PBC (St-James,
De Man, & Stout, 2006). Therefore, we propose hypothesis 3 below.
H3: There is a significantly positive relationship
between PBC and US consumer intention to purchase
activewear for casual use.
2.2. Expanding the TPB model
The TPB model is, in some cases, found lacking in it
multidimensionality and requires an expansion to the
classic variables when investigating purchase intention
of apparel (Cowan & Kinley, 2014; Muzaffar, 2015;
Zheng & Chi, 2015). Ajzen (1991) indicates the TPB
model is, in principle, open to the inclusion of additional
variables for improving its explanatory power. To better
understand what influence US consumers’ purchase
3
intention of activewear for casual use, an expanded
TPB model is necessary.
2.2.1. Past activewear PB
Past behaviour is arguably a significant determinant of
future behaviour (Ajzen, 1991; Conner & Armitage,
1998). Many prior studies have investigated the impact
of past behaviours on attitude that is a common predictor for behavioural intention (d’Astous, Colbert, &
Montpetit, 2005; Kim & Karpova, 2010; Zheng & Chi,
2015). Past behaviour is the execution of a behaviour
or reaction to overt or covert stimuli in the past
(Sommer, 2011). d’Astous et al. (2005) found that consumers tend to use their previous experiences in performing a behaviour to justify their decision to perform again.
They believe that attitudes are defined by the information drawn from past experiences. In a study examining consumers’ purchase intention of fashion counterfeit
goods, Kim and Karpova (2010) found that those who
had previous experience in purchasing fashion counterfeit goods held more positive attitude towards the behaviour of purchasing those products. Dean, Raats, and
Shepherd (2011) found that past behaviour predicted
the PB of organic food. In their empirical study of US
consumer purchase intention towards environmentally
friendly apparel, Zheng and Chi (2015) found that past
PB of environmentally friendly apparel led to a more
favourable consumer attitude towards environmentally
friendly apparel. Therefore, hypothesis 4 is proposed as
follows.
H4: There is a significantly positive relationship
between past PB of activewear and US consumer attitude towards activewear.
2.2.2. Consumer lifestyle orientation
Behaviour models can be enhanced with the inclusion of
the concept of lifestyle with consideration to personal
characteristics, society, habits, and value (Kucukemiroglu, 1999). Bourdieu (1984) believed that the constitution
of an individual is greatly related to consumption behaviours, which forms the lifestyle and tastes of the
individual.
Individuals with strong health values place a higher
level of importance on exercise and are more willing to
accept exercise behaviour (Bephage, 2000; Markula,
2017). Moreover, individuals who highly value exercise
and wellness behaviours are more likely to adopt visual
symbols of dress in order to project the lifestyle to others
(St-James et al., 2006). Those with a higher regard for a
wellness-based lifestyle are more likely to wear activewear in daily life than consumers without this lifestyle
regard. Health consciousness has been found to
4
L. WATTS AND T. CHI
significantly motivate consumption (First & Brozina,
2009; Markula, Grant, & Denison, 2001). The importance that an individual places on the value of participation in physical activity could affect consumption of
activewear. Kraft and Goodell (1993) determined that
consumer lifestyle orientation is an important factor
when investigating health-related behaviours. In an
effort to provide clarity in health and wellness studies,
Kraft and Goodell (1993) developed a wellness scale to
construct psychographic profiles for market segmentation. They believe that lifestyle has a significant impact
on attitude towards health-related behaviours because
those who are currently proactive in their health would
have a more positive outlook on healthy behaviours.
The wellness scale measures personal health attitudes
and behaviours through the dimensions of health
environment sensitivity, physical fitness, personal health
responsibility, and nutrition and stress management
(Kraft & Goodell, 1993). The orientation towards a
health-conscious lifestyle is indicative of consumption
preferences. This study employed this approach to
measure the relationship between lifestyle and attitude
towards the purchase of activewear for casual use as
seen in the hypothesis below.
H5: There is a significantly positive relationship
between the consumer lifestyle orientation and US consumer attitude towards activewear.
3. Proposed research model and developed
survey instrument
Based on the extensive literature review above, a research
model with proposed hypotheses was developed to reveal
the antecedents of US consumer’s intention to purchase
activewear for casual use. The demographic factors
including age, gender, education level, income level,
and ethnicity were included as control variables. The
model is illustrated in Figure 1.
The survey instrument consists of one section to collect demographic information and multiple sections to
gather the responses to the measures of antecedents
and consumer’s intention to purchase activewear. The
measures and scales applied were adapted from the previous pertinent studies and presented in Table 1. This
procedure provides proof of the content validity of
adopted measures and scales (Mariadoss, Chi, Tansuhaj,
& Pomirleanu, 2016).
4. Methodology
4.1. Data collection
The developed survey instrument was firstly reviewed by
three professors who were familiar with research topic
and survey techniques. Then, the instrument was tested
through a pilot study with 10 US consumers. The suggestions from the participants were used to refine the instrument with regard to arrangement, wording accuracy, and
relevance (Zheng & Chi, 2015). This process helped to
make the final survey instrument more valid and clearer
(Mariadoss et al., 2016). The primary data were collected
by an online survey of US consumers. The professional
survey website used is Amazon Mechanical Turk
(https://www.mturk.com), which can reach a wide
range of consumers with high reliability (Goodman, Cryder, & Cheema, 2012; Paolacci, Chandler, & Ipeirotis,
2010). The qualified survey participants were the US
consumers who had purchased activewear for casual
use in the past year. A total of 146 eligible responses
were received. The profile of survey respondents is presented in Table 2. Among 146 respondents, the gender
distribution is quite balanced although it skews a bit
towards male (42% vs. 58%). The ages of the respondents
show a relatively normal distribution with the majority at
an age range of 20–55. Most of the respondents have
relatively high education level at 98.6% with some college
education or higher, which are considered the major
consumer segment for casual activewear (Chi &
Kilduff, 2011; Green, 2017). Most of the respondents
are Caucasian (73%) that is generally consistent with
the US population but are relatively lower in African
American (6%) and Hispanic and Latino (5%) while
higher in Asian and Pacific islanders (16%). The income
distribution of the respondents is generally aligned with
the US population (US Census Bureau, 2018).
4.2. Data analysis methods
Figure 1. The proposed research model.
The statistical assumptions including variable normality,
multicollinearity, and correlations were first examined.
Multivariate normality is assessed through the skewness
and the kurtosis of each variable. There is violation of
normality assumption, if the results are greater than
INTERNATIONAL JOURNAL OF FASHION DESIGN, TECHNOLOGY AND EDUCATION
5
Table 1. Constructs and corresponding measures and scales.
Construct
Attitude (A)
Measure and scale (factor loading)
Five-point Likert scale (from ‘Strongly disagree = 1’ to ‘Strongly agree = 5’.)
A1: Purchasing activewear for me would be a good idea. [0.789]
A2: I like the idea of purchasing casual activewear. [0.787]
A3: I have a favourable attitude towards the behaviour of purchasing casual
activewear. [0.773]
Source
Kim and Karpova (2010)
and Zheng and Chi (2015)
Subjective
norm (SN)
SN1: Most people important to me think that I should purchase casual activewear. [0.652]
SN2: Close friends and family think that it is acceptable for me to purchase
casual activewear. [0.719]
SN3: People I listen to could influence me to purchase casual activewear. [0.777]
Fitzmaurice (2005)
PBC
PBC1: In general, shopping for activewear is difficult. R [0.844]
PBC2: It is hard to find the products I want when shopping for casual activewear. R [0.808]
PBC3: I have the resources to purchase casual activewear. [0.676]
PBC4: If I want to, I have complete control of purchasing casual activewear. [0.828]
Kim and Karpova (2010)
Consumer lifestyle
orientation (L)
L1: I am concerned about my health all the time. [0.565]
L2: I try to exercise at least 30 minutes a day, 3 days each week. [0.650]
L3: I believe that the ‘wellness’ idea is a fad. R [0.600]
L4: My daily meals are nutritionally balanced. [0.822]
L5: I think of myself as a health-conscious consumer. [0.861]
Kraft and Goodell (1993) and
Sparks and Shepherd (1992)
Purchase
intention (PI)
PI1: I would purchase casual activewear. [0.739]
PI2: The probability that I would consider buying activewear is low. R [0.671]
PI3: I would consider buying casual activewear. [0.744]
Huang, Lee, and Ho (2004)
Past PB
Five-point Likert scale Never = 1, Have once or twice = 2, Several times in the
past 6 months = 3, Several times a MONTH = 4, Several times a WEEK = 5) PB1:
Over the past 6 months, how often have you purchased activewear?
Chaudary, Ahmed, Gill, and
Rizwan (2014)
Note: R – reverse measures. The factor loading of each measure to its latent construct is provided in parenthesis.
+1.0 or smaller than −1.0 (George & Mallery, 2010).
Multicollinearity is a statistical phenomenon in which
two or more predictor variables in a multiple regression
model are highly correlated, meaning that one can be linearly predicted from the others with a non-trivial degree
of accuracy. Variance-inflation factor (VIF) values of less
than 5.0 indicate that no multicollinearity problem
occurs (Ott, Longnecker, & Ott, 2001). In addition,
Field (2009) suggests that correlation coefficient between
Table 2. Demographic information of the survey respondents.
Percentage
Gender
Male
Female
Education level
Some college
Bachelor’s degree
Master’s degree
Doctorate
Others
Personal annual income level
(before taxation)
<$5000
$5000–$9999
$10,000–$14,999
$15,000–$24,999
Percentage
Age
58.2
41.8
42.5
48.5
6.2
1.4
1.4
5.5
6.8
6.8
17.8
$25,000–$34,999
17.1
$35,000–$49,999
17.2
$50,000–$74,999
15.8
$75,000 and more
13.0
Note: Total eligible responses are 146.
20–25
26–30
31–35
36–40
41–45
46–50
51–55
56–60
>61
Ethnicity
Caucasian
African American
Asian, Pacific
islanders
Hispanic, Latino
Others
14.4
29.5
22.6
10.3
11.0
4.1
4.8
2.6
0.7
72.6
5.5
15.6
4.8
1.5
variables should not go beyond 0.8 to avoid multicollinearity. Then, exploratory factor analysis using varimax
rotation method was utilised to reduce a larger number
of measurement items to a smaller number of factors
(Stewart, 1981). Measurement items with low factor
loadings (less than 0.50), high cross-loadings (greater
than 0.4), and item-to-total correlations (less than 0.3)
were excluded from the factor matrices (Anderson &
Gerbing, 1988). This also established the evidences of
unidimensionality for the investigated constructs.
Since each construct in the proposed research model
is measured by multiple items, unidimensionality,
reliability, and construct validity were tested for proving
construct measurement adequacy (Chi, Kilduff, &
Gargeya, 2009). First, unidimensionality refers to the
existence of one underlying measurement construct
(dimension) that accounts for variation in examinee
responses. Second, Cronbach’s alpha is a coefficient of
internal consistency. It is commonly used as an estimate
of the reliability of a psychometric test for a sample of
examinees. Third, convergent validity refers to the extent
to which indicators of a specific construct ‘converge’ or
share a high proportion of variance in common. Convergent validity is valid when average variance extracted
(AVE) scores for all latent constructs are above the
desired threshold of 0.50. Fourth, discriminant validity
refers to the extent to which a construct is truly distinct
6
L. WATTS AND T. CHI
from other constructs. Comparing the AVE scores to the
squared correlation between the two constructs of interest, the AVEs should be greater than the squared correlation in order to demonstrate satisfactory discriminant
validity. Table 3 presents the correlations and properties
of all constructs and demographic variables. Cronbach’s
coefficient alphas of all latent constructs are greater than
0.70, indicating reliability was rigorously met (Nunnally,
1978). The AVE scores for all latent constructs were
above the desired threshold of 0.50, suggesting convergent validity. All AVE scores are greater than the squared
correlation between the two constructs of interest, which
demonstrate satisfactory discriminant validity.
Once the adequacies of all constructs were demonstrated, the average score of the multi-items for each
construct was computed and used in multiple regression
analysis (Wang & Barnes, 2007). To minimise the
volume of linear constructs and non-linear construct
measures in a model, the use of single indicants for constructs in the model has been recommended by many
scholars (Agarwal & Malhotra, 2005; Cadogan, Cui,
Morgan, & Story, 2006; Chi & Sun, 2013). Multiple
regression analysis was applied to analyse the relationship (i.e. the proposed hypotheses). SPSS software was
used for statistical analysis.
5. Hypothesis testing results and discussion
Once the adequacies of all latent constructs were demonstrated, the proposed hypotheses (H1–H5) in the model
were tested. Table 4 presents the results of hypothesis
testing. Four out of five proposed hypotheses were proven statically significant at p < .05 level. The results
showed that consumer intention to purchase activewear was significantly affected by their attitude (β =
0.472, t = 6.254), PBC (β = 0.321, t = 4.872), but not by
subjective norm (β = 0.052, t = 0.779). The effects of
demographic variables on consumer intention to purchase activewear were all insignificant at p < .05 level.
Thus, H1 and H3 were supported while H2 was rejected.
This indicates that US consumers are more likely to purchase activewear for casual purpose when they show
positive attitude towards wearing activewear outside of
exercise environments. This positive relationship is congruent with the findings from previous apparel purchase
intention studies (Wang, 2014; Yan et al., 2010; Zheng &
Chi, 2015). Increasingly perceived control of the
resources (e.g. money, time, information) towards activewear consumption and the self-confidence to wearing
activewear outside of exercise environments lead to US
consumers’ higher willingness to purchasing activewear.
This was consistent with previous empirical studies concerning purchase intention towards apparel (Jin & Kang,
2010; Shim et al., 2001). In contrast, individual’s perceived opinions of other important figures in their lives
(e.g. family, friends, and peers) towards activewear consumption do not significantly affect their own decisions
on purchasing activewear. The lack of concern over the
expectations and opinions of important others suggests
that the purchase of activewear is tied to self-interest.
Vallerand, Deshaies, Cuerrier, Pelletier, and Mongeau
(1992) argue that subjective norm is less significant in
the explanation of purchase intention because it is a
remote concept. As activewear is often worn for solitary
leisure activities, remoteness is relevant in that the influence of significant others is potentially less pervasive.
Table 3. Correlations and properties of all constructs and demographic variables.
Attitude
SN
PBC
L
PB
PI
Age
Gender
Ethnicity
Education
Income
Mean
SD
Cronbach’s alpha
AVE
Skewness
Kurtosis
VIF
Attitude
SN
PBC
L
PB
PI
Age
Gender
Ethnicity
Education
Income
1
0.150
0.166
0.196
0.030
0.415
–
–
–
–
–
4
0.7
0.814
0.613
−0.706
0.787
1.810
0.387**
1
0.012
0.097
0.012
0.071
–
–
–
–
–
3
0.8
0.726
0.520
0.281
0.069
1.325
0.407**
0.094
1
0.123
0.001
0.285
–
–
–
–
–
4
0.7
0.702
0.630
−0.298
0.036
1.323
0.443**
0.311**
0.350**
1
0.014
0.206
–
–
–
–
–
4
0.8
0.723
0.504
−0.163
0.002
1.469
0.172*
0.108
−0.024
0.119
1
0.002
–
–
–
–
–
3
0.7
–
0.531
0.522
0.451
1.092
0.644**
0.267**
0.534**
0.454**
−0.045
1
–
–
–
–
–
4
0.9
0.771
0.517
−0.394
−0.509
–
0.299**
−0.044
0.141
0.046
−0.083
0.240**
1
–
–
–
–
–
–
–
–
–
–
1.313
−0.191*
0.040
−0.049
−0.099
−0.017
−0.136
−0.231**
1
–
–
–
–
–
–
–
–
–
1.138
0.054
0.023
0.069
−0.035
0.025
−0.058
−0.170*
0.122
1
–
–
–
–
–
–
–
–
1.003
0.111
−0.107
0.098
0.181
0.078
0.132
0.229**
0.019
−0.046
1
–
–
–
–
–
–
–
1.202
0.198*
0.136
0.234*
0.281**
0.144
0.242**
0.090
0.111
0.097
0.274**
1
–
–
–
–
–
–
1.261
Note: The numbers in the upper left section are the squared correlations. PI, purchase intention; SN, subjective norm; PBC, perceived behavioural control; L, consumer lifestyle orientation.
*Correlation is significant at the.05 level (two-tailed).
**Correlation is significant at the.01 level (two-tailed).
9.313 (7/138)
.321
5.419
3.968
5.378
–
.141
.401
Constant
PB
Lifestyle
Yes
Yes
H4
H5
Attitude
Yes
No
Yes
H1
H2
H3
Note: PI, intent to purchase casual activewear; PBC, perceived behavioural control; SN, subjective norm; PB, past purchase behaviour; lifestyle, lifestyle orientation. Std. coef. stands for standardised coefficients.
<.001
<.001
−0.183
6.254
0.779
4.872
Constant
Attitude
SN
PBC
PI
–
.472
.052
.321
<.001
.036
<.001
F-value (df1/df2)
18.825 (8/137)
.524
.334
.952
.083
.693
.277
<.001
.181
.098
.536
.516
0.304
−0.372
−1.748
0.396
1.092
4.911
−1.343
1.665
−0.621
0.652
.020
−.023
−.107
.025
.070
.296
−.099
.120
−.047
.050
Age
Gender
Ethnicity
Education
Income
Age
Gender
Ethnicity
Education
Income
Total R 2
Sig. at p < .05
t-value
Std. coef. (β)
Control variable
Sig. at p < .05
t-value
Std. coef. (β)
Independent variable
Dependent variable
Hypothesis
Table 4. Results of hypothesis testing.
.855
<.001
.438
<.001
Sig. at p < .05
INTERNATIONAL JOURNAL OF FASHION DESIGN, TECHNOLOGY AND EDUCATION
7
Overall, the model exhibits a high explanatory power for
US consumer purchase intention towards casual activewear, collectively accounting for 52.4% of variance in
the purchase intention (R 2 = 52.4%).
Hypotheses 4 and 5 tested the impacts of consumer
past PB and lifestyle orientation (L) on their attitude
towards purchasing activewear. Both past PB and lifestyle orientation (L) significantly affect US consumers’
attitude towards purchasing activewear (β = 0.141, t =
3.968; β = 0.4011, t = 5.378, respectively). Thus, H4 and
H5 were supported. US consumers’ past PB positively
influenced attitude towards purchasing activewear for
casual use. This finding was congruent with previous
studies (Dean et al., 2011; Kim & Karpova, 2010). Likewise, individuals who place a higher level of importance
on health value and exercise are more likely to show a
positive attitude towards wearing activewear outside of
exercise environments. This result was consistent with
previous studies (First & Brozina, 2009; Honkanen, Verplanken, & Olsen, 2006; Ogle, Hyllegard, & Dunbar,
2004). The effects of demographic variables on consumer
attitude towards wearing activewear were all insignificant at p < .05 level except age. Age shows a positive
effect on consumer attitude towards wearing activewear
for casual use (β = 0.296, t = 4.911). The model shows a
good explanatory power for US consumer attitude
towards purchasing activewear for casual use, accounting
for 32.1% variance in US consumer attitude towards purchasing activewear (R 2 = 32.1%).
6. Conclusions and implications
In recent years, the activewear industry has grown tremendously. As consumers have become increasingly
interested in healthy lifestyles, the demand for athletic
performance apparel to meet their needs has risen. However, activewear is no longer contained to the gym
(Friedman, 2015). It is visible in places such as grocery
store, movie theatre, and classroom. To better understand this phenomenon of activewear purchased to be
worn as casualwear, this study determined the key factors influencing US consumers’ intentions to purchase
activewear for casual use.
Overall, this study made contributions to extant literature in four ways. First, taking a holistic approach,
this study integrated the existing social psychology theory for examining factors that govern human intentions
(i.e. the Theory of Planned Behaviour (TPB)) and
additional important identifiable variables (i.e. past PB
and lifestyle orientation) to propose an enhanced consumer purchase intention model for casual activewear.
Second, the psychometric properties of the proposed
extended TPB model were examined using gathered
8
L. WATTS AND T. CHI
primary US consumer survey data. Factor analysis and
multiple regression method were utilised for data analysis and hypothesis testing. The significant factors
influencing the US consumers’ intentions to purchase
activewear for casual use were determined. Attitude
and PBC were found to have positive effects on intent
to purchase activewear. The proposed model shows a
high explanatory power for US consumer purchase
intention towards activewear, accounting for 52.4% of
variance in the purchase intention.
Third, this study found that the additional variables of
past PB and consumer lifestyle orientation within the
extended TPB model significantly influenced consumer
attitude towards the purchase of activewear. Past activewear PB and lifestyle orientation towards health and
wellness help US consumers gain positive attitude
towards shopping activewear for casual purpose.
Finally, demographic variables do not directly influence consumer intention to purchase activewear. However, age affects consumer attitude towards wearing
activewear in casual occasions. Older consumers show
more positive attitudes towards wearing activewear for
causal purposes.
This study also offers some managerial implications
based on the findings. First, as consumer attitudes influence their intent to purchase activewear, companies
should develop marketing strategies and promotional
campaigns to promote and cultivate favourable attitudes
among target consumers towards wearing activewear in
daily occasions. Second, since consumer PBC influences
their intent to purchase activewear, companies should
increase the availability of activewear products through
different sales channels to ensure products are conveniently accessible to target consumers. Information
concerning product characteristics and properties should
be made readily available to consumers to aid in consumers’ informed decision-making. Effort should be made
to help consumers perceive affordable prices instead of
high prices through the use of marketing strategies
such as discounts, advertisements, and new product
development. As consumers’ lack of self-confidence
can also inhibit their ability to purchase activewear, companies should offer products in a variety of styles and fits
to provide consumers with the best product to fit their
psychological needs. Third, since past activewear PB
positively influences consumer attitude, to encourage
repeat PB, companies should focus on service quality
and consumer satisfaction. That is, if consumers are satisfied with quality of service provided and the purchased
product, they will revisit, repurchase, and recommend to
others. Managers can identify their competitive strengths
and weaknesses from the perspective of the consumer in
order to evaluate and improve quality of service and
products. Last, since consumer lifestyle orientation significantly influences their attitudes, these details should
be more focal for marketers in developing advertising
strategies. Aspects concerning health, well-being, and
fitness should be emphasised in advertisement for
activewear.
7. Limitations and future studies
Although the findings from this study contribute to our
understanding of US consumer behaviour towards activewear, there are some limitations and opportunities
for the future studies that need to mention. First, the
conclusions and implications drawn from this study
are limited to activewear. The generalisation of findings
to other apparel products needs further validation.
Second, this study is focused on activewear and the proposed research model is an activewear purchase intention model, the survey instrument is product-specific.
The application of the survey instrument and research
model requires appropriate revisions. Third, in this
study, the intent to purchase is investigated instead of
actual purchase of activewear. Future studies should pursue the investigation of the measurement of actual purchase of activewear since the consumption of
activewear in the US is rapidly increasing.
Disclosure statement
No potential conflict of interest was reported by the authors.
References
Agarwal, J., & Malhotra, N. K. (2005). An integrated model
of attitude and affect. Journal of Business Research, 58(4),
483–493.
Ajzen, I. (1985). From intentions to actions: A theory of
planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action
control: From cognition to behavior (pp. 11–39).
Heidelberg: Springer.
Ajzen, I. (1991). The theory of planned behavior.
Organizational Behavior and Human Decision Processes,
50, 179–211.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation
modeling in practice: A review and recommended two-step
approach. Psychological Bulletin, 103(3), 411–423.
Athleisure. (2015). In Merriam-Webster.com. Retrieved from
http://www.merriam-webster.com/blog/athleisure-wordswere-watching.htm
Banjo, S. (2016, April 13). Has the activewear fashion fad run
its course? Retrieved from https://www.bloomberg.com/
gadfly/articles/2016-04-13/lululemon-inspired-fitnessfashion-trend-cools-down
Bephage, G. (2000). Social and behavioral sciences for nurses,
an integrated approach. Philadelphia, PA: Churchill
Livingstone Press.
INTERNATIONAL JOURNAL OF FASHION DESIGN, TECHNOLOGY AND EDUCATION
Bourdieu, P. (1984). Distinction: A social critique of the judgement of taste. London: Routledge & Kegan Paul.
Cadogan, J. W., Cui, C., Morgan, R. E., & Story, V. (2006).
Factors facilitating and impeding the development of export
market-oriented behavior: A study of Hong Kong manufacturing exporters. Industrial Marketing Management, 35,
634–647.
Chaudary, M. W., Ahmed, F., Gill, M. S., & Rizwan, M. (2014).
The determinants of purchase intention of consumers
towards counterfeit shoes in Pakistan. Journal of Public
Administration and Governance, 4(3), 20–38.
Chi, T. (2013). The effects of contingency factors on perceived
values of casual sportswear. Asia Pacific Journal of
Marketing and Logistics, 25(2), 249–262.
Chi, T., & Kilduff, P. P. D. (2011). Understanding consumer
perceived value of casual sportswear: An empirical study.
Journal of Retailing and Consumer Services, 18(5), 422–429.
Chi, T., Kilduff, P. P. D., & Gargeya, V. B. (2009). Alignment
between business environment characteristics, competitive
priorities, supply chain structures, and firm business performance. International Journal of Productivity and
Performance Management, 58(7), 645–669.
Chi, T., & Sun, Y. (2013). Development of firm export market
oriented behavior: Evidence from an emerging economy.
International Business Review, 22(1), 339–350.
Chi, T., & Zheng, Y. (2016). Understanding environmentally
friendly apparel consumption: An empirical study of
Chinese consumers. International Journal of Sustainable
Society, 8(3), 206–227.
Conner, M., & Armitage, C. J. (1998). Extending the theory of
planned behavior: A review and avenues for further
research. Journal of Applied Social Psychology, 28(15),
1429–1464.
Cowan, K., & Kinley, T. (2014). Green spirit: Consumer empathies for green apparel. International Journal of Consumer
Studies, 38(5), 493–499.
d’Astous, A., Colbert, F., & Montpetit, D. (2005). Music piracy
on the web: How effective are antipiracy arguments?
Evidence from the Theory of Planned Behavior. Journal of
Consumer Policy, 28, 289–310.
Dean, M., Raats, M. M., & Shepherd, R. (2011). The role of
self-identity, past behavior, and their interaction in predicting intention to purchase fresh and processed organic food.
Journal of Applied Social Psychology, 42(3), 669–688.
Euromonitor International. (2014, June). Global participative
sport as consumption: The new rules of the game.
Retrieved from Euromonitor Passport database.
Field, A. (2009). Discovering statistics using SPSS. Thousand
Oaks, CA: Sage.
First, I., & Brozina, S. (2009). Cultural influences on motives
for organic food consumption. EuroMed Journal of
Business, 4(2), 185–199.
Fitzmaurice, J. (2005). Incorporating consumers’ motivations
into the theory of reasoned action. Psychology and
Marketing, 22(11), 911–92.
Friedman, V. (2015, November 4). Adventures in athleisure
clothing. The New York Times. Retrieved from http://
www.nytimes.com/2015/11/05/fashion/adventures-inathleisure-clothing.html
Fromm, J. (2016, July 6). The Lululemon lifestyle: Millennials
seek more than just comfort from athleisure wear.
Retrieved from http://www.forbes.com/sites/jefffromm/
9
2016/07/06/the-lululemon-lifestyle-millennials-seek-morethan-just-comfort-from-athleisure-wear/#cfe871a36584
George, D., & Mallery, M. (2010). SPSS for windows step by
step: A simple guide and reference, 17.0 update (10th ed.).
Boston, MA: Pearson.
Goodman, J. K., Cryder, C. E., & Cheema, A. (2012). Data collection in a flat world: The strengths and weaknesses of
Mechanical Turk samples. Journal of Behavioral Decision
Making, 26(3), 213–224.
Green, D. (2017). Athleisure is not just a trend – It’s a fundamental shift in how Americans dress. Retrieved from
http://www.businessinsider.com/athleisure-is-more-than-atrend-2017-2
Gupta, D. (2011). Functional clothing – Definition and classification. Indian Journal of Fibre & Textile Research, 36(4),
321–326.
Honkanen, P., Verplanken, B., & Olsen, S. O. (2006). Ethical
values and motives driving organic food choice. Journal of
Consumer Behaviour, 5(5), 420–430.
Horton, K., Ferrero-Regis, T., & Payne, A. (2016). The hard
work of leisure: Healthy life, activewear and Lorna Jane.
Annals of Leisure Research, 19(2), 180–193.
Huang, J., Lee, B. C., & Ho, S. H. (2004). Consumer attitude
toward gray market goods. International Marketing
Review, 21(6), 598–614.
Jin, B., & Kang, J. H. (2010). Face or subjective norm? Chinese
college students’ purchase behaviors toward foreign brand
jeans. Clothing and Textiles Research Journal, 28(3), 218–
233.
Jin, B., & Kang, J. H. (2011). Purchase intention of Chinese
consumers toward a US apparel brand: A test of a composite
behavior intention model. Journal of Consumer Marketing,
28(3), 187–199.
Kim, H., & Karpova, E. (2010). Consumer attitudes toward
fashion counterfeits: Application of the theory of planned
behavior. Clothing and Textiles Research Journal, 28(2),
79–94.
Knowles, S. R., Hyde, M. K., & White, K. M. (2012). Predictors
of young people’s charitable intentions to donate money: An
extended theory of planned behavior perspective. Journal of
Applied Social Psychology, 42(9), 2096–2110.
Kraft, F., & Goodell, P. (1993). Identifying the health conscious consumer. Journal of Health Care Monitoring, 13
(3), 18–25.
Kucukemiroglu, O. (1999). Market segmentation by using consumer lifestyle dimensions and ethnocentrism. European
Journal of Marketing, 33(5/6), 470–487.
Lac, A., Crano, W. D., Berger, D. E., & Alvaro, E. M. (2013).
Attachment theory and theory of planned behavior: An
integrative model predicting underage drinking.
Developmental Psychology, 49(8), 1579–1590.
Lau, M., Chang, M., Moon, K., & Liu, W. (2006). The brand
loyalty of sportswear in Hong Kong. Journal of Textile
and Apparel, Technology and Management, 5(1), 1–13.
Mariadoss, B. J., Chi, T., Tansuhaj, P., & Pomirleanu, N.
(2016). Influences of firm orientations on sustainable supply
chain management. Journal of Business Research, 69(9),
3406–3414.
Markula, P. (2017). Are fit people healthy? Health, exercise,
active living and the body in fitness discourse. Waikato
Journal of Education, 3(1). Retrieved from http://wje.org.
nz/index.php/WJE/article/view/492
10
L. WATTS AND T. CHI
Markula, P., Grant, B. C., & Denison, J. (2001). Qualitative
research and aging and physical activity: Multiple ways of
knowing. Journal of Aging and Physical Activity, 9(3),
245–264.
Martin, K. A., Sinden, A. R., & Fleming, J. C. (2000). Inactivity
may be hazardous to your image: The effects of exercise participation on impression formation. Journal of Sport and
Exercise Psychology, 22(4), 283–291.
Molina, L. (2016, May 17) Overall apparel market struggles
under dominance of athleisure trend. APLF. Retrieved
from
http://www.aplf.com/en-US/leather-fashion-newsand-blog/news/31386/overall-apparel-market-strugglesunder-dominance-of-athleisure-trend
Muzaffar, N. (2015). Developing an extended model of theory
of planned behavior to explore green purchase behavior of
Pakistani consumers. American Journal of Business and
Management AJBM, 4(2), 85–101.
Norman, P. (2011). The theory of planned behavior and binge
drinking among undergraduate students: Assessing the
impact of habit strength. Addictive Behaviors, 36(5), 502–
507.
Nunnally, J. C. (1978). Psychometric theory (2nd ed.).
New York, NY: McGraw-Hill.
Ogle, J. P., Hyllegard, K. H., & Dunbar, B. H. (2004). Predicting
patronage behaviors in a sustainable retail environment:
Adding retail characteristics and consumer lifestyle orientation to the belief-attitude-behavior intention model.
Environment and Behavior, 36(5), 717–741.
Ott, L., Longnecker, M., & Ott, R. L. (2001). An introduction to
statistical methods and data analysis. Pacific Grove, CA:
Duxbury.
Paolacci, G., Chandler, J., & Ipeirotis, P. G. (2010). Running
experiments on Amazon Mechanical Turk. Judgment and
Decision Making, 5(5), 411–419.
Shim, S., Eastlick, M. A., Lotz, S. L., & Warrington, P. (2001).
An online prepurchase intention model: The role of intention to search. Journal of Retailing, 77(3), 397–416.
Simmel, G. (1957). Fashion. American Journal of Sociology, 62
(6), 541–558.
Sommer, L. (2011). The theory of planned behaviour and the
impact of past behaviour. International Business &
Economics Research Journal, 10(1), 91–110.
Sparks, P., & Shepherd, R. (1992). Self-identity and the theory
of planned behavior: Assessing the role of identification
with ‘green consumerism’. Social Psychology Quarterly, 55
(4), 388.
Stewart, D. W. (1981). The application and misapplication of
factor analysis in marketing research. Journal of
Marketing Research, 18(1), 51–62.
View publication stats
St-James, M. B., De Man, A. F., & Stout, D. (2006). Athletic
style of dress and perceived social distance. North
American Journal of Psychology, 8(3), 541–548.
Tong, X., & Hawley, J. M. (2009). Measuring customer-based
brand equity: Empirical evidence from the sportswear market in china. Journal of Product & Brand Management, 18
(4), 262–271.
U.S. Census Bureau. (2018). Retrieved from https://www.
census.gov/en.html
Vallerand, R. J., Deshaies, P., Cuerrier, J. P., Pelletier, L. G., &
Mongeau, C. (1992). Ajzen and Fishbein’s theory of
reasoned action as applied to moral behavior: A confirmatory analysis. Journal of Personality and Social Psychology:
Personality Processes and Individual Differences, 62(1), 98–
109.
Wang, Y. (2014). Consumers’ purchase intentions of shoes:
Theory of planned behavior and desired attributes.
International Journal of Marketing Studies, 6(4), 50–58.
Wang, S., & Barnes, S. (2007). Exploring the acceptance of
mobile auctions in China. Paper presented at the
Management of Mobile Business, 2007. ICMB 2007.
International Conference on Mobile Business.
Wasilenko, K. A., Kulik, J. A., & Wanic, R. A. (2007). Effects of
social comparisons with peers on women’s body satisfaction
and exercise behavior. International Journal of Eating
Disorders, 40(8), 740–745.
Wray, A. Z., & Hodges, N. N. (2008). Response to activewear
apparel advertisements by US baby boomers: An examination of cognitive versus chronological age factors. Journal
of Fashion Marketing and Management: An International
Journal, 12(1), 8–23.
Xu, Y., Chen, Y., Burman, R., & Zhao, H. (2014). Second-hand
clothing consumption: A cross-cultural comparison
between American and Chinese young consumers.
International Journal of Consumer Studies, 38(6), 670–677.
Yan, R., Hyllegard, K. H., & Blaesi, L. F. (2012). Marketing ecofashion: The influence of brand name and message explicitness. Journal of Marketing Communications, 18(2), 151–168.
Yan, R., Ogle, J. P., & Hyllegard, K. H. (2010). The impact of
message appeal and message source on Gen Y consumers’
attitudes and purchase intentions toward American apparel.
Journal of Marketing Communications, 16(4), 203–224.
Yoon, C. (2011). Theory of planned behavior and ethics theory
in digital piracy: An integrated model. Journal of Business
Ethics, 100(3), 405–417.
Zheng, Y., & Chi, T. (2015). Factors influencing purchase
intention towards environmentally friendly apparel: An
empirical study of US consumers. International Journal of
Fashion Design, Technology and Education, 8(2), 68–77.