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The role of virtual try-on technology in online purchase decision from consumers’ aspect

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Purpose Online shopping has continued to grow in popularity, and the advance of internet technology has enhanced customers’ experiences. One technology online retailers have been using to increase sales is virtual try-on (VTO). The purpose of this paper is to investigate how such technology affects online consumers’ purchase decision process towards purchase intention, especially from an integration of utilitarian, hedonic and risk perspectives, by using advanced partial least square (PLS) approaches. Design/methodology/approach This study applied a web-based survey approach for data collection from online apparel retailing websites. The survey instrument was developed by adapting previously validated measurement items. The valid data collected were analysed using PLS with multi-group analyses. Advanced PLS techniques such as examination of discriminant validity using heterotrait-monotrait ratio, tests of out-of-sample prediction performance, and measurement invariance of composite models were applied. Findings The results of examining the proposed model reveal that customers’ attitude towards VTO technology can affect their intention to purchase a garment online, which is affected by perceived usefulness, perceived enjoyment and perceived privacy risk. Perceived ease of use is found to affect perceived usefulness and perceived helpfulness. The results also show no significant differences among age groups and genders in terms of the role of VTO technology in the full decision process towards online purchase intention. Originality/value This study enhances the understanding of the roles that VTO technology plays in consumers’ online purchase intention by providing an integrative view of its utilitarian value, hedonic value and risk. This study demonstrates the feasibility of applying advanced PLS techniques to investigate online consumer behaviour, particularly in the field of VTO application in online retailing. Implications for online retailers and designers of VTO technology are also derived from the findings.
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Internet Research
The role of virtual try-on technology in online purchase decision from consumers’
aspect
Tingting Zhang, William Yu Chung Wang, Ling Cao, Yan Wang,
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Tingting Zhang, William Yu Chung Wang, Ling Cao, Yan Wang, (2019) "The role of virtual try-
on technology in online purchase decision from consumers’ aspect", Internet Research, https://
doi.org/10.1108/IntR-12-2017-0540
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The role of virtual try-on
technology in online purchase
decision from consumersaspect
Tingting Zhang
University of Science and Technology Beijing, Beijing, China
William Yu Chung Wang
University of Waikato, Hamilton, New Zealand
Ling Cao
Nanjing University of Information Science and Technology, Nanjing, China, and
Yan Wang
Shaanxi Business College, Xian, China
Abstract
Purpose Online shopping has continued to grow in popularity, and the advance of internet technology has
enhanced customersexperiences. One technology online retailers have been using to increase sales is virtual
try-on (VTO). The purpose of this paper is to investigate how such technology affects online consumers
purchase decision process towards purchase intention, especially from an integration of utilitarian, hedonic
and risk perspectives, by using advanced partial least square (PLS) approaches.
Design/methodology/approach This study applied a web-based survey approach for data collection from
online apparel retailing websites. The survey instrument was developed by adapting previously validated
measurement items. The valid data collected were analysed using PLS with multi-group analyses. Advanced
PLS techniques such as examination of discriminant validity using heterotrait-monotrait ratio, tests of
out-of-sample prediction performance, and measurement invariance of composite models were applied.
Findings The results of examining the proposed model reveal that customersattitude towards VTO
technology can affect their intention to purchase a garment online, which is affected by perceived
usefulness, perceived enjoyment and perceived privacy risk. Perceived ease of use is found to affect
perceived usefulness and perceived helpfulness. The results also show no significant differences among age
groups and genders in terms of the role of VTO technology in the full decision process towards online
purchase intention.
Originality/value This study enhances the understanding of the roles that VTO technology plays
in consumersonline purchase intention by providing an integrative view of its utilitarian value, hedonic
value and risk. This study demonstrates the feasibility of applying advanced PLS techniques to investigate
online consumer behaviour, particularly in the field of VTO application in online retailing. Implications
for online retailers and designers of VTO technology are also derived from the findings.
Keywords Online retailing, Purchase intention, Partial least squares, Multi-group analysis,
Use and gratification theory, Virtual try-on
Paper type Research paper
1. Introduction
The expansion of online marketplaces has dramatically changed shopping patterns in the
worldwide retailing environment. It is reported that US customers made 51 per cent of
their purchases online in 2016 (Farber, 2016). Clothing and accessories have been the
leading online merchandise category in the past decade (Huang and Shiau, 2017).
For example, US online retail sales in apparel, footwear and accessories generated $81bn
in 2017 and is expected to increase to $123bn by 2022 (Statista, 2018). Despite the steady
rise in online apparel sales over the years, the inability to try-on clothing is a major
obstacle to online purchases, which is usually termed as the suit, fit and match dilemma
(Pachoulakis and Kapetanakis, 2012).
Internet Research
© Emerald Publishing Limited
1066-2243
DOI 10.1108/IntR-12-2017-0540
Received 31 December 2017
Revised 26 April 2018
16 May 2018
4 July 2018
Accepted 7 July 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1066-2243.htm
Online
purchase
decision
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Online apparel retailers have tried various approaches in recent years to address this
issue. A key approach is adopting virtual try-on (VTO) technologies (Lin and Wang, 2015),
which consist of website features that enable creation and manipulation of product or
environment images to simulate (or surpass) actual experience with the product or
environment(Fiore, Kim and Lee, 2005, p. 39). It is believed that adopting VTO technology
is trendy and is likely to represent the future of online clothing retailing (Greene, 2011). Such
a trend is even observed among online retailers of other products such as Ray-Bans
eyewear and MACs cosmetics.
Similar to the industry interest in improving VTO technology and its implementation,
research on the role of such technology on online retailing marketing was called for by a
number of researchers, and numerous studies have been conducted ( Jennifer, 2009).
Indeed, research has found that VTO plays an important role in online consumers
purchase decision making (Merle et al., 2012). Traditionally, this technology adds
shopping value from the utilitarian perspective by addressing the suit, fit and match
dilemma. It can also provide hedonic value through the pleasure and enjoyment
experienced by consumers while interacting with VTO technology. However, despite how
useful and enjoyable it is to interact with this technology, online consumers may decide
not to purchase clothing from online retailers if using it requires too much effort or has
some risks (Merle et al., 2012).
Overall, extant literature has investigated how VTO applications affect online
consumersdecision making from different perspectives (Zhang et al., 2017). For example,
while Kim and Forsythe (2010) focus on utilitarian and hedonic perspectives and Lee et al.
(2010) on utilitarian and risk perspectives, Yang and Wus (2009) study has a focus on
utilitarian and hedonic perspectives with risk as the moderator. Nevertheless, there is not
a holistic view combining the utilitarian, hedonic and risk perspectives of VTO technology
to investigate the said topic. This study makes an effort towards this direction. In
addition, in examining the role of this particular technology in consumersonline purchase
decision process, prior studies mostly adopt covariance-based structure equation
modelling (CB-SEM) techniques.
This study intends to deepen our understanding of the role of VTO technology in
consumersdecisional process towards online purchase intention by extending the use of
partial least square SEM (PLS-SEM) techniques to this research area. The rest of this
paper is organised as follows. Section 2 elaborates related literature and the development
of hypotheses. Section 3 discusses data collection and data analysis methods, followed
by data analysis and presentation of results. Section 5 discusses the findings and
concludes this study.
2. Literature review and hypotheses development
2.1 Perspectives of VTO technology
Online consumerspurchase decision process involves five steps: problem recognition,
information search, evaluation, decision and post-purchase behaviour (Hakan, 2016).
Online retailers introduced VTO technology to enable online consumers to select
complementary clothes from the online shop, try various matches freely and see the
outfits on the screen, which assists with their evaluation of clothes (Fiore, Jin and Kim,
2005). Hence, this technology plays an important role in the evaluation stage, which in
turn affects userspurchase decision (e.g. Merle et al., 2012; Kim, 2016). Indeed, it is
reported that online consumersattitude towards VTO technology is related to
behavioural intention towards online retailers or their online retailing websites (Lee et al.,
2006; Kim and Forsythe, 2009), while its values and usage are associated with
purchase intention in certain ways (Fiore, Jin and Kim, 2005; Fiore, Kim and Lee, 2005;
Merle et al., 2012; Beck and Crié, 2018).
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The literature claims that VTO technology has utilitarian and hedonic values. Utilitarian
value involves helping consumers address the suit, fit and match dilemma. For example,
Baytar et al. (2016) find that as VTO applications could potentially provide consumers with
helpful information related to apparel attributes (e.g., size, and colour) during
online shopping, consumers are interested in this application for apparel fit evaluation.
Baytar et al.s (2016) argument is also supported by an early study (Faust and Carrier, 2011)
in which 70.2 per cent of participants used this technology to confirm the fit of clothes. The
utilitarian value of VTO technology has also been assessed by identifying its usefulness and
ease of use (Hirst and Omar, 2007).
Interaction with VTO provides an enjoyable shopping experience for customers,
demonstrating its hedonic value. This technology allows online consumers to enlarge or
rotate an outfit, to customise a virtual model using their own body information (Pachoulakis
and Kapetanakis, 2012), or even to upload a picture of their own face to make the model look
like themselves (Merle et al., 2012). VTO applications can also provide social value, allowing
consumers to seek their friendsopinions by sharing how an outfit looks on the customised
model (Kang and Johnson, 2013).
Despite its utilitarian and hedonic values, using VTO technology is not risk free. It is
reported that the return rates for apparel and accessories bought online are normally
between 35 and 40 per cent (Dennis, 2017), suggesting that one risk is possible inconsistent
fit between the actual result and the virtual trying-on outcome. In addition, while
customising a virtual model, customers usually need to provide confidential information,
such as facial image, height, weight, bust size, waist size and body shape (Merle et al., 2012;
Pachoulakis and Kapetanakis, 2012). In such cases, there is a danger of information leakage,
suggesting the possibility of privacy risk.
Prior studies have investigated VTO application from a utilitarian perspective (e.g. Beck
and Crié, 2018) or a hedonic perspective (e.g. Merle et al., 2012) separately, and a combination
of utilitarian and hedonic perspective (e.g. Fiore, Jin and Kim, 2005; Fiore, Kim and Lee, 2005;
Lee et al., 2006; Hirst and Omar, 2007; Kim and Forsythe, 2007, 2008, 2009; Yen et al., 2017),
of utilitarian and risk perspective (e.g. Cho and Fiorito, 2009; Cho and Wang, 2010; Huang
and Qin, 2011), and of hedonic and risk perspective (e.g. Lee et al., 2010; Huang and Qin,
2011). A summary of related studies is presented in Table I. Nevertheless, there seems a lack
of focus on a holistic view of the utilitarian value, hedonic value and risk of VTO application,
with socialisation being seldom included.
2.2 Uses and gratifications theory
As a visualisation technology, VTO application is a media format that requires extensive
interaction between consumers and the technology. Originated from the effectiveness
perspective on media communication (Luo, 2002), uses and gratifications (U&G) theory
assumes a user-directed nature of a media and that the media requires a high level of
interactivity from its users (Huang, 2008). It has been applied to investigate users
experience and behaviour associated with online shopping, especially in the area of
consumersmotivations and attitudes towards interaction with various online media, and
the resulting impacts on their purchasing intentions (Lim and Ting, 2012). Thus, U&G is
considered appropriate for investigating consumersperceptions about VTO applications
and their impact on consumersattitudes and behaviour (Yaoyuneyong et al., 2014).
Prior studies provide extensive reviews of the applications of U&G for understanding
online consumer behaviours (e.g. Luo, 2002; Lim and Ting, 2012; Yaoyuneyong et al., 2014).
The literature demonstrates the three key aspects of U&G, namely, informativeness,
entertainment and irritation, are equally important for understanding online consumers
attitudes towards web-based visualisation technology and their behaviours (Lim and Ting,
2012). While informativeness, which describes the value of information delivered by the
Online
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decision
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Source Perspectives Method Independent variable Dependent variable
Socioeconomic
factor Findings
Beck and
Crié
(2018)
Utilitarian Experiment,
MANCOVA,
mediation test
Virtual fitting room usage,
curiosity about product, intention
to patronise
Intention to purchase Virtual fitting room usecuriosity about
product both online and offline; curiosity
patronage intention both online and
offline; patronage intention purchase
intention both online and offline; virtual
fitting room usepurchase intention
offline
Yen et al.
(2017)
Utilitarian;
Hedonic
Survey; multiple
linear regression
Perceived usefulness, perceived
ease of use, perceived enjoyment,
attitude towards product
Intention toward using a
virtual fitting system
PUINT; PEINT; ATTINT
Kim and
Forsythe
(2009)
Utilitarian;
Hedonic
Online survey,
CB-SEM
PU, PEOU, PE, ATU, actual use of
SET
ATU, intended use of
VTO, post-use
evaluation of SET,
purchase, reuse SET,
revisit the site
PU, PEATU; ATUactual use
Kim and
Forsythe
(2008)
Utilitarian;
Hedonic
Online survey,
factor analysis,
CB-SEM
PU, PEOU, PE, ATU, intended use
of VTO
ATU, intended use of
VTO, post-use
evaluation of VTO
Gender PU, PEATU; PEOUPU, PE; ATU
intended use of VTO; Perceived
entertainment has a stronger effect on
attitude for women than for men
Hirst and
Omar
(2007)
Utilitarian;
Hedonic
Email survey,
factor analysis,
multivariate and
univariate
analysis
PU, PEOU, PE, ATP Attitudes towards
online shopping,
intention to shop online
Gender Online shoppers tended to be more
matured with high incomes and are willing
to provide personal information (credit
card and purchasing information) online
Kim and
Forsythe
(2007)
Utilitarian;
Hedonic
Focus group and
online survey,
CB-SEM
Perceived usefulness, perceived
entertainment value
ATT, Use PUATT; PEATT, ATTUSE
Lee et al.
(2006)
Utilitarian;
Hedonic
Experiment,
factor analysis,
CB-SEM
Utilitarian shopping orientation,
hedonic shopping orientation, level
of IIT, PU, PEOU, PE
PU, PEOU, PE, ATR,
behavioural intention
towards online retailers
Age, gender,
major
Utilitarian shopping orientation, level of
IITperceived usefulness; utilitarian
shopping orientation, level of IITPEOU;
(continued )
Table I.
A summary of
literature on VTO
adoption by
online consumers
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Source Perspectives Method Independent variable Dependent variable
Socioeconomic
factor Findings
hedonic shopping orientation, level of
IITperceived enjoyment; PU, PEOU,
PEattitude towards the online retailer;
attitudebehavioural intention
Fiore
et al.
(2005a)
Utilitarian;
Hedonic
Experiment,
CB-SEM
Arousal, Pleasure, trying image
interactivity as stimulating
experience, OSL (consciousness-
emotion-value), recreational
shopping
Global attitude,
willingness to purchase,
willingness to patronise
Age, gender,
major
Consciousness-emotion-value, recreational
shoppingtrying image interactivity;
trying image interactivitypleasure and
willingness to purchase
Cho and
Wang
(2010)
Utilitarian;
Risk
Online survey,
factor analysis,
CB-SEM
PU, PEOU, PS, trust, perceived
security, PEOU, PU, trust, ATR
Trust, ATR, Age, education,
income
PEOU, PSPU; PU, PSTrust; PU,
TrustATR
Merle
et al.
(2012)
Hedonic Experiment,
factor analysis,
CB-SEM
Self-congruity, body esteem,
confidence in apparel fit, utilitarian
value, hedonic value
Confidence in fit,
utilitarian value,
purchase intentions
Gender Body esteemself-congruity, confidence in
fit; Self-congruityconfidence in fit,
utilitarian value, hedonic value; utilitarian,
hedonic valuePI
Lee et al.
(2010)
Hedonic;
Risk
Experiment,
factor analysis,
CB-SEM
LIIT, PE, PR, experimenting with
appearance
PE, PR, ATR LIITPE, PR, experimenting with
appearancePE, PR; PE, PRATR
Huang
and Qin
(2011)
Hedonic;
risk
Survey, factor
analysis, CB-
SEM
Performance Expectancy, effort
expectancy, social influence,
facilitating conditions, privacy
concerns, security concerns,
perceived risk
Perceived risk, Intention
to use
Gender, age,
education,
prior
experience
Performance expectancy, effort
expectancy, social influence has positive
impact on intention to use. Perceived risk
has negative impact on intention to use.
Privacy concerns and security concerns
have positive impact on intention to use
Notes: PU, perceived usefulness; PEOU, perceived ease of use; PS, perceived security; PE, perceived enjoyment/entertainment; PR, perceived risk; LIIT, level of IIT;
ATU, attitude towards using IIT; ATP, attitude towards purchasing online; ATR, attitude towards online retailer; PI, purchase intention
Table I.
Online
purchase
decision
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media for consumers, and entertainment, which measures the pleasure consumers can gain
from using the media, are related to consumerspositive attitude towards the media,
irritation, which refers to the distraction, control and offensiveness felt by consumers, would
lead to negative impressions of the media (Yaoyuneyong et al., 2014). Consumers tend to
expect their interactions with innovative web technology to be as informative and
entertaining as possible, with little irritation (Yaoyuneyong et al., 2014).
The three aforementioned aspects of U&G form a solid theoretical foundation for a
holistic view of the perceived values and risks of VTO technology and the subsequent
purchase decision. The primary goal of the information delivered via this technology is to
address the suit, fit and match dilemma in apparel online shopping, which is of utilitarian
value. Virtually trying-on apparel can be entertaining, suggesting the hedonic value of such
an application. Consumers may feel irritated if they have to return a garment, whether or not
they virtually tried it on, or believe that the personal information provided to use the VTO
application might be in danger of leakage, which is related to risk.
2.3 Hypotheses development
Online consumersinteraction with shopping-assisting applications can affect their
purchase intention decision making in an online setting (Kim, 2016). Online purchase
intention refers to whether or not online consumers intend to make an online purchase in
the near future (Law and Ng, 2016), which is subject to consumersattitude towards
shopping-assisting applications (Noordin et al., 2017). Attitude is widely used for
predicting behavioural intentions (e.g. Davis, 1989) and found to be a key predictor of
purchase intention (Law and Ng, 2016). Through VTO applications, which provide
visualised shopping assistance, online consumers may obtain additional information
about the clothing by enlarging and rotating the product images and/or mixing-and-
matching various clothing items (Fiore and Jin, 2003). With sufficient information about
clothes acquired from using technology, consumers tend to change their attitudes towards
the technology, which in turn is likely to affect online consumersintention to purchase
clothes from online retailers (Beck and Crié, 2018):
H1. Online consumersattitudes towards VTO technology positively influences intention
to purchase.
According to the literature, online consumersattitudes towards VTO technology are related
to its perceived usefulness and perceived enjoyment (Childers et al., 2001). Hirst and Omars
(2007) study shows that online consumersattitude towards online shopping is directly
influenced by their perceptions of the usefulness and enjoyment, with an indirect influence
from perceived ease of use. VTO technology can help to address the suit, fit and match
dilemma by providing a rich online shopping experience. Such assistance can be useful as it
provides visual information (Dennis et al., 2010). In addition, the perceived ease of use for
VTO applications would enrich consumersshopping experience as it provides clues about a
productsphysical attributes to help consumers evaluate the product (Dennis et al., 2010),
thus enhancing its perceived usefulness:
H2. Perceived usefulness has a positive influence on online consumersattitude towards
VTO technology.
H3. Perceived ease of use has a positive influence on perceived usefulness.
The enjoyment of interactive shopping isfound to be a strong predictor of attitude in an online
shopping context. In addition to assisting with product evaluation, enjoyment of shopping
online can be enhanced by the interactive nature of the VTO application (Kim, 2016; Pantano
et al., 2017). Kim and Forsythe (2008) indicate that perceived entertainment value of virtual
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trying-on has a positive influence on attitudes towards the enabling technology as consumers
tend to enjoy immersing themselves in the virtual simulation and that the easier a VTO
application is to use, the more useful and enjoyable it would be perceived to be:
H4. Perceived enjoyment has a positive influence on online consumersattitude towards
VTO technology.
H5. Perceived ease of use has a positive influence on perceived enjoyment.
Socialisation is another factor that has been intensively studied in the context of online
clothing shopping (Kim, 2011). VTO application could enhance the shopping experience
further as the trying-on outcome can often be easily shared with friends and family (Dennis
et al., 2010). Thus, it can be a socialisation channel, especially when incorporated with social
media applications, which provides a pleasant and interactive shopping experience for
online consumers (Pachoulakis and Kapetanakis, 2012):
H6. Perceived socialisation has a positive influence on online consumersattitude
towards VTO technology.
Based on the literature, risk perception is related to insecure transactions and confidential
information (such as personal information) transferred to other parties (Law and Ng, 2016).
Online consumers may not use a particular internet technology if using it has some risks,
which in turn may negatively affect their attitude towards technology (Merle et al., 2012).
Choi and Lee (2003) found that the level of perceived product risk for online apparel
consumers is higher than for online non-apparel consumers because they could not try
clothes on. Some prior studies have found evidence that virtually trying-on could potentially
reduce the risk regarding apparel fit when shopping online (Shim and Lee, 2011; Kim, 2016).
Yet, consumers are not confident that the clothes they purchase can meet their expectation,
their attitude towards the application tends to change (Merle et al., 2012; Shin and Baytar,
2014). For example, perceptions towards risks of using VTO technology are found to help
explain online consumersattitudes towards it (Huang and Qin, 2011):
H7. Perceived product risk has a negative influence on online consumersattitude
towards VTO technology.
Online consumers are also concerned about the process of collecting their personal
information (e.g. body size) when using VTO technology, which is referred to as perceived
privacy risk (Sekhavat, 2017). For example, online consumers are reportedly uncomfortable
with using such technology because of the process of collecting their body information
(Loker et al., 2004) and the potential loss of control over their personal information
(Chiu et al., 2014), especially when they have short- and large-body types (Nam et al., 2009).
Online consumersconcern for their privacy is suggested as one of the major factors
influencing their attitude towards online shopping applications (e.g. VTO technology)
(Grewal et al., 2003; Huang and Qin, 2011):
H8. Perceived privacy risk has a negative influence on online consumersattitude
towards VTO technology.
Some prior studies suggest that the age of a person affects the adoption of internet
technologies and the individuals online purchase decision related to those technologies. For
example, younger people have higher abilities and levels of acceptance in using
technological devices and are major online buyers (Law and Ng, 2016). Also, young
consumersonline garment shopping experiences are found to be related to the utilitarian
and hedonic effects of the garment presentation (McCormick and Livett, 2012). However,
other researchers demonstrate no differences in the decisional process underlying online
shopping behaviour between different age groups (Hernández et al., 2011). With regard to
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this controversy, this study examines whether there are any differences between younger
and other online consumers in terms of their perceived values and risks of VTO application,
attitude towards it, and online purchase intention:
H9. There are significant differences in the role of VTO technology between younger and
other consumersonline purchase decision process including perceived values and
risks of using the technology, attitude towards it, and online purchase intention.
Gender is another key socioeconomic factor to investigate differences in customer purchase
decisions in an online setting; specifically, men and women differ in their perceptions
associated with online purchase intentions (Pascual-Miguel et al., 2015). When purchasing
garments, females are considered to have a higher need of product information and are more
likely to use VTO technology as they are more concerned about size and fit (Shin and
Baytar, 2014). Prior studies also found that mens behaviours are more inclined to be based
on benefit and utilitarian motivation than women, while female consumers with hedonic
orientation are likely to be attracted by perceived social features of the VTO application
since they are more concerned about social relations and like web technologies that allow
them to socialise more (Dennis et al., 2010; Law and Ng, 2016). With regard to this
controversy, this study examines whether there are any differences between male and
female online consumers in terms of their perceived values and risks of VTO application,
attitude towards it and online purchase intention:
H10. There are significant differences in the role of VTO technology between male and
female consumersonline purchase decision process in terms of their perceived
values and risks of VTO application, attitude towards it, and online purchase
intention.
3. Research methodology
3.1 Measurement development
The measurement items for each construct are adapted from previously validated items by
carefully revising them to fit the context of this study. Items measuring online consumers
intention to purchase clothes after using VTO technology were adapted from Fiore, Kim and
Lee (2005). Items used by Kim and Forsythe (2009) are adapted to measure consumers
attitude towards VTO technology. Perceived usefulness and perceived ease of use are
measured using items adapted from Davis (1989). Items measuring perceived enjoyment,
perceived socialisation, perceived product risk and perceived privacy risk are adapted from
Kim and Forsythe (2009), Kim (2011), Choi and Lee (2003) and Cho and Wang (2010),
respectively. A five-point Likert scale ranging from Strongly disagreeto Strongly agree
is used to measure all of the items.
3.2 Data collection
This study took a web-based survey approach to collect data for the following reasons.
First, the focus of this study suggests a web-based survey is an appropriate tool to collect
empirical data to examine online consumersintention to purchase clothes online. Second, a
web-based survey could maintain the anonymity of respondents, ensuring that the
participants would not know the researcher and vice versa. An anonymous web-based
survey can reduce the response bias because participants may give biased responses if they
know the researcher (Andrews et al., 2008). Third, a web-based survey provides
response control functions to make sure that participants complete the questions
required to be answered, thus reducing missing data (Andrews et al., 2008). In addition, a
web-based survey can be set up to prevent a participant from taking the survey multiple
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times. Fourth, a web-based survey automatically stores survey responses into a database
directly, which eliminates transcription errors.
An invitation message regarding the recruitment of participants for the survey was
posted on selected online apparel retailing websites. The message outlined the
objective of this study, the sampling procedure, protection of participantsprivacy,
confidentiality, anonymity and plans for disclosing survey results, which could help build
trust between participants and researchers (Andrews et al., 2008). A scanning quick
response (QR) code for and a URL link to the web-based survey were also provided
in the invitation message so that those who were interested were able to participate in the
survey by scanning the QR code or clicking on the URL link. Before proceeding to
answering any questions, respondents were shown three examples of VTO application
with explanations and retailers that had adopted these technologies, such as Uniqlo
(http://uniqlo.bigodata.com.cn/u2/mini.php).
Overall, 470 attempts were made to participate in the survey, with 208 completions,
resulting in a response rate of 52.55 per cent. In terms of the approach used for accessing
the survey, 208 completions out of 411 attempts accessed the survey through QR code,
with a response rate of 50.61 per cent. In total, 31 completions out of 59 attempts were
made by directly visiting the URL of the survey, with a response rate of 66.10 per cent.
After carefully examining the responses, 11 responses were removed because the
respondents either left the textbox for entering age empty or entered an abnormal age like
1000. Eventually, 236 valid responses were retained for further analysis. Table II shows
that 67.8 per cent of the respondents were adults (i.e. 19 years old or above), which is two
times more than the portion of minor respondents (i.e. 18 years old or below). There were
more female than male respondents. More than half of the respondents spent 500-1500
RMB per month. Nearly, 45 per cent of the respondents had 23 years of online shopping
experience, and 22.46 per cent of respondents had 45 years of experience.
Frequency %
Age
Minors (18 or below) 76 32.20
Adults (19 or above) 160 67.80
Gender
Male 103 43.64
Female 133 56.36
Monthly expense (RMB)
500 and below 33 13.98
5011000 59 25.00
10011500 69 29.24
15012000 31 13.14
20012500 10 4.24
25013000 6 2.54
3001 or above 28 11.86
Years of online shopping
1 or below 32 13.56
23 102 43.22
45 53 22.46
67 31 13.14
89 6 2.54
10 or above 12 5.08
Table II.
Demographic profiles
of the respondents
Online
purchase
decision
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The non-response bias was assessed using t-tests to compare the difference between
answers by the initial 40 respondents and the last 40 respondents. Because late respondents
are theorized to have some similarities with non-respondents, one approach would be to
compare scores on key metrics from both the initial respondents and the late respondents
(Armstrong and Overton, 1977). As Table III shows, the results of the t-test indicate
no significant differences between the two groups ( pW0.05) (Liang et al., 2007). Thus,
non-response bias is not an issue in this study.
Since the data were collected using a single web-based survey and the self-reported
answers related to each construct were conceptual, common method bias could be a potential
problem. To minimise common method bias, respondents were assured of anonymity before
they took part in the survey (Podsakoff et al., 2012). An unrotated principal axis factoring
analysis was conducted (Podsakoff et al., 2003). The results reveal that five factors emerged
from the data set with the first factor accounting for 42.42 per cent of the variance, indicating
that common method bias is tolerable in the data set.
4. Data analysis and results
Upon completion of data collection, a two-step analytical approach recommended by
Sarstedt et al. (2017) was used to first assess the measurement model and then to validate
the SEM techniques. The PLS path modelling approach was chosen among different SEM
methods because it is not only able to estimate path models with latent variables, but also
supports conducting multi-group analyses (MGA) (Sarstedt et al., 2016; Rasoolimanesh et al.,
2017). In addition, it does not require the data set to be normally distributed. The sample
size of this study for MGA is relatively small, in which case PLS would be a better option
than CB-SEM for testing the research model (Sarstedt et al., 2016; Hair, Hult, Ringle, Sarstedt
and Thiele, 2017). As the PLS calculation does not generate formal significance test results
for each parameter, a bootstrap technique was adopted to obtain the t-statistics and
standard errors (Rasoolimanesh et al., 2017). In this study, data analyses were conducted
using the SmartPLS three software package, with bootstrapping conducted with 5000
re-samples (Ringle et al., 2015).
4.1 Validating the measurement model
The research model is a reflective one, including eight reflective constructs. The assessment
of the reflective measurement model requires an evaluation of the reliability and validity of
each variable. Convergent validity is assessed through items loading, composite reliability
(CR) of each item, and average variance extracted (AVE) for each construct. Loadings of all
items on their corresponding variables are above 0.70 at the significance level of 0.001, as
shown in Table IV. In addition, all the AVEs, as presented in Table IV, range from 0.715 to
0.811, which exceed the recommended 0.50 threshold (Hair, Hult, Ringle and Sarstedt, 2017).
Table IV shows that values of CR for all constructs ranged from 0.887 to 0.941, highly
exceeding the recommended level of 0.70 (Gefen et al., 2011), indicating high construct
reliability. Hence, the measurement model yields satisfactory convergent validity.
Discriminant validity is established using heterotrait-monotrait (HTMT) ratio (Henseler
et al., 2015; Franke and Sarstedt, 2018). As shown in Table V, the HTMT value of each pair
of reflective constructs is below 0.90, confirming that satisfactory discriminant validity has
been established. In order to detect potential multicollinearity, the variance inflation factor
coefficient is calculated.
4.2 Validating the structural model
As depicted in Figure 1, all paths are significant at 0.01 levels, except for the paths from
perceived socialisation and perceived product risk to attitude. Path analysis of attitude
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Initial 40 respondents Last 40 respondents Levenes test for equality of variances t-test for equality of means
Construct Item Mean SD Mean SD FPtP
Perceived usefulness (PU) PU1 3.90 0.871 3.78 0.768 0.899 0.346 0.681 0.498
PU2 3.78 0.891 3.60 0.744 1.045 0.310 0.953 0.343
PU3 3.68 0.859 3.73 0.784 1.002 0.320 0.272 0.786
PU4 3.75 0.870 3.75 0.809 0.918 0.341 0.000 1.000
Perceived ease of use (PEOU) PEOU1 3.68 0.888 3.53 0.751 1.322 0.254 0.816 0.417
PEOU2 3.83 0.781 3.53 0.679 0.093 0.761 1.834 0.070
PEOU3 3.73 0.960 3.40 0.841 0.752 0.388 1.610 0.111
Perceived enjoyment (PE) PE1 3.68 0.944 3.68 0.829 0.488 0.487 0.000 1.000
PE2 3.80 0.823 3.80 0.758 0.125 0.725 0.000 1.000
PE3 3.65 0.893 3.55 0.904 0.008 0.928 0.498 0.620
PE4 3.73 0.905 3.78 0.733 1.677 0.199 0.271 0.787
Perceived socialisation (PS) PS1 3.55 1.011 3.60 0.841 1.444 0.233 0.240 0.811
PS2 3.35 1.122 3.30 0.939 1.347 0.249 0.216 0.829
PS3 3.75 0.927 3.58 0.903 0.009 0.923 0.856 0.395
Perceived product risk (PROR) PROR1 3.88 0.822 3.75 0.670 3.239 0.076 0.745 0.458
PROR2 3.78 0.862 3.40 0.928 0.073 0.788 1.872 0.065
PROR3 3.75 0.776 3.53 0.640 0.990 0.323 1.414 0.161
PROR4 3.88 0.791 3.75 0.809 0.548 0.462 0.699 0.487
Perceived privacy risk (PRIR) PRIR1 3.63 0.897 3.55 0.783 0.742 0.392 0.398 0.691
PRIR2 3.35 1.027 3.28 0.877 0.468 0.496 0.351 0.726
PRIR3 3.35 0.975 3.30 0.823 0.691 0.408 0.248 0.805
PRIR4 3.25 1.056 3.18 0.984 0.007 0.935 0.329 0.743
Attitude towards VTO technology (ATT) ATT1 3.90 0.709 3.73 0.816 0.557 0.458 1.024 0.309
ATT2 3.78 0.832 3.65 0.802 0.002 0.964 0.684 0.496
ATT3 3.60 0.871 3.58 0.712 2.356 0.129 0.141 0.889
Intention to purchase (INT) INT1 3.75 0.927 3.63 0.925 0.344 0.559 0.604 0.548
INT2 3.73 0.847 3.75 0.742 1.204 0.276 0.140 0.889
INT3 3.85 0.736 3.70 0.723 0.003 0.957 0.920 0.361
Table III.
Results of testing non-
response bias
Online
purchase
decision
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shows a value of 0.824 towards intention. The path coefficients of perceived ease of use
towards perceived usefulness and perceived enjoyment are 0.735 and 0.675, respectively.
Path analysis values towards attitude are 0.33 for perceived usefulness, 0.304 for perceived
enjoyment and 0.267 for perceived privacy risk. The effect sizes ( f
2
) were also calculated.
f
2
values are defined as 0.02 (small), 0.15 (medium) and 0.35 (large). As shown in Table VI,
all f
2
values show at least a small size, more than 0.02, except for the paths from perceived
socialisation and perceived product risk to attitude. Generally, all the hypotheses are
supported by the results of the model except for H6 and H7.
Construct Item Mean SD Factor loading
Perceived usefulness (PU) PU1 3.771 0.858 0.896
(AVE ¼0.800, CR ¼0.941) PU2 3.661 0.851 0.881
PU3 3.682 0.862 0.903
PU4 3.691 0.840 0.898
Perceived ease of use (PEOU) PEOU1 3.53 0.820 0.898
(AVE ¼0.811, CR ¼0.928) PEOU2 3.627 0.800 0.906
PEOU3 3.581 0.862 0.897
Perceived enjoyment (PE) PE1 3.657 0.876 0.861
(AVE ¼0.769, CR ¼0.930) PE2 3.699 0.822 0.909
PE3 3.441 0.907 0.836
PE4 3.669 0.839 0.899
Perceived socialisation (PS) PS1 3.462 0.894 0.861
(AVE ¼0.723, CR ¼0.887) PS2 3.292 0.954 0.845
PS3 3.597 0.870 0.844
Perceived product risk (PROR) PROR1 3.636 0.825 0.843
(AVE ¼0.715, CR ¼0.909) PROR2 3.559 0.824 0.873
PROR3 3.631 0.751 0.829
PROR4 3.678 0.769 0.836
Perceived privacy risk (PRIR) PRIR1 3.530 0.778 0.789
(AVE ¼0.722, CR ¼0.912) PRIR2 3.305 0.893 0.885
PRIR3 3.309 0.835 0.882
PRIR4 3.191 0.940 0.840
Attitude towards VTO technology ATT1 3.737 0.769 0.878
(ATT) ATT2 3.695 0.765 0.888
(AVE ¼0.765, CR ¼0.907) ATT3 3.602 0.809 0.858
Intention to purchase (INT) INT1 3.661 0.841 0.906
(AVE ¼0.799, CR ¼0.923) INT2 3.648 0.802 0.894
INT3 3.691 0.771 0.882
Notes: CR, composite reliability; AVE, average variance explained
Table IV.
Descriptive analysis,
PLS factor loadings,
AVE and CR
PU PEOU PE PS PROR PRIR ATT
PEOU 0.816
PE 0.764 0.753
PS 0.753 0.705 0.873
PROR 0.551 0.622 0.679 0.495
PRIR 0.471 0.490 0.592 0.677 0.559
ATT 0.777 0.714 0.809 0.736 0.621 0.688
INT 0.748 0.641 0.735 0.698 0.594 0.614 0.856
Notes: PU, perceived usefulness; PEOU, perceived ease of use; PE, perceived enjoyment; PS, perceived
socialisation; PROR, perceived product risk; PRIR, perceived privacy risk; ATT, attitude towards VTO
technology; INT, intention to purchase
Table V.
Heterotrait-monotrait
ratios
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The research model explains 67.8 per cent of variance in intention and 62.5 per cent of the
variance in attitude, suggesting a high level of in-sample prediction. However, the R
2
does
not capture the out-of-sample predictive performance of the research model. Shmueli et al.
(2016, p. 4556) point out that fundamental to a proper predictive procedure is the ability to
predict measurable information on new cases. This study adopts Shmueli et al.s (2016)
approach to assess the models out-of-sample predictive power using the PLS predict
algorithm in the SmartPLS software package. As shown in Tables VII and VIII, the Q
2
values for all the constructs and indicators are positive, which suggests that the prediction
error of using the PLS-SEM model is smaller than that of using the mean values. This means
that the PLS-SEM model demonstrates better predictive performance (Shmueli et al., 2016).
In addition, the PLS-SEM results have greater Q
2
values and lower root mean squared
errors (RMSE) and mean absolute errors (MAE) than the linear regression model (LM)
results, suggesting that the PLS-SEM path model improves the predictive performance
compared with LM (Evermann and Tate, 2016).
Intention to
purchase
Attitude
towards VTO
technology
Perceived
usefulness
Perceived
enjoyment
Perceived
ease of use
Perceived
socialisation
Perceived
product risk
Perceived
privacy risk
0.824***
0.333***
0.304***
–0.005 ns
0.058 ns
0.267***
0.735***
0.675***
R2= 0.62 R2= 0.678
0.539
0.453
Significant path
Non-significant path
Note: ***p<0.01
Figure 1.
Results of testing the
structural model with
full sample
Hypothesis Path Path coefficient t-value f
2
H1 ATT INT 0.824 4.970 2.121
H2 PU ATT 0.333 27.056 0.140
H3 PEOU PU 0.735 10.373 1.177
H4 PE ATT 0.304 13.399 0.078
H5 PEOU PE 0.675 3.501 0.837
H6 PS ATT 0.005 0.752 0.000
H7 PROR ATT 0.058 0.051 0.005
H8 PRIR ATT 0.267 2.940 0.115
Notes: PU, perceived usefulness; PEOU, perceived ease of use; PE, perceived enjoyment; PS, perceived sociali-
sation; PROR, perceived product risk; PRIR, perceived privacy risk; ATT, attitude towards VTO technology; INT,
intention to purchase
Table VI.
Results of hypotheses
testing (H1H8)
Online
purchase
decision
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4.3 Multi-group analysis
Prior to performing multi-group analysis to compare the path coefficients between different
groups, the acceptability of the measurement models and measurement invariance should
be established (Podsakoff et al., 2003). To determine measurement invariance, Henseler et al.
(2016) suggest a three-step procedure to analyse the measurement invariance of composite
models (MICOM) for PLS-SEM, which involves determining configural invariance,
compositional invariance and the equality of composite mean values and variances. If both
configural and compositional invariance are established, partial measurement invariance is
established; otherwise, no measurement invariance is established. If configural invariance,
compositional invariance, and the equality of composite mean values and variances are all
established, full measurement invariance is established (Henseler et al., 2016). In the
proposed research model, different age groups and gender groups have identical indicators,
data treatment and algorithm settings or optimisation criteria, indicating configural
invariance. The compositional invariance analysis and the equality of mean values and
variances test were performed in SmartPLS 3. The testing results show that the research
model indicates partial measurement invariance for gender groups and full measurement
invariance for age groups, which allows for comparing the standardized path coefficients
between the groups (see Tables IX and X).
Table XI shows that the MGA results of the two age groups reveal no significant
differences at the 0.05 significance level, with the exception of the path from perceived
RMSE MAE Q
2
PU 0.571 0.373 0.496
PE 0.581 0.370 0.360
ATT 0.524 0.361 0.444
INT 0.467 0.324 0.341
Notes: PU, perceived usefulness; PE, perceived enjoyment; ATT, attitude towards VTO technology; INT,
intention to purchase
Table VII.
Out-of-sample
predictive
performance
of constructs
PLS LM PLS-LM
RMSE MAE Q
2
RMSE MAE Q
2
RMSE MAE Q
2
PU1 0.658 0.447 0.457 0.662 0.478 0.410 0.004 0.031 0.047
PU2 0.624 0.429 0.492 0.625 0.440 0.466 0.001 0.011 0.026
PU3 0.637 0.455 0.462 0.644 0.457 0.446 0.007 0.002 0.016
PU4 0.593 0.458 0.397 0.666 0.463 0.378 0.073 0.005 0.019
PE1 0.659 0.414 0.439 0.701 0.492 0.365 0.042 0.078 0.074
PE2 0.580 0.405 0.506 0.634 0.471 0.412 0.054 0.066 0.094
PE3 0.672 0.424 0.458 0.774 0.555 0.280 0.102 0.131 0.178
PE4 0.647 0.458 0.411 0.710 0.506 0.291 0.063 0.048 0.120
ATT1 0.581 0.406 0.435 0.616 0.466 0.365 0.035 0.060 0.070
ATT2 0.588 0.400 0.414 0.608 0.459 0.373 0.020 0.059 0.041
ATT3 0.640 0.452 0.370 0.645 0.481 0.370 0.005 0.029 0.000
INT1 0.709 0.480 0.296 0.719 0.531 0.276 0.010 0.051 0.020
INT2 0.633 0.447 0.384 0.652 0.507 0.347 0.019 0.060 0.037
INT3 0.629 0.459 0.342 0.635 0.492 0.329 0.006 0.033 0.013
Notes: PU, perceived usefulness; PE, perceived enjoyment; ATT, attitude towards VTO technology; INT,
intention to purchase; RMSE, root mean squared error; MAE, mean absolute error; PLS, partial least squares
path model; LM, linear regression model
Table VIII.
Out-of-sample
predictive
performance
of indicators
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Equal mean value Equal variance
Constructs
Configural
invariance
established
c value
(¼1)
Confidence
interval
(CIs)
Permutation
p-values
Partial
measurement
invariance
established Differences
Confidence
interval (CIs)
Permutation
p-values Differences
Confidence
interval (CIs)
Permutation
p-values
Full
measurement
invariance
established
PU Yes 1.000 (1.000,1.000) 0.973 Yes 0.013 (0.282,0.274) 0.930 0.176 (0.464,0.448) 0.478 Yes
PEOU Yes 1.000 (0.999,1.000) 0.624 Yes 0.096 (0.280,0.271) 0.500 0.091 (0.490,0.463) 0.721 Yes
PE Yes 0.999 (0.999,1.000] 0.318 Yes 0.059 (0.287,0.264) 0.675 0.063 (0.530,0.472) 0.810 Yes
PS Yes 0.998 (0.992,1.000] 0.467 Yes 0.197 (0.278,0.271) 0.155 0.416 (0.530,0.455) 0.100 Yes
PROR Yes 0.997 (0.993,1.000) 0.329 Yes 0.269 (0.279,0.275) 0.057 0.286 (0.460,0.429) 0.230 Yes
PRIR Yes 0.998 (0.994,1.000) 0.265 Yes 0.255 (0.272,0.272) 0.067 0.130 (0.467,0.429) 0.586 Yes
ATT Yes 1.000 (0.999,1.000) 0.66 Yes 0.134 (0.282,0.270) 0.344 0.113 (0.425,0.406) 0.607 Yes
INT Yes 1.000 (0.999,1.000) 0.922 Yes 0.081 (0.278,0.269) 0.566 0.016 (0.398,0.384) 0.937 Yes
Notes: PU, perceived usefulness; PEOU, perceived ease of use; PE, perceived enjoyment; PS, perceived socialisation; PROR, perceived product risk; PRIR, perceived privacy risk;
ATT, attitude towards VTO technology; INT, intention to purchase
Table IX.
MICOM test
results for different
age groups
Online
purchase
decision
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Equal mean value Equal variance
Constructs
Configural
invariance
established
cvalue
(1)
Confidence
interval
(CIs)
Permutation
p-values
Partial
measurement
invariance
established Differences
Confidence
interval (CIs)
Permutation
p-values Differences
Confidence
interval (CIs)
Permutation
p-values
Full
measurement
invariance
established
PU Yes 1.000 (1.000,1.000) 0.562 Yes 0.073 (0.264,0.260) 0.588 0.246 (0.429,0.424) 0.278 Yes
PEOU Yes 1.000 (0.999,1.000) 0.207 Yes 0.005 (0.253,0.259) 0.966 0.426 (0.435,0.438) 0.056 Yes
PE Yes 1.000 (0.999,1.000) 0.365 Yes 0.002 (0.263,0.263) 0.992 0.696 (0.470,0.476) 0.003 No
PS Yes 0.998 (0.994,1.000) 0.032 Yes 0.107 (0.269,0.254) 0.417 0.325 (0.478,0.442) 0.166 Yes
PROR Yes 0.998 (0.995,1.000) 0.350 Yes 0.073 (0.261,0.261) 0.583 0.139 (0.425,0.415) 0.531 Yes
PRIR Yes 0.998 (0.995,1.000) 0.351 Yes 0.014 (0.265,0.264) 0.908 0.525 (0.444,0.423) 0.017 No
ATT Yes 1.000 (0.999,1.000) 0.525 Yes 0.038 (0.262,0.266) 0.765 0.341 (0.403,0.394) 0.094 Yes
INT Yes 1.000 (0.999,1.000) 0.581 Yes 0.040 (0.263,0.255) 0.756 0.406 (0.366,0.358) 0.026 No
Notes: PU, perceived usefulness; PEOU, perceived ease of use; PE, perceived enjoyment; PS, perceived socialisation; PROR, perceived product risk; PRIR, perceived
privacy risk; ATT, attitude towards VTO technology; INT, intention to purchase
Table X.
MICOM test
results for different
gender groups
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socialisation to attitude, with a MGA p-value of 0.01. No significant differences between
males and females are found (see Table XII). The permutation test results confirm no
significant difference between female and male groups or between the two age groups for
the structural model, as all permutation p-values are above the 0.05 threshold. Hence, H9
and H10 are not supported.
5. Discussion and conclusions
This study proposes that online consumersusage experiences with and attitude towards
VTO technology play an important role in their online purchase decision intention. The
results of examining the proposed PLS model reveal that customersattitude towards this
technology predicts their intention to purchase a garment online. In turn, attitude is found to
be affected by perceived usefulness, perceived enjoyment and perceived privacy risk, where
perceived usefulness and perceived enjoyment are affected by perceived ease of use. To a
certain extent, the model can be used to explain differences in terms of the determinants of
attitude towards VTO applications between different age groups.
5.1 Discussion
The results of examining the proposed model reveal that customersattitude towards VTO
technology can affect their intention to purchase a garment online. This confirms results of
prior studies that positive attitudes towards this kind of technology would stimulate
Relationships
Path
coefficient
(minor)
Path
coefficient
(adult) CIs (minor) CIs (adult) MGA p-value
Permutation
p-value
ATT INT 0.782 0.845 (0.630, 0.881) (0.765, 0.896) 0.811 0.196
PU ATT 0.432 0.286 (0.247, 0.618) (0.096, 0.453) 0.144 0.292
PEOU PU 0.833 0.697 (0.716, 0.911) (0.512, 0.815) 0.058 0.445
PE ATT 0.102 0.413 (0.062, 0.351) (0.152, 0.646) 0.969 0.288
PEOU PE 0.555 0.730 (0.187, 0.804) (0.601, 0.818) 0.839 0.470
PS ATT 0.266 0.106 (0.044, 0.523) (0.327, 0.103) 0.010 0.482
PROR ATT 0.185 0.015 (0.154, 0.44) (0.226, 0.147) 0.126 0.732
PRIR ATT 0.103 0.323 (0.127, 0.289) (0.135, 0.542) 0.928 0.254
Notes: PU, perceived usefulness; PEOU, perceived ease of use; PE, perceived enjoyment; PS, perceived
socialisation; PROR, perceived product risk; PRIR, perceived privacy risk; ATT, attitude towards VTO
technology; INT, intention to purchase
Table XI.
Results of testing
age difference
Relationships
Path coefficient
( female)
Path coefficient
(male) CIs ( female) CIs (male)
MGA
p-value
Permutation
p-value
ATT INT 0.868 0.787 (0.802, 0.913) (0.666, 0.871) 0.925 0.205
PU ATT 0.391 0.355 (0.230, 0.611) (0.115, 0.522) 0.582 0.308
PEOU PU 0.691 0.780 (0.486, 0.823) (0.596, 0.885) 0.210 0.447
PE ATT 0.117 0.346 (0.303, 0.423) (0.107, 0.576) 0.147 0.285
PEOU PE 0.743 0.630 (0.616, 0.836) (0.373, 0.799) 0.826 0.457
PS ATT 0.065 0.109 (0.343, 0.147) (0.116, 0.338) 0.149 0.482
PROR ATT 0.083 0.060 (0.140, 0.241) (0.114, 0.253) 0.580 0.728
PRIR ATT 0.392 0.150 (0.131, 0.660) (0.018, 0.323) 0.924 0.254
Notes: PU, perceived usefulness; PEOU, perceived ease of use; PE, perceived enjoyment; PS, perceived
socialisation; PROR, perceived product risk; PRIR, perceived privacy risk; ATT, attitude towards VTO
technology; INT, intention to purchase
Table XII.
Results of testing
gender difference
Online
purchase
decision
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consumerspurchase intention as it affects the intended use of the technology and
consequently the post-use evaluation of the product (Kim and Forsythe, 2008). Online
consumersattitude towards VTO technology is found to be affected by perceived
usefulness and perceived enjoyment, both of which are in turn influenced by perceived ease
of use. These findings are consistent with prior research in the sense that enhanced
shopping experiences enabled by interactive technologies lead to stronger purchase
intention than passive product presentation (Kim and Forsythe, 2008).
However, consumersattitude towards VTO technology is not affected by perceived
product risk. One of the reasons might be that, with more information about the product
being delivered by this technology, consumers feel they would be less likely to return the
product (Yaoyuneyong et al., 2014), suggesting little perceived product risk. Another reason
could be that the cost of returning a product is low. For example, a number of countries,
such as the UK (Parliament of the United Kingdom, 2013, pp. 13-18) and China (State
Administration for Industry and Commerce, 2017, pp. 1-4), have set up laws or regulations
that e-online retailers allow termless return within a certain period.
Interestingly, perceived privacy risk is found to be positively related to attitude towards
VTO technology. One possible explanation is the privacy paradox, in which a person might
state that they would not use an online service when having privacy concerns, but would
not care when actually faced with the possibility of convenience (Bélanger and Crossler,
2011). Particularly, consumers tend to have a positive feeling towards websites with privacy
information made more salient and accessible and are even willing to pay a premium for
using the privacy protective website while shopping online (Tsai et al., 2011).
Overall, findings of this study suggest that the utilitarian, hedonic and risk perspectives of
VTO technology are roughly equally important for predicting consumersattitudes towards it
within an integrated framework. This finding is consistent with prior studies in the sense that
the three perspectives are predictors of consumersattitude towards this technology, even
though the relative importance of each perspective varies among these studies. For example,
when considering utilitarian and hedonic value together, hedonic value has a stronger impact
on attitude towards VTO technology than utilitarian value (Kim and Forsythe, 2007). When
considering utilitarian value and risk together, utilitarian seems a strong predictor (Cho and
Wang, 2010).When comparing hedonic value and risk, Lee et al. (2010) find that hedonic value
is a stronger predictor, whereas Huang and Qin (2011) find the opposite.
The results of multi-group analysis suggest no statistically significant differences in either
different age groups or different gender groups, which aligns with prior studies that find no
significant gender or age difference in the overall VTO technology adoption process (e.g. Kim
and Forsythe, 2008). However, this study has identified some differences in the determinants
of attitude towards this technology between online customers of different ages. For example,
the impacts of perceived socialisation and perceived product risk on attitude towards using
this technology are positive for minor customers and negative for adult customers. Such a
finding is consistent with the literature, in that the ageof a person affects technology adoption
on the internet and the individuals behavioural pattern related to those technologies (Law and
Ng, 2016). In particular, this finding aligns with Yenset al. (2017) argument that people born
before the millennium are more attracted to new technology such as VTO application and
social networking functions than people born after that time and are less concerned with
provision of personal information while shopping online (Lian and Yen, 2014).
5.2 Implications
The present study contributes to the body of literature on the influence of VTO technology on
consumersonline apparel purchase intention. Guided by the U&G theory and the technology
adoption literature, this study justifies the need to adopt an integration of utilitarian, hedonic
and risk perspectives for estimating how VTO applications affect online consumerspurchase
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decision process. A holistic view combining the utilitarian, hedonic and risk perspectives
could further our understanding of how this technology affects online consumerspurchase
intention. In addition, by using the PLS approach to validate the research model, this study
applies another analytical approach for investigating this research topic. This study shows
the feasibility of applying advanced PLS techniques in the topic of online behaviour,
especially in the field of adoption and impact of VTO technology in online apparel retailing.
Particularly, the use of HTMT, PLS predict and PLS-MGA provide insights into the validity of
the measurement model, the reliability of the predictive accuracy of the structural model and
the differences in proposed relationships between various groups.
The present study enhances the role of VTO technology in consumersonline purchase
decision-making process because of the utilitarian and hedonic value and reduces the risk
associated with this technology. It is important that online apparel retailers make full use of
advanced technologies such as the one investigated in this study to assist customers with
their purchase decisions. Moreover, online apparel retailers should endeavour to increase
consumerspositive attitude towards VTO technology, as improved attitude is meaningful
for promoting purchase intention. Online retailers who focus on a particular age group may
consider customised implementation strategies of VTO technology. For example, for minor
customers, online apparel retailers could emphasise the advanced socialisation features of
the technology that allows sharing try-on presentation with friends via various social media.
For older customers, online retailers could proactively and explicitly acknowledge the
privacy policy of using such a technology to assure customers of its security.
Designers of VTO technology may also benefit from the findings. Some design
guidelines can be generated to promote a positive attitude towards this technology.
Designers could integrate advanced virtual reality and augment reality technologies with
vivid trying-on scenes in order to bridge the gap between the online and offline shopping
experience. For instance, it could be designed to allow customers to try-on clothes on
different predefined or customised body types on a mobile device and move their avatars to
see how they look in the chosen garment in different scenes or occasions.
5.3 Limitations and future research
While the results of this study provide insightful implications for research and practice, these
results should be viewed with respect to certain limitations. As our study analyses cross-
sectional data, it can only provide a static perspective on online consumersbehavioural patterns
(Rindfleisch et al., 2008). Further study may extend the findings from this study to understand
possible changes in online consumersattitude towards using VTO technology and their
intention to purchase apparel online over time. Future studies could also extend the research
model by evaluating a relationship between behavioural intention and actual behaviour to
examine how the self-reported intention to make purchases predicts actual purchase behaviour.
In addition, future research could test the validated model in online retailing of other products
such as eyewear and cosmetics and deepen use of the PLS approach during the examination.
References
Andrews, D., Nonnecke, B. and Preece, J. (2008), Conducting research on the internet: online survey
design, development and implementation guidelines,International Journal of Human-Computer
Interaction, Vol. 16 No. 2, pp. 185-210.
Armstrong, J.S. and Overton, T.S. (1977), Estimating nonresponse bias in mail surveys,Journal of
Marketing Research, Vol. 14 No. 3, pp. 396-402.
Baytar, F., Chung, T.-l. D. and Shin, E. (2016), Can augmented reality help e-shoppers make informed
purchases on apparel fit, size, and product performance,International Textile and Apparel
Association Annual Conference Proceedings,Vancouver, and Knoxville, TN, pp. 1-2.
Online
purchase
decision
Downloaded by University of Waikato Library At 15:39 31 March 2019 (PT)
Beck, M. and Crié, D. (2018), I virtually try it I want it ! Virtual fitting room: a tool to increase on-line
and off-line exploratory behavior, patronage and purchase intentions,Journal of Retailing and
Consumer Services, Vol. 40, pp. 279-286.
Bélanger, F. and Crossler, R.E. (2011), Privacy in the digital age: a review of information privacy
research in information systems,MIS Quarterly, Vol. 35 No. 4, pp. 1017-1042.
Childers, T.L., Carr, C.L., Peck, J. and Carson, S. (2001), Hedonic and utilitarian motivations for online
retail shopping behavior,Journal of Retailing, Vol. 77 No. 4, pp. 511-535.
Chiu, C.-M., Wang, E.T.G., Fang, Y.-H. and Huang, H.-Y. (2014), Understanding customersrepeat
purchase intentions in B2C E-commerce: the roles of utilitarian value, hedonic value and
perceived risk,Information Systems Journal, Vol. 24 No. 1, pp. 85-114.
Cho, H. and Fiorito, S.S. (2009), Acceptance of online customization for apparel shopping,
International Journal of Retail & Distribution Management, Vol. 37 No. 5, pp. 389-407.
Cho, H. and Wang, Y. (2010), Cultural comparison for the acceptance of online apparel customization,
Journal of Consumer Marketing, Vol. 27 No. 6, pp. 550-557.
Choi, J. and Lee, K.H. (2003), Risk perception and e-shopping: a cross-cultural study,Journal of
Fashion Marketing & Management, Vol. 7 No. 7, pp. 49-64.
Davis, F.D. (1989), Perceived usefulness, perceived ease of use, and user acceptance of information
technology,MIS Quarterly, Vol. 13 No. 3, pp. 319-340.
Dennis, C., Morgan, A., Wright, L.T. and Jayawardhena, C. (2010), The influences of social e-shopping
in enhancing young womens online shopping behaviour,Journal of Customer Behaviour, Vol. 9
No. 2, pp. 151-174.
Dennis, S. (2017), Many unhappy returns: E-commerces Achilles heel, available at:
www.forbes.com/sites/stevendennis/2017/08/09/many-unhappy-returns-e-commerces-achilles-
heel/#2722b9ee4bf2 (accessed 20 December 2017).
Evermann, J. and Tate, M. (2016), Assessing the predictive performance of structural equation model
estimators,Journal of Business Research, Vol. 69 No. 10, pp. 4565-4582.
Farber, M. (2016), Consumers are now doing most of their shopping online, available at: http://
fortune.com/2016/06/08/online-shopping-increases/ (accessed 12 December 2017).
Faust, M.E. and Carrier, S. (2011), How computer technologies may change the way women buy apparel,
Proceedings of the 2011 International Conference on Computer and Management,Wuhan, pp. 1-4.
Fiore, A.M. and Jin, H.J. (2003), Influence of image interactivity on approach responses towards an
online retailer,Internet Research, Vol. 13 No. 1, pp. 38-48.
Fiore, A.M., Jin, H.J. and Kim, J. (2005), For fun and profit: hedonic value from image interactivity and
responses toward an online store,Psychology & Marketing, Vol. 22 No. 8, pp. 669-694.
Fiore, A.M., Kim, J. and Lee, H.H. (2005), Effect of image interactivity technology on consumer
responses toward the online retailer,Journal of Interactive Marketing, Vol. 19 No. 3, pp. 38-53.
Franke, G. and Sarstedt, M. (2018), Heuristics versus statistics in discriminant validity testing: a
comparison of four procedures,Internet Research, forthcoming.
Gefen, D., Rigdon, E.E. and Straub, D. (2011), An update and extension to SEM guidelines for
administrative and social science research,MIS Quarterly, Vol. 35 No. 2, pp. iii-xiv.
Greene, L. (2011), Next big trend: virtual fitting rooms, available at: www.ft.com/cms/s/2/57b1fea6-
1f55-11e0-8c1c-00144feab49a.html#axzz26RCYH5Zm (accessed 3 January 2017).
Grewal, D., Munger, J.L., Iyer, G.R. and Levy, M. (2003), The influence of internet-retailing factors on
price expectations,Psychology & Marketing, Vol. 20 No. 6, pp. 477-493.
Hair, J.F., Hult, G.T.M., Ringle, C.M. and Sarstedt, M. (2017), A Primer on Partial Least Squares
Structural Equation Modeling, 2nd ed., Sage, Thousand Oaks, CA.
Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M. and Thiele, K.O. (2017), Mirror, mirror on the wall: a
comparative evaluation of composite-based structural equation modeling methods,Journal of
the Academy of Marketing Science, Vol. 45 No. 5, pp. 616-632.
INTR
Downloaded by University of Waikato Library At 15:39 31 March 2019 (PT)
Hakan, C. (2016), The functionality of online shopping site within the customer service life cycle: a
literature review, in In, L. (Ed.), Encyclopedia of E-Commerce Development, Implementation, and
Management, IGI Global, Hershey, PA, pp. 791-803.
Henseler, J., Ringle, C.M. and Sarstedt, M. (2015), A new criterion for assessing discriminant validity in
variance-based structural equation modeling,Journal of the Academy of Marketing Science,
Vol. 43 No. 1, pp. 115-135.
Henseler, J., Ringle, C.M. and Sarstedt, M. (2016), Testing measurement invariance of composites using
partial least squares,International Marketing Review, Vol. 33 No. 3, pp. 405-430.
Hernández, B., Jiménez, J. and Martín, M.J. (2011), Age, gender and income: do they really moderate
online shopping behaviour?,Online Information Review, Vol. 35 No. 1, pp. 113-133.
Hirst, A. and Omar, O.E. (2007), Assessing womens apparel shopping behaviour on the internet,
Journal of Retail Marketing Management Research, Vol. 1 No. 1, pp. 32-40.
Huang, E. (2008), Use and gratification in e-consumers,Internet Research, Vol. 18 No. 4, pp. 405-426.
Huang, L.C. and Shiau, W.L. (2017), Factors affecting creativity in information system development:
insights from a decomposition and PLS-MGA,Industrial Management & Data Systems, Vol. 117
No. 3, pp. 496-520.
Huang, N. and Qin, G. (2011), A study of online virtual fitting room adoption based on UTAUT,
Proceedins of the International Conference on e-Business and E-Government,Shanghai, pp. 1-4.
Jennifer, R. (2009), Online branding strategies of UK fashion retailers,Internet Research, Vol. 19 No. 3,
pp. 348-369.
Kang, J.-Y.M. and Johnson, K.K.P. (2013), How does social commerce work for apparel shopping?
Apparel social e-shopping with social network storefronts,Journal of Customer Behaviour,
Vol. 12 No. 1, pp. 53-72.
Kim, D.-E. (2016), Psychophysical testing of garment size variation using three-dimensional virtual
try-on technology,Textile Research Journal, Vol. 86 No. 4, pp. 365-379.
Kim, J. and Forsythe, S. (2007), Hedonic usage of product virtualization technologies in online apparel
shopping,International Journal of Retail & Distribution Management, Vol. 35 No. 6, pp. 502-514.
Kim, J. and Forsythe, S. (2008), Adoption of virtual try-on technology for online apparel shopping,
Journal of Interactive Marketing, Vol. 22 No. 2, pp. 45-59.
Kim, J. and Forsythe, S. (2009), Adoption of sensory enabling technology for online apparel shopping,
European Journal of Marketing, Vol. 43 No. 9, pp. 1101-1120.
Kim, J. and Forsythe, S. (2010), Adoption of virtual try-on technology for online apparel shopping,
Journal of Interactive Marketing, Vol. 22 No. 2, pp. 45-59.
Kim, S. (2011), Web-interactivity dimensions and shopping experiential value,Journal of Internet
Business, No. 9, pp. 1-25.
Law, M. and Ng, M. (2016), Age and gender differences: understanding mature online users with the
online purchase intention model,Journal of Global Scholars of Marketing Science, Vol. 26 No. 3,
pp. 248-269.
Lee, H.H., Fiore, A.M. and Kim, J. (2006), The role of the technology acceptance model in explaining
effects of image interactivity technology on consumer responses,International Journal of Retail
& Distribution Management, Vol. 34 No. 8, pp. 621-644.
Lee, H.H., Kim, J. and Fiore, A.M. (2010), Affective and cognitive online shopping experience,Clothing
& Textiles Research Journal, Vol. 28 No. 2, pp. 140-154.
Lian, J.W. and Yen, D.C. (2014), Online shopping drivers and barriers for older adults: age and gender
differences,Computers in Human Behavior, Vol. 37 No. 37, pp. 133-143.
Liang, H., Saraf, N., Hu, Q. and Xue, Y. (2007), Assimilation of enterprise systems: the effect of
institutional pressures and the mediating role of top management,MIS Quarterly, Vol. 31
No. 1, pp. 59-87.
Online
purchase
decision
Downloaded by University of Waikato Library At 15:39 31 March 2019 (PT)
Lim, W.M. and Ting, D.H.T. (2012), E-shopping: an analysis of the uses and gratifications theory,
Modern Applied Science, Vol. 6 No. 5, pp. 48-63.
Lin, Y.L. and Wang, M.J.J. (2015), The development of a clothing fit evaluation system under virtual
environment,Multimedia Tools & Applications, Vol. 75 No. 13, pp. 1-13.
Loker, S., Cowie, L., Ashdown, S. and Lewis, V.D. (2004), Female consumersreactions to body
scanning,Clothing and Textiles Research Journal, Vol. 22 No. 4, pp. 151-160.
Luo, X. (2002), Uses and gratifications theory and e-consumer behaviors,Journal of Interactive
Advertising, Vol. 2 No. 2, pp. 34-41.
McCormick, H. and Livett, C. (2012), Analysing the influence of the presentation of fashion garments
on young consumersonline behaviour,Journal of Fashion Marketing and Management: An
International Journal, Vol. 16 No. 1, pp. 21-41.
Merle, A., Senecal, S. and St-Onge, A. (2012), Whether and how virtual try-on influences consumer
responses to an apparel web site,International Journal of Electronic Commerce, Vol. 16 No. 3,
pp. 41-64.
Nam, Y., Park, J., Lee, Y., Lee, K.H. and Choi, K.M. (2009), Apparel consumersbody type and their
shopping characteristics,Journal of Fashion Marketing & Management, Vol. 13 No. 13, pp. 372-393.
Noordin, S., Ashaari, N.S. and Wook, T.S.M.T. (2017), Virtual fitting room: the needs for usability and
profound emotional elements,Proceedings of the 6th International Conference on Electrical
Engineering and Informatics,Langkawi, pp. 1-6.
Pachoulakis, I. and Kapetanakis, K. (2012), Augmented reality platforms for virtual fitting rooms,
International Journal of Multimedia & Its Applications, Vol. 4 No. 4, pp. 35-46.
Pantano, E., Rese, A. and Baier, D. (2017), Enhancing the online decision-making process by using
augmented reality: a two country comparison of youth markets,Journal of Retailing and
Consumer Services, Vol. 38 No. 5, pp. 81-95.
Parliament of the United Kingdom (2013), The Consumer Contracts (Information, Cancellation and
Additional Charges) Regulations, in Parliament of the United Kingdom (Ed.), 2013 No. 3134,
The Stationery Office Limited, pp. 13-18.
Pascual-Miguel, F.J., Agudo-Peregrina, Á.F. and Chaparro-Peláez, J. (2015), Influences of gender and
product type on online purchasing,Journal of Business Research, Vol. 68 No. 7, pp. 1550-1556.
Podsakoff, P.M., MacKenzie, S.B. and Podsakoff, N.P. (2012), Sources of method bias in social science
research and recommendations on how to control it,Annual Review of Psychology, Vol. 63 No. 1,
pp. 539-569.
Podsakoff, P.M., MacKenzie, S.B., Lee, J.-Y. and Podsakoff, N.P. (2003), Common method biases in
behavioral research: a critical review of the literature and recommended remedies,Journal of
Applied Psychology, Vol. 88 No. 5, pp. 879-903.
Rasoolimanesh, S.M., Ringle, C., Jaafar, M. and Ramayah, T. (2017), Urban vs. rural destinations:
residentsperceptions, community participation and support for tourism development,
Tourism Management, Vol. 60, pp. 147-158.
Rindfleisch, A., Malter, A.J., Ganesan, S. and Moorman, C. (2008), Cross-sectional versus longitudinal
survey research: concepts, findings, and guidelines,Journal of Marketing Research, Vol. 45
No. 3, pp. 261-279.
Ringle, C.M., Wende, S. and Becker, J.-M. (2015), SmartPLS 3. Bönningstedt: SmartPLS, available at:
www.smartpls.com (accessed 10 August 2017).
Sarstedt, M., Hair, J.F. and Ringle, C.M. (2017), Partial least squares structural equation modeling,in
Homburg, C., Klarmann, M. and Vomberg, A. (Ed.), Handbook of Market Research, Springer,
Heidelberg, pp. 1-40.
Sarstedt, M., Hair, J.F., Ringle, C.M., Kai, O.T. and Gudergan, S.P. (2016), Estimation issues with PLS
and CBSEM: where the bias lies! ,Journal of Business Research, Vol. 69 No. 10, pp. 3998-4010.
Sekhavat, Y.A. (2017), Privacy preserving cloth try-on using mobile augmented reality,IEEE
Transactions on Multimedia, Vol. 19 No. 5, pp. 1041-1049.
INTR
Downloaded by University of Waikato Library At 15:39 31 March 2019 (PT)
Shim, S.I. and Lee, Y. (2011), Consumers perceived risk reduction by 3D virtual model,International
Journal of Retail & Distribution Management, Vol. 39 No. 12, pp. 945-959.
Shin, E. and Baytar, F. (2014), Apparel fit and size concerns and intentions to use virtual try-on:
impacts of body satisfaction and images of modelsbodies,Clothing & Textiles Research
Journal, Vol. 32 No. 1, pp. 20-33.
Shmueli, G., Ray, S., Velasquez Estrada, J.M. and Chatla, S.B. (2016), The elephant in the room: predictive
performance of PLS models,Journal of Business Research, Vol. 69 No. 10, pp. 4552-4564.
State Administration for Industry and Commerce (2017), Interim measures on return of goods bought
online without reasons within 7 days, in State Administration for Industry and Commerce (Ed.),
90, The State Council of the Peoples Republic of China, Beijing, pp. 1-4.
Statista (2018), Apparel, footwear and accessories retail E-commerce revenue in the United States from
2016 to 2022, available at: www.statista.com/statistics/278890/us-apparel-and-accessories-
retail-e-commerce-revenue/ (accessed 5 March 2018).
Tsai, J.Y., Egelman, S., Cranor, L. and Acquisti, A. (2011), The effect of online privacy information on
purchasing behavior: an experimental study,Information Systems Research, Vol. 22 No. 2,
pp. 254-268.
Yang, H.E. and Wu, C.C. (2009), Effects of image interactivity technology adoption on e-shoppers
behavioural intentions with risk as moderator,Production Planning & Control, Vol. 20 No. 4,
pp. 370-382.
Yaoyuneyong, G., Foster, J. and Flynn, L. (2014), Factors impacting the efficacy of augmented reality
virtual dressing room technology as a tool for online visual merchandising,Journal of Global
Fashion Marketing, Vol. 5 No. 4, pp. 283-296.
Yen, Y.Y., Narayanasamy, K., Lin, C.Y., Rasiah, D. and Ramasamy, S. (2017), Consumers perception
towards real-time virtual fitting system,Proceedings of the 6th International Conference on
Computing and Informatics,Kuala Lumpur, Universiti Utara Malaysia, Sintok, pp. 311-316.
Zhang, T., Cao, L. and Wang, W.Y.C. (2017), The impact of virtual try-on image interaction
technology on online shopperspurchase decision,Proceedings of the 8th International
Conference on E-Education, E-Business, E-Management and E-Learning,Kuala Lumpur, and
New York, NY, pp. 6-10.
Corresponding author
William Yu Chung Wang can be contacted at: william.wang@waikato.ac.nz
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... Further important characteristics to attain desired consumer responses in marketing practice are the informativeness of the AR application (e.g., the quality and degree of provided information, product contextuality, information sharing capabilities, etc.) (Heller et al., 2019a;Kowalczuk et al., 2021;Pantano et al., 2017) as well as aspects pertaining to the quality or performance of the AR solution (e.g., the visual quality, mapping quality, responsiveness of the AR app, etc.) (Kowalczuk et al., 2021;Pantano et al., 2017;Park & Yoo, 2020). In terms of the psychological outcomes, AR can give rise to diverse cognitive responses, such as usefulness perceptions (Pantano et al., 2017;Zhang et al., 2019), perceived informativeness (Feng & Xie, 2018;Qin et al., 2021;Smink et al., 2019), curiosity (Beck & Crié, 2018;, brand awareness (Feng & Xie, 2019), creativity (Jessen et al., 2020) as well as self-referencing (T.-L. Huang, 2019), which is decisive in creating a personalized experience with products, forging ownership perceptions (Song et al., 2020), and attachment with a brand (Yuan et al., 2021). ...
... In addition to the cognitive and affective responses that have been encountered, the analyzed body of literature also indicates that it can be imperative to cultivate social dynamics between consumers. Albeit considered only to a marginal extent, the reviewed studies indicate that AR can be combined with social media features (Zhang et al., 2019) and other social constituents, such as point-of-view sharing or further communicative acts. It is assumed, that social experiences can be facilitated via AR, which can result in viral marketing behavior and unpaid brand endorsements (Sung, 2021). ...
... Therefore, it seems that the positive aspects of AR can overcome potential hesitancies of consumers when it comes to sharing personal information. Besides word-of-mouth intentions and willingness to share personal data, the reviewed literature chiefly agrees that AR can induce intentions to revisit stores (Javornik, 2016;Park & Yoo, 2020) that support AR functionality as well as increase purchase intentions (Beck & Crié, 2018;Brengman et al., 2019;Moriuchi et al., 2021;Zhang et al., 2019). This is important for marketers, as it pinpoints AR technology as a medium with the potential not only to increase sales conversion but also to maintain customer relations. ...
Chapter
Recent years have seen a swift embracement of augmented reality (AR) as an interactive marketing tool, which has been accelerated even more rapidly by the COVID-19 pandemic. However, the general attitude toward the technology as well as the factors that inhibit or facilitate its adoption from both, the consumers, and practitioners, remain elusive. This prevents marketers from fully exploiting the potential related to AR marketing. This chapter (1) draws on current literature to conceptualize consumer experience in AR marketing and (2) complements these findings with a practitioner perspective by conducting interviews with small retailers. The results of the present chapter indicate that, from the consumer perspective, AR can give rise to diverse cognitive, affective, and social-psychological outcomes, which can translate into behavioral outcomes, including purchase intentions, word-of-mouth intentions, and brand engagement. From the practitioner’s perspective, initial interview results reveal that advancements toward an easy integration of AR within existing IT infrastructures, as well as efficient ways to create virtual product replicas are crucial for the adoption of AR by small retailers. Based on the combined observations from literature and the conducted interviews, a comprehensive framework of interactive AR marketing is provided, and a way forward is discussed by addressing the emergent trends of AR as an interactive marketing technology.KeywordsAugmented realityInteractive marketingShoppingRetailConsumer experience
... For example, while some papers seek to explain purchase intentions as the primary outcome (e.g., Baek et al., 2018;Fan et al., 2020), others focus on the perceived ease of use as the outcome variable (e.g., Mishra et al., 2021). Notably, some studies conceptualize ease of use as a predictor (e.g., Zhang et al., 2019), whereas others specify this as a mediator (e.g., Plotkina and Saurel, 2019) that translates into purchase intentions as the dependent variable. We will now discuss the function of different variables from the perspective of the individual studies (Are they predictors, mediators, moderators, or outcomes?) ...
... Moderating variables include aspects of the product, such as product type (Poushneh, 2018;Rauschnabel et al., 2019;Fan et al., 2020;Mishra et al., 2021), product contextuality (Heller et al., 2019a), consumer's brand attachment (Yuan et al., 2021), and price-value trade-off (Heller et al., 2019a). Other moderators relate to the national background (Pantano et al., 2017) or sociodemographics (Zhang et al., 2019). Some studies rely on consumer-centred moderators, including technology anxiety (Kim and Forsythe 2008), technology-as-solution-belief (Joerß et al., 2021), involvement (Kim and Forsythe 2008), AR familiarity/experience (Yim et al., 2017;Song et al., 2019;Bonnin, 2020), processing fluency (Hilken et al., 2017;Heller et al., 2019a), or assessment orientation (Heller et al., 2019b;Jessen et al., 2020) as moderators. ...
... Core aspects of the user experience involve perceived enjoyment, spatial/telepresence, flow experience, and perceived ease of use. According to the technology acceptance model, user's experience will also improve perceptions of user benefits (e.g., Huang and Liao, 2015;Pantano et al., 2017;Rese et al., 2017;Zhang et al., 2019). The user benefits of AR have initially been described as rather hedonic. ...
Article
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The application of augmented reality (AR) is receiving great interest in e-commerce, m-commerce, and brick-and-mortar-retailing. A growing body of literature has explored several different facets of how consumers react to the upcoming augmented shopping reality. This systematic literature review summarizes the findings of 56 empirical papers that analyzed consumers’ experience with AR, acceptance of AR, and behavioral reactions to AR in various online and offline environments. The review synthesizes current knowledge and critically discusses the empirical studies conceptually and methodologically. Finally, the review outlines the theoretical basis as well as the independent, mediating, moderating, and dependent variables analyzed in previous AR research. Based on this synthesis, the paper develops an integrative framework model, which helps derive directives for future research on augmented shopping reality.
... The second theme is about the effects of AR on customers' experience, product evaluation, and purchase decision making. Specifically, 24 articles examined the impacts of AR on customer experience (Barhorst et al., 2021;Heller et al., 2019a;Huang and Liao, 2017;Song et al., 2019) and purchase intention (Zhang et al., 2019;Smink et al., 2020). ...
... The second category of literature delineated AR characteristics and their effects on customers' purchase intention. AR characteristics examined include interactivity (Park and Yoo, 2020;Song et al., 2019), responsiveness (Poushneh, 2021;Song et al., 2019), personalisation (Smink et al., 2020), enjoyment (Zhang et al., 2019), spatial presence (Smink et al., 2020), sensory control (Heller et al., 2019b), information quality (Song et al., 2019), aesthetic quality (Song et al., 2019), intrusiveness (Smink et al., 2020), and privacy risk (Zhang et al., 2019). Articles in this stream also delved into the boundary conditions such as product category, brand popularity, and customer characteristics, in examining the effects of AR use or AR characteristics on purchase intention. ...
... The second category of literature delineated AR characteristics and their effects on customers' purchase intention. AR characteristics examined include interactivity (Park and Yoo, 2020;Song et al., 2019), responsiveness (Poushneh, 2021;Song et al., 2019), personalisation (Smink et al., 2020), enjoyment (Zhang et al., 2019), spatial presence (Smink et al., 2020), sensory control (Heller et al., 2019b), information quality (Song et al., 2019), aesthetic quality (Song et al., 2019), intrusiveness (Smink et al., 2020), and privacy risk (Zhang et al., 2019). Articles in this stream also delved into the boundary conditions such as product category, brand popularity, and customer characteristics, in examining the effects of AR use or AR characteristics on purchase intention. ...
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The emergence and proliferation of augmented reality (AR) technology in retailing has revolutionised consumer shopping and service experience. A body of research on AR in business applications, particularly for retailing, is quickly developing. This research shed light on the current status of the scholarly works on AR in retailing by conducting a systematic literature review using bibliometric analysis and thematic analysis. Specifically, this research examines 51 peer-reviewed journal articles using bibliometric analysis. It provides a detailed view of the literature, including research trends, publication venues, and authorships. Moreover, it classifies and reviews three major themes and summarises the articles in each theme. Finally, this research identifies and discusses the possible directions for future research.
... The recent advancements in smartphone technology and social media platforms have increased the popularity of Artificial Intelligence (AI) color cosmetics. For instance, cosmetics try on commonly taken place in the physical cosmetic stores can be replaced by AI color cosmetics in the virtual environment (Zhang et al., 2019). Moreover, facial enhancement technology, such as AI color cosmetics applications, may appeal to those who simply wish to look attractive online. ...
... According to Zhang et al. (2019), AI color cosmetics applications may also serve as an e-commerce platform. After users have finished editing their selfies by applying virtual color cosmetics, the applications may direct them to the official online shops of branded color cosmetics. ...
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... Those who did so have focused either on cosmetics or accessories VTOs, or on hedonic use of AR in social media (Cowan, Javornik, & Jiang, 2021;Smink, van Reijmersdal, van Noort, & Neijens, 2020). Several studies (Merle, Senecal, & St-Onge, 2012;Zhang, Wang, Cao, & Wang, 2019) tested a research model for online apparel retailing, but not in mobile or highly personalised contexts. Ours is the first study to examine how privacy concern and body image factors influence user acceptance of an interface with two modes of personalisation and/or privacy. ...
... Similar value from using in-store or desktop VTOs has already been shown by J.-Y. M. Kang (2014) and Zhang et al. (2019) to positively impact perceived usefulness. To operationalise SV for our mobile context, we turn to the self-presentation and impression management benefits derived by social media users, as they regulate their personal image in the eyes of others via selfies, videos, and likes (Rosenberg & Egbert, 2011). ...
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Virtual try-on (VTO) apps are now used by many fashion consumers, but VTOs for the apparel category have met with resistance. This study examines privacy concern, body image and social value as antecedents to adoption intention towards an apparel VTO with two types of photorealistic avatars. Twenty users first tried out the app in lab sessions, then 301 completed an online survey with a video of the VTO. A majority of participants were concerned about potential misuse of their uploaded picture and preferred to use a pre-loaded avatar of a model with a similar body. This option explains why privacy concern had a weak negative impact on adoption intention in our model, albeit at the expense of self-presentation benefits. The trait of privacy disposition best predicted consumer responses overall, yet other motives were also revealed. Discussed are the implications of this study’s results and limitations to privacy calculus research.
... Therefore, customers who utilize more hedonic motivation must fulfill their needs of the fun experience, fantasy, and sensory experience. On the other hand, using virtual try-on as one of the AR applications in the clothing and garment online retail can provide an integrative view of its utilitarian value, hedonic value, and risk toward customer purchase decisions across different ages and genders (Zhang et al. 2019). ...
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Although online stores extend the traditional offer of the brick and mortar ones, the limited possibilities to virtually try the product before the effective buying makes the online purchase decision a complex process for consumers. Therefore, online retailers face new challenges for supporting consumers consisting of the introduction of advanced technologies such as augmented reality systems. The present study investigates the effect of augmented reality technologies on consumer behaviour within the online retail environments, by comparing two different cultural settings. Drawing upon the technology acceptance model (TAM), new constructs related to the technology characteristics (e.g. quality of information, aesthetic quality, interactivity, and response time) developed a new conceptual model. This model has been tested for a new technology for virtual try-on (a smart mirror for virtual glasses). Focusing on young consumers, data collected in Italy and Germany yielding a total of 318 participants was used. Findings across these two markets reflect cross-market similarities, but also dissimilarities, related to consumers’ motivation to employ augmented reality systems for supporting their online purchase decision. These insights should prove helpful to retailers in better manage the online channels, that could be easily extended to the mobile one.
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