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How do consumers perceive mobile
self-checkout in fashion
retail stores?
Yuli Liang
Mobile selfcheckout in
fashion retail
stores
677
Texas State University, San Marcos, Texas, USA, and
Seung-Hee Lee and Jane E. Workman
Fashion Design and Merchandising, Southern Illinois University,
Carbondale, Illinois, USA
Received 11 August 2020
Revised 20 August 2020
30 September 2020
26 February 2021
30 April 2021
Accepted 26 July 2021
Abstract
Purpose – Mobile self-checkout refers to scanning products using a mobile device inside a brick-and-mortar
store and completing the checkout process on mobile devices. Even though mobile self-checkout has been used
in other industries for several years, it is a new application in the fashion industry and only limited numbers of
retailers have implemented mobile self-checkout in their stores. The purpose of this study is to understand
consumers’ acceptance of mobile self-checkout in fashion retail stores by analyzing determinants of using a
new system.
Design/methodology/approach – Part of the Unified Theory of Acceptance and Use of Technology
(UTAUT) was used as a theoretical framework. Openness to experience, variety seeking and adventure
shopping were added to the model. Empirical data (with 229 valid responses) were collected from the top 20
metropolitan areas in the US via Qualtrics Panel services. Exploratory factor analysis, confirmatory factor
analysis, structural equation modeling and multi-group moderation were used to estimate construct validity
and test the proposed hypotheses and theoretical framework.
Findings – The results indicated that consumers’ intentions toward using mobile self-checkout in fashion
retail stores were predicted by facilitating conditions, social influence and openness to experience. Moreover,
consumers’ previous experience of using mobile self-checkout in fashion retail stores moderated the path from
facilitating conditions to behavioral intention and the path from social influence to behavioral intention. In
addition, different genders and smartphone usage frequency did not vary significantly on the model paths.
Practical implications – The findings show how fashion retailers can understand consumers’ preference
and their willingness to use mobile self-checkout in fashion retail stores. Moreover, the authors addressed ways
for fashion retailers to promote mobile self-checkout in the future.
Originality/value – As a new technology in the fashion industry, literature is deficient concerning
consumers’ intention to adopt mobile self-checkout. This research provided suggestions for fashion retailers
about adopting and improving acceptance of mobile self-checkout. Results will lead to theoretical and
managerial implications for future technology development.
Keywords Mobile self-checkout, UTAUT, Consumer perception, Technology in fashion industry
Paper type Research paper
Introduction
Due to the spread of digital technology, new business models are emerging in the fashion
industry. Digital technology-based innovations in retail are oriented to enhance consumer
shopping activity as well as business profitability (Pantano and Vissone, 2014). Innovative
technologies are being employed globally to increase customer loyalty and retailer
performance. The proliferation of mobile technologies contributes to an increase in
customer satisfaction with the shopping process. With the increasing popularity of
smartphones, retailers have started to offer more services through mobile apps. In addition to
searching for information, conducting mobile shopping and dealing with mobile banking,
consumers are encouraged to use the checkout functions on their apps (Ahuja, 2018).
Consumers are interested in using new systems and exploring checkout functions on
smartphone apps. A GPShopper survey found that 48% of US Internet users thought scan-
International Journal of Retail &
Distribution Management
Vol. 50 No. 6, 2022
pp. 677-691
© Emerald Publishing Limited
0959-0552
DOI 10.1108/IJRDM-08-2020-0299
IJRDM
50,6
678
and-go technology would make shopping easier and 43% would prefer to try scan-and-go
than spend time waiting in a checkout line (Kats, 2019). Self-checkout with a smartphone is
one of a new generation of self-service systems developed by retailers. In this study, mobile
self-checkout refers to scanning products using a mobile device inside a brick-and-mortar
store and completing the checkout process on mobile devices (Andriulo et al., 2015). Mobile
self-checkout is being widely adopted by fast-moving consumer goods retail companies,
grocery stores and convenience stores (Andriulo et al., 2015; Pucci, 2020; Demoulin and
Djelassi, 2016).
Mobile self-checkout has potential to generate more traffic for brick-and-mortar stores and
enrich the customer’s buying experience with an added benefit for retailers—it is costeffective (e.g. cashierless stores such as AmazonGo). Some retailers found increased sales
from customers who use mobile self-checkout. Further, mobile self-checkout saves printing
costs, frees up floor space that can be used for other purposes, and improves inventory
control, marketing, reward schemes and customer service (Taylor, 2016).
Mobile self-checkout has been used in other industries for several years, but it is a new
application in the fashion industry. Fashion retailers such as Macy’s and Nike have
implemented mobile self-checkout in their stores (Alvarez, 2018; CBS Denver, 2018;
VanHoose, 2020). Predictions are that more fashion retailers will use this service in the near
future because of benefits for customers, that is, mobile self-checkout will reduce waiting time
for check-out, enable social distancing and add a novel element to the shopping experience.
However, despite this new significant addition in fashion retail stores; it has received little
academic attention. Customers’ perceptions and factors that encourage or discourage using
mobile self-checkout in fashion retail stores are still understudied. Results of research
investigating technology acceptance in other types of stores may not be entirely applicable to
fashion retail brick-and-mortar stores. Fashion retail stores have unique characteristics that
differentiate them from other types of retail stores (e.g. offering products related to customer
identity). Therefore, the purpose of this research is to study consumers’ intentions of using
mobile self-checkout by analyzing determinants of using a new system. Results will provide
information for retailers about improving adoption and acceptance of mobile self-checkout.
The literature review will highlight the gaps in research regarding mobile self-checkout in
fashion retail stores.
Literature review
Conceptual framework
This research applied part of the Unified Theory of Acceptance and Use of Technology
(UTAUT) (Venkatesh et al., 2003) as a guideline to conceptualize research constructs and
develop the research model. UTAUT has been widely used in information system acceptance
and mobile technology research (e.g. Khalilzadeh et al., 2017). Venkatesh et al. (2003) proposed
four core determinants of using a new system: performance expectancy, effort expectancy,
social influence, facilitating conditions. Because many consumers have used mobile phones to
order online or pay bills or for other financial activities, presumably, they would know what
to expect in terms of performance and effort involved. Thus, this study did not examine
performance or effort expectancy, but instead, focused on facilitating conditions and social
influence. Openness to experience (Thompson, 2008), variety seeking (Donthu and Garcia,
1999) and adventure shopping (Kim and Hong, 2011) were added to the model. Gender,
consumers’ experience of having used (or not used) mobile self-checkout in fashion retailers,
and smartphone usage were tested for moderating effects across different groups.
Facilitating conditions
Once an infrastructure is enabled, facilitating conditions such as guidance, support and
training support the system (Sivathanu, 2019). Facilitating conditions affected behavioral
intentions to use mobile technologies such as mobile payments and mobile applications (e.g.
Sivathanu, 2019). Facilitating conditions refer to consumers’ perceptions of resources and
support available to help them use mobile self-checkout (Venkatesh et al., 2012). When
fashion retailers adopted mobile self-checkout, they used signage in the store and on the
mobile app to guide consumers in how to use the system (CBS Denver, 2018). Also, sales
associates are ready to answer customers’ questions. These facilitating conditions will
increase consumers’ intention to try to use mobile self-checkout. Previous research supported
that facilitating conditions had a positive influence on consumers’ behavioral intention to use
mobile apps (Hew et al., 2015); the greater the accessibility to facilitating conditions, the
greater the behavioral intention to adopt a mobile app (Madan and Yadav, 2016). However,
research has not examined how facilitating conditions influence consumers’ intention to use
an app to complete mobile self-checkout in fashion retail stores. Therefore, we propose:
H1. Facilitating conditions will positively influence consumers’ intention of using mobile
self-checkout.
Social influence
Social influence represents the social pressure exerted on a person to adopt a new technology
(Martins et al., 2014). Social influence occurs when an individual’s perceptions, feelings,
thoughts or behaviors are influenced by others, society or their surroundings (Turner and
Oakes, 1986). Social influence also occurs when users believe that people around them should
use certain technologies (Venkatesh et al., 2012). The opinions or values of significant others
(e.g. friends, family and social groups) influence an individual’s perception of technology.
Social influence has a significant effect on behavioral intentions, especially in mobile
applications (e.g. Alshare and Mousa, 2014). Social influence is the extent to which consumers
perceive that important others believe they should or should not use mobile self-checkout
(Venkatesh et al., 2003). Social influence is one of the most influential determinants of
behavioral intention (Dwivedi et al., 2011). The greater the degree of social influence, the
greater was the behavioral intention to adopt a mobile wallet (Madan and Yadav, 2016).
Therefore, an individual who believes that important others approve his/her usage of mobile
self-checkout will be more inclined to trust and use mobile self-checkout. However, no
research has examined the effect of social influence on consumers’ intention to use an app for
mobile self-checkout in fashion retail stores. Therefore, we propose:
H2. Social influence will positively influence consumers’ intention of using mobile selfcheckout.
Openness to experience
Openness to experience is one of the big five personality traits (McEachern and Warnaby, 2008)
that relates to sensitivity to imagination, art, intellectual abilities, knowledge and information
search. Openness to experience “reflects an individual’s propensity to be flexible with their
thoughts, be curious, and pursue activities” (Kim and Jeong, 2015, p. 401). Openness to
experience is the personality trait most likely to be related to seeking and testing new functions
on the web (Kim and Jeong, 2015). Individuals who score higher on the dimension of openness to
experience tend to be more flexible, creative, innovative, imaginative, curious and untraditional
(McCrae, 2007). Previous research openness to experience is associated with a drive for new
adventure and motivation to try new options (e.g. Lu and Chen, 2017; Moslehpour et al., 2018).
However, no research has examined openness to experience as a personality trait that may
affect consumers’ intention to use an app for mobile self-checkout in fashion retail stores. Thus,
it can be hypothesized that if a consumer is more open to try new options, they will have greater
intention of using mobile self-checkout. Therefore, H3 was proposed:
Mobile selfcheckout in
fashion retail
stores
679
IJRDM
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680
H3. Openness to experience will positively influence consumers’ intention of using
mobile self-checkout.
Variety seeking
Kahn (1995) defined variety seeking as the tendency of individuals to seek diversity in their
choice of services or goods. Variety-seeking behavior in the context of consumer purchasing
behavior is related to emotional and psychosocial motivations rather than cognitive
processes or functional interests (Tian et al., 2018).
Internet shoppers are more likely to be variety seekers (Donthu and Garcia, 1999).
Consumers’ variety-seeking behavior will positively influence consumers’ consumption
online but consumers might exhibit choice overload when confronted with too many choices
(Nagar and Gandotra, 2016). When consumers are shopping at fashion retailers, they may like
to try different items, a variety of styles and new/special services. When consumers have an
opportunity to try a new service, those who have a greater need for variety will likely have a
greater intention to try it. However, no research has examined variety seeking as a tendency
that may affect consumers’ intention to use an app for mobile self-checkout in fashion retail
stores. Therefore, we propose:
H4. Variety seeking will positively influence consumers’ intention of using mobile selfcheckout.
Adventure shopping
Adventure shopping was defined as “shopping for stimulation, adventure, and the feeling of
being in another world,” can be considered shopping for sensory stimulation (Arnold and
Reynolds, 2003, p. 80) and can create hedonic shopping value (Babin et al., 1994). Because
consumers shop to try new things and get new ideas, the exploration process in shopping
could be adventurous and shopping experiences provided by retailers could satisfy
consumers’ adventure shopping motivation (Triantafillidou et al., 2017). Mobile self-checkout
is a new system offered by some fashion retailers, but no research has examined if consumers
might be interested in experimenting with the innovative technology that allows mobile selfcheckout. Therefore, we propose:
H5. Adventure shopping will positively influence consumers’ intention toward using
mobile self-checkout.
Moderating effects
Research indicates that men and women exhibit different consumption behaviors when
shopping with fashion retailers and using mobile apps (Leon, 2018; Walsh et al., 2017). Gender
has been shown to be a variable that moderates the relationships among the variables within
the framework of UTAUT (Venkatesh et al., 2012). Especially, gender moderates the effects of
facilitating conditions on behavioral intention (Venkatesh et al., 2012). Compared with
women, men are willing to spend more effort to overcome different constraints and difficulties
to pursue their goals and they rely less on facilitating conditions when considering use of a
new technology, whereas women tend to place emphasis on external supporting factors
(Rotter and Portugal, 1969; Venkatesh et al., 2012). Compared with men, women like to go
shopping to gain “adventure, thrills, stimulation, excitement, and entering a different
universe of exciting sights, smells, and sounds” (Arnold and Reynolds, 2003, p. 80). However,
no research has examined how gender differences will influence other variables as consumers
form an intention to use an app for mobile self-checkout in fashion retail stores. Therefore, we
propose:
H6. Gender will moderate the path between: adventure shopping and behavioral
intention (H6a), variety seeking and behavioral intentional (H6b), openness to
experience and behavioral intentional (H6c), social influence and behavioral
intention (H6d), facilitating conditions and behavioral intention (H6e).
The UTAUT identified experience as a moderator of theorized relationships, finding that the
effect of behavioral intention on technology use will decrease as experience increases
(Venkatesh et al., 2012). For example, frequency of car use reduced the effect of behavioral
intention on future car use (Verplanken et al., 1998). But Lennon et al. (2007) found that
consumers with online shopping experience show greater intention to use online shopping
again. Mobile self-checkout is a new application in fashion retail stores but no research has
examined how experience will influence other variables as consumers form an intention to
use an app for mobile self-checkout in fashion retail stores. Therefore, we propose:
H7. Experience will moderate the path between: adventure shopping and behavioral
intention (H7a), variety seeking and behavioral intention (H7b), openness to
experience and behavioral intention (H7c), social influence and behavioral intention
(H7d), facilitating conditions and behavioral intention (H7e).
Regarding consumers’ personality traits and smartphone usage, extraversion was positively
associated with smartphone usage frequency, the duration of calls and intensive usage of
communication-related apps (messaging) (Chittaranjan et al., 2013; Montag et al., 2015).
Conscientiousness was a negative predictor for smartphone usage for shopping and finance
matters (Chittaranjan et al., 2013; Kim et al., 2015). Moreover, consumers with high or medium
mobile experience use a greater number of shopping apps than users with low mobile
experience (Kim et al., 2017). But no research has examined how smartphone usage influences
consumers’ intention to use an app for mobile self-checkout in fashion retail stores even
though mobile self-checkout can only be completed by using a smartphone. Therefore, we
propose:
H8. Smartphone usage will moderate the path between: adventure shopping and
behavioral intention (H8a), variety seeking and behavioral intention (H8b), openness
to experience and behavioral intention (H8c), social influence and behavioral
intention (H8d), facilitating conditions and behavioral intention (H8e).
The review of related literature shows that many variables (e.g. facilitating conditions, social
influence, adventure shopping, variety seeking, openness to experience, gender, experience
and smartphone usage) have potential to influence consumers’ intention to use an app to
complete mobile self-checkout in fashion retail stores. But no research has directly examined
these variables with regard to what is a recent innovation for fashion retail stores. The
conceptual framework is presented in Figure 1.
Research method
Research instrument
An online self-administered questionnaire was created using Qualtrics and sent to potential
participants in the top 20 US metropolitan areas. Participants watched a short video about
mobile self-checkout (CBS Denver, 2018) and completed a questionnaire with measures from
established research of facilitating conditions, social influence, openness to experience,
variety seeking, adventure shopping and behavioral intention. Measures to assess research
constructs were adopted or adapted from previous research based on validity and reliability
(see Table 1). Participants were asked whether they have used mobile self-checkout in fashion
retail stores and how many hours they use a smartphone daily. The questionnaire took about
15 minutes to complete.
Mobile selfcheckout in
fashion retail
stores
681
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Figure 1.
Conceptual framework
and testing results
Note(s): **p < 0.01, *p < 0.05. Dash arrow lines indicate moderating effects and ns
indicate non-significant
Construct
Variable type
Source
Format after adapted
Facilitating
conditions
Social influence
Exogenous
Variable
Exogenous
Variable
Exogenous
Variable
Exogenous
Variable
Exogenous
Variable
Endogenous
Variable
Venkatesh et al.
(2012)
Venkatesh et al.
(2012)
Thompson (2008)
4-item scale; 7-point Likert-type (Strongly
disagree/strongly agree)
3-item scale; 7-point Likert-type (Strongly
disagree/strongly agree)
8-item scale; 5-point (inaccurate/accurate)
Donthu and Garcia
(1999)
Kim and Hong
(2011)
Dodds et al. (1991)
3-item scale; 7-point Likert-type (Strongly
disagree/strongly agree)
4-item scale; 7-point Likert-type (Strongly
disagree/strongly agree)
3-item scale; 7-point Likert-type (Very low/
very high)
Openness to
experience
Variety seeking
Adventure
shopping
Table 1.
Research measurement Behavioral
Intention
and its source
Analysis
Descriptive statistics, reliability, structural equation modeling (SEM), moderation test
with linear regression and T-tests were used with SPSS and Amos statistical software
programs.
Results
Sample
Participants (n 5 229) were recruited via Qualtrics Panel services over a one-week period (see
Table 2). The majority of respondents were male (55.99%), 57.2% live in the top five
metropolitan areas, 76.3% ranged between 19 and 45 years old (range 5 18 to 65), 115
participants had experience using mobile self-checkout in fashion retailers. Participants
spent an average of 7.32 hours daily on their smartphone (median 5 5.5, range 5 0–24).
Characteristic
Percent
Gender
Male
Female
55.900
44.100
Age
18–24
25–34
35–44
45–54
55–64
65 and above
9.500
25.100
37.400
16.600
10.500
0.900
Ethnicity
Caucasian
African American
Asian/Asian American
Hispanic/Latino
Native American
Other
68.600
17.000
5.200
7.900
0.900
0.400
Employment
Employed full time (40 or more hours per week)
Employed part time (up to 39 hours per week)
Unemployed and currently looking for work
Unemployed and not currently looking for work
Graduate student
Undergraduate student
Retired
Homemaker
Self-employed
Unable to work
60.700
12.700
7.400
1.700
0.400
2.200
7.000
3.500
3.100
1.300
Characteristic
Education
Less than high school
High school graduate
Some college
2-year degree
4-year degree
Professional degree
Doctorate
Total Household Income
Less than $5,000
$5,000–$9,000
$10,000–$19,999
$20,000–$29,999
$30,000–$39,999
$40,000–$49,999
$50,000–$59,999
$60,000–$69,999
$70,000–$79,999
$80,000–$89,999
$90,000–$99,999
$100,000–$149,999
$150,000–$199,999
$200,000–$249,999
$250,000 or more
Marital Status
Married
Single
Other
Previous experience
Yes
No
Percent
1.700
11.800
17.900
12.200
26.600
21.000
8.700
Mobile selfcheckout in
fashion retail
stores
683
2.200
3.500
6.100
7.000
8.300
5.200
10.000
4.400
7.900
6.100
4.400
14.400
10.000
4.400
6.100
51.500
45.000
3.500
50.200
49.800
Exploratory factor analysis (EFA)
Exploratory factor analysis (EFA) using a principal component analysis method was
performed on 25 items of exogenous and endogenous variables. Because of low loading
(0.40; Hair et al., 2018). The final factor analysis solution had 19 items
measuring six factors and accounted for approximately 82.823% of total variance explained.
All commonalities ranged between 0.657 and 0.937; Cronbach’s alpha ranged from 0.776 to
0.961; demonstrating the good reliability of the scales; EFA loadings ranged from 0.701 to
0.909 (see Table 3).
Confirmatory factor analysis (CFA)
In confirmatory factor analysis (CFA), one item (from openness to experience) of high
modification indices was dropped, all other items within six factors remained. CFA on
remaining 18 items showed an excellent fit (χ 2 5 186.62; df 5 120; χ 2/df 5 1.555; p < 0.001;
root mean square residual [RMSEA] 5 0.049; comparative fit index [CFI] 5 0.981; Bentler–
Bonett normed fit index [NFI] 5 0.948), providing evidence of convergent validity. The
good fit indices lend support for the construct validity of individual constructs in the model,
as indicated by the earlier EFA.
Table 2.
Demographic
characteristics of
research
sample (n 5 229)
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Factor
Scale item
Facilitating
conditions
I have the resources necessary to use mobile checkout (FC 1)
I have the knowledge necessary to use mobile checkout
(FC 2)
Mobile checkout is compatible with other technologies I use
(FC 3)
People who are important to me think that I should use
mobile checkout (SI 1)
People who influence my behavior think that I should use
mobile checkout (SI 2)
People whose opinions I value prefer that I use mobile
checkout (SI 3)
Intellectual (OTE 1)
Intelligent (OTE 2)
Philosophical (OTE 3)
Deep (OTE 4)
I like to try the different products in fashion retail stores
(VS 1)
I like a great deal of variety available in fashion retail stores
(VS 2)
I like the new and different styles available when shopping
in fashion retail stores (VS 3)
To me, shopping is an adventure (AS 1)
I find shopping stimulating (AS 2)
Shopping makes me feel like I am in my own universe (AS 3)
The likelihood that I would use mobile checkout in fashion
retail stores (BI 1)
The probability that I would use mobile checkout in fashion
retail stores (BI 2)
My willingness to use mobile checkout in fashion retail
stores (BI 3)
Social influence
Openness to
experience
Variety seeking
Adventure
shopping
Behavioral
intention
Table 3.
Exploratory factor
analysis results
EFA
loadings
0.845
0.852
Reliability
0.903
0.816
0.85
0.943
0.872
0.829
0.729
0.701
0.724
0.755
0.773
0.776
0.911
0.853
0.773
0.829
0.874
0.819
0.830
0.896
0.961
0.852
0.848
As reported in Table 4, each item loaded significantly on its proposed constructs, with
composite reliabilities above 0.75, providing evidence of the reliability of the measures (Hair
et al., 2018). Results showed good internal consistency of multiple indicators for each
construct. The average variance extracted (AVE), which ranged from 0.507 to 0.893, exceeded
the recommended value of 0.50 (Fornell and Larcker, 1981). All standardized CFA loadings
were significant (p < 0.001) and exceeded 0.50 (ranging from 0.545 to 0.97), showing good
convergent validity (items that are indicators of a specific construct share a high proportion
of variance in common; Hair et al., 2018). AVE for each construct was greater than the
estimates of squared correlations between constructs (see Table 5), confirming discriminant
validity (extent to which a construct is truly distinct from other constructs; Fornell and
Larcker, 1981; Hair et al., 2018).
Model development and hypotheses testing
SEM (Hair et al., 2018; Kline, 2015) was used to test the research model. The model fit was very
good (χ 2 5 186.62; df 5 120; χ 2/df 5 1.555; p < 0.001; RMSEA 5 0.049; CFI 5 0.981;
NFI 5 0.948). R2 behavioral intention is 0.523. A comparison of these values against
recommended values suggests that the model estimation result is satisfactory (Kline, 2015;
MacCallum et al., 1996). Results of hypothesized relationships are presented in Figure 1—
facilitating conditions, social influence, openness to experience positively influence
Scale item
Composite reliability
AVE
0.904
0.759
Facilitating conditions
FC 1
FC 2
FC 3
Social influence
SI 1
SI 2
SI 3
Openness to experience
OTE 1
OTE 2
OTE 4
Variety seeking
VS 1
VS 2
VS 3
Adventure shopping
AS 1
AS 2
AS 3
Behavioral intention
BI 1
BI 2
BI 3
Social
influence
CFA loadings
0.868
0.894
0.851
0.944
0.848
0.898
0.929
0.935
0.750
Mobile selfcheckout in
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685
0.507
0.800
0.764
0.545
0.913
0.778
0.884
0.880
0.882
0.897
0.744
0.842
0.875
0.871
0.961
0.893
0.970
0.962
0.901
Behavior
intention
Adventure
shopping
Facilitating
conditions
Variety
seeking
Table 4.
Confirmatory factor
analysis results of
measurement
properties
Openness to
experience
Social
0.921
influence
Behavioral
0.612
0.945
intention
Adventure
0.536
0.482
0.863
shopping
Facilitating
0.430
0.563
0.454
0.871
conditions
Variety
0.520
0.563
0.573
0.614
0.882
seeking
Openness to
0.308
0.465
0.373
0.463
0.575
0.712
experience
Note(s): Values along the diagonal indicate the average variance extracted for each construct. Off-diagonal
values indicate squared correlations between constructs
consumers’ intention of using mobile self-checkout. Therefore, H1, H2 and H3 were
supported; H4 and H5 were not supported.
Testing moderating effects
To test moderating effects, respondents were separated into two groups for each moderating
test. Consumers’ smartphone usage frequency was categorized by the median score (5 hours/
day). They were separated based on gender (men n 5 128; women n 5 101), previous
Table 5.
Squared correlation
matrix with AVE on
the diagonal
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experience using mobile self-checkout in fashion retailers (with experience n 5 115; without
experience n 5 114) and frequency of using smartphone (less frequent user n 5 107; frequent
user n 5 122). MANOVA showed that men were older, have higher level of education and
higher income than women, there was no difference in ethnicity between the groups.
Consumers who have experience of using mobile self-checkout in fashion retailers have
higher level of education and higher income, but no difference in age and ethnicity. There was
no difference in age, ethnicity, education and income between frequent and less frequent
smartphone users. Multiple group comparisons were conducted to examine differences
between two groups in the magnitude of influence on behavioral intention from adventure
shopping, variety seeking, openness to experience, social influence and facilitating conditions
(Hair et al., 2018).
The moderating effect of gender was tested by estimating a constrained multi-group
model (Model 1/Base Model—no moderating effects). Each structural weight was
constrained to be equal across the two groups. This mode had an acceptable fit
(χ 2 5 385.096, df 5 245; χ 2/df 5 1.572; CFI 5 0.956; RMSEA 5 0.052). An unconstrained
multi-group model (Model 2—moderating effects) was then estimated, in which the structural
weights were estimated uniquely for each group. The unconstrained model exhibited an
acceptable fit (χ 2 5 378.407, df 5 240; χ 2/df 5 1.577; CFI 5 0.957; RMSEA 5 0.053). The chisquare difference (Δχ 2 5 6.689, df 5 5; ns) between the two models was not significant at the
group level, indicating the influences from adventure shopping, variety seeking, openness to
experience, social influence and facilitating conditions on behavioral intention do not differ
for men and women. H6 was not supported.
The same method was used to test the moderating effects of previous experience and
smartphone usage frequency. When testing moderating effects of previous experience, both
the constrained multi-group model (χ 2 5 360.575, df 5 245; χ 2/df 5 1.472; CFI 5 0.964;
RMSEA 5 0.046) and unconstrained model (χ 2 5 324.046, df 5 240; χ 2/df 5 1.350;
CFI 5 0.975; RMSEA 5 0.039) exhibited an acceptable fit. The chi-square difference
(Δχ 2 5 36.529, df 5 5; p < 0.001) between the two models was significant at the group level,
indicating the influences from adventure shopping, variety seeking, openness to experience,
social influence and facilitating conditions on behavioral intention do differ for consumers
with (vs without) previous experience of using mobile self-checkout at fashion retailers. To
further test the influence from each exogenous variable, each path was constrained at a time
and the chi-square difference was compared with chi-square threshold.
The results indicate that the relationship between facilitating condition and behavioral
intention is significantly different (with 95% confidence) for consumers who have previous
experience of using mobile self-checkout at fashion retailers (β 5 0.315) than for consumers
who do not have previous experience (β 5 0.245). They also indicate that the relationship
between social influence and behavioral intention is significantly different (with 99%
confidence) for consumers who have previous experience (β 5 0.290) than for consumers who
do not have previous experience (β 5 0.231). H7d and H7e were supported but H7a, H7b and
H7c were not supported.
When testing moderating effects of smartphone usage, both the constrained multi-group
model (χ 2 5 382.579, df 5 245; χ 2/df 5 1.562; CFI 5 0.959; RMSEA 5 0.050) and unconstrained
model (χ 2 5 369.343, df 5 240; χ 2/df 5 1.539; CFI 5 0.962; RMSEA 5 0.049) exhibited an
acceptable fit. The chi-square difference (Δχ 2 5 13.236, df 5 5; p < 0.05) between the two
models was significant at the group level. When comparing the chi-square difference by
constraining each path at a time, no significant difference was identified. H8 was not supported.
Discussions and implications
This study examined determinants of consumers’ intention of using mobile self-checkout in
fashion retail stores based on UTAUT (Venkatesh et al., 2003). First, consistent with previous
studies (Venkatesh et al., 2012; Hew et al., 2015), results confirmed the positive influence of
facilitating conditions toward behavioral intention. This result seems logical because when
consumers can seek help from in-store signage, from apps and from knowledgeable sales
associate
