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A moderated mediating Omnichannel

customer
mechanism of omnichannel experiences

customer experiences
Angelina Nhat Hanh Le 595
School of Management, University of Economics Ho Chi Minh City,
Ho Chi Minh City, Vietnam, and Received 14 February 2020
Revised 29 February 2020
Xuan-Doanh Nguyen-Le 18 March 2020
10 July 2020
International School of Business, University of Economics Ho Chi Minh City, 12 October 2020
Ho Chi Minh City, Vietnam Accepted 10 December 2020

Abstract
Purpose – The purpose of the paper is to create a well-integrated and unified customer experience anytime,
anywhere, through any channel is the leading objective of omnichannel retailers. Scholars advocate the crucial
role of channel integration quality (CIQ)–specifically its components of channel-service configuration and
integrated interactions–in formulating the customer experience, which in turn determines their patronage
intention. However, a dearth of research exists on the dynamic nature of this particular mechanism. The
potential mediating and moderating effects of customer empowerment and Internet usage have hardly been
considered at all in the context of omnichannel retailing. These research gaps will be addressed in this study.
Design/methodology/approach – Based on a data set of 312 omnichannel customers, the partial least
square–structural equation model (PLS-SEM) was employed to test the hypothesised relationships.
Findings – The results reveal the dynamic mechanism in which channel-service configuration and integrated
interactions are the key factors that not only directly enable omnichannel retailers to deliver customers with a
seamless shopping experience but also empower customers to shape their own consumption experiences. The
findings also demonstrate the contingency role of consumers’ Internet usage in such a dynamic mechanism.
Finally, the notion that a strong customer experience increases their intention for patronage is supported by the
empirical evidence.
Originality/value – This study contributes to the existing literature by quantitatively examining the
moderated mediating mechanism of forming customer experience and its subsequent patronage behaviour in
the context of omnichannel retailers.
Keywords Omnichannel retailers, Channel integration quality, Customer experience, Customer empowerment
Paper type Research paper

Introduction
Over the last few years, retailing has advanced dramatically, while technological advancement
has enabled retailers to connect and conduct transactions with their customers through various
channels such as websites, mobile apps, social media and so on (Lynch and Barnes, 2020;
Savastano et al., 2019). With multiple channels and interactive touchpoints during customer
shopping journeys, it is crucial for retailers to apply omnichannel strategies to serve customers
with seamless switching among all available channels and across every touchpoint (Ieva and
Ziliani, 2018; Keyser et al., 2020; Savastano et al., 2019; Shen et al., 2018). According to Lee et al.
(2019), the ultimate goal of omnichannel retailers is to create a well-integrated and unified
customer experience at any time, any place and through any channel (Kuppelwieser and Klaus,
2020; Lynch and Barnes, 2020; Jocevski et al., 2019; Picot-Coupey et al., 2016; Ye et al., 2018).
Historically, the concept of the customer experience has been studied in different contexts of
retailing, from physical-store retailing (Mohd-Ramly and Omar, 2017; Terblanche, 2018) International Journal of Retail &
Distribution Management
Vol. 49 No. 5, 2021
pp. 595-615
© Emerald Publishing Limited
0959-0552
This research is funded by the University of Economics Ho Chi Minh City, Vietnam. DOI 10.1108/IJRDM-02-2020-0054
IJRDM to e-retailing (Izogo and Jayawardhena, 2018; Pandey and Chawla, 2018; Rose et al., 2012),
49,5 m-retailing (McLean et al., 2018) and even multichannel retailing (Lemon and Verhoef, 2016).
However, for omnichannel retailers, the seamless customer experience in which customers
rationally and emotionally respond to an omnichannel retailer (c.f., McLean et al., 2018) remains
scant, and the mechanism that underpins such a seamless experience is not fully understood
(Lemon and Verhoef, 2016). To the best of our knowledge, the existing studies that attempt to
conceptualise and describe the omnichannel experience have mainly been qualitative or
596 exploratory in nature (e.g. Ieva and Ziliani, 2018; Parise et al., 2016; Picot-Coupey et al., 2016).
Thus, much uncertainty still exists about the formation of the omnichannel customer
experience as well as subsequent behavioural outcomes such as patronage intention.
Channel integration quality (CIQ hereafter) is regarded as a key factor determining the
ability of omnichannel retailers to manage customer relationships across channels and
deliver customers with a seamless purchasing experience throughout their shopping journey
(Kembro and Norrman, 2019; Lee et al., 2019; Ye et al., 2018). According to Sousa and Voss
(2006), CIQ is comprised of two components: channel-service configuration and integrated
interactions. The former refers to the wide range and flexible combination of various online
and offline channel services, while the latter describes the consistency and uniformity of both
content and process attributes through different channels provided by omnichannel retailers.
In recent years, a number of novel service combinations and functional attributes with regard
to CIQ have been implemented by omnichannel retailers. For instance, big-box omnichannel
retailers like Walmart and Target have been successful in launching the “buy online, pick up
in-store” (BOPS) and “click and collect” services (Walk-Morris, 2019). Moreover, in order to
exclude the natural boundaries between channels and provide customers with a seamless
experience, many in-store technologies (e.g. in-store interactive digital kiosks, interactive
fitting rooms, price-checkers) and robust mobile app features (e.g. scan-and-go, push
notifications for in-store, online promotions) have been invested in by omnichannel retailers
(Jocevski et al., 2019; Savastano et al., 2019). Tesco’s Scan Pay Go app allows customers to
scan and pay for their purchase by using their smartphones without actually visiting the
store cashier, while Amazon Go offers shoppers a brick-and-mortar shopping experience
without the check-out line. With these tremendous efforts to improve CIQ, it is critical to
evaluate the effectiveness of CIQ on enhancing the seamless customer experience in the
context of omnichannel retailers.
Customer empowerment refers to the level of control that customers receive during their
shopping journey over exactly where, when and how to shop and receive deliveries (Zhang
et al., 2018). According to Prentice et al. (2016), the Internet and advanced technologies
increasingly provide business firms with the opportunity to empower customers at their
fingertips. Indeed, a number of customers today are avid users of touchpoints and are
technology savvy (Ieva and Ziliani, 2018); as such, empowering customers with the ability to
shape their own consumption experiences has become an inevitability for online businesses.
In the context of omnichannel retailers, we anticipate the vital role of customer empowerment
in influencing the omnichannel experience and presume that the process of applying
advanced technologies to integrate various online and physical channels provides an
increasing independence/autonomy for customers to make their own choices at all stages of
their shopping journey (Savastano et al., 2019). In other words, customer empowerment is
predicted to play a mediating role in the linkage from CIQ to a seamless omnichannel
experience. This proposition will be explored in this study. In addition, according to Chang
and Chen (2008), customers who spend more time online tend to accumulate more internet-
related knowledge, experiences and skills. Consequently, they are confident in navigating
information-searching tasks across multiple channels and become more familiar with
omnichannel retailers’ available offerings (Daunt and Harris, 2017). Thus, we contend that
Internet usage exhibits a contingency role in affecting customer perception and evaluation in Omnichannel
the context of omnichannel retailing settings. customer
Given the above voids in the extant literature, the current study aims to contribute to the
scarce literature on customers’ seamless experience with omnichannel retailers by offering
experiences
relevant insights into the dynamic mechanisms of forming the omnichannel experience and
its subsequent patronage behaviour. In particular, this empirical study attempts to
(1) examine the effects of the two components of CIQ (i.e. channel-service configuration and
integrated interactions) on the customer experience; (2) explore the mediating role of customer 597
empowerment on the relationship between CIQ and the customer experience; (3) identify the
moderating role of Internet usage on the effects of CIQ and customer empowerment on the
customer experience; and (4) assess exactly how this customer experience results in
patronage intention. The findings of this work offer important practical knowledge for
omnichannel retailers to optimise their channel management to deliver a seamless shopping
experience for their customers.

Literature review and hypothesis development

Customer experience with omnichannel retailers
Omnichannel retailers refer to those with a retailing business model operating in numerous
online and physical channels with a synergistic integration for the purpose of providing
customers with a smooth and borderless shopping journey across channels (Lee et al., 2019;
Lynch and Barnes, 2020). Contemporary consumers no longer shop in a single channel;
instead, they move across channels during different stages of their purchasing process
(Kuppelwieser and Klaus, 2020; Lynch and Barnes, 2020; Zhang et al., 2018). For instance,
they may search for information on websites, check prices on their mobile apps and order
products at physical stores or execute the process in whichever order they please. Customers
are expected to obtain services from any channel with the same customer identity/account
(Lynch and Barnes, 2020; Zhang et al., 2018), with all support and offerings requiring
consistency along multiple touchpoints across different channels (Ieva and Ziliani, 2018;
Lynch and Barnes, 2020; Picot-Coupey et al., 2016). These changes in customer behaviour and
expectations have required retailers to integrate all of their channel activities across the areas
of information exchange, joint operations, logistics, pricing, promotion, inventories, order
fulfilment and even after-sales services through their omnichannel strategy (Lee et al., 2019;
Li et al., 2018). Delivering customers with seamless, consistent and unified experiences
regardless of the channel or purchasing stage is cited as a top priority of omnichannel
retailers (Kuppelwieser and Klaus, 2020; Lee et al., 2019; Lynch and Barnes, 2020).
Several definitions of customer experience exist in the literature (also see, Becker and
Jaakkola, 2020; Brun et al., 2017; Lemon and Verhoef, 2016; Klaus et al., 2013); however, the
major stream of research advocates that the overall customer experience is multidimensional
and holistic in nature, emphasising sensorial, cognitive, affective, behavioural and social
responses to a firm’s offerings during the customer’s entire purchase journey (Becker and
Jaakkola, 2020; Bustamante and Rubio, 2017; Keyser et al., 2020; Lemon and Verhoef, 2016;
Manthiou et al., 2020; McLean et al., 2018). The multidimensional perspective of the customer
experience is derived from/rooted in the premise that, during a purchasing journey,
consumers engage in rational evaluations while undergoing emotional arousal at every
touchpoint, in turn driving them to make particular decisions (Brun et al., 2017; Keyser et al.,
2020; Klaus et al., 2013; Kuppelwieser and Klaus, 2020; Schmitt, 1999). Thus, in compliance
with the broadly advocated stream of experiential marketing, omnichannel customer
experiences can be delineated from a holistic process and synergistic combination of both
rational and emotional evaluations during a shopping journey across multiple channels in
IJRDM which the seamless switching among available channels is critical to the omnichannel
49,5 customer experience (Ieva and Ziliani, 2018).
Consumers are driven by both rationality and emotion, and given that “no strong
customer experience scales have been developed” and “many such scales are still being
evaluated and reviewed for their internal and external validity” (see Lemon and Verhoef,
2016, p. 81), according to McLean et al. (2018), the two widely documented and well-validated
variables of customer satisfaction and customer emotions can be deployed to capture the
598 rational evaluations and emotional perceptions of an experience. Satisfaction is
conceptualised as resulting from a rational comparison between customer expectations
and the actual delivered performance; as such, it is considered as a central element in
understanding the customer experience (Lemon and Verhoef, 2016; McLean et al., 2018).
Moreover, customer emotions have been studied as a crucial dimension of the customer
experience not only in the general consumption context (Keyser et al., 2020; Manthiou et al.,
2020) but also in various retailing settings such as physical store retailing (Das and
Varshneya, 2017), e-retailing (Rose et al., 2012), m-retailing (McLean et al., 2018) and even
omnichannel retailing (Lynch and Barnes, 2020). Therefore, consistent with this prior
relevant retailing research, the omnichannel experience can be operationalised as a second-
order construct of two dimensions, satisfaction with the experience and positive emotions.
This approach allows us to not only investigate customers’ rational evaluation about the
omnichannel experience offered by retailers (referring to the “satisfaction with experience”
dimension), but also examine customer emotions during the purchase journey across all
available touchpoints with omnichannel retailers (referring to the “positive emotions”
dimension).

Stimulus-organism-response (SOR) framework

The SOR framework (Mehrabian and Russell, 1974) is one of the most extensively adopted
theoretical frameworks for explaining customer shopping behaviours in various contexts of
retailing such as offline retailing (Kumar and Kim, 2014), e-retailing (Izogo and
Jayawardhena, 2018; Rose et al., 2012), multichannel retailing (Pantano and Viassone, 2015)
and omnichannel retailing (Zhang et al., 2018). This framework points out that environmental
stimulus (S) affects customers’ internal organism (O), which in turn leads to their subsequent
behavioural responses (R). In line with Lee et al. (2019), in the current study, two components
of CIQ (i.e. channel-service configuration and integrated interactions) are considered to be the
stimulus. In addition, according to Zhang et al. (2018), organism represents customers’
internal states, which consist of not only internal activities (e.g. perception, feeling and
thinking) but also affective, emotional and cognitive states (e.g. pleasure and satisfaction).
Thus, customer empowerment and customer experience are regarded as the organism in the
analytic framework. Finally, customer patronage intention is proposed to stand for the
behavioural response in the SOR framework. In summary, the current study’s research
framework (Figure 1) is primarily drawn from the SOR framework that serves as a basis for
the development of the following hypotheses.

The influence of channel integration quality (CIQ) on customer experience

CIQ refers to the degree to which a retailer coordinates operations and interactions across its
multiple channels to provide a unified shopping journey for its customers (Zhang et al., 2018).
Based on the SOR framework, CIQ as an environmental stimulus is expected to affect
customers’ internal states, such as customer experience (Becker and Jaakkola, 2020). Since the
CIQ of omnichannel retailers is comprised of channel-service configuration and integrated
interactions (Sousa and Voss, 2006), customer experience should be determined by these two
characteristics.
 Channel Integration
Omnichannel
Internet usage customer
Quality
H3a: 0.130c
experiences
f2 : 0.040 H3b: 0.083a
f2: 0.016
Trust in
Channel- retailer
H3c: 0.110b
service f2: 0.030
configuration 0.205c
599
f2: 0.049

Customer Customer Patronage

empowerment experience intention
R2 : 0.347; Q2 : 0.330 R2 : 0.541; Q2 : 0.443
H4: 0.600c R : 0.532; Q : 0.512
2 2

f2: 0.395
-0.100a
Integrated f2: 0.019

interactions Variety-
First-order construct
seeking
Second-order construct

Control variable
Figure 1.
Mediating effect; [ ] 95% confidence interval Research framework
and analysis results
Note(s): p: a < 0.05, b ≤ 0.01, c ≤ 0.001; H3a, H3b, H3c: moderating effects SRMR: 0.079

Channel-service configuration reflects the structure of available channels and flexible

combinations across all channels provided by omnichannel retailers (Lee et al., 2019). A good
configuration of channel integration exhibits a high degree to which customers can choose
alternative channels for a given service and can accomplish the preferred tasks of the service
through certain channels of their own choice (Shen et al., 2018). According to Sousa and Voss
(2006), with a broad number of available channels that retailers offer to their customers, it is
convenient for them to shop flexibly with alternative channels. In addition, customers can
enjoy hassle-free choice at all shopping stages and freely switch among available channels
according to their preferences (Kazancoglu and Aydin, 2018); operating as such, the chosen
service or shopping combinations are the best fit to fulfil their expectations (Lee and Kim,
2010). As a result, customers will experience positive emotions like pleasure, encouragement
and satisfaction through their shopping journey with these broad choice omnichannel
retailers.
In addition to the wide range of alternative channels, the transparency of similarities and
differences of alternative channels and combination options will provide rich information and
round comprehensiveness to customers (Kembro and Norrman, 2019; Shen et al., 2018).
Customers are well-informed and feel certainty during their shopping journey with a good
channel-service configuration retailer (Lee et al., 2019). Indeed, they deliver an actual valuable
experience for their customers compared to omnichannel retailers who do not provide such a
wide breadth of choices and transparency of channel-service configuration. Thus, we
hypothesise:
H1a. Channel-service configuration is positively associated with the customer
experience.
Integrated interactions refer to the consistency and uniformity of a retailer’s content and
process attributes through different channels (Lee et al., 2019; Sousa and Voss, 2006).
IJRDM According to Ieva and Ziliani (2018), consistency throughout multiple touchpoints within a
49,5 customer journey contributes immensely to the overall customer experience. The more
consistent content (e.g. price, product information, promotion) offered by retailers across
available channels, the less doubt and confusion felt by their customers during their shopping
journey. In the context of omnichannel retailers, a large assortment of products and wide
range of pricing are usually the case; thus, consistent content will help remove barriers
towards purchases by reducing the time spent and eliminating the hassle of comparing
600 products and prices which can in turn bring forth a pleasant sentiment and improve the
customer experience (Kembro and Norrman, 2019; Li et al., 2018; Verhoef et al., 2015).
Furthermore, uniformity in process attributes (e.g. the feel, image, and delivery speed of
services) can offer customers a frictionless purchase journey through different channels,
consequently resulting in their overall satisfaction with the shopping experience.
Recently, shoppers have been able to interact with omnichannel retailers to get consistent
content via a number of channels, such as calling a call centre or communicating online
through live chat systems (Rae, 2017). With online live chat systems, omnichannel retailers
provide online-based synchronous media with a human service representative who provides
answers through such media (McLean and Osei-Frimpong, 2017). Customers are served in
real-time, much like the way a store’s staff communicate in brick-and-mortar locations,
leading to a high level of customer satisfaction (Rae, 2017). In addition, by enabling customers
to see and touch merchandise virtually, the application of virtual and augmented reality
technologies can help omnichannel retailers ameliorate the limitations of natural boundaries
and provide a consistent feeling of services between online-offline channels (Keyser et al.,
2020; Pilkington, 2019; Savastano et al., 2019), thus satisfying customers’ expectations of
embracing a seamless experience. Previous empirical evidence shows that process
consistency between online and offline channels of land-based retailers mitigates
perceived risks (Lynch and Barnes, 2020) and positively impact online perceived value
(Wu and Chang, 2016). Li et al. (2018) also identified that the integrated information and
functions of multiple channels significantly enhance identity attractiveness while
diminishing retailer uncertainty. In the same vein, we posit that omnichannel retailers with
a high level of integrated interactions can bring a better experience to their customers.
H1b. Integrated interactions are positively associated with the customer experience.

The mediating mechanism: CIQ–customer empowerment–the customer experience

Customer empowerment is defined as the extent to which customers have control during their
shopping journey (Zhang et al., 2018). As mentioned earlier, compared with omnichannel
retailers with low CIQ, those with high CIQ can serve customers with not only more shopping
choices (referring to channel-service configuration) but also consistent content and processes
(referring to integrated interactions), thus offering consumers the ability to control over the
entire shopping journey (Yrj€ol€a et al., 2018). The high CIQ where customer data are
continuously accumulated, updated and then synchronised across multiple channels and
touchpoints will enable customers to have flexible and personalised shopping preferences
(Jocevski et al., 2019). According to Broniarczyk and Griffin (2014), choice freedom and
extensive information are the two key factors influencing customer empowerment. When
customers can interchangeably and simultaneously utilise any channels and any touchpoints
suited to their shopping needs at their convenience, they feel strongly empowered (Lee and
Kim, 2010). Li et al. (2018) also pointed out that cross-channel integration in a multichannel
context empowers customers to shop freely among channels. In practice, omnichannel
retailers can apply new technologies like scan-and-go as a part of their strategy to enhance
CIQ (Wallis, 2017). Scan-and-go is a self-check-out form that allows shoppers to scan, pack
and pay for products based on smartphone apps without visiting the store cashier; thus,
omnichannel customers are able to gain full control over their shopping experience (Grewal Omnichannel
et al., 2017; Savastano et al., 2019; Yrj€ol€a et al., 2018). Therefore, a high level of CIQ in customer
omnichannel retailers can provide customers with increased empowerment.
As noted by Lemon and Verhoef (2016), as human beings are continually trying to pursue
experiences
autonomy, customer empowerment is thus deemed an important driver of their perceived
experience. Prior studies also confirm that customer empowerment will enhance customers’
perception of a satisfactory seamless experience (Berraies and Hamouda, 2018). Retailers who
focus on customer empowerment can provide more personalised services and customised 601
options that make customers feel in control of what they are looking for (Jocevski et al., 2019).
The high level of control can give rise to close matching between customer demand and the
offerings of retailers (Zhang et al., 2018). This fit can leave customers with positive emotions
and satisfying shopping outcomes, endowing the shopping journey with overall positive
experience.
Taken all together, we posit that omnichannel retailers with a higher level of CIQ can
provide customers with greater empowerment, which in turn leads to a higher level of
positive customer experience. Thus, the next hypothesis is stated as follows:
H2. Customer empowerment mediates the influences of CIQ (consisting of (a) channel-
service configuration and (b) integrated interactions) on the customer experience.

The moderating effect of Internet usage

Internet usage is understood here as the length of time customers spend online (Daunt and
Harris, 2017; Park and Jun, 2003). The knowledge and experience customers have with the
Internet might depend on their Internet usage. To date, Internet experience has typically been
studied as a moderator in different contexts such as website shopping behaviour (Chang and
Chen, 2008) and online/offline channel preference and usage during a customer’s shopping
journey (Frambach et al., 2007). Compared to customers who spend less time online, those
with more online time may have more experiences, then manifest different perceptions as well
as judgements pertaining to online and offline marketing channels accordingly (cf. Chang and
Chen, 2008). Internet usage, therefore, can be a potential moderating variable in studies
focussing on the evaluation of omnichannel retailers.
According to Daunt and Harris (2017), customers with less frequent Internet usage are
likely to feel low confidence in their ability to navigate the alternative channels of
omnichannel retailers. In contrast, customers who have had a longer time exposure to
interactive interfaces and various touchpoints provided by omnichannel retailers can better
understand the availability and possible combinations of the salient features, functions and
attributes of various online and physical channels. This will increase customers’ ability to
take advantage of the omnichannel integration so as to fit their own needs (i.e. a given
shopping task). Customers with Internet experience will feel comfortable and fully in control
during the interaction and communication processes with omnichannel retailers (Frambach
et al., 2007). As a result, they will value the benefits that the high omnichannel integration
quality brings to them and become satisfied with their omnichannel retailer experiences.
Based on the above arguments, Internet usage is expected to positively moderate the effects
of CIQ, itself comprised of channel-service configuration and integrated interactions, as well
as customer empowerment on customer experience in the context of omnichannel retailers.
Thus, we propose the following hypothesis:
H3. Customer Internet usage strengthens the positive influence of (a) channel-service
configuration, (b) integrated interactions and (c) customer empowerment regarding
the customer experience.
IJRDM The influence of the customer experience on patronage intention
49,5 According to the SOR framework, customers’ internal states (i.e. customer experience) could
result in their response to omnichannel retailers (i.e. patronage intention). Consumer
patronage intention is represented as their likelihood to continue to shop in a particular
retailer’s online and physical stores (Savastano et al., 2019). Previous studies advocate that
positive experiences entice patronage intention and customer loyalty in the context of
e-retailing (Pandey and Chawla, 2018; Shobeiri et al., 2015), while overall customer experience
602 significantly enhances the frequency of using retailers’ mobile apps in m-retailing (McLean
et al., 2018). As mentioned above, the current study defines customer experience as a second-
order construct of two dimensions: satisfaction with the experience and positive emotions.
A number of supportive arguments and extensive empirical evidence are found for the
positive impacts of these two dimensions on the behavioural intentions of customers. For
example, Murali et al. (2016) argued that a higher level of satisfaction would lead to a higher
level of customer retention. This view is also confirmed by Mahmoud et al. (2018), who found
that satisfaction significantly enhances customer retention. Similarly, a meta-analysis study
has shown that satisfaction is an important antecedent of customer repurchase behaviour
(Blut et al., 2015). In the retailing industry, a large number of research projects have been
conducted to confirm the positive impact of satisfaction on loyalty and patronage intention
(Chang et al., 2015; Garaus, 2017; Pandey and Chawla, 2018).
With respect to another component of customer experience, positive emotions, according
to Das and Varshneya (2017), consumption feelings/emotions such as pleasure and arousal in
physical store retailing have a significant positive effect on patronage intentions. In an
m-retailing context, data from the research of McLean et al. (2018) indicated that a higher level
of customer experience (i.e. satisfaction and positive emotions) would lead to a higher
frequency of revisiting m-retailing applications. Based on the aforementioned arguments and
evidence, we posit that the greater the degree to which customers experience satisfaction and
positive emotions, the higher their intention to patronise an omnichannel retailer. Overall, we
hypothesise:
H4. The customer experience is positively associated with patronage intention.

Methodology
Sample and data collection
In the omnichannel retailing context, customers use both online (e.g. websites, mobile apps)
and physical stores to complete their purchasing journey. According to Picodi (2018), a global
e-commerce platform operating in Vietnam, half of Vietnamese online customers (49%) were
aged between 25 and 34 years old. Moreover, 60% of them were women, and 40% were men.
Thus, the respondents of the current study were limited to those aged 25–34, and purposive
sampling based on gender (see Table 1) was employed.
The data collection was conducted in Ho Chi Minh (HCM) City, where the retail business
activities are striking and leading (Thuy Mien, 2018). Moreover, all of the well-known
Vietnamese omnichannel retailers do business in HCM City. Therefore, the current study
employs HCM City for data collection. The survey was conducted at the five busiest shopping
malls and office buildings in the metropolitan area of HCM City (i.e. Vincom Center, Saigon
Square, Takashimaya Vietnam, Diamond Plaza and Parkson Plaza) to approach potential
respondents. After being presented with the definition of omnichannel retailers in the survey
questionnaire, in order to be able to fill in the questionnaire, participants either chose one
well-known omnichannel retailer – Nguyen Kim (electronic appliances), FPT Shop/The Gioi
Di Dong (mobile carriers and devices), or Concung (mother and baby products) – or self-
declared the omnichannel retailer they were most familiar with. Next, to be included in the
 Realised quota Planned quota Purchase frequency (websites/apps/
Omnichannel
Gender Freq. % % stores) Freq. % customer
experiences
Male 126 40.4 40.0 Several times a week 29 9.3
Female 186 59.6 60.0 A few times a month 148 47.4
Total 312 100.0 100.0 A few times a year 100 32.1
Rarely (only once or twice) 35 11.2
Total 312 100.0 603
Education Freq. % Average order value (websites/apps/stores) (*) Freq. %

High school 3 1.0 Less than 1,000 129 41.3

College or university 257 82.4 1,000 to < 3,000 92 29.5
Post-graduate and above 51 16.3 3,000 to < 5,000 44 14.1
Others 1 0.3 5,000 and up 47 15.1
Total 312 100.0 Total 312 100.0

Monthly income (*) Freq. % Daily Internet usage Freq. %

Less than 5,000 9 2.9 Less than 2 h 51 16.3

5,000 to < 9,000 77 24.7 2 h to < 5 h 131 42.0
9,000 to < 15,000 116 37.2 5 h to < 8 h 68 21.8
15,000 and up 110 35.3 8 h and up 62 19.9 Table 1.
Total 312 100.0 Total 312 100.0 Sample demographic
Note(s): (*) thousand Vietnamese Dong (VND); US$ 1 5 VND 23,215 at the time of the survey characteristics

survey, filtering questions were used to ensure that the person (1) had visited both the online
(websites/mobile apps) and physical stores of the chosen omnichannel retailer; (2) had made
at least one purchase either online or physical stores of this omnichannel retailer and (3) was
aged 25–34. If any of these three conditions were not met, the questionnaire was not given.
A small souvenir was also offered to them in appreciation of their support.

Measurements
The current study consists of three multi-dimensional constructs, two single-dimensional
constructs, one single-item construct and two control variables. The measurements for these
constructs were adopted from prior studies with some minor modifications to fit the current
research context (see Appendix). Specifically, the two multi-dimensional constructs that
belong to CIQ (channel-service configuration and integrated interactions) had two
dimensions for each, with scales adopted from Lee et al. (2019). In particular, channel-
service configuration was comprised of breadth of channel-service choice and transparency
of channel-service configuration, while integrated interactions encompassed both content
consistency and process consistency. Each of these constructs was measured by four items.
Another multi-dimensional construct, customer experience, consisted of satisfaction with
experiences and positive emotions that were measured by three- and ten-item indices taken
from McLean et al. (2018). Unidimensional constructs of customer empowerment and
patronage intention were adapted from Zhang et al.’s (2018) scales of five and three items,
respectively. Internet usage was assessed based on Fioravanti et al.’s (2012) single-item
construct. Regarding control variables, trust in retailers was measured with four items
adopted from Chiu et al. (2012), while variety-seeking was measured with seven items taken
from Menidjel et al. (2017). All items were measured with a seven-point Likert scale
IJRDM (1 5 strongly disagree, 7 5 strongly agree) and were translated into Vietnamese, the official
49,5 language of the current research context.
The data were collected over a five-week period in 2019 at different times of the day and on
both weekdays and weekends. After close scrutiny, 312 valid responses were used for further
analysis. The details about respondents’ profiles and purchase behaviours are presented in
Table 1.
604
Data analysis and results
This research employed SmartPLS 3.2.8 (Ringle et al., 2015) and applied the partial least
square–structural equation model (PLS-SEM) to test the accuracy of measurement scales and
the structural model. The analysis results are shown below.

Assessment of measurement scales

The research framework had unidimensional, multidimensional and even single-item
constructs; then, as recommended by Becker et al. (2012), a two-stage approach was applied.
In stage I, the repeated indicators approach was applied to obtain the latent variable scores.
These scores were saved in the data set for further analysis in stage II. Then, in stage II, the
scores of stage I became the indicators for their corresponding constructs. The results of scale
accuracy (i.e. reliability and validity) of the studied constructs were presented in Tables 2
and 3.
To assess the reliability of the constructs, the thresholds of Cronbach’s α (0.7) and
composite reliability (0.7) (Hair et al., 2017) were applied; the data in Table 2 indicates the
satisfactory level of scale reliability. Convergent validity for the studied constructs was also
verified, with the minimum requirement of indicator reliability (0.5) and average variance
extracted (AVE) values above the cut-off point of 0.5 being satisfied. In addition, to assess the
discriminant validity of the measurement model, cross-loadings, Fornell–Larcker criterion,
and the Heterotrait-Monotrait Ratio (HTMT) were used. Each indicator’s loading on its
corresponding construct was higher than all of its cross-loadings on the other constructs.
Also, as can be seen from Table 3, the square root of the AVE of each construct was higher
than the construct’s highest correlations with the other constructs. Moreover, all HTMT
values fell below the conservative maximum level of 0.85. Overall, both the reliability and
validity of the measurement model were assured.

Test for common method bias

Since the data were collected based on respondents’ self-reported subjective perceptions, it
was important to assess whether the common method bias (CMB) could threaten the research
results. In this regard, two statistical tests were used to check the seriousness of CMB. First,
Harman’s one-factor test was applied by putting all indicators together into an exploratory
factor analysis, while the principal component analysis without rotation was used to
determine the number of extracted factors. The results showed that the largest factor
accounted for only 34.98% of the total variance. Thus, there is no single factor emerging, nor
could one general factor explain the majority of the covariance among the scale indicators.
Secondly, following Liang et al. (2007), a PLS model with a common method factor was
supplemented. The results indicated that 90% (44/49) of the method factor loadings were
insignificant, while the substantive factor loadings of the principal constructs’ indicators
were all significant. Moreover, the average substantively explained variance of the indicators
was 0.673, while the average method-based variance was only 0.005. The ratio of substantive
variance to method variance was about 135:1. According to the above two tests, there was no
problem with CMB in this study.
Assessment stage I
Hierarchical measurement model
Studied constructs (dimensions) No. of scale itemsa Alpha CRb AVEc Item loading/highest cross-loading

Channel-service configuration Breadth of channel-service choice 4 0.837 0.891 0.672 0.800/0.581; 0.833/0.440; 0.843/0.484; 0.804/0.476
Transparency of channel-service configuration 4 0.841 0.894 0.679 0.806/0.534; 0.893/0.578; 0.879/0.530; 0.705/0.370
Integrated interactions Content consistency 4 0.822 0.883 0.655 0.810/0.499; 0.858/0.497; 0.849/0.479; 0.711/0.504
Process consistency 4 0.815 0.880 0.648 0.673/0.501; 0.856/0.576; 0.851/0.487; 0.826/0.504
Customer empowerment 5 0.822 0.876 0.586 0.715/0.486; 0.714/0.494; 0.764/0.438; 0.840/0.527;
0.785/0.490
Customer experience Satisfaction with experience 3 0.894 0.934 0.826 0.870/0.650; 0.933/0.676; 0.923/0.707
Positive emotions 10 0.932 0.943 0.623 0.784/0.624; 0.764/0.577; 0.811/0.617; 0.690/0.433;
0.785/0.540; 0.749/0.553; 0.787/0.605;
0.836/0.640; 0.822/0.598; 0.852/0.646
Patronage intention 3 0.896 0.935 0.828 0.912/0.619; 0.917/0.625; 0.901/0.590
Internet usage 1 n.a. n.a. n.a. n.a.
Control variables
Trust in retailer 4 0.829 0.886 0.661 0.778/0.536; 0.836/0.509; 0.838/0.539; 0.797/0.520
Variety-seeking 7 0.950 0.958 0.763 0.836/0.315; 0.908/0.332; 0.934/0.323; 0.890/0.302;
0.881/0.416; 0.821/0.316; 0.840/0.367

Assessment stage II
Hierarchical measurement model
Studied constructs No. of scale dimensions Alpha CRb AVEc Dimension loading/highest cross-loading

Channel-service configuration 2 0.751 0.889 0.800 0.903/0.473; 0.886/0.481

Integrated interactions 2 0.759 0.892 0.805 0.879/0.491; 0.915/0.612
Customer experience 2 0.852 0.931 0.871 0.934/0.489; 0.932/0.667
Note(s): abased on a 1–7 Likert scale; bComposite reliability; cAverage variance extracted; n.a., not applicable
experiences
Omnichannel
customer

605

analysis: reliability and

convergent validity
Scale accuracy
Table 2.
 49,5

606

validity
Table 3.
IJRDM

Scale accuracy
analysis: discriminant
Assessment stage Stage I
CSC InI Cexp
Studied constructs (dimensions) BCSC TCSC CC PC Cemp SE PE PI IU Trust Seek

Channel-service Breadth of channel-service choice 0.820 0.700 0.468 0.486 0.534 0.545 0.471 0.538 0.070 0.511 0.115
configuration (CSC) (BCSC)
Transparency of channel-service 0.601 0.824 0.550 0.503 0.486 0.471 0.459 0.457 0.064 0.443 0.280
configuration (TCSC)
Integrated interactions Content consistency (CC) 0.388 0.454 0.809 0.752 0.567 0.561 0.502 0.408 0.087 0.561 0.276
(InI) Process consistency (PC) 0.396 0.414 0.611 0.805 0.626 0.685 0.622 0.516 0.038 0.744 0.334
Customer empowerment (Cemp) 0.448 0.408 0.468 0.514 0.765 0.721 0.694 0.497 0.043 0.632 0.443
Customer experience Satisfaction with experience (SE) 0.467 0.406 0.480 0.588 0.620 0.909 0.810 0.730 0.090 0.735 0.308
(Cexp) Positive emotions (PC) 0.415 0.409 0.437 0.542 0.612 0.742 0.789 0.728 0.037 0.707 0.316
Patronage intention (PI) 0.465 0.403 0.351 0.442 0.434 0.651 0.667 0.910 0.080 0.681 0.143
Internet usage (IU) 0.065 0.058 0.080 0.033 0.031 0.086 0.014 0.075 n.a. 0.030 0.055
Trust in retailer (trust) 0.431 0.378 0.461 0.611 0.528 0.632 0.625 0.592 0.005 0.813 0.228
Variety-seeking (seek) 0.112 0.243 0.238 0.292 0.381 0.289 0.299 0.148 0.042 0.203 0.874

Stage II
Studied constructs CSC InI Cemp Cexp PI IU Trust Seek

Channel-service configuration (CSC) 0.895 0.681 0.541 0.635 0.560 0.079 0.516 0.220
Integrated interactions (InI) 0.512 0.897 0.625 0.760 0.506 0.072 0.687 0.349
Customer empowerment (Cemp) 0.469 0.547 n.a. 0.710 0.421 0.034 0.520 0.400
Customer experience (Cexp) 0.509 0.616 0.656 0.933 0.765 0.058 0.728 0.341
Patronage intention (PI) 0.486 0.445 0.421 0.707 n.a. 0.076 0.588 0.130
Internet usage (IU) 0.069 0.060 0.034 0.054 0.076 n.a. 0.006 0.057
Trust in retailer (trust) 0.448 0.605 0.520 0.672 0.588 0.006 n.a. 0.202
Variety-seeking (seek) 0.187 0.307 0.400 0.315 0.130 0.057 0.202 n.a.
Note(s): The lower and upper of the diagonal are bivariate correlations and HTMT ratios, respectively; diagonal italic values are the square root of AVE (average
variance extracted); n.a., not applicable
Assessment of the structural model Omnichannel
Following the procedure to evaluate the structural model as proposed by Hair et al. (2017), the customer
collinearity issues among each set of predictor variables were firstly checked; all VIF values
of less than 5.0 demonstrated that collinearity was unlikely to be a concern. To assess the
experiences
quality of the structural model, the SRMR value of 0.079–less than the threshold (0.08)–
asserted a good fit of the model for theory testing. In addition, the predictive power
and predictive relevance of the proposed research model were assessed through the R2 and Q2
of the endogenous constructs, respectively. The R2 values of 0.26, 0.13 and 0.02 represent 607
substantial, moderate and weak levels of predictive accuracy, respectively, while the
predictive relevance of Q2 values should be higher than zero. As can be seen from Figure 1, the
R2 values of customer empowerment (0.347), customer experience (0.541) and patronage
intention (0.532) all reached the substantial level. The Q2 values of three endogenous were
above zero. These indicated that the exogenous constructs had substantial explanatory
capability and adequate predictive relevance for the three endogenous constructs in the
model. Overall, the quality of the structural model was assured. Next, the hypothesis testing
results comprised of direct effects, mediating effects and moderating effects are
presented below.
Direct effects. A t-test calculated from the bootstrapping procedure of 5,000 samples was
applied to test the direct effects in the research model, while Cohen’s indicator (f2) was used to
measure the effect sizes with the values of 0.02, 0.15, and 0.35 representing small, medium and
large effects, respectively (Hair et al., 2017). Figure 1 illustrates how all of the three direct
hypotheses (H1a, H1b and H4) were supported at a minimum of 95% confidence level. The
effect sizes of channel-service configuration and integrated interactions on customer
experience were between small and medium (0.036 and 0.129, respectively), while the effect of
customer experience on patronage intention was rather large (0.395).
Mediating effects. Following Zhao et al.’s (2010) mediation analysis approach, we used one
bootstrap test (5,000 samples) to replace both the Baron-Kenny’s procedure and the Sobel’s
test to examine the indirect, mediating effects. The bootstrapping results pointed out that
both indirect effects stipulated in H2a and H2b were positive and significant and the 95% bias
corrected confidence intervals did not include zero; thus, H2a and H2b were supported.
Further identifying the typology of mediations was conducted. In addition to the above
significant and positive indirect effects, the direct effects of channel service configuration and
integrated interactions on customer experience were also positive and significant, thus
customer empowerment was identified as a complementary mediation of the proposed direct
effects.
Moderating effects. The study examined the moderating effects of Internet usage on the
positive relationships between customer experience and its precursors (i.e. channel-service
configuration, integrated interactions, and customer empowerment). Three interaction terms
were created for moderating effect analysis. The results from Figure 1 asserted that all three
moderating effects (H3a, H3b, and H3c) were supported. According to Kenny’s standard, the
effect size in tests of moderation might be 0.005, 0.01 and 0.025 for small, medium and large,
respectively (Hair et al., 2017). The interaction term’s f2 effect size in the current study had
values of 0.040, plus 0.030 and 0.016, indicating large and medium effects.
Control variables. The analysis of control variables suggested that there were significant
positive and negative effects of trust in retailers and variety-seeking on patronage intention,
respectively.

Discussion and conclusion

The current study examines a moderated mediating model of customer experience in the
context of omnichannel retailers. Based on the SOR framework, we propose that CIQ with two
IJRDM components, channel-service configuration and integrated interactions, could enrich
49,5 customer experience through the mediating role of customer empowerment as well as the
moderating role of Internet usage, which in turn determine customer patronage behaviour.
The results show that there is a positive relationship between two CIQ components and
customer experience. This finding confirms the view of Lemon and Verhoef (2016) who
considered service quality (and its constituent elements) to be an antecedent of customer
experience. In particular, integrated interactions, in contrast to the channel-service
608 configuration, have a stronger impact on customer experience. This finding implies that
customers strongly demand consistent messaging across channels during their purchase
journey. If omnichannel retailers try to expand to more channel choices without a high level of
uniformity among these channels, they will fail to bring about positive emotions as well as
satisfaction for their customers. Another important finding is that customer experience
greatly affects patronage intention. This result is consistent with other studies that have
found the positive influences of satisfaction and emotions (two dimensions of customer
experience) on patronage intention (Chang et al., 2015; McLean et al., 2018).
In addition, in this study, customer empowerment is found to mediate the impact of CIQ on
customer experience. By offering higher levels of CIQ, omnichannel retailers better empower
customers, leading to a higher level of satisfaction during the shopping experience (also see
Zhang et al., 2018). As mentioned in the literature review and according to the SOR
framework, customer empowerment and customer experience are defined as customers’
internal states. This finding broadly supports the work of other studies that link CIQ with
customers’ internal states (Lemon and Verhoef, 2016; Li et al., 2018; Shen et al., 2018).
Furthermore, the current study confirms the moderating effect of Internet usage on the
positive relationships between customer experience and its precursors (i.e. channel-service
configuration, integrated interactions, and customer empowerment). It may be explained by
the fact that the more customers use the Internet, the greater their familiarity with
omnichannel retailers’ online channels (i.e. websites, mobile apps) and/or in-store
technologies. Consequently, customers could complete any task more easily during the
purchase journey, which leads them to experience greater enjoyment in their shopping as well
as feeling a deeper sense of satisfaction. This result is in line with those of previous studies
regarding the contingency role of Internet experience in the context of e-retailer website
(Chang and Chen, 2008) and multichannel retailing (Frambach et al., 2007).
Control variables, including trust in a particular retailer and variety-seeking, are also
found to have significant impacts on patronage behaviour. In particular, trust in retailers
positively affects patronage intention, while variety-seeking has a negative influence.

Theoretical and managerial implications

The current research contributes to the literature on omnichannel retailers and customer
experience in two aspects. First, although a number of early research papers on omnichannel
retailing have focused on channel integration (Lee et al., 2019; Li et al., 2018; Shen et al., 2018;
Zhang et al., 2018), it should be recognised that creating a seamless, consistent and unified
experience is the ultimate aim of omnichannel retailers (Kuppelwieser and Klaus, 2020; Lynch
and Barnes, 2020; Jocevski et al., 2019; Ye et al., 2018). This study empirically demonstrates
the centrality of seamless shopping experience in omnichannel strategies. In addition, while
previous researches on customer experience in omnichannel retailing contexts are generally
qualitative and exploratory in nature, this study makes a major contribution to the existing
literature by quantitatively examining the moderated mediating mechanism of forming
customer experience and its subsequent patronage behaviour.
Based on the empirical findings, meaningful practical implications could be drawn from a
managerial standpoint. First, the current study points out that omnichannel experience acts
as the key determinant of patronage intention. Thus, omnichannel retailers are advised to Omnichannel
optimise their channel management to deliver a seamless, consistent and unified shopping customer
experience to their customers. By integrating all available channels, omnichannel retailers
make customers feel more empowered, which could enrich their shopping experience and lead
experiences
to patronage behaviour.
Second, integrated interactions, compared with channel-service configuration, are found
to have a stronger impact on customer experience. Thus, in order to optimise channel
management to achieve great customer experience, this study suggests that omnichannel 609
retailers should focus on the consistency of their content and process. In particular, all
retailers’ messaging (e.g. product information, prices, promotion, and stock availability)
should be uniform across channels such as physical stores, websites and mobile apps.
Moreover, retailers’ processes are advised to be consistent, either online or in-store, so that
customers could perceive the consistency of services across channels during their purchasing
journey. A greater effort should be devoted to building a strong and robust order
management system in which standard operating procedures (SOPs) are the core
components. In addition, continuously applying new technologies for supply chain
operations to keep track of orders and inventory is strongly recommended. For instance,
blockchain helps omnichannel retailers create robust and integrated order management
systems. Drawing on multiple nodes, this technology allows omnichannel retailers to ensure
consistency by synchronising orders and inventory information among all available
channels as well as offering real-time and accurate responses to their customers. As a result,
customers can feel confident and satisfied with their shopping experiences.
Third, omnichannel retailers should also pay attention to the moderating role of Internet
usage. As can be seen from the research findings, Internet usage strengthens the impacts of
channel-service configuration, integrated interactions and customer empowerment regarding
enhancing the customer experience. In this regard, for omnichannel retailers, especially those
with limits in terms of resources, the customer segment who represents a high level of Internet
usage should be selected as their target market.

Limitations and future research

The current study inherits a few limitations which may provide avenues for future research.
First of all, as the channel scope of omnichannel retailers is growing quickly with the
development of social media and modern technologies (Shen et al., 2018), future research
should continue to update new creative channels and provide a more complete picture of CIQ
as well as its impact on customer experience. CIQ has been operationalised using a reflective
approach (i.e. Lee et al., 2019) in this study, there also exists a formative approach of
elaborating CIQ (e.g. Zhang et al., 2018), so a comparison of the two approaches can provide a
deep understanding of the nature of the construct. Moreover, regarding the measurement of
customer experience, the current study takes into account only the prevalent cognitive and
affective dimensions of the experience, many more dimensions such as social and sensorial
ones (Becker and Jaakkola, 2020) have been overlooked, thus this limitation should be
addressed in future research. In addition, according to Lemon and Verhoef (2016) “no strong
customer experience scales have been developed” and “many such scales are still being
evaluated and reviewed for their internal and external validity” (p. 81); there is an abundance
of opportunities for future research focussing on scale development and validation of
customer experience in general as well as its separated dimensions in particular.
Furthermore, as the current research framework was empirically investigated in the
context of omnichannel retailers, there exists the potential to test the framework in some other
contexts such as banking, food and beverage services (F&B) and tourism. Finally, we cannot
deny the existence of other factors that are related to the mechanisms of forming the customer
IJRDM experience in omnichannel models. For instance, customers’ personality traits (e.g. customer
49,5 innovativeness; Park and Jun, 2003) and tendencies (e.g. showrooming and webrooming;
Goraya et al., 2020) should be considered as moderators on the relationships between
customer experience and its precursors. Furthermore, it can be argued that other constructs
such as commitment and value co-creation (Lemon and Verhoef, 2016) are also related to
customer experience in the omnichannel context. Thus, further studies that take these
constructs into account should be undertaken.
610
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IJRDM Appendix
49,5
Construct Adapted items

Breadth of channel-service choice (1) I can purchase products via both online and physical stores of X
(2) I can get support through both online and physical stores of X
(3) I can give feedback about products through both online and
614 physical stores of X
(4) I can get detailed product descriptions from both online and
physical stores of X
Transparency of channel-service (1) I am aware of the available services of both online and physical
configuration stores of X
(2) I am familiar with the available services of both online and physical
stores of X
(3) I know how to utilise the available services of both online and
physical stores of X
(4) I know the differences in the available services offered by both
online and physical stores of X
Content consistency (1) X provides consistent product information for both online and
physical stores
(2) Product prices are consistent for both online and physical stores of X
(3) X provides consistent promotion information for both online and
physical stores
(4) X provides consistent stock availability for both online and physical
stores
Process consistency (1) The service images are consistent for both online and physical
stores of X
(2) The levels of customer service are consistent for both online and
physical stores of X
(3) The feelings of service are consistent for both online and physical
stores of X
(4) The online and physical stores of X have consistent performance in
their speed of service delivery
Customer empowerment (1) In my dealings with X, I feel I am in control
(2) During the shopping process at X, I can select products and services
freely
(3) I can influence the choice-set offered to me by X
(4) The ability to influence the offerings and services of X is beneficial
to me
(5) My influence over X has increased relatively in comparison to the
past
Internet usage How many hours do you use the internet per day?
Less than 2 h; 2 h to < 5 h; 5 h to < 8 h; 8 h and up
Satisfaction with experience (1) I am satisfied with the shopping experience at X
(2) The shopping experience at X is exactly what I need
(3) The shopping experience at X has worked out as well as I thought it
would
Table A1.
Measurement scales (continued )
Construct Adapted items
Omnichannel
customer
Positive emotions (1) I feel frustrated when shopping at X. (R) experiences
(2) I feel confident when shopping at X
(3) I feel assured when shopping at X
(4) I feel confused when shopping at X. (R)
(5) I feel optimistic when shopping at X
(6) I feel uncertain when shopping at X (R) 615
(7) I feel disappointed when shopping at X (R)
(8) I feel relieved when shopping at X
(9) I feel doubtful when shopping at X (R)
(10) I feel satisfied when shopping at X
Patronage intention (1) I am likely to continue to purchase products from X
(2) I am likely to recommend X to my friends
(3) I am likely to choose X as a preferred retailer if I need to buy such
products
Trust in retailer (1) X is a trustworthy retailer
(2) X cares about its customers
(3) X keeps its promises to its customers
(4) X is not overly opportunistic
Variety-seeking (1) When shopping, I find myself spending a lot of time checking out
new websites/apps/physical stores
(2) I take advantage of the first available opportunity to find out about
new websites/apps/physical stores
(3) I like to investigate information about new websites/apps/physical
stores
(4) I like information sources that introduce new websites/apps/
physical stores
(5) I frequently keep watching for new websites/apps/physical stores
(6) I seek out situations in which I will be exposed to new and different
sources of websites/apps/physical store information
(7) I am continually seeking out new websites/apps/physical stores
Note(s): X refers to the well-known or self-declared omnichannel retailer chosen by the respondent;
(R) indicates reversed items Table A1.

About the authors

Angelina Nhat Hanh Le is Senior Lecturer of Marketing, School of Management, University of
Economics HCM City, Vietnam, where she teaches marketing management and consumer behavior. Her
research interests include marketing channels, Internet marketing, brand management, green
marketing, and meta-analysis. Her research has been published in Journal of the Academy of
Marketing Science, Journal of International Marketing, International Journal of Advertising, Journal of
Consumer Behavior, Management Decision, Asia Pacific Journal of Marketing and Logistics and so on.
Angelina Nhat Hanh Le is the corresponding author and can be contacted at: hanhln@ueh.edu.vn
Xuan-Doanh Nguyen-Le is a research fellow, International School of Business, University of
Economics HCM City, Vietnam. Her research interests include marketing channels, Internet marketing,
brand management. Her research has been presented in several international and national conferences.

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