Luận Văn How Does Channel Integration Quality Enrich Customer Experiences With Omnichannel Retailers

MINISTRY OF EDUCATION AND TRAINING
UNIVERSITY OF ECONOMICS HO CHI MINH CITY
──────────────────
NGUYEN LE XUAN DOANH
HOW DOES CHANNEL INTEGRATION QUALITY ENRICH
CUSTOMER EXPERIENCES WITH OMNICHANNEL
RETAILERS? AN EXAMINATION OF MEDIATING AND
MODERATING MECHANISMS
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MASTER BY RESEARCH THESIS
Ho Chi Minh City – 2022

MINISTRY OF EDUCATION AND TRAINING
UNIVERSITY OF ECONOMICS HO CHI MINH CITY
──────────────────
NGUYEN LE XUAN DOANH
HOW DOES CHANNEL INTEGRATION QUALITY ENRICH
CUSTOMER EXPERIENCES WITH OMNICHANNEL
RETAILERS? AN EXAMINATION OF MEDIATING AND
MODERATING MECHANISMS
Specialization: Commercial Business
Specialization code: 8340121
MASTER BY RESEARCH THESIS
SUPERVISOR: Dr. LE NHAT HANH
Ho Chi Minh City - 2022

DECLARATION
I, Nguyen Le Xuan Doanh, declare that the Master by Research thesis entitled
“How does channel integration quality enrich customer experiences with
omnichannel retailers? An examination of mediating and moderating mechanisms”
has been composed solely by myself, with the enduring support, instruction, and
insight from my supervisor Dr. Le Nhat Hanh. Except where states otherwise by
reference or acknowledgment, the work presented is entirely my own.
Signed:
Nguyen Le Xuan Doanh
Date:

ACKNOWLEDGMENTS
I owe a debt of gratitude to my supervisor, Dr. Le Nhat Hanh, for her enduring
support, instruction, and insight. She helped direct my interests towards relevant
topics and methodological innovations in retailing, and made my experience as a
master by research student truly memorable.
Besides, I am grateful to all of my teachers at School of International Business
- Marketing, University of Economics Ho Chi Minh City for their help and support
throughout my studies.

TABLE OF CONTENTS
SECOND TITLE PAGE
DECLARATION
ACKNOWLEDGMENTS
TABLE OF CONTENTS
LIST OF ABBREVIATIONS
LIST OF TABLES
LIST OF FIGURES
ABSTRACT
CHAPTER 01: INTRODUCTION..........................................................................1
1.1. Research background and statement of the problem.....................................1
1.2. Research objectives .......................................................................................4
1.3. Subject and scope of research........................................................................5
1.4. Research method............................................................................................5
1.5. Research contribution....................................................................................6
1.6. Research structure..........................................................................................6
CHAPTER 02: LITERATURE REVIEW AND HYPOTHESIS
DEVELOPMENT .....................................................................................................8
2.1. Omnichannel retailers....................................................................................8
2.2. Channel integration quality with omnichannel retailers................................9
2.3. Customer experience with omnichannel retailers........................................11
2.4. Prior relevant studies ...................................................................................14
2.5. Research framework and hypothesis development .....................................21
2.5.1. Stimulus-Organism–Response (SOR) framework ............................21

TABLE OF CONTENTS
2.5.2. The influences of channel integration quality (CIQ) on customer
experience .........................................................................................................21
2.5.3. The mediating mechanism: CIQ – customer empowerment – the
customer experience..........................................................................................24
2.5.4. The moderating effect of internet usage............................................25
2.5.5. The influence of the customer experience on patronage intention....26
2.6. Summary......................................................................................................29
CHAPTER 03: RESEARCH METHOD ..............................................................30
3.1. Research process..........................................................................................30
3.2. Measurement scales.....................................................................................32
3.3. Questionnaire design ...................................................................................36
3.4. Sample and data collection..........................................................................36
3.5. Sample characteristics .................................................................................38
3.6. Data analysis method...................................................................................39
3.7. Summary......................................................................................................41
CHAPTER 04: DATA ANALYSIS AND RESULTS ..........................................42
4.1. Assessment of measurement model.............................................................42
4.2. Test for common method bias .....................................................................46
4.3. Assessment of structural model...................................................................48
4.4. FIMIX analysis for data heterogeneity........................................................52
4.5. Summary......................................................................................................52
CHAPTER 05: DISCUSSION AND CONCLUSION..........................................54
5.1. Discussion of results....................................................................................54
5.2. Research implications..................................................................................56

TABLE OF CONTENTS
5.3. Limitations and futher research...................................................................58
LIST OF PUBLICATIONS
REFERENCES
APPENDICES

LIST OF ABBREVIATIONS
SOR: Stimulus-Organism-Response
CIQ: Channel integration quality (CSC: Channel-service configuration, BCSC:
Breadth of channel-service choice, TCSC: Transparency of channel-service
configuration, InI: Integrated interactions, CC: Content consistency, PC: Process
consistency)
CCI: Cross-channel integration
HCM: Ho Chi Minh
PLS-SEM: Partial least squares structural equation modeling
CMB: Common method bias
VIF: Variance inflation factor
SRMR: Standardized root mean square residual
FIMIX-PLS: Finite mixture PLS
HTMT: Heterotrait-Monotrait ratio
AVE: Average variance extracted

LIST OF TABLES
Table 2.1. Definitions and examples of the sub-dimensions of CIQ........................9
Table 3.1. Measurement scales..................................................................................33
Table 3.2. Sample demographic characteristics ........................................................39
Table 4.1. Scale accuracy analysis.............................................................................44
Table 4.2. Scale accuracy analysis: Discriminant validity assessment......................45
Table 4.3. Test for common method bias (CMB)......................................................46
Table 4.4. Inner VIF value.........................................................................................48
Table 4.5. Significance testing results of the structural model path coefficients ......49
Table 4.6. Significance testing results of the total indirect effects ...........................50
Table 4.7. FIMIX-PLS results for the relative segment sizes and retention criteria 52

LIST OF FIGURES
Figure 2.1. Research model of Lee et al. (2019) .......................................................15
Figure 2.2. Research model of Zhang et al. (2018)....................................................16
Figure 2.3. Research model of McLean et al. (2018) ................................................17
Figure 2.4. Research model of Shen et al. (2018)......................................................19
Figure 2.5. Research model of Li et al. (2018)...........................................................20
Figure 2.6. Research framework and hypotheses.......................................................28
Figure 3.1. Research process.....................................................................................31
Figure 4.1. Research model in Stage I........................................................................42
Figure 4.2. Research model in Stage II......................................................................43
Figure 4.3. Analysis results........................................................................................51

ABSTRACT
1. Title
Thesis title: “How does channel integration quality enrich customer experiences
with omnichannel retailers? An examination of mediating and moderating
mechanisms”.
Presented by: Nguyen Le Xuan Doanh
Supervisor: Dr. Le Nhat Hanh
Submitted to: University of Economics Ho Chi Minh City
2. Abstract
While omnichannel has been a bloated retail buzzword for years, little is known
about the dynamic mechanism of forming customer experience and the subsequent
patronage behavior in the context of omnichannel retailers. Drawing upon the
stimulus-organism-response (SOR) framework, this thesis fills this important
research gap by examining the effects of channel integration quality (CIQ) on
customer experience through the mediating role of customer empowerment as well
as the moderating role of internet usage, which in turn results in patronage intention.
The partial least squares structural equation modeling (PLS-SEM) with two-stage
approach is employed to empirically test the research framework with 312 customers
of the omnichannel retailers in Vietnam. The findings reveals that two dimensions of
CIQ (i.e., channel-service configuration and integrated interactions) significantly
affect customer experience, which in turn leads to patronage intention. Moreover,
customer empowerment complementarily mediates the impacts of CIQ dimensions
on customer experience, while internet usage strengthens the positive relationships
between customer experience and its precursors. This thesis was concluded with the
meaningful practical implications for omnichannel retailers to optimize their channel
management that delivers a seamless shopping experience to their customers.
3. Keywords: Omnichannel retailers; Channel integration quality; Customer
experience; Customer empowerment.

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CHAPTER 01: INTRODUCTION
1.1. Research background and statement of the problem
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. The way retailers interact with their customers has also been
reshaped completely. For instance, with artificial intelligence, it is predicted that 90%
of traditional human retail interactions will be replaced by online shopbots;
meanwhile, virtual and augmented reality will allow customers to see and touch
merchandise virtually (Pilkington, 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 (Shen et al., 2018). According to
Walk-Morris (2019), about 67% of U.S. retailers consider the omnichannel strategy
to be a top priority, as it helps them capture the contemporary showrooming and
webrooming shopping trends of customers while maintaining competitive advantages
(Graham, 2017; Radial, 2016; Lee et al., 2019). A recent report by IDC Retail Insights
indicates that retailers have gained an increase in 15–35% in average transaction size
and 5–10% in loyal customer profitability by using the omnichannel strategy
(Winkler, 2019).
In Vietnam, according to We Are Social’s report, the number of internet users
has reached 64 million, equivalent to 67% of the population. Meanwhile, the rate of
owning internet access devices among Vietnamese consumers has increased (i.e.,
72% of the Vietnamese adult population use smartphone, 43% use laptop or desktop
computer, and 13% use tablet) (Kemp, 2018). Along with these rapid development,
omnichannel retailing has recently emerged as a new trend in Vietnam (Anh Hoa,
2017). According to a study conducted by Sapo – an omnichannel retailing platform
operating in Vietnam, 97% of retail store owners applied omnichannel strategy to
their business in 2018 (Tuyet An, 2019). Omnichannel approach is also considered

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as a new weapon to help Vietnamese retailers maintain and expand market share
(Vietnamnet, 2017).
Omnichannel retailers refer to those businesses using the retailing business
model which operates in a number of channels and touchpoints with synergetic
management that excludes natural borders among channels (Lee et al., 2019; Verhoef
et al., 2015; Zhang et al., 2018). Creating a well-integrated and unified customer
experience at anytime, anywhere, through any channel is the ultimate aim of
omnichannel retailers (Frazer and Stiehler, 2014; Jocevski et al., 2019). Historically,
the concept of customer experience has been studied in different contexts of retailing,
from physical-store retailing (Bäckström and Johansson, 2017; Jones et al., 2010;
Sachdeva and Goel, 2015; Terblanche, 2018) to e-retailing (Martin et al., 2015;
Pandey and Chawla, 2018; Rose et al., 2012), m-retailing (McLean et al., 2018; Tseng
and Yazdanifard, 2015), and even multichannel retailing (Blázquez, 2014; Lemon
and Verhoef, 2016). However, in the context of omnichannel retailers, the seamless
customer experience in which customers cognitively and affectively respond to an
omnichannel retailer (McLean et al., 2018) continues to be a challenge for both
practitioners and academia. Nearly 80% of retailers admit their lack of success in
offering customers a unified experience across channels (Periscope, 2016).
According to a recent survey conducted by TNS, 61% of U.S. customers have
difficultly switching from one channel to another when interacting with omnichannel
retailers (Dreyer, 2014); while 87% of global customers think that brands need to put
more effort into delivering a seamless experience (Zendesk, 2013). With respect to
the academic side, research on the omnichannel experience remains scant and the
mechanisms that underpin the seamless customer experience are not fully understood
(Lemon and Verhoef, 2016). To our best knowledge, the few existing studies attempt
to conceptualize and describe the omnichannel experience have been qualitative and
exploratory in nature (Cook, 2014; Frazer and Stiehler, 2014; Melero et al., 2016;
Parise et al., 2016; Peltola et al., 2015). Thus, much uncertainty still exists about the

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formation of the omnichannel customer experience as well as subsequent behavioral
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 (c.f., Lee et al., 2019). 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 later 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” or “click and collect” service (Walk-Morris, 2019b). Moreover, in order to
excluding the natural boundaries between channels and providing customers with a
seamless experience, many of the in-store technologies (e.g., in-store interactive
digital kiosks, interactive fitting rooms, price-checkers) as well as the robust mobile
app features (e.g., scan-and-go, push notifications for in-store, online promotions)
have been invested in by omnichannel retailers (Grant, 2018; Jocevski et al., 2019;
Sopadjieva et al., 2017). Tesco’s Scan Pay Go app allows customers to scan and pay
for their purchase by using their smartphones without visiting the store cashier, while
the Amazon Go offers shoppers a brick-and-mortar shopping experience without the
checkout line (Reuters, 2018; Wood, 2018). In Vietnam, VinMart and Co.opmart are
vanguard retailers in scan-and-go technology, which helps customers save time when
shopping by offering a prompt payment option (Dantri, 2019; Hai Kim, 2019). With
these tremendous efforts to improve CIQ, it is important to evaluate the effectiveness
of CIQ on enhancing the seamless customer experience in the context of omnichannel
retailers.

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Customer empowerment refers to the level of control over where, when and how
to shop and to get delivery that customers receive during their shopping journey
(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 (Sopadjieva et al., 2017) and are technology-savvy (Azhari and Bennett,
2015); 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, this thesis expects the important role of customer
empowerment in influencing the omnichannel experience and presume that the
process of integrating various online and physical channels should provide an
increasing autonomy for customers to make their own choices at all stages of their
shopping journey. In other words, customer empowerment is predicted to play a
mediating role on the linkage from CIQ to a seamless omnichannel experience. This
proposition will be explored in the current study. In addition, according to Chang and
Chen (2008), customers who spend more time online tend to accumulate more
internet-related knowledge and skills, and consequently they should be more familiar
with omnichannel retailers’ available offerings. Thus, this thesis contend that internet
usage exhibits a contingency role in affecting customer perception and evaluation in
the context of omnichannel retailing settings.
1.2. Research objectives
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 relevant insights into the dynamic mechanisms of
forming the omnichannel experience and its subsequent patronage behavior. 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.

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(2) Explore the mediating role of customer 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.
(4) Assess exactly how this customer experience results in patronage intention.
The findings of this work offer important practical knowledge for omnichannel
retailers to optimize their channel management that delivers a seamless shopping
experience to their customers.
1.3. Subject and scope of research
The subject of this research is customers who used to have experience of
shopping with omnichannel retailers. We targets customers from four well-known
omnichannel retailers (i.e., Nguyen Kim, FPT Shop, The Gioi Di Dong, Concung).
Besides, respondents can also self-declared the omnichannel retailer they are most
familiar with. Data for the current thesis were collected at the five busiest shopping
mall and office buildings in the metropolitan area of Ho-Chi-Minh City (i.e., Vincom
Center, Saigon Square, Takashimaya Vietnam; Diamond Plaza; Parkson Plaza).
Research was conducted from 06/2019 to 11/2019.
1.4. Research method
The current thesis is defined as an empirical research. The partial least squares
structural equation modeling (PLS-SEM) approach is applied to quantitatively
examine the dynamic mechanisms of forming the customer experience and its
subsequent patronage behavior in omnichannel retailers context. According to Hair
et al. (2017), there are two types of theory which be required when develop path
models in PLS-SEM: measurement theory and structural theory. The former
represents how the studied constructs are measured, while the later describes the
relationships between them. In this study, all studied constructs are modeled based
on a reflective measurement model, which the scales are adopted from prior studies.

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From literature review, a structural model for this work is also drawn upon the
stimulus-organism-response (SOR) framework.
After translating all measurement scales into Vietnamese - the official
language of the current research context, a questionnaire is designed, pre-tested with
20 customers and then modified to ensure its clarity before distribution. The next
stage is to conduct a paper-based survey for data collection. Then, the dataset is
analyzed using SmartPLS 3.2.8 and consisted of the following steps: assessment of
measurement model, test for common method bias (CMB), assessment of the
structural model with hypotheses testing, and the FIMIX analysis for data
heterogeneity.
1.5. Research contribution
This project provides an important opportunity to advance the understanding of
customer experience with omnichannel retailers. First of all, the current thesis
empirically demonstrates the centrality of a well-integrated 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 dynamic mechanisms of forming customer
experience and its subsequent patronage behavior. And finally, the findings of this
work offer some important insights into optimizing the channel management to help
omnichannel retailers delivering a seamless, consistent and unified shopping
experience to their customers.
1.6. Research structure
After Chapter 01 – Introduction, the current thesis is composed of four themed
chapters:
Chapter 02 - Literature review and hypothesis development: This section (1)
gives a brief overview of the recent history of omnichannel retailers and customer
experience, as well as defining the term “channel integration quality” (CIQ) and its

7
dimensions/ sub-dimensions; (2) reviews five studies that are relevant to this thesis;
(3) introduces the stimulus - organism – response (SOR) framework; and finally (4)
presents the research model and the associated hypotheses.
Chapter 03 – Research method: This chapter is concerned with the method used
for the current thesis, including the research process, measurement scales,
questionnaire design, sample and data collection, as well as the sample characteristics
and data analysis method.
Chapter 04 – Data analysis and results: This section analyzes the dataset of the
research. It consists of the following steps: assessment of measurement model, test
for common method bias, assessment of structural model, and FIMIX analysis for
data heterogeneity.
Chapter 05 – Discussion and conclusion: This final chapter briefs the important
results of the current thesis and provides actionable insights for omnichannel retailers
to optimize their channel management. Moreover, the research limitations and
recommendations for further research are also mentioned.

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CHAPTER 02: LITERATURE REVIEW AND HYPOTHESIS
DEVELOPMENT
2.1. Omnichannel retailers
Omnichannel retailers refer to those with retailing business model of operating
in numerous channels and touchpoints with a synergetic management that excludes
the natural borders among channels (Lee et al., 2019; Verhoef et al., 2015; Zhang et
al., 2018). Although omnichannel is shifted from multichannel (Shen et al., 2018),
these two concepts are definitely different in a number of respects. First of all, in
constrast to multichannel retailers which mainly focus on physical store, website and
direct marketing (e.g., catalog) (Verhoef et al., 2015); the channel scope of
omnichannel is broader, including brick-and-mortar store, website, mobile app, social
media, as well as all other customer touchpoints (Shen et al., 2018). Morever,
multichannel retailers usually design and manage the channels separately, with a
limited integration between channels (Shen et al., 2018). On the other hand,
omnichannel retailers try to co-ordinate all their channel management activities
across areas of information exchange, joint operations, logistics, pricing, promotion,
inventories, order fulfillment and even after sales services (Li et al., 2018; Lee et al.,
2019). Finally, whereas multichannel retailers gear towards optimizing customer
experience with each channel (Shen et al., 2018), the ultimate aim of omnichannel
retailers is serving customers with a seamless, consistent and well-integrated
experience at anytime, anywhere, through any channels (Frazer and Stiehler, 2014;
Jocevski et al., 2019).
According to Lee et al. (2019), omnichannel studies can be categorized into two
streams: organizational-level studies and individual-level studies. Organizational-
level studies approach this topic from the point of view of firm’s management, such
as examining the impact of channels on retailer’s performance (Cao and Li, 2015);
how to measure and manage channel distribution (Ailawadi and Farris, 2017); or
related marketing issues that retailers must to care (Melero et al., 2016). Individual-
level studies, on the other hand, focus on customer behavior. Most studies in the field

9
of omnichannel customer behavior have concerned the purchase intention (Cook,
2014; Juaneda-Ayensa et al., 2016) as well as the channel choice (Park and Lee, 2017;
Xu and Jackson, 2019). However, in regard to omnichannel customer experience,
researchers have not treated it in much detail. Although the few of prior studies have
dealt with the impact of channel integration quality on customer respones to
omnichannel retailers (Shen et al., 2018; Zhang et al., 2018; Lee et al., 2019), research
on the relationship between channel integration quality and customer experience has
been still deficient, especially the empirical studies with fully mechanism.
2.2. Channel integration quality with omnichannel retailers
Channel integration quality (CIQ) is regarded as a key factor that determine
omnichannel retailers’ ability to manage customer relationships across channels and
deliver customers with a seamless purchasing experience during their entire shopping
journey (c.f., Lee et al., 2019). In their major study, Sousa and Voss (2006) propose
a conceptual framework for CIQ in general context with two dimensions: channel-
service configuration and integrated interactions. Each of them has two sub-
dimensions as shown particularly in Table 2.1.
Table 2.1. Definitions and examples of the sub-dimensions of CIQ
Dimension
Sub-
dimension
Definition Example
Channel-
service
configuration
Breadth of
channel-
service
choice
The degree to which
customers can choose
alternative channels for a
given service or can
accomplish preferred
tasks through an
individual channel.
It’s very easy for
customers to know the
product details through
both the retailer’s
online and offline
channels.

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Transparency
of channel-
service
confguration
The degree to which
customers are aware of
the existence channels
and services as well as the
diﬀerences between such
service attributes across
channels.
Customers can come to
physical stores to find
and evaluate products
but finish the purchase
online.
The consistency of
content offered by
retailers across channels, Price and promotion
Content which allows customer to are consistent for both
consistency receive the same response the retailer’s online and
to an enquiry posted offline channels.
through diﬀerent
Integrated
interactions
channels.
The degree of
Regardless of which
customers call the
consistency of relevant customer care hotline
Process
consistency
and comparable process
attributes across channels
(e.g., the feel, image, and
on a retailer's website
or meet staff at the
physical store to ask
delivery speed of about product
services). warranty, they are
served similarly.
Sources: Sousa and Voss (2006); Shen et al. (2018); Lee et al. (2019)
To date, serveral studies have attempted to examine the critical role played by
CIQ in different contexts such as multichannel banking service (Hsieh et al., 2012;
Seck and Philippe, 2013), multichannel retailers (Lee and Kim, 2010; Wu and Chang,

11
2016), omnichannel catering service (Shen et al., 2018), omnichannel retailing (Lee
et al., 2019). Drawing upon Sousa and Voss (2006)’s framework and in line with
these previous studies, the current thesis defines channel-service configuration and
integrated interactions as the two components of CIQ. The former refers to the wide
range and flexible combination of various online and offline channel services, while
the later describes the consistency and uniformity of both content and process
attributes through different channels provided by onnichannel retailers.
2.3. Customer experience with omnichannel retailers
Modern customers’ behavior become even more complex and sophisticated
nowadays. Instead of shopping on an individual channel, they move across channels
anytime, anywhere, at any stages during their purchasing process (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 they can do things the other
ways around. Customers are expected to obtain services from any channel with the
same customer identity/account (Zhang et al., 2018), and all of the supports and
offerings require consistency in multiple touchpoints across channels (Ieva and
Ziliani, 2018). These changes in customer behavior and expectations require retailers
to integrate all their channel activities across areas of information exchange, joint
operations, logistics, pricing, promotion, inventories, order fulfillment 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
(Frazer and Stiehler, 2014; Lee et al., 2019).
Historically, the concept of customer experience have been studied in different
contexts of retailing, from physical stores retailing (Jones et al., 2010; Sachdeva and
Goel, 2015; Bäckström and Johansson, 2017; Terblanche, 2018) to e-retailing (Rose
et al., 2012; Martin et al., 2015; Pandey and Chawla, 2018) and m-retailing (Tseng
and Yazdanifard, 2015; McLean et al., 2018). For example, McLean et al. (2018)

12
develop a “Mobile Application Customer Experience Model” which highlights the
impact of utilitarian factors of technology on customer experience during use of
retailers’ mobile applications. On the other hand, a number of authors have recently
considered customer experience in the context of multichannel retailing (Blázquez,
2014) and multichannel marketing (Lemon and Verhoef, 2016; Brun et al., 2017).
Standing out among these studies, Lemon and Verhoef (2016) conceptualize
customer experience throughout the customer journey with a firm across multiple
touchpoints, as customer behavior have become more complex in multichannel
context.
Despite practitioners’ consistent emphasis on the crucial role of creating and
managing customer experience throughout the entire shopping journey, according to
Lemon and Verhoef (2016) the extant customer experience literature is still in its
nascent stage; as such, the customer experience will be one of the most challenging
research topics in the coming years. In the context of omnichannel retailers, the
empirical works directly related to the customer experience are even scarcer. The few
existing studies attempt to conceptualize and describe the customer experience, thus
being qualitative and exploratory in nature. In particular, Melero et al. (2016)
approach this phenomenon from marketing’s viewpoint and point out key challenges
to develop an integrated omnichannel customer experience, including adopting a
customer centric approach, unifying all touchpoints across all channels, delivering
personalized customer experiences, integrating the available channels and delighting
customers across channels. Similarly, some other studies such as Cook (2014), Frazer
and Stiehler (2014), Parise et al. (2016), Peltola et al. (2015) qualitatively explore the
customer experience from different perspectives, such as in-store experience,
experiential marketing, operational management and digital technology. The
literature review also reveals two quantitative studies by Azhari and Bennett (2015)
and Ieva and Ziliani (2018) on the omnichannel experience. Using the descriptive
statistics method, Azhari and Bennett (2015) explore the role of digital technology in
physical stores to create an emotional and sensory experience; while Ieva and Ziliani

13
(2018) focus on the customer experience management perspective, using latent class
cluster analysis to segment customers. Overall to date, what we know about the
omnichannel experience comes from qualitative perspectives; while a few
quantitative studies focus solely on the individual channel experience (Azhari and
Bennett, 2015) or examined it from one of management perpsectives (Ieva and
Ziliani, 2018). Such approaches, however, fail to empirically address a seamless
experience cross all available channels, as well as to understand the dynamic
mechanisms of forming customer experience and its subsequent patronage behavior
in the context of omnichannel retailers.
Although a number of deﬁnitions of customer experience exist in the literature
(also see, Lemon and Verhoef, 2016; McLean et al., 2018; Rose et al., 2012), the
major stream of research advocates that the customer experience is holistic in nature
and defined as a multidimensional psychological perspective (Azhari and Bennett,
2015; Brun et al., 2017; Frazer and Stiehler, 2014; Ieva and Ziliani, 2018; Lemon and
Verhoef, 2016; McLean et al., 2018). According to McLean et al. (2018), customer
experience is comprised of cognitive and affective dimensions that customers have
with a company through all cues and touchpoints among the entire customer journey.
Customer satisfaction with an experience reflects their cognitive component of the
experience (Lemke et al., 2011; Lemon and Verhoef, 2016), while customer emotions
can represent the affective aspect of the experience (Oliver, 1993).
A number of authors have considered customer satisfaction to be a central
element in understanding the customer experience (Lemon and Verhoef, 2016;
McLean et al., 2018). Furthermore, customer emotions have been also studied as a
dimension to measure customer experience in various retailing settings such as
physical store retailing (Grace and O’Cass, 2005), e-retailing (Kim et al., 2007) and
m-retailing (McLean et al., 2018). Consistent with the prior relevant retailing
research, the current thesis defines the omnichannel experience as a second-order
construct of two dimensions, satisfaction with experience and positive emotions. This
approach allows us to not only investigate customers’ cognitive evaluation about the

14
overall experience that omnichannel retailers offer to them (referring to the
“satisfaction with experience” dimension), but also examine customer
affections/emotions during the purchase journey across all available touchpoints with
omnichannel retailers (referring to the “positive emotions” dimension).
2.4. Prior relevant studies
(1) Customer engagement through omnichannel retailing: The eﬀects of
channel integration quality (Lee et al., 2019)
This study explores the influences of channel integration quality (CIQ) on
customer engagement in omnichannel retailing context, as well as the positive
outcomes resulting from such engagement. Based on social exchange theory, Lee et
al. (2019) posit two dimensions of CIQ (i.e., channel-service configuration and
integrated interactions, with two sub-dimensions for each of them) as the antecedents
of customer engagement; while customer engagement is a second-order construct
(including conscious attention, enthused participation, and social connection). The
outcomes are repurchase intention and positive word-of-mouth (see Figure 2.1).
Data analysis from 490 U.S. shoppers reveals that all the CIQ dimensions
positively affect customer engagement, which in turn leads to repurchase intention
and positive word-of-mouth. However, the effects are definitely different between
high-involvement products (represented by Apple) and low-involvement products
(represented by Kroger). These findings make an important contribution to the field
of customer engagement in the context of omnichannel retailing and at the individual
level; and also provide useful ideas for retailers to engage customers across channels.

15
Note: CSC: Channel-service configuration, BCSC: Breadth of channel-service choice, TCSC:
Transparency of channel-service configuration, InI: Integrated interactions, CC: Content
consistency, PC: Process consistency, WOM: word-of-mouth.
Figure 2.1. Research model of Lee et al. (2019)
(2) The impact of channel integration on consumer responses in omnichannel
retailing: The mediating effect of consumer empowerment (Zhang et al., 2018)
The purpose of this paper is to examine the impact of channel integration on
consumer respones in the context of omnichannel retailing; and the mediating role of
consumer empowerment in this relationship. Drawing upon the stimulus – organism
- response (SOR) framework, Zhang et al. (2018) define channel integration as a
second – order formative construct which promotes consumer empowerment; in turn
Channel integration quality
Enthused
participation
BCSC Conscious
attention
Social
connection
CSC
Repurchase
intention
TCSC H1 H3
Customer
engagement
CC
H2 H4
InI
Positive
WOM
PC
Control variables:
Physical store quality
Virtual store quality
Demographics

16
Trust
H2 H5
Consumer
perception of
channel
integration
H1
Consumer
empowerment H4
Patronage
intention
H3 H6
Satisfaction
leads to increased trust and satisfaction and improved patronage behavior (see Figure
2.2.).
Data analysis from 155 Chinese shoppers demonstrates that channel integration
has a positive relationship with consumer patronage intention and this relationship is
mediated by consumer empowerment. Moreover, consumer empowerment is
positively related to perceived trust and satisfaction. This study makes a major
contribution to research on omnichannel retailing by not only demonstrating the
critical role of channel integration but also explaining how it can enhance positive
consumer respones and patronage behavior.
Figure 2.2. Research model of Zhang et al. (2018)
(3) Developing a Mobile Applications Customer Experience Model (MACE) -
Implications for Retailers (McLean et al., 2018)
This research attempts to examine customer experience in the context of m-
commerce by developing a Mobile Applications Customer Experience Model. Based

17
Moderators: Gender, Screen size
Customer experience
+ Satisfaction with the
experience
+ Positive emotions
Enjoyment
Frequency
of use
on Technology Acceptance Model, Flow Theory and Expectancy Confirmation
Theory with Information Technology, McLean et al. (2018) posit utilitarian factors
of technology (including three dimensions: ease of use, convenience and
customisation), timeliness and enjoyment as the key variables influencing customer
experience, which in turn results in customers’ frequency of use (see Figure 2.3).
Data are collected from 1024 UK consumers, in the context of shopping with
the four retailers’ mobile applications (i.e., H&M, Next, John Lewis and Marks &
Spencer). The results highlight the importance of utilitarian factors in delivering an
excellent customer experience. Moreover, this paper reveals that customers have a
negative experience if they perceive to spend longer time than necessary when using
the mobile application. On the other hand, gender and smartphone screen-size play a
moderating role on the customer experience. This project provides an important
opportunity to advance the understanding of customer experience in m-retailing
through Mobile Applications Customer Experience Model and provide the key
insights for retailers on how to enrich their customer experience with mobile
application channel.
Figure 2.3. Research model of McLean et al. (2018)
Utilitarian factors of
technology
+ Ease of use
+ Convenience
+ Customisation
Timeliness

18
(4) Channel integration quality, perceived fluency and omnichannel service
usage: The moderating roles of internal and external usage experience (Shen et al.,
2018)
This paper investigates the factors that affect omnichannel service usage.
Following Wixom & Todd framework, Shen et al. (2018) develop a research
framework including object-based beliefs (which is represented by channel
integration quality with four dimensions: channel choice breadth, channel service
transparency, content consistency and process consistency) and behavioral beliefs
(which is represented by perceived fluency). Besides, behavior-based traits (i.e.,
internal and external usage experience) are considered as moderators for the
relationship between behavioral beliefs and usage behavior (see Figure 2.4).
Data are collected from 401 users of an omnichannel catering service platforms
in Mainland China. The findings indicate that channel integration quality
significantly affects customers’ perceived fluency across channels, which in turn
leads to omnichannel service usage. Moreover, internal usage experience weakens,
while external usage experience strengthens the positive relationship between
perceived fluency and usage behavior. This project provides an important opportunity
to advance the understanding of omnichannel service from customer behavior’s
viewpoint and also suggests several insights for omnichannel service providers to
optimize their channel management for delivering a smooth service experience to
their customers.

19
Object-based beliefs Behavior-based traits
Figure 2.4. Research model of Shen et al. (2018)
(5) Customer's reaction to cross-channel integration in omnichannel retailing:
The mediating roles of retailer uncertainty, identity attractiveness, and switching
costs (Li et al., 2018)
This paper gives an account of the mechanisms through which customers react
to cross-channel integration (CCI) in the context of omnichannel retailing. Following
the Push-Pull-Mooring framework, Li et al. (2018) develop a research framework
which retailer uncertainty, identity attractiveness, and switching costs play pushing,
pulling, and mooring roles, respectively, in shaping customers’ respones to CCI (i.e.,
customer retention and interest in alternatives); while showrooming behavior acts as
a moderator in these relationships (see Figure 2.5).
Channel
choice breadth
H2a,b,c,d
Internal usage
experience
Channel
service
transparency
H3a
Perceived
fluency
Omnichannel
service usage
H1
Content
consistency
H3b
Behavioral beliefs
External usage
experience
Process
consistency

20
The analysis results of 259 Chinese shoppers reveal that retailer uncertainty,
identity attractiveness, and switching costs partially mediate the effect of CCI on
customer retention, while fully mediating the relationship between CCI and interest
in alternatives. Furthermore, the showrooming behavior is found to strengthen the
negative relationship between CCI and retailer uncertainty. This empirical work
presented here provides an investigation into how customers react to CCI through the
dynamic mechanisms and points out important insights for omnichannel retailers to
implement their CCI strategy.
Figure 2.5. Research model of Li et al. (2018)
Push-pull effects
Showrooming
H1a,b,c
H4a,b,c
Retailer
uncertainty
Customer
retention
Cross-
channel
integration
H3a,b,c
Identity
attractiveness
Service investment
Switching
costs
Interest in
alternatives
H2a,b,c
Mooring effect

21
2.5. Research framework and hypothesis development
2.5.1. 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
behaviors in various contexts of retailing such as offline retailing (Morin et al., 2007),
e-retailing (Eroglu et al., 2001; Wang et al., 2011; Wu et al., 2013), multichannel
retailing (Hsieh et al., 2012; Pantano and Viassone, 2015) and omnichannel retailing
(Lazaris et al., 2017; Zhang et al., 2018). This framework points out the relationship
among the stimulus (S), consumers’ internal states (O) and subsequent behavior (R).
The stimulus affects consumers’ internal states, which in turn results in their respones.
In particular, stimulus refers to the retail environmental stimuli, such as in-store
music, store atmosphere, channel availability, channel integration (Morin et al., 2007;
Pantano and Viassone, 2015; Zhang et al., 2018). In line with Lee et al. (2019), in the
current thesis, 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 research
framework. Finally, customer patronage intention is proposed to stand for the
behavioral response in the SOR framework. In summary, the current study’s research
framework (Figure 2.6) is primarily drawn from the SOR framework that serves as a
basis for the development of the following hypotheses.
2.5.2. The influences of channel integration quality (CIQ) on customer
experience
Channel integration quality 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

22
an environmental stimulus is expected to affect customers’ internal states, such as
customer experience. Since CIQ of omnichannel retailers are comprised of channel-
service configuration and integrated interactions (Sousa and Voss, 2006), customer
experience should be determined by these two characteristics.
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 a 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; operating as such, the chosen service or shopping
combinations are the best fit to fulfill their needs (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 (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
offer a valued experience for their customers compared to omnichannel retailers who
do not provide such wide breadth of choices and transparency of channel-service
configuration. Thus, we hypothesize:
H1a. Channel-service configuration is positively associated with the customer
experience.

23
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). The more retailers offer consistent content (e.g., price, product
information, promotion) across all available channels, the less their customers feel
doubtful or confused 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 products and prices,
which can in turn improve customer experience (c.f., Li et al., 2018). Furthermore,
the 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 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 center 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, 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 by allowing customers to see and touch merchandise virtually (Brynjolfsson
et al., 2013; Pilkington, 2019). Previous empirical evidence shows that process
consistency between online and offline channels of land-based retailers 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
enhances identity attractiveness while diminishing retailer uncertainty. In the same

24
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.
2.5.3. 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). According to Broniarczyk and Griffin (2014), choice freedom and
extensive information are the two key factors influencing customer empowerment. In
addition, when customers can freely utilize any channels suited to their need at their
convenience, they feel strongly empowered (Lee and Kim, 2010). Li et al. (2018) also
point 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 et al., 2017). Therefore, high level of CIQ in omnichannel
retailers can provide customers with increased empowerment.
As noted by Lemon and Verhoef (2016), as human beings are continually trying
to pursue autonomy, customer empowerment is thus deemed an important driver of
their perceived experience. Prior empirical studies also confirm that customer
empowerment will enhance customers’ perception of a satisfactory experience
(Castillo, 2018, 2017; Hunter and Garnefeld, 2008). Retailers that focus on customer

25
empowerment will try to provide more personalized services and customized options
that make customers feel like the retailers offer them exactly what they need. 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 satisfied shopping outcomes, endowing the shopping journey
with an 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.
2.5.4. The moderating effect of internet usage
Internet usage is understood here as the length of time customers spend online
(Park and Jun, 2003). The knowledge and experience customers have with the internet
might depend on their use of internet. To date, internet experience has typically been
studied as a moderator in different contexts such as website shopping behavior
(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 a larger amount of online time may accumulate
more online experiences, manifesting 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
focusing on the evaluation of omnichannel retailers.
According to Daunt and Harris (2017), customers with less frequent internet
usage are likely to feel low confidence with regard to their ability to navigate the
alternative channels of omnichannel retailers. In contrast, customers who have had a

26
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).
Internet experienced customers 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 bring 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 regarding 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.
2.5.5. The influence of the customer experience on patronage intention
According to the SOR framework, customers’ internal states (i.e., customer
experience) could result in their response to omnichannel retailers (i.e., patronage
intention). Previous studies demonstrate that experiential values positively affect
website patronage intentions in the e-retailing context (Shobeiri et al., 2015), while
overall customer experience 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 behavioral intentions of customers. For example,

27
Anderson and Sullivan (1993) argue that a higher level of satisfaction will lead to a
higher level of customer retention. This view is also confirmed by Ranaweera and
Prabhu (2003) who declare that satisfaction significantly enhances customer
retention. Similarly, several studies have shown that satisfaction is an important
antecedent of customer repurchase behavior (Fang et al., 2011; Lee et al., 2009;
Olsen, 2002). In the retailing industry, a large number of research projects have been
conducted to confirm the positive impact of satisfaction on patronage intention
(Chang et al., 2015; Grace and O’Cass, 2005; Wang, 2009). With respect to another
component of customer experience, positive emotions, according to Grace and
O’Cass (2005), consumption feelings/emotions such as pleasure or excitement in
physical store retailing have a significant positive effect on patronage intentions.
Similarly, Wang (2009) confirms that a positive attitude will lead to customer
patronage intentions. In an e-retailing context, data from the research of Kim et al.
(2007) indicate that a higher level of shopping enjoyment will lead to a higher level
of patronage intention. 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 patronize an omnichannel retailer.
Overall, we hypothesize:
H4. The customer experience is positively associated with patronage intention.

28
Channel Integration Quality
Breadth of
channel-
service
choice
Transparency
of channel-
service
configuration
Content
consistency
Channel-
service
configuration
Internet
usage
H3c
Customer
empwerment
Satisfaction
w. exp.
Positive
emotion
Customer
experience
H4
Patronage
intention
Process
consistency
Integrated
interaction
First-order constructs
Second-order constructs
Indirect effects
Stimulus (S) Organism (O) Respone (R)
Figure 2.6. Research framework and hypotheses
Control var.:
Trust, Seek

29
2.6. Summary
Overall, this chapter presented the research framework which be drawn upon
the SOR framework and the literature review on each construct of the research model,
as well as five studies that are relevant to this thesis. Futhermore, four hypotheses
were proposed. First, CIQ’s dimensions (i.e., (a) channel-service configuration and
(b) integrated interactions) are positively associated with the customer experience.
Second, customer empowerment mediates the influences of CIQ (consisting of (a)
channel- service configuration and (b) integrated interactions) on the customer
experience. Third, customer internet usage strengthens the positive influence of (a)
channel-service configuration, (b) integrated interactions, and (c) customer
empowerment regarding the customer experience. And finally, the customer
experience is positively associated with patronage intention. The next chapter would
be concerned with the method used for the current thesis.

30
CHAPTER 03: RESEARCH METHOD
3.1. Research process
The research process in this thesis consisted of ten steps as presented in Figure
3.1. The first step was to review the literature and prior relevant papers to adopt the
measurement scales for all studied constructs (i.e., breadth of channel-service choice,
transparency of channel-service configuration, content consistency, process
consistency, customer empowerment, satisfaction with experience, positive
emotions, patronage intention, internet usage, trust on retailer, and variety seeking);
with some minor modifications to fit the current research context. All the items of
these constructs were then translated into Vietnamese, the official language of the
current research context. Following the measurement scales, a questionnaire was
designed and pre-tested with 20 customers (i.e., 10 MBA students and 10 office
staffs). The questionnaire was then modified to ensure its clarity before finalization
and distribution.
After that, this research conducted a paper-based interview with participants
who used to have experience of shopping with omnichannel retailers and aged 25 -
34; with a purposive sampling based on gender (i.e., 60% women and 40% men). The
survey was conducted at the five busiest shopping mall and office buildings in the
metropolitan area of Ho-Chi-Minh City (i.e., Vincom Center, Saigon Square,
Takashimaya Vietnam; Diamond Plaza; Parkson Plaza). After collection, the dataset
was analyzed using SmartPLS 3.2.8. To begin this process, a two-stage approach was
applied to assess the measurement model. The reliability of the studied constructs
was represented by Cronbach’s alpha and composite reliability, while the convergent
validity was represented by indicator’s outer loading and average variance extracted
(AVE). Furthermore, cross loadings, Fornell-Larcker criterion, and the Heterotrait-
Monotrait ratio (HTMT) were used to assess the discriminant validity of the
measurement model. The next step was checking whether the common method bias
(CMB) could threaten the research results. After that, the structural model was
assessed through a number of different criteria, such as: the VIF values for checking

31
5. Main survey
(n = 312)
4. Modified
questionnaire
3. Pre-test
(n = 20)
6. Assessment of the measurement
model (two-stage approach)
7. Test for common
method bias (CMB)
9. FIMIX analysis for data
heterogeneity
8. Assessment of the structural
model
10. Conclusions and
managerial implications
the collinearity issues, the SRMR value to evaluate the model fit, the R2
and Q2
of
the endogenous constructs to assess the predictive power and predictive relevance of
the proposed research model, respectively. On testing the hypotheses of the current
thesis, a bootstrapping procedure of 5,000 samples was applied to test the direct
effects, the mediating effects, as well as the moderating effects. Finally, the FIMIX-
PLS approach was applied to evaluate whether the research findings were distorted
by the unobserved heterogeneity. The research process was ended with some
conclusions and managerial implications for omnichannel retailers.
Figure 3.1. Research process
2. Measurement scales
& Draft questionnaire
1. Literature
review

32
3.2. Measurement scales
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 Table 3.1). Specifically, the two
multi-dimensional constructs that belong to CIQ (channel-service configuration and
integrated interactions) had two dimensions for each, with scales were 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 scales (2018)
of five and three items, respectively. Internet usage was assessed based on Gross's
(2004) 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 Adjei and Clark (2010). All items were measured with a seven-point
Likert scale (1 = strongly disagree, 7 = strongly agree) and were translated into
Vietnamese, the official language of the current research context.

33
Table 3.1. Measurement scales
Constructs Items
Channel-
service
configuration
(CSC)
Breadth of
channel-
service
choice
(BCSC)
1. I can purchase products via the online or physical
stores of X. (BCSC1)
2. I can get support through the online or physical
stores of X. (BCSC2)
3. I can give feedback about the products through
the online or physical stores of X. (BCSC3)
4. I can get detailed product description from the
online or physical stores of X. (BCSC4)
Transparency
of channel-
service
configuration
(TCSC)
1. I am aware of available services of the online and
physical stores of X. (TCSC1)
2. I am familiar with available services of both the
online and physical stores of X. (TCSC2)
3. I know how to utilize available services of the
online and physical stores of X. (TCSC3)
4. I know the diﬀerences of available services
between the online and physical stores of X.
(TCSC4)
Integrated
interactions
(InI)
Content
consistency
(CC)
1. X provides consistent product information across
the online and physical stores. (CC1)
2. The product prices are consistent across the
online and physical stores of X. (CC2)
3. X provides consistent promotion information
across the online and physical stores. (CC3)
4. X provides consistent stock availability across
the online and physical stores. (CC4)

34
Process
consistency
(PC)
1. The service images are consistent across the
online and physical stores of X. (PC1)
2. The levels of customer service are consistent
across the online and physical stores of X. (PC2)
3. The feelings of service are consistent across the
online and physical stores of X. (PC3)
4. The online and physical stores of X have
consistent performance in the speed of service
delivery. (PC4)
Customer empowerment
(Cemp)
1. In my dealings with X, I feel I am in control.
(Cemp1)
2. During the shopping process at X, I can select
products and services freely. (Cemp2)
3. I can influence the choice-set offered to me by X.
(Cemp3)
4. The ability to influence the goods and services of
X is beneficial to me. (Cemp4)
5. My influence over X has increased relative to the
past. (Cemp5)
Internet usage (IU)
How many hours do you use the internet per day?
Less than 2h; 2h to < 5h; 5h to < 8h; 8h and
more
Customer
experience
(Cexp)
Satisfaction
with
experience
(SE)
1. I am satisfied with the shopping experience at X.
(SE1)
2. The shopping experience at X is exactly what I
needed. (SE2)
3. The shopping experience at X has worked out as
well as I thought it would. (SE3)

35
Positive
emotions
(PE)
1. I feel encouraged when shopping at X. (PE1)
2. I feel confident when shopping at X. (PE2)
3. I feel sure when shopping at X. (PE3)
4. I feel unconfused when shopping at X. (PE4)
5. I feel optimistic when shopping at X. (PE5)
6. I feel certain when shopping at X. (PE6)
7. I feel content when shopping at X. (PE7)
8. I feel relieved when shopping at X. (PE8)
9. I feel undoubtful when shopping at X. (PE9)
10. I feel satisfied when shopping at X. (PE10)
Patronage intention (PI)
1. I am likely to continue to purchase products from
X. (PI1)
2. I am likely to recommend X to my friends. (PI2)
3. I am likely to choose X as a preferred retailer if I
need the products that I will buy. (PI3)
Trust in retailer (Trust)
1. X is a trustworthy retailer. (Trust1)
2. X cares about its customers. (Trust2)
3. X keeps its promises to its customers. (Trust3)
4. X is not opportunistic. (Trust4)
Variety-seeking (Seek)
1. When shopping, I find myself spending a lot of
time checking out new websites/apps/physical
stores. (Seek1)
2. I take advantage of the first available opportunity
to find out about new websites/apps/physical stores.
(Seek2)
3. I like to investigate information about new
websites/apps/physical stores. (Seek3)

36
4. I like information source that introduce new
5. I frequently look out for new
6. I seek out situations in which I will be exposed
to new and different sources of
websites/apps/physical store information. (Seek6)
7. I am continually seeking out new
Note: X refers to the listed well-known or the self-declared omnichannel retailer by the respondent.
Sources: Lee et al. (2019); Zhang et al. (2018); McLean et al. (2018); Chiu et al.
(2012); Adjei and Clark (2010).
3.3. Questionnaire design
The paper-based questionnaire was designed in three sections. Section 1
contained an explanation of omnichannel retailers and screening questions to identify
eligible respondents. The second section included measurement of the research
constructs. Finally, the last section contained the respondent’s demographic
information. We pre-tested the questionnaire with 10 MBA students at a well-known
public university and 10 office staffs. The questionnaire was then modified to ensure
its clarity before finalization and distribution.
3.4. 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. Li et al. (2018)
also note that omnichannel shoppers are online customers. 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. Similarly, a report from Nielsen Vietnam also
pointed out that 60% of Vietnamese online customers were women and 40% were

37
men, with the age bracket of 25 – 29 totaling 55% (Uyen Phuong, 2018). Thus, the
respondents of the current study were limited to those aged 25 – 34, and purposive
sampling based on gender (see Table 3.2) was employed.
The data collection was conducted in Ho-Chi-Minh (HCM) City, where the
retail business activities are striking. According to Tran (2019), HCM’s retail sales
and service revenue reached more than 4.07 billion USD in April 2019, up 14.4%
from the same time last year. In the first five months of 2018, statistical data from
General Statistics Office of Vietnam also pointed out that HCM was the city with the
fastest growth of retail goods sales (13.5%) in Vietnam (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 mall and office buildings
in the metropolitan area of HCM City (i.e., Vincom Center, Saigon Square,
Takashimaya Vietnam; Diamond Plaza; Parkson Plaza) to approach potential
respondents (aged 25 – 34). After presenting the definition of omnichannel retailers
in the survey 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 survey,
filtering questions were used to ensure that the person: (1) has visited both the online
(websites/mobile apps) and physical stores of one of the four omnichannel retailers;
(2) has made at least one purchase either online or physical store 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. The data were collected over a five-week period in 2019
(from 8 July 2019 to 11 August 2019) at different times of day and on both weekdays
and weekends.

38
The process approached more than 400 respondents who decided to participate
in this study. After presenting the definition of omnichannel retailers and the three
filtering questions, 356 respondents met the conditions to continue answering the
questionnaire. After close scrutiny, 312 valid responses were used for further
analysis.
According to Hair et al. (2017), one of the characteristics of PLS-SEM is that it
allows us to use a very small sample size, for example less than 100. The current
thesis used G*Power analysis to caculate the sample size. In particular, as this study
focus on the significance of single effects instead of the variance explained by the
overall regression equation, the “Linear multiple regression: Fixed model, single
regression coefficient” was chosen as the method. Then, as this thesis had an
expectation for the standardized coefficient of 0.2 and an overall R² of 0.25, so an
effect size (f²) of 0.053 was added. Moreover, the research model has four predictors
for the construct customer experience. Overall, this would require sample size of 206.
On the other hand, larger sample size increase the consistency of PLS-SEM
estimations, so the sample size of 312 of the current study was strong enough.
3.5. Sample characteristics
The majority of respondents (82.4%) had graduated from a college or university.
The most common monthly income level bracket was from 9,000,000 VND and up.
In terms of omnichannel shopping, nearly half of the respondents (47.4%) made
purchases a few times per month, with the value for each order most commonly less
than 1,000,000 VND (41.3%). Furthermore, the most common daily internet usage
range was from 2 hours to just under 5 hours (42%). More details about respondents’
profiles and purchase behaviors are presented in Table 3.2.

39
Table 3.2. Sample demographic characteristics
Gender Freq.
Realized
quota %
Planed
quota %
Purchase frequency
(webs/apps/stores)
Freq. %
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
Education Freq. %
Average order value
(webs/apps/stores) (*)
Freq. %
High school 3 1.0 Less than VND 1,000,000 129 41.3
College or university 257 82.4
VND 1,000,000 to <
3,000,000
92 29.5
Post graduate and above 51 16.3
VND 3,000,000 to <
5,000,000
44 14.1
Others 1 0.3 VND 5,000,000 and more 47 15.1
Total 312 100.0 Total 312 100.0
Monthly income (*) Freq. % Daily internet usage Freq. %
Less than VND 5,000,000 9 2.9 Less than 2h 51 16.3
VND 5,000,000 to <
9,000,000
77
24.7 2h to < 5h 131 42.0
VND 9,000,000 to <
15,000,000
116
37.2 5h to < 8h 68 21.8
VND 15,000,000 and more 110 35.3 8h and more 62 19.9
Total 312 100.0 Total 312 100.0
(*) US$ 1 = Vietnamese Dong (VND) 23,215 at the time of the survey
3.6. Data analysis method
Statistical analysis was performed using SmartPLS software (version 3.2.8). To
begin this process, a two-stage approach was applied, following Becker et al. (2012).
For the purpose of evaluating the measurement model, these criteria were considered
(Hair et al., 2017):
(1) Cronbach’s α and composite reliability should be higher than 0.7 to ensure
the internal consistency reliability.
(2) For the satisfaction of convergent validity, indicator’s outer loading should
be higher than 0.7, while average variance extracted (AVE) values should be above
the cut-off point of 0.5.
(3) In regarding to discriminant validity: cross loadings, Fornell-Larcker
criterion, and the Heterotrait-Monotrait ratio (HTMT) were used.

40
The next step was to applying Harman’s one-factor test, as well as common
method factor approach (Liang et al., 2007) to check whether the common method
bias (CMB) could threaten the research results, as the dataset of this study was
collected based on respondents’ self-reported subjective perceptions.
After that, the structural model was assessed by the following steps as proposed
by Hair et el. (2017):
(1) For collinearity issues, VIF value should be higher than 0.2 and less than 5.
(2) SRMR value of less than 0.08 asserted a good fit of the model for theory
testing.
(3) The predictive power and predictive relevance of the proposed research
model were assessed through the R2
and Q2
of the endogenous constructs,
respectively. In particular, the R2
values of 0.26, 0.13 and 0.02 represented
substantial, moderate and weak levels of predictive accuracy, respectively (Cohen,
1988); while the predictive relevance of Q² values should be higher than zero.
For hypotheses testing, the current thesis assessed the direct effects, mediating
effects and moderating effects using bootstrapping procedure of 5,000 samples. The
hypotheses were supported in case p value < 0.05, as well as the 95% confidience
intervals bias corrected did not include zero. In regarding to the effect size of the
direct effects, 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). Whereas, Kenny’s standard was used for evaluating
the effect size in tests of moderation, with the value of 0.005, 0.01 and 0.025
representing small, medium, and large, respectively (Hair et al., 2017).
Finally, following Becker et al. (2013), FIMIX- PLS approach was applied to
check whether unobserved heterogeneity in the selected sample can prevent the
derivation of accurate findings.

41
3.7. Summary
This chapter dealt with the method used for the current thesis, including the
research process, measurement scales, questionnaire design, sample and data
collection, sample characteristics, as well as the data analysis method. In particular,
the study consisted of three multi-dimensional constructs (i.e., channel-service
configuration, integrated interactions and customer experience), two single-
dimensional constructs (customer empowerment and patronage intention), one
single-item construct (internet usage), and two control variables (trust in retailers and
variety seeking). All measurements for these constructs were adopted from prior
studies of Lee et al. (2019); McLean et al. (2018); Zhang et al. (2018); Gross’s (2004);
Chiu et al. (2012); and Adjei and Clark (2010). Data were collected from customers
who used to have experience of shopping with omnichannel retailers and aged 25 -
34, with a purposive sampling based on gender; at five busiest shopping mall and
office buildings in the metropolitan area of HCM City. After close scrutiny, 312 valid
responses would be used for further analysis using SmartPLS 3.2.8 in the next
chapter.

42
CHAPTER 04: 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 model and the structural model. The analysis results are shown below.
4.1. Assessment of measurement model
The research framework had unidimensional, multidimensional, and even
single-item constructs (see Figure 2.6); 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 (see Figure 4.1). These scores were saved
in the dataset for further analysis in Stage II. Then, in Stage II, the scores of Stage I
became the indicators for their corresponding constructs (see Figure 4.2). The results
of scale accuracy (i.e., reliability and validity) of the studied constructs were
presented in Table 4.1 and Table 4.2.
Note: See Table 3.1 for all abbreviations of the respective constructs in the model
Figure 4.1. Research model in Stage I

43
Note: See Table 3.1 for all abbreviations of the respective constructs in the model
Figure 4.2. Research model in Stage II
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 4.1
indicates the satisfactory level of scale reliability. Convergent validity for the studied
constructs were also verified, with the minimum requirement of indicator loadings
(0.7) and average variance extracted (AVE) values above the cut-off point of 0.5
being satisfied (i.e., the AVE values: CSC - 0.800, InI – 0.805, Cemp - 0.586, Cexp -
0.871, PI - 0.828, Trust - 0.661 and Seek - 0.763). 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 4.2, 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

44
of 0.85. Overall, both the reliability and validity of the measurement model were
assured.
Table 4.1. Scale accuracy analysis
Assessment Stage I
Hierarchical measurement model No of scale
itemsa Alpha CRb
AVEc Item loading/ highest
cross-loading
Studied constructs (Dimensions)
Channel-
service
configuration
(CSC)
Breadth of channel-
service choice
4 .837 .891 .672 .800/.581, .833/.440,
.843/.484, .804/.476
Transparency of
channel-service
configuration
4 .841 .894 .679 .806/.534, .893/.578,
.879/.530, .705/.370
Integrated
interactions
(InI)
Content consistency 4 .822 .883 .655 .810/.499, .858/.497,
.849/.479, .711/.504
Process consistency 4 .815 .880 .648 .673/.501, .856/.576,
.851/.487, .826/.504
Customer empowerment (Cemp) 5 .822 .876 .586 .715/.486, .714/.494,
.764/.438, .840/.527,
.785/.490
Customer
experience
(Cexp)
Satisfaction with
experience
3 .894 .934 .826 .870/.650, .933/.676,
.923/.707
Positive emotions 10 .932 .943 .623 .784/.624, .764/.577,
.811/.617, .690/.433,
.785/.540, .749/.553,
.787/.605, .836/.640,
.822/.598, .852/.646
Patronage intention (PI) 3 .896 .935 .828 .912/.619, .917/.625,
.901/.590
Internet usage (moderator) 1 n.a n.a n.a n.a
Control variables
Trust on retailer (Trust) 4 .829 .886 .661 .778/.536, .836/.509,
.838/.539, .797/.520
Variety Seeking (Seek) 7 .950 .958 .763 .836/.315, .908/.332,
.934/.323, .890/.302,
.881/.416, .821/.316,
.840/.367
Assessment Stage II
Hierarchical measurement model No of scale
dimensiona Alpha CRb
AVEc Dimension loading/
highest cross-loading
Studied constructs
Channel-service configuration
(CSC)
2 .751 .889 .800 .903/.473, .886/.481
Integrated interactions (InI) 2 .759 .892 .805 .879/.491, .915/.612
Customer experience (Cexp) 2 .852 .931 .871 .934/.489, .932/.667
Note: a
based on a 1-7 Likert scale; b
Composite Reliability; c
Average Variance Extracted; n.a.: not applicable

45
Table 4.2. Scale accuracy analysis: Discriminant validity assessment
Assessment Stage Stage I
Studied constructs
(dimensions)
CSC InI Cem
p
Cexp
PI IU
Tru
st
See
k
BCSC TCSC CC PC SE PE
Channel-
service
confgurati
on (CSC)
Transparency
of channel-
service
configuration
.820 .700 .468 .486 .534 .545 .471 .538 .070 .511 .115
Transparency
of channel-
service
configuration
.601 .824 .550 .503 .486 .471 .459 .457 .064 .443 .280
Integrated
interaction
s (InI)
Content
consistency
.388 .454 .809 .752 .567 .561 .502 .408 .087 .561 .276
Process
consistency
.396 .414 .611 .805 .626 .685 .622 .516 .038 .744 .334
(Cemp)
.448 .408 .468 .514 .765 .721 .694 .497 .043 .632 .443
Customer
experience
(Cexp)
Satisfaction
with
experience
.467 .406 .480 .588 .620 .909 .810 .730 .090 .735 .308
Positive
emotions
.415 .409 .437 .542 .612 .742 .789 .728 .037 .707 .316
Patronage intention (PI) .465 .403 .351 .442 .434 .651 .667 .910 .080 .681 .143
Internet usage (IU)
(moderator)
-.065 -.058
-
.080
-
.033
-.031
-
.086
-
.014
-
.075 n.a .030 .055
Trust on retailer (Trust) .431 .378 .461 .611 .528 .632 .625 .592
-
.005 .813 .228
Variety Seeking (Seek) .112 .243 .238 .292 .381 .289 .299 .148
-
.042
.203 .874
Stage II
Studied constructs
CSC InI Cem
p
Cexp PI IU Tru
st
See
k
Channel-service confguration
(CSC) .895 .681 .541 .635 .560 .079 .516 .220
Integrated interactions (InI) .512 .897 .625 .760 .506 .072 .687 .349
(Cemp)
.469 .547 n.a .710 .421 .034 .520 .400
Customer experience (Cexp) .509 .616 .656 .933 .765 .058 .728 .341
Patronage intention (PI) .486 .445 .421 .707 n.a .076 .588 .130
Internet usage (IU)
(moderator)
-.069 -.060 -.034 -.054
-
.076 n.a .006 .057
Trust on retailer (Trust) .448 .605 .520 .672 .588
-
.006 n.a .202
Variety Seeking (Seek) .187 .307 .400 .315 .130
-
.057
.202 n.a
Note: The lower and upper of the diagonal are bivariate correlations and HTMT ratios, respectively; diagonal
bold values are the square root of AVE (average variance extracted); n.a.: not applicable

46
4.2. Test for common method bias
Since the data was 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. Second, 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 (see Table 4.3). 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.
Table 4.3. Test for common method bias (CMB)
Construct Indicator
Substantive
Factor
Loadings (R1)
R1
2
Method
Factor
Loading (R2)
R2
2
BCSC
BCSC_1 .079c
.624 -.076 .006
BCSC_2 .839 c
.703 -.032 .001
BCSC_3 .845 c
.715 -.006 .000
BCSC_4 .806 c
.649 .115a
.013
TCSC
TCSC_1 .799 c
.638 -.071 .013
TCSC_2 .889 c
.790 .014 .006
TCSC_3 .878 c
.770 .061 .011
TCSC_4 .722 c
.521 -.013 .008
CC
CC_1 .811 c
.657 .114a
.005
CC_2 .863 c
.744 -.080 .000

47
CC_3 .853 c
.728 -.103a
.004
CC_4 .700 c
.490 .089 .000
PC
PC_1 .658 c
.432 .041 .002
PC_2 .853 c
.728 .087 .008
PC_3 .859 c
.739 -.081 .006
PC_4 .834 c
.695 -.040 .002
Cemp
Cemp_1 .690 c
.476 .126 .016
Cemp_2 .687 c
.471 .125 .016
Cemp_3 .788 c
.621 .182c
.033
Cemp_4 .851 c
.725 -.003 .000
Cemp_5 .802 c
.644 -.046 .002
SE
SE_1 .975 c
.951 -.008 .000
SE_2 .937 c
.877 -.056 .003
SE_3 .924 c
.854 .019 .000
PE
PE_1 .782 c
.612 .157a
.025
PE_2 .763 c
.582 .074 .005
PE_3 .810 c
.657 -.001 .000
PE_4 .693 c
.480 -.078 .006
PE_5 .787 c
.619 -.001 .000
PE_6 .750 c
.562 .075 .006
PE_7 .787 c
.620 -.112 .012
PE_8 .835 c
.698 -.032 .001
PE_9 .822 c
.675 -.048 .002
PE_10 .852 c
.725 -.030 .001
PI
PI_1 .910 c
.828 .031 .001
PI_2 .916 c
.839 .006 .000
PI_3 .903 c
.816 -.038 .001
Trust
TRUST_1 .743 c
.552 .131 .017
TRUST_2 .831 c
.690 -.018 .000
TRUST_3 .858 c
.737 -.068 .005
TRUST_4 .819 c
.671 -.033 .001
Seek
Seek_1 .810 c
.656 -.009 .000
Seek_2 .886 c
.784 .024 .001
Seek_3 .931 c
.867 -.033 .001
Seek_4 .873 c
.762 -.002 .000
Seek_5 .895 c
.802 .063 .004
Seek_6 .863 c
.745 -.030 .001
Seek_7 .883 c
.780 -.014 .000
Internet Usage
(moderator)
IU .006 .000 -.064 .004
Average .809 .673 -.001 .005
Note: p: a
< .05, b
≤ .01, c
≤ .001; see Table 3.1 for all abbreviations of the respective constructs.

48
4.3. Assessment of structural model
Following the procedure to evaluate the structural model as proposed by Hair et
al. (2017), the collinearity issues among each set of predictor variables were firstly
checked; all VIF values (see Table 4.4) of higher than 0.2 and less than 5.0
demonstrated that collinearity was unlikely to be a concern.
Table 4.4. Inner VIF value
Antecedents
Customer
empowerment
Customer
experience
Patronage
intention
Channel-service configuration 1.355 1.459
Integrated interactions 1.355 1.621
Customer empowerment 1.532
Customer experience 1.944
Trust in retailer 1.826
Variety-seeking 1.110
To assess the 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 (see
Figure 4.3). 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 substantial, moderate and
weak levels of predictive accuracy, respectively (Cohen, 1988), while the predictive
relevance of Q² values should be higher than zero. As can be seen from Figure 4.3,
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 indicate 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 hypotheses 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

49
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).
Table 4.5 and Figure 4.3 illustrates how all of the three direct hypotheses (H1a, H1b
and H4) were supported at at least 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).
Table 4.5. Significance testing results of the structural model path coefficients
Path
coefficients
p Value
95% Confidence
intervals
Significancea
(p < 0.05)?
CSC  Cexp (H1a) 0.156 0.003 [0.051, 0.264] Yes
Cexp  PI (H4) 0.600 0.000 [0.491, 0.721] Yes
InI  Cexp (H1b) 0.310 0.000 [0.215, 0.409] Yes
Seek  PI -0.100 0.015 [-0.191, -0.029] Yes
Trust  PI 0.205 0.001 [0.082, 0.321] Yes
Moderating effect
1 (H3a)
0.130 0.001 [0.053, 0.205] Yes
Moderating effect
2 (H3b) 0.083 0.029 [0.012, 0.159] Yes
Moderating effect
3 (H3c) 0.110 0.008 [0.026, 0.188] Yes
Note: a
The 95% confidience intervals bias corrected not included zero is also considered as a
criterion for significance testing
Mediating effects: Following Zhao et al., (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 (see Table 4.6 and Figure 4.3); thus, H2a and H2b were supported. Further
identifying the typology of mediations was conducted. In addition to the above

50
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.
Table 4.6. Significance testing results of the total indirect effects
Total
indirect
effects
p Value
95% Confidence
intervals
Significancea
(p < 0.05)?
CSC ---> Cexp 0.106 0.000 [0.054, 0.162] Yes
InI ---> Cexp 0.172 0.000 [0.120, 0.239] Yes
Note: a
The 95% confidience intervals bias corrected not included zero is also considered as a
criterion for significance testing
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 Table 4.5 and Figure 4.3 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 (H3a), plus 0.030 (H3c) and 0.016 (H3b), 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 (Table 4.5 and Figure 4.3).

 p: a
< .05, b
≤ .01, c
≤ .001
 H3a, H3b: moderating role
 SRMR: .079
Channel Integration Quality
Internet
usage
H3a: .130c
f2
: .040
Channel-service
configuration H3c: .110b
f2
: .030
H3b: .083a
f2
: .016
.205c
f2
: .049
Customer
empowerment
(R2
: .347, Q2
: .330)
Customer
experience
(R2
: .541, Q2
: .443)
Patronage
intention
(R2
: .532, Q2
: .512)
H4: .600c
f2
: .395
Integrated
interactions
-.100a
f2
: .019
First-order constructs
Second-order constructs
Variety
seeking
Trust in
retailer
51
Control variables
Indirect effects (H2a, H2b)
Figure 4.3. Analysis results

52
4.4. FIMIX analysis for data heterogeneity
Unobserved heterogeneity in the selected sample can prevent the derivation of
accurate findings (Becker et al., 2013); thus, the current study applied the FIMIX-
PLS approach to evaluate whether unobserved heterogeneity is explanatory (see
Table 4.7). The FIMIX-PLS algorithm was calculated for different numbers of
segments (K). The process started with K=1 and stopped at K = 4, as the fourth
segment of K = 4 occupied only 3% (9 observations) of the sample, which was lacking
in producing reliable statistics. Then, as the value of EN was not available in the K=1
segment solution, only the three solutions (K = 2 to K = 4) were compared to find the
best segmentation solution via two criteria: (1) lowest values of the CAIC, AIC3 and
BIC, and (2) EN of at least 0.50 were applied (Hair et al., 2017). The results indicated
none of the segmentation solutions were better than the others; consequently,
unobservable variables remained unidentified and the research findings were unlikely
be distorted by the unobserved heterogeneity.
Table 4.7. FIMIX-PLS results for the relative segment sizes and retention
criteria
K (Number of
pre-specified segments)
Relative segment sizes (%)
Segment I Segment II Segment III Segment IV Sample Sum
K=1 312 (100%) 312 (100%)
K=2 184 (59%) 128 (41%) 312 (100%)
K=3 184 (59%) 97 (31%) 31 (10%) 312 (100%)
K=4 187 (60%) 97 (31%) 19 (6%) 9 (3%) 312 (100%)
Segment retention criteria (fit indices) K=1 K=2 K=3 K=4
CAIC (Consistent Akaike’s Information Criterion) 2,124.28 2,142.86 2,190.28 2,231.46
AIC3 (Modified AIC with Factor 3) 2,079.36 2,049.28 2,048.05 2,040.56
BIC (Bayesian Information Criteria) 2,112.28 2,117.86 2,152.28 2,180.46
EN (Entropy Statistic) # .43 .57 .71
4.5. Summary
This chapter analyzed the dataset of the research, including: assessment of
measurement model, test for common method bias, assessment of structural model,
and FIMIX analysis for data heterogeneity. In summary, the results showed that CIQ
components (i.e., channel-service configuration and integrated interactions)

53
significantly affect customer experience, which in turn leads to patronage intention.
Moreover, customer empowerment complementarily mediates the impacts of CIQ
components on the customer experience, while internet usage strengthens the positive
relationships between the customer experience and its precursors. The next chapter,
therefore, moves on to discuss the research findings and managerial implications.

Luận Văn How Does Channel Integration Quality Enrich Customer Experiences With Omnichannel Retailers

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