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 MECHANISM
MASTER BY RESEARCH THESIS
Ho Chi Minh City – 2019
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 MECHANISM
Specialization: Commercial Business
Specialization code: 8340121
MASTER BY RESEARCH THESIS
SUPERVISOR: Dr. LE NHAT HANH
Ho Chi Minh City - 2019
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:
Acknowledgements
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
Declaration
Acknowledgements
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
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 scale ......................................................................................32
3.3. Questionnaire design ...................................................................................36
3.4. Sample and data collection ..........................................................................36
3.5. Sample characteristics .................................................................................38
3.6. Summary ......................................................................................................39
CHAPTER 04: DATA ANALYSIS AND RESULTS ..........................................40
4.1. Assessment of measurement scales .............................................................40
4.2. Test for common method bias .....................................................................44
4.3. Assessment of structural model ...................................................................46
4.4. FIMIX analysis for data heterogeneity ........................................................50
4.5. Summary ......................................................................................................50
CHAPTER 05: DISCUSSION AND CONCLUSION..........................................52
5.1. Discussion of results ....................................................................................52
5.2. Research implications ..................................................................................53
5.3. Limitations and futher research ...................................................................56
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
BOPS: Buy online, pick up in-store
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
SMEs: Small and medium-sized enterprises
RFID: Radio frequency identification
SOP: Standard operating procedure
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…………………………………….38
Table 4.1. Scale accuracy analysis………………………………………………….42
Table 4.2. Scale accuracy analysis: Discriminant validity assessment……………..43
Table 4.3. Test for common method bias (CMB)…………………………………..44
Table 4.4. Inner VIF value………………………………………………………….46
Table 4.5. Significance testing results of the structural model path coefficients…...47
Table 4.6. Significance testing results of the total indirect effects…………………48
Table 4.7. FIMIX-PLS results for the relative segment sizes and retention criteria..50
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………………………………………………40
Figure 4.2. Research model in Stage II……………………………………………..41
Figure 4.3. Analysis results…………………………………………………………49
How does channel integration quality enrich customer experiences with
omnichannel retailers? An examination of mediating and moderating
mechanisms
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-organismresponse (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. Structural equation model with two-stage approach is
employed to 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. The
current thesis was concluded with the meaningful practical implications for omnichannel
retailers.
Keywords: Omnichannel retailers; Channel integration quality; Customer experience;
Customer empowerment.
1
1. 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 is recently emerging as a new trend in Vietnam (Anh Hoa,
2017). According to a recent study conducted by Sapo – an omnichannel retailing
platform operating in Vietnam, 97% retail store owners applied omnichannel strategy
to their business in 2018 (Tuyet An, 2019). Omnichannel approach is also considered
2
as a new weapon to help 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
3
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” (BOPS) 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.
4
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, we expect 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 this study. In addition, according to Chang and Chen
(2008), customers who spend more time online tend to accumulate more internetrelated knowledge and skills, and consequently they should be more familiar with
omnichannel retailers’ available offerings. Thus, we 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.
5
(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 the customer experiences with omnichannel
retailers. Data were collected from HCM’s customers who used to have experiences
of shopping with the omnichannel retailers and aged 25 - 34; with a purposive
sampling based on gender (i.e., 60% women and 40% men). Participants either chose
one of the listed well-known omnichannel retailers (i.e., Nguyen Kim, FPT Shop, The
Gioi Di Dong, Concung) - or self-declared the omnichannel retailer they were most
familiar with. 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). 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.
1.4. Research method
A combination of qualitative and quantitative approaches was used in this
thesis. In the first stage, the measurement scales for all studied constructs were
adopted from prior studies and were translated into Vietnamese. Next, the
questionnaire was designed, pre-tested with 20 customers and then modified to ensure
its clarity before distribution.
6
The second stage was quantitative study which including the main survey and
data analysis. The survey was conducted at the five busiest shopping mall and office
buildings in the metropolitan area of HCM City over a five-week period. After
collection, the dataset was analyzed using SmartPLS 3.2.8 and consisted of the
following steps: assessment of measurement scales, 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 provided 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
dimensions/ sub-dimensions; (2) reviews the studies relevant to this thesis; (3)
introduces stimulus - organism – response (SOR) framework; and finally (4) presents
the research model and the associated hypotheses.
7
Chapter 03 – Research method: This chapter is concerned with the method used
for the current thesis, including the research
process, measurement scale,
questionnaire design, sample and data collection, as well as the sample
characteristics.
Chapter 04 – Data analysis and results: This section analyzes the dataset of the
research. It consists of the following steps: assessment of measurement scales, 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.
8
2. 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 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. Organizationallevel 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). Individuallevel 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, regarding 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: channelservice configuration and integrated interactions. Each of them has two subdimensions as shown particularly in Table 2.1.
Table 2.1. Definitions and examples of the sub-dimensions of CIQ
Dimension
Sub-
Definition
dimension
Example
The degree to which
Channelservice
configuration
Breadth of
channelservice
choice
customers
can
choose
alternative channels for a
given service
accomplish
tasks
or can
preferred
through
individual channel.
an
It’s
very
easy
for
customers to know the
product details through
both
the
online
channels.
and
retailer’s
offline
10
The degree to which
Transparency
of channelservice
confguration
customers are aware of Customers can come to
the existence channels physical stores to find
and services as well as the and evaluate products
differences between such but finish the purchase
service attributes across online.
channels.
The
consistency
of
offered
by
content
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
Integrated
different
channels.
interactions
The
degree
of
consistency of relevant
Process
consistency
and comparable process
attributes across channels
(e.g., the feel, image, and
delivery
services).
speed
of
When customers call
the
hotline
on
the
website or meeting the
staff in-store to ask for
product 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,
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
11
these previous studies, the current thesis defines channel-service configuration and
integrated interactions as 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 in a single 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 aftersales 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)
developed a “Mobile Application Customer Experience Model” which highlighted
the impact of utilitarian factors of technology on customer experience during use of
12
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) conceptualized
customer experience throughout the customer journey with a firm across multiple
touch points, 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 delight
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
(2018) focus on the customer experience management perspective, using latent class
cluster analysis to segment customers. Overall to date, what we know about the
13
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, in the current study, we define the omnichannel experience as a secondorder construct of two dimensions, satisfaction with experience and positive
emotions. This approach allows us to not only investigate customers’ cognitive
evaluation about the overall experience that omnichannel retailers offer to them
(referring to the “satisfaction with experience” dimension), but also examine
14
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
Enthused
participation
Channel integration quality
BCSC
Conscious
attention
Social
connection
CSC
Repurchase
intention
H1
TCSC
H3
Customer
engagement
CC
H2
InI
PC
H4
Positive
WOM
Control variables:
Physical store quality
Virtual store quality
Demographics
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) defines channel integration as a
second – order formative construct which promotes consumer empowerment; in turn
16
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.
Trust
H2
Consumer
perceptions
of channel
integration
H1
Consumer
empowerment
H5
Patronage
intention
H4
H3
H6
Satisfaction
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 mcommerce by developing a Mobile Applications Customer Experience Model. Based
17
on Technology Acceptance Model, Flow Theory and Expectancy Confirmation
Theory with Information Technology, McLean et al. (2018) posits 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.
Moderators: Gender, Screen size
Enjoyment
Utilitarian factors of
technology
+ Ease of use
+ Convenience
+ Customisation
Customer experience
+ Satisfaction with the
experience
+ Positive emotions
Timeliness
Figure 2.3. Research model of McLean et al. (2018)
Frequency
of use
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 provided 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 for the customers.
19
Channel
choice breadth
Internal usage
experience
H2a,b,c,d
Channel
service
transparency
H3a
Omnichannel
service usage
Perceived
fluency
Content
consistency
H1
H3b
Behavioral beliefs
External usage
experience
Process
consistency
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 Yang (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 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).
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. The empirical work
presented here provides the investigation into how customers react to CCI through
the dynamic mechanisms and provides insights for omnichannel retailers to
implement their CCI strategy.
Push-pull effects
Showrooming
Retailer
uncertainty
H4a,b,c
Crosschannel
integration
H3a,b,c
H1a,b,c
Customer
retention
Identity
attractiveness
Service investment
Interest in
alternatives
Switching
costs
H2a,b,c
Mooring effect
Figure 2.5. Research model of Li et al. (2018)
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 channelservice 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 hasslefree 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) channelservice 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
channelservice
choice
Transparency
of channelservice
configuration
Internet
usage
Channelservice
configuratio
n
H3c
Positive
emotion
Satisfaction
w. exp.
Customer
experience
Customer
empwerment
Patronage
intention
H4
Content
consistency
Integrated
interaction
Process
consistency
First-order constructs
Second-order constructs
Control var.:
Trust, Seek
Indirect effects
Stimulus (S)
Organism (O)
Figure 2.6. Research framework and hypotheses
Respone (R)
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 model, as well
as five studies 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) channelservice configuration and (b) integrated interactions) on the customer experience.
Third, customer internet usage strengthens the positive influence of (a) channelservice 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
3. CHAPTER 03: RESEARCH METHOD
3.1. Research process
In the current thesis, the mixed method was chosen, including both qualitative
(step 1 – 4 in Figure 3.1) and quantitative (step 5 – 9 in Figure 3.1) methods.
Qualitative methods offer an effective way of modifying and ensuring the plausibility
as well as the clarity of the questionnaire. The first step in this process was to review
the literature and prior relevant papers to adopt the measurement scale 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 scale, 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.
The second stage of the study comprised 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 reliability 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
31
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
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 FIMIXPLS approach was applied to evaluate whether the research findings were distorted
by the unobserved heterogeneity. Research process includes the following steps as
illustrated in Figure 3.1.
-
1. Literature
2. Measurement scale
3. Pre-test
review
& Draft questionnaire
(n = 20)
5. Main survey
4. Modified
(n = 312)
questionnaire
6. Assessment of the measurement
7. Test for common
model (two-stage approach)
method bias (CMB)
Reliability
(cronbach’s
alpha
and
8. Assessment of the structural
composite reliability)
model
- Convergent validity (indicator reliability
and average variance extracted - AVE)
- The collinearity issues (VIF value)
- Discriminant validity (cross loadings,
- The model fit (SRMR value)
Fornell-Larcker
- The predictive power (R2) and predictive
criterion,
and
Heterotrait-Monotrait Ratio - HTMT)
the
relevance (Q2)
- Hypotheses testing (bootstrapping 5,000):
9. FIMIX analysis
for data
heterogeneity
direct effects, mediating effects, and the
moderating effects.
Figure 3.1. Research process
32
3.2. Measurement scale
The current study consists of three multi-dimensional constructs, two singledimensional 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 multidimensional 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
1. I can purchase products via the online or physical
stores of X. (BCSC1)
Breadth of
channelservice
choice
(BCSC)
Channel-
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)
service
1. I am aware of available services of the online and
configuration
physical stores of X. (TCSC1)
(CSC)
Transparency 2. I am familiar with available services of both the
of channelservice
online and physical stores of X. (TCSC2)
3. I know how to utilize available services of the
configuration online and physical stores of X. (TCSC3)
(TCSC)
4. I know the differences of available services
between the online and physical stores of X.
(TCSC4)
1. X provides consistent product information across
the online and physical stores. (CC1)
Integrated
Content
interactions
consistency
(InI)
(CC)
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
1. The service images are consistent across the
online and physical stores of X. (PC1)
2. The levels of customer service are consistent
Process
consistency
(PC)
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)
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)
Customer empowerment
(Cemp)
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)
How many hours do you use the internet per day?
Internet usage (IU)
Less than 2h; 2h to < 5h; 5h to < 8h; 8h and
more
1. I am satisfied with the shopping experience at X.
Customer
experience
(Cexp)
Satisfaction
with
experience
(SE)
(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
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)
Positive
emotions
(PE)
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)
1. I am likely to continue to purchase products from
X. (PI1)
Patronage intention (PI)
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)
1. X is a trustworthy retailer. (Trust1)
Trust in retailer (Trust)
2. X cares about its customers. (Trust2)
3. X keeps its promises to its customers. (Trust3)
4. X is not opportunistic. (Trust4)
1. When shopping, I find myself spending a lot of
time checking out new websites/apps/physical
stores. (Seek1)
Variety-seeking (Seek)
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
websites/apps/physical stores. (Seek4)
5.
I
frequently
look
out
for
new
websites/apps/physical stores. (Seek5)
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
websites/apps/physical stores. (Seek7)
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
3.5. Sample characteristics
After close scrutiny, 312 valid responses were used for further analysis. 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. More details about respondents’ profiles and purchase
behaviors are presented in Table 3.2.
Table 3.2. Sample demographic characteristics
Gender
Realized Planed
Purchase frequency
quota % quota %
(webs/apps/stores)
126
40.4
40.0 Several times a week
186
59.6
60.0 A few times a month
A few times a year
312
100.0 100.0 Rarely (only once or twice)
Total
Average order value
Freq.
%
(webs/apps/stores) (*)
3
1.0
Less than VND 1,000,000
VND 1,000,000 to
<
257
82.4
3,000,000
VND 3,000,000 to
<
51
16.3
5,000,000
1
0.3
VND 5,000,000 and more
312
100.0
Total
Freq.
%
Daily internet usage
9
2.9
Less than 2h
77
24.7
2h to < 5h
Freq.
Freq.
%
29
148
100
35
312
Freq.
9.3
47.4
32.1
11.2
100.0
%
129
41.3
92
29.5
44
14.1
47
312
Total
Monthly income (*)
Freq.
Less than VND 5,000,000
51
VND 5,000,000 to
<
131
9,000,000
VND 9,000,000 to
<
116
37.2
5h to < 8h
68
15,000,000
VND 15,000,000 and more
110
35.3
8h and more
62
312
100.0
312
Total
Total
(*) US$ 1 = Vietnamese Dong (VND) 23,215 at the time of the survey
15.1
100.0
%
16.3
Male
Female
Total
Education
High school
College or university
Post graduate and above
Others
42.0
21.8
19.9
100.0
39
3.6. Summary
This chapter dealt with the method used for the current thesis, including the
research
process, measurement scale, questionnaire design, sample and data
collection, as well as the sample characteristics. 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 was collected from customers who used to have experience of
shopping with the 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 in the next chapter.
40
4. 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 scales and the structural model. The analysis results are shown below.
4.1. Assessment of measurement scales
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
41
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
(0.5) 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
42
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
Studied constructs (Dimensions)
Breadth of channelChannelservice choice
service
configuration Transparency
of
(CSC)
channel-service
configuration
Content consistency
Integrated
interactions
(InI)
Process consistency
No of scale
itemsa
4
Item loading/ highest
cross-loading
.800/.581,
.833/.440,
.843/.484, .804/.476
.806/.534,
.893/.578,
.879/.530, .705/.370
Alpha
CRb
AVEc
.837
.891
.672
4
.841
.894
.679
4
.822
.883
.655
4
.815
.880
.648
5
.822
.876
.586
3
.894
.934
.826
10
.932
.943
.623
Patronage intention (PI)
3
.896
.935
.828
Internet usage (moderator)
Control variables
Trust on retailer (Trust)
1
n.a
n.a
n.a
4
.829
.886
.661
Variety Seeking (Seek)
7
.950
.958
.763
Alpha
CRb
AVEc
.751
.889
.800
Dimension loading/
highest cross-loading
.903/.473, .886/.481
.759
.852
.892
.931
.805
.871
.879/.491, .915/.612
.934/.489, .932/.667
Customer empowerment (Cemp)
Customer
experience
(Cexp)
Satisfaction
with
experience
Positive emotions
Assessment Stage II
Hierarchical measurement model
Studied constructs
Channel-service configuration
(CSC)
Integrated interactions (InI)
Customer experience (Cexp)
No of scale
dimensiona
2
2
2
.810/.499,
.858/.497,
.849/.479, .711/.504
.673/.501,
.856/.576,
.851/.487, .826/.504
.715/.486,
.714/.494,
.764/.438,
.840/.527,
.785/.490
.870/.650,
.933/.676,
.923/.707
.784/.624,
.764/.577,
.811/.617,
.690/.433,
.785/.540,
.749/.553,
.787/.605,
.836/.640,
.822/.598, .852/.646
.912/.619,
.917/.625,
.901/.590
n.a
.778/.536,
.836/.509,
.838/.539, .797/.520
.836/.315,
.908/.332,
.934/.323,
.890/.302,
.881/.416,
.821/.316,
.840/.367
Note: a based on a 1-7 Likert scale; b Composite Reliability; c Average Variance Extracted; n.a.: not applicable
43
Table 4.2. Scale accuracy analysis: Discriminant validity assessment
Assessment Stage
Studied constructs
(dimensions)
Transparency
of channelservice
Channelconfiguration
service
confgurati Transparency
on (CSC)
of channelservice
configuration
Content
Integrated
consistency
interaction
Process
s (InI)
consistency
Customer empowerment
(Cemp)
Satisfaction
with
Customer
experience experience
(Cexp)
Positive
emotions
Patronage intention (PI)
Internet usage (IU)
(moderator)
CSC
BCSC TCSC
InI
CC
PC
Stage I
Cexp
Cem
p
SE
PE
PI
IU
Tru
st
See
k
.820
.700
.468
.486
.534
.545
.471
.538
.070
.511
.115
.601
.824
.550
.503
.486
.471
.459
.457
.064
.443
.280
.388
.454
.809
.752
.567
.561
.502
.408
.087
.561
.276
.396
.414
.611
.805
.626
.685
.622
.516
.038
.744
.334
.448
.408
.468
.514
.765
.721
.694
.497
.043
.632
.443
.467
.406
.480
.588
.620
.909
.810
.730
.090
.735
.308
.415
.409
.437
.542
.612
.742
.789
.728
.037
.707
.316
.465
.403
.651
.086
.667
.014
.910
.075
.080
.681
.143
-.058
.442
.033
.434
-.065
.351
.080
n.a
.030
.055
Trust on retailer (Trust)
.431
.378
.461
.611
.528
.632
.625
.592
.813
.228
Variety Seeking (Seek)
.112
.243
.238
.292
.381
.289
.299
.148
.203
.874
CSC
Stage II
InI
.895
-.031
.005
.042
Cem
p
Cexp
PI
IU
Tru
st
See
k
.681
.541
.635
.560
.079
.516
.220
.512
.897
.625
.760
.506
.072
.687
.349
.469
.547
n.a
.710
.421
.034
.520
.400
.509
.486
.616
.445
.656
.421
.933
.707
.058
.076
.728
.588
.341
.130
-.069
-.060
-.034
-.054
.765
n.a
.076
n.a
.006
.057
Trust on retailer (Trust)
.448
.605
.520
.672
.588
n.a
.202
Variety Seeking (Seek)
.187
.307
.400
.315
.130
.202
n.a
Studied constructs
Channel-service confguration
(CSC)
Integrated interactions (InI)
Customer
empowerment
(Cemp)
Customer experience (Cexp)
Patronage intention (PI)
Internet
usage
(IU)
(moderator)
.006
.057
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
44
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% (45/49) 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 substantivelyexplained 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
BCSC
TCSC
CC
Indicator
BCSC_1
BCSC_2
BCSC_3
BCSC_4
TCSC_1
TCSC_2
TCSC_3
TCSC_4
CC_1
CC_2
Substantive
Factor
Loadings (R1)
.079c
.839 c
.845 c
.806 c
.799 c
.889 c
.878 c
.722 c
.811 c
.863 c
R1 2
.624
.703
.715
.649
.638
.790
.770
.521
.657
.744
Method
Factor
Loading (R2)
-.076
-.032
-.006
.115a
-.071
.014
.061
-.013
.114a
-.080
R2 2
.006
.001
.000
.013
.013
.006
.011
.008
.005
.000
45
PC
Cemp
SE
PE
PI
Trust
Seek
Internet Usage
(moderator)
Average
CC_3
CC_4
PC_1
PC_2
PC_3
PC_4
Cemp_1
Cemp_2
Cemp_3
Cemp_4
Cemp_5
SE_1
SE_2
SE_3
PE_1
PE_2
PE_3
PE_4
PE_5
PE_6
PE_7
PE_8
PE_9
PE_10
PI_1
PI_2
PI_3
TRUST_1
TRUST_2
TRUST_3
TRUST_4
Seek_1
Seek_2
Seek_3
Seek_4
Seek_5
Seek_6
Seek_7
.853 c
.700 c
.658 c
.853 c
.859 c
.834 c
.690 c
.687 c
.788 c
.851 c
.802 c
.975 c
.937 c
.924 c
.782 c
.763 c
.810 c
.693 c
.787 c
.750 c
.787 c
.835 c
.822 c
.852 c
.910 c
.916 c
.903 c
.743 c
.831 c
.858 c
.819 c
.810 c
.886 c
.931 c
.873 c
.895 c
.863 c
.883 c
.728
.490
.432
.728
.739
.695
.476
.471
.621
.725
.644
.951
.877
.854
.612
.582
.657
.480
.619
.562
.620
.698
.675
.725
.828
.839
.816
.552
.690
.737
.671
.656
.784
.867
.762
.802
.745
.780
-.103a
.089
.041
.087
-.081
-.040
.126
.125
.182c
-.003
-.046
-.008
-.056
.019
.157a
.074
-.001
-.078
-.001
.075
-.112
-.032
-.048
-.030
.031
.006
-.038
.131
-.018
-.068
-.033
-.009
.024
-.033
-.002
.063
-.030
-.014
.004
.000
.002
.008
.006
.002
.016
.016
.033
.000
.002
.000
.003
.000
.025
.005
.000
.006
.000
.006
.012
.001
.002
.001
.001
.000
.001
.017
.000
.005
.001
.000
.001
.001
.000
.004
.001
.000
IU
.006
.000
-.064
.004
.809
.673
-.001
.005
Note: p: a < .05, b ≤ .01, c ≤ .001; see Table 3.1 for all abbreviations of the respective constructs.
46
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 less than 5.0 demonstrated that collinearity
was unlikely to be a concern.
Table 4.4. Inner VIF value
Antecedents
Channel-service configuration
Integrated interactions
Customer empowerment
Customer experience
Trust in retailer
Variety-seeking
Customer
Customer Patronage
empowerment experience intention
1.355
1.459
1.355
1.621
1.532
1.944
1.826
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
47
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 channelservice 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
48
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, plus 0.030 and 0.016, indicating large and medium effects.
Control variables: The analysis of control variables suggested that there were
significant positive and negative effects of trust in retailers and variety-seeking on
patronage intention, respectively (Table 4.5 and Figure 4.3).
49



Internet
usage
Channel Integration Quality
p: a < .05, b ≤ .01, c ≤ .001
H3a, H3b: moderating role
SRMR: .079
H3a: .130c
f2: .040
Channel-service
configuration
H3c: .110b
f2: .030
Customer
empowerment
(R2: .347, Q2: .330)
Trust
in
retailer
H3b: .083a
f2: .016
.205c
f2: .049
Customer
experience
(R2: .541, Q2: .443)
Patronage
intention
2
(R : .532, Q2: .512)
H4: .600c
f2: .395
-.100a
f2: .019
Integrated
interactions
First-order constructs
Second-order constructs
Control variables
Indirect effects (H2a, H2b)
Figure 4.3. Analysis results
Variety
seeking
50
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 FIMIXPLS 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. Three solutions (K = 1 to K = 3) were then 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)
K=1
K=2
K=3
K=4
Segment I
312 (100%)
184 (59%)
184 (59%)
187 (60%)
Segment retention criteria (fit indices)
CAIC (Consistent Akaike’s Information Criterion)
AIC3 (Modified AIC with Factor 3)
BIC (Bayesian Information Criteria)
EN (Entropy Statistic)
Relative segment sizes (%)
Segment II Segment III Segment IV
128 (41%)
97 (31%)
97 (31%)
31 (10%)
19 (6%)
9 (3%)
Sample Sum
312 (100%)
312 (100%)
312 (100%)
312 (100%)
K=1
2,124.28
2,079.36
2,112.28
#
K=2
2,142.86
2,049.28
2,117.86
.43
K=3
2,190.28
2,048.05
2,152.28
.57
K=4
2,231.46
2,040.56
2,180.46
.71
4.5. Summary
This chapter analyzed the dataset of the research, including: assessment of
measurement scales, 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)
significantly affect customer experience, which in turn leads to patronage intention.
51
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.
52
5. CHAPTER 05: DISCUSSION AND CONCLUSION
5.1. Discussion of results
The current thesis examines customer experience with omnichannel retailers.
Based on the SOR framework, we propose that CIQ with two components, channelservice configuration and integrated interactions, could enrich customer experience
through the mediating role of customer empowerment as well as the moderating role
of internet usage, which in turn determine customer patronage behavior. The results
show that there is a positive relationship between two CIQ components and customer
experience. This finding confirms the view of Lemon and Verhoef (2016) who
consider service quality (and its constituent elements) as an antecedent of customer
experience. In particular, integrated interactions, in contrast to the channel-service
configuration, have a stronger impact on customer experience. This finding implies
that customers strongly demand consistent messaging across channels during their
purchase journey. If omnichannel retailers try to expand to more channel choices
without a high level of uniformity among these channels, they will fail to bring about
positive emotions as well as satisfaction for their customers. Another important
finding is that customer experience greatly affects patronage intention. This result is
consistent with some other research which found the positive influences of
satisfaction and emotions (two dimensions of customer experience) on patronage
intention (Chang et al., 2015; Grace and O’Cass, 2005; Kim et al., 2007; Wang,
2009).
In addition, in this thesis, customer empowerment is found to mediate the
impact of CIQ on customer experience. By offering higher levels of CIQ,
omnichannel retailers better empower customers, leading to a higher level of
satisfaction during the shopping experience (also see Zhang et al., 2018). As
mentioned in the literature review and according to the SOR framework, customer
empowerment and customer experience are defined as customers’ internal states. This
finding broadly supports the work of other studies that link CIQ with customers’
internal states (Lemon and Verhoef, 2016; Li et al., 2018; Shen et al., 2018).
53
Furthermore, the current thesis confirms the moderating effect of internet usage on
the positive relationships between customer experience and its precursors (i.e.,
channel-service configuration, integrated interactions, and customer empowerment).
It may be explained by the fact that the more customers use the internet, the greater
their familiarity with omnichannel retailers’ online channels (i.e, websites, mobile
apps) and/or in-store technologies. Consequently, customers could complete any
tasks more easily during the purchase journey, which leads them to experience greater
enjoyment in their shopping as well as feeling a deeper sense of satisfaction. This
result is in line with those of previous studies regarding the contingency role of
internet experience in the multichannel retailing context (Frambach et al., 2007;
Verhoef et al., 2007).
Control variables, including trust in a particular retailer and variety-seeking, are
also found to have significant impacts on patronage behavior. In particular, trust in
retailers positively affects patronage intention, while variety-seeking has a negative
influence. These results corroborate the ideas of Chiu et al. (2012) and Zhang et al.
(2018), who suggested that trust might influence customer patronage intention
towards retailers. Moreover, it is encouraging to compare our results with that found
by Gountas and Gountas (2007) who found that customer’s personality traits (i.e.,
variety seeking in our research) might be related to their repurchase intentions.
In summary, the combination of findings provides some support for
understanding the dynamic mechanisms of forming customer experience and the
subsequent patronage behavior in omnichannel retailers context. Consistent with the
literature, this research indicates that creating a seamless experience is the aim of
omnichannel strategy (Frazer and Stiehler, 2014; Peltola et al., 2015; Ieva and Ziliani,
2018; Jocevski et al., 2019).
5.2. Research implications
The current research contributes to the literature on omnichannel retailers and
customer experience in two aspects. First, although a number of early research papers
54
on omnichannel retailing have focused on channel integration (Lee et al., 2019; Li et
al., 2018; Shen et al., 2018; Zhang et al., 2018), it should be recognized that creating
a seamless, consistent and unified experience is the ultimate aim of omnichannel
retailers (Frazer and Stiehler, 2014; Jocevski et al., 2019). This 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 thesis makes
a major contribution to the existing literature by quantitatively examining the
dynamic mechanisms of forming customer experience and its subsequent patronage
behavior.
Based on the empirical findings, meaningful practical implications could be
drawn from a managerial standpoint. First, the current thesis points out that
omnichannel experience acts as the key determinant of patronage intention. Thus,
omnichannel retailers are advised to optimize their channel management to deliver a
seamless, consistent and unified shopping experience to their customers. By
integrating all available channels, omnichannel retailers make customers feel more
empowered, which could enrich their shopping experience and lead to patronage
behavior.
Second, integrated interactions, compared with channel-service configuration,
are found to have a stronger impact on customer experience. Thus, in order to
optimize channel management to achieve a great customer experience, this study
suggests that omnichannel retailers should focus on the consistency of their content
and process. In particular, all retailers’ messaging (e.g., product information, prices,
promotion, and stock availability) should be uniform across channels such as physical
stores, websites and mobile apps. In the context of omnichannel retailers, customers
are served with a wide range of products, thus highly requiring omnichannel
assortment and pricing (Verhoef et al., 2015; Li et al., 2018). These two aspects (i.e.,
assortment and pricing) are related to the retailer’s category management process
(Basuroy et al., 2001; Cachon and Kök, 2007). Therefore, omnichannel retailers are
55
advised to improve category management activity to well integrate their assortment
and prices across channels. Whereas, for the purpose of serving customers with a
synchronized inventory information across channels, it’s essential that omnichannel
retailers build a strong and robust order management system (Konczal, 2019; Dang,
2018), as well as apply new technologies for supply chain operations. For instance,
with the radio frequency identification (RFID) tags, omnichannel retailers can track
and record the movement of inventory among all available channels (Donaldson,
2019). This technology allows omnichannel retailers to ensure the consistency of
inventory data across channels and respone to their customers accurately. Moreover,
retailers’ processes are also advised to be consistent, either online or in-store, so that
customers could perceive the consistency of services across channels during their
purchasing journey. Staff should be trained to follow standard operating procedure
(SOP) in their daily activities. Besides, the new technologies should also be invested
to exclude the natural boundaries with regard to business steps between channels. For
instance, live online chat, an emergent form of customer service, allows omnichannel
retailers to serve their customers in real-time, much like the way a store’s staff
communicate in brick-and-mortar locations. It can help shoppers feel that they are
served by the same way, through the same steps at anytime, anywhere, through any
channels during their shopping journey.
Third, omnichannel retailers should also pay attention to the moderating role of
internet usage. As can be seen from the research findings, internet usage strengthens
the impacts of channel-service configuration, integrated interactions and customer
empowerment regarding enhancing the customer experience. In this regard, for
omnichannel retailers, especially those with limits in term of resources (e.g., small
and medium-sized enterprises - SMEs), the customer segment who represents a high
level of internet usage should be selected as their target market.
56
5.3. Limitations and futher research
The current study inherits a few limitations which may provide avenues for
future research. First of all, this thesis focus on the main channels, including physical
stores, websites and mobile apps. However, the channel scope of omnichannel
retailers is growing quickly with the development of social media and modern
technologies (Shen et al., 2018). Future research should continue to update new
creative channels and provide a more complete picture of CIQ as well as its impact
on customer experience. In addition, as the current research framework was
empirically investigated in the context of omnichannel retailers, there exists the
potential to test the framework in some other contexts such as banking, food and
beverage services (F&B), and tourism. Finally, we cannot deny the existence of other
factors that may be related to the mechanisms of forming the customer experience in
omnichannel models. For instance, customers’ personality traits (e.g., customer
innovativeness) can be considered as moderators on the relationships between
customer experience and its precursors. Furthermore, it can be argued that other
constructs such as commitment (Lemon and Verhoef, 2016) and value co-creation are
also related to customer experience in the omnichannel context. Thus, further studies
which take these constructs into account should be undertaken.
LIST OF PUBLICATIONS
Angelina Nhat Hanh Le and Xuan-Doanh Nguyen-Le, 2019. How does channel
integration quality enrich customer experiences with omnichannel retailers? An
examination of mediating and moderating mechanisms. Journal of Retailing and
Consumer Services, <Under Review>.
[Accessed 18 November 2019]
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APPENDIX A. QUESTIONNAIRE – VIETNAMESE VERSION
Số phiếu: …….
BẢNG CÂU HỎI KHẢO SÁT
Kính chào Anh/Chị,
Tôi tên là Nguyễn Lê Xuân Doanh – Học viên Cao học Trường Đại học Kinh tế TP.HCM. Tôi đang thực hiện nghiên cứu về trải nghiệm mua sắm
đa kênh (omnichannel) của người tiêu dùng. Rất mong Anh/Chị dành khoảng 10 phút giúp tôi trả lời phiếu khảo sát này. Sự hỗ trợ của Anh/ Chị có
ý nghĩa rất lớn để tôi có thể hoàn thành nghiên cứu. Tôi xin cam kết tất cả thông tin thu thập chỉ được sử dụng cho mục đích học thuật và dữ liệu được
xử lý ẩn danh, bảo mật nghiêm ngặt.
PHẦN 1: THÔNG TIN CHUNG
*Ghi chú: Mua sắm đa kênh (omnichannel) là mua hàng trên nhiều kênh khác nhau (ví dụ: tại cửa hàng, trên website, trên ứng dụng di động
– mobile apps…) mà những kênh này được tích hợp và kết nối đồng bộ với nhau; cho Anh/ Chị một trải nghiệm mua sắm liền mạch, nhất
quán; KHÔNG có sự khác biệt về giá cả, chất lượng, dịch vụ hậu mãi…giữa các kênh.
Anh/ Chị vui lòng chọn MỘT Nhà bán lẻ bên dưới mà Anh/ Chị quen thuộc nhất và thỏa mãn đồng thời cả 3 điều kiện sau:
- Anh/ Chị đã từng đến cửa hàng của nhà bán lẻ này.
- Anh/ Chị đã từng truy cập vào website/ ứng dụng di động của nhà bán lẻ này.
- Anh/ Chị đã từng mua hàng của nhà bán lẻ này ít nhất một lần tại cửa hàng (hoặc trên website/ ứng dụng di động).
Nguyễn Kim
FPT Shop
Thế Giới Di Động
Con Cưng (concung.com)
Khác: …………………
PHẦN 2: KHẢO SÁT CHÍNH
1. Phần này khám phá trải nghiệm mua sắm của Anh/ Chị đối với Nhà bán lẻ mà Anh/ Chị đã chọn ở trên (sau đây được gọi chung là X). Xin
vui lòng cho biết mức độ đồng ý của Anh/ Chị đối với các phát biểu bên dưới bằng cách khoanh tròn vào con số thích hợp theo qui ước sau:
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Hoàn toàn không
Rất không đồng Không đồng ý
Trung lập
Đồng ý
Rất đồng ý
Hoàn toàn đồng ý
đồng ý
ý
Hoàn toàn không đồng ý  Hoàn toàn đồng ý
Khi mua sắm tại X, tôi có thể
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Nhận được sự tư vấn, hỗ trợ qua kênh trực tuyến hoặc tại các cửa hàng của X.
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Phản hồi về sản phẩm qua kênh trực tuyến hoặc tại các cửa hàng của X.
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Có được thông tin chi tiết về sản phẩm qua kênh trực tuyến hoặc tại các cửa hàng của X.
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Đối với các dịch vụ (vd: đặt hàng, giao hàng, hậu mãi...) mà X cung cấp
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Tôi biết về sự khác nhau của các dịch vụ giữa kênh trực tuyến và tại các cửa hàng của X.
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X cung cấp cho khách hàng
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10 Giá cả sản phẩm nhất quán cả trên kênh trực tuyến lẫn tại các cửa hàng của X.
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11 Thông tin khuyến mãi nhất quán cả trên kênh trực tuyến lẫn tại các cửa hàng của X.
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12 Thông tin lượng tồn sản phẩm nhất quán cả trên kênh trực tuyến lẫn tại các cửa hàng của X.
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
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đồng
ý
Cảm nhận của tôi về X
Hình ảnh của X (thiết kế cửa hàng, màu sắc, logo…) nhất quán cả trên kênh trực tuyến lẫn tại các cửa
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hàng.
14 Dịch vụ chăm sóc khách hàng của X nhất quán cả trên kênh trực tuyến lẫn tại các cửa hàng.
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15 Tôi có cảm nhận như nhau khi mua sắm trên kênh trực tuyến và tại các cửa hàng của X.
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16 Tốc độ phục vụ khách hàng của X như nhau cả trên kênh trực tuyến lẫn tại các cửa hàng.
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17 X là một nhà bán lẻ lớn mà mọi người đều nhận ra.
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18 X là một nhà bán lẻ nổi tiếng.
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X có danh tiếng tốt.
X là một nhà bán lẻ đáng tin cậy.
21 X quan tâm đến khách hàng của mình.
22 X giữ lời hứa với khách hàng của mình.
23 X không lợi dụng khách hàng của mình.
Trong khi mua sắm tại X
24 Tôi cảm thấy mình kiểm soát được tình hình mua sắm.
25 Tôi có thể tự do lựa chọn sản phẩm và dịch vụ như mong muốn.
26 Tôi có thể ảnh hưởng đến các nhóm hàng hóa và dịch vụ mà X cung cấp cho tôi.
27 Khả năng ảnh hưởng đến các hàng hóa và dịch vụ mà X cung cấp mang lại lợi ích cho tôi khi mua sắm.
28 Ảnh hưởng của tôi đến nhà bán lẻ X tăng lên so với trước đây.
Cảm nhận của tôi về trải nghiệm mua sắm tại X
29 Tôi hài lòng với trải nghiệm mua sắm tại X.
30 Trải nghiệm mua sắm tại X chính là những gì tôi cần.
31 Trải nghiệm mua sắm tại X tuyệt vời đúng như tôi nghĩ.
Khi mua sắm tại X
32 Tôi cảm thấy phấn khởi.
33 Tôi cảm thấy tự tin.
34 Tôi có cảm giác chắc chắn.
35 Tôi KHÔNG cảm thấy phân vân.
36 Tôi cảm thấy lạc quan.
37 Tôi có cảm nhận rõ ràng.
38 Tôi KHÔNG cảm thấy thất vọng.
39 Tôi cảm thấy thoải mái.
40 Tôi KHÔNG cảm thấy nghi ngờ.
41 Tôi cảm thấy thỏa mãn.
Dự định của tôi đối với X
42 Rất có thể tôi sẽ tiếp tục mua sản phẩm tại X.
43 Rất có thể tôi sẽ giới thiệu X cho bạn bè của mình.
44 Rất có thể tôi sẽ ưu tiên chọn X khi cần mua sản phẩm trong tương lai.
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2. Xin vui lòng cho biết mức độ đồng ý với những phát biểu sau về chính bản thân Anh/Chị:
Khi đang ở tại một cửa hàng mua sắm bất kỳ
45 Tôi vừa ngắm sản phẩm được bày bán vừa sử dụng các thiết bị di động (như điện thoại, máy tính bảng…).
46 Tôi hay dùng thiết bị di động để tìm hiểu các sản phẩm được bày bán tại cửa hàng.
47 Tôi dùng các thiết bị công nghệ để tìm kiếm mức giá tốt hơn trên kênh trực tuyến.
48 Tôi dùng các thiết bị công nghệ trong khi vẫn đang ở tại cửa hàng.
Đối với các “website mua sắm-ứng dụng mua sắm trên điện thoại di động-cửa hàng mua sắm mới”
(được ký hiệu là “webs-apps-CH mới”)
49 Nói chung, tôi thường là người đầu tiên trong nhóm bạn của mình thử mua sắm tại đó.
50 Khi tôi nghe về các “webs-apps-CH mới”, tôi sẽ rất thích thú được thử mua sắm tại đó.
Nói chung, tôi thường là người đầu tiên trong nhóm bạn của mình biết đến tên của các “webs-apps-CH
51
mới”.
Tôi sẽ thử mua sắm tại đó ngay cả khi tôi không nghe thấy bất cứ thông tin gì về các “webs-apps-CH
52
mới” từ bạn bè của mình.
53 Thông thường, tôi biết đến tên của các “webs-apps-CH mới” trước mọi người.
54 Khi mua sắm, tôi thấy mình thường dành nhiều thời gian tìm hiểu các “webs-apps-CH mới”.
55 Tôi thường tận dụng cơ hội để tìm hiểu về các “webs-apps-CH mới”.
56 Tôi thích khám phá thông tin về các "webs-apps-CH mới”.
57 Tôi thích những nguồn thông tin giới thiệu về các “webs-apps-CH mới”.
58 Tôi thường xuyên tìm kiếm các “webs-apps-CH mới”.
Tôi tìm kiếm các cơ hội/ tình huống để có thể có được các nguồn thông tin mới và đa dạng về các “webs59
apps-CH mới”.
60 Tôi liên tục tìm kiếm các “webs-apps-CH mới”.
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2
2
3
3
3
3
3
3
4
4
4
4
4
4
5
5
5
5
5
5
6
6
6
6
6
6
7
7
7
7
7
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
PHẦN 3: THÔNG TIN CÁ NHÂN
Xin vui lòng cho biết một số thông tin cá nhân chung của Anh/ Chị bằng cách đánh dấu vào ô trả lời thích hợp.
Giới tính:
Nam
Độ tuổi:
18 – 24
Trình độ học vấn:
Nghề nghiệp:
Nữ
25 – 34
35 – 44
Cao đẳng, đại học
Trung học phổ thông
Học sinh, sinh viên
Thu nhập trung bình hàng tháng:
45 – 54
55 – 64
Thạc sĩ, Tiến sĩ
Viên chức, nhân viên văn phòng
≥ 65
Khác:…………
Khác:…………
Nội trợ
< 5 triệu đồng
Từ 5 – dưới 9 triệu đồng
Từ 9 – dưới 15 triệu đồng
≥ 15 triệu đồng
Tần suất Anh/Chị mua hàng qua website mua sắm/ứng dụng mua sắm trên điện thoại di động/hay tại cửa hàng mua sắm:
Khoảng vài lần 1 tuần
Khoảng vài lần 1 tháng
Khoảng vài lần 1 năm
Rất ít (mới chỉ 1 hoặc 2 lần)
Giá trị trung bình cho mỗi đơn hàng Anh/ Chị mua qua website mua sắm/ ứng dụng mua sắm trên điện thoại di động/ hay tại cửa hàng mua
sắm:
< 1 triệu đồng
Từ 1 – dưới 3 triệu đồng
Từ 3 – dưới 5 triệu đồng
≥ 5 triệu đồng
Từ 5 – dưới 8 tiếng/ ngày
≥ 8 tiếng/ ngày
Thời gian trung bình Anh/ Chị sử dụng Internet:
< 2 tiếng/ ngày
Từ 2 – dưới 5 tiếng/ ngày
XIN CHÂN THÀNH CÁM ƠN ANH/ CHỊ!
APPENDIX B. RESPONDENT’S DEMOGRAPHIC
Gender
Frequency
Percent
Valid Percent
Cumulative
Age
Percent
Valid
Frequency
1
126
40.4
40.4
40.4
2
186
59.6
59.6
100.0
Total
312
100.0
100.0
Percent
Valid Percent
Valid
2
312
100.0
100.0
100.0
Occup
Valid Percent
Cumulative
Frequency
Percent
Valid Percent
Percent
Valid
Cumulative
Percent
Edu
Frequency
Percent
Cumulative
Percent
1
3
1.0
1.0
1.0
1
1
.3
.3
.3
2
257
82.4
82.4
83.3
2
299
95.8
95.8
96.2
3
51
16.3
16.3
99.7
3
1
.3
.3
96.5
4
1
.3
.3
100.0
4
11
3.5
3.5
100.0
312
100.0
100.0
312
100.0
100.0
Total
Valid
Total
Income
Frequency
Percent
Purchase frequency
Valid Percent
Cumulative
Frequency
Percent
Valid Percent
Percent
Valid
Percent
1
9
2.9
2.9
2.9
2
77
24.7
24.7
27.6
3
116
37.2
37.2
64.7
4
110
35.3
35.3
100.0
Total
312
100.0
100.0
Valid
1
29
9.3
9.3
9.3
2
148
47.4
47.4
56.7
3
100
32.1
32.1
88.8
4
35
11.2
11.2
100.0
312
100.0
100.0
Total
Internet Usage
Purchase value
Frequency
Percent
Valid Percent
Frequency
Cumulative
Percent
Valid Percent
Cumulative
Percent
Percent
Valid
Cumulative
1
129
41.3
41.3
41.3
1
51
16.3
16.3
16.3
2
92
29.5
29.5
70.8
2
131
42.0
42.0
58.3
3
44
14.1
14.1
84.9
3
68
21.8
21.8
80.1
4
47
15.1
15.1
100.0
4
62
19.9
19.9
100.0
312
100.0
100.0
312
100.0
100.0
Total
Valid
Total
APPENDIX C. BOOTSTRAPPING RESULTS FOR HYPOTHESIS TESTING
1. Path Coefficients
Mean, STDEV, T-Values, P-Values
Original Sample
(O)
Standard
Deviation
(STDEV)
Sample
Mean (M)
T Statistics
(|O/STDEV|)
P Values
Channel-service configuration -> Customer empowerment
0.257
0.259
0.063
4.100
0.000
Channel-service configuration -> Customer experience
0.156
0.157
0.053
2.944
0.003
Customer empowerment -> Customer experience
0.413
0.414
0.048
8.561
0.000
Customer experience -> Patronage intention
0.600
0.599
0.060
10.056
0.000
Integrated interactions -> Customer empowerment
0.415
0.415
0.052
8.028
0.000
Integrated interactions -> Customer experience
0.310
0.307
0.049
6.266
0.000
-0.100
-0.100
0.041
2.444
0.015
0.205
0.207
0.062
3.297
0.001
Seeking -> Patronage intention
Trust -> Patronage intention
Confidence Intervals Bias Corrected
Original Sample (O)
Sample Mean (M)
Channel-service configuration -> Customer empowerment
0.257
0.259
0.002
0.126
0.370
Channel-service configuration -> Customer experience
0.156
0.157
0.001
0.051
0.264
Customer empowerment -> Customer experience
0.413
0.414
0.000
0.318
0.506
Customer experience -> Patronage intention
0.600
0.599
-0.001
0.491
0.721
Integrated interactions -> Customer empowerment
0.415
0.415
0.000
0.297
0.508
Integrated interactions -> Customer experience
0.310
0.307
-0.003
0.215
0.409
-0.100
-0.100
0.000
-0.191
-0.029
0.205
0.207
0.002
0.082
0.321
Seeking -> Patronage intention
Trust -> Patronage intention
Bias
2.5%
97.5%
2. Total Indirect Effects
Mean, STDEV, T-Values, P-Values
Original
Sample (O)
Channel-service configuration -> Customer
empowerment
Channel-service configuration -> Customer
experience
Channel-service configuration -> Patronage
intention
Customer empowerment -> Customer
experience
Customer empowerment -> Patronage
intention
Customer experience -> Patronage intention
Integrated interactions -> Customer
empowerment
Integrated interactions -> Customer
experience
Integrated interactions -> Patronage intention
Sample Mean
(M)
Standard Deviation
(STDEV)
T Statistics
(|O/STDEV|)
P
Values
0.106
0.107
0.028
3.791
0.000
0.157
0.159
0.038
4.138
0.000
0.248
0.248
0.039
6.383
0.000
0.172
0.172
0.030
5.629
0.000
0.289
0.287
0.038
7.577
0.000
Internet -> Customer experience
Seeking -> Patronage intention
Trust -> Patronage intention
Confidence Intervals Bias Corrected
Original Sample (O)
Sample Mean (M)
Bias
2.5%
97.5%
Channel-service configuration -> Customer experience
0.106
0.107
0.001
0.054
0.162
Channel-service configuration -> Patronage intention
0.157
0.159
0.001
0.083
0.229
Channel-service configuration -> Customer empowerment
Customer empowerment -> Customer experience
Customer empowerment -> Patronage intention
0.248
0.248
0.000
0.180
0.330
Integrated interactions -> Customer experience
0.172
0.172
0.000
0.120
0.239
Integrated interactions -> Patronage intention
0.289
0.287
-0.002
0.220
0.366
-0.006
-0.006
0.000
-0.051
0.036
Customer experience -> Patronage intention
Integrated interactions -> Customer empowerment
Internet -> Customer experience
Internet -> Patronage intention
Seeking -> Patronage intention
Trust -> Patronage intention