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Volume 2, Issue 2, 2020
e-ISSN 2682-8170
1Graduate School of Business, Universiti Tun Abdul Razak (alexander.tay159@ur.unirazak.edu.my)
2Graduate School of Business, Universiti Tun Abdul Razak
The Effect of Expectations and Service Quality on Customer
Experience in the Marketing 3.0 Paradigm
Alexander Tay Guan Meng1, and Samsinar Md. Sidin2
Publication Details: Received 03/01/20; Revised 05/03/20; Accepted: 30/05/20
ABSTRACT
The Marketing 3.0 era calls for a holistic approach to marketing practices, where value creation
is driven by positive customer experiences that stimulate customer engagement and interaction.
It is thus increasingly important for firms, particularly in the service sector, to improve
customer experience to enhance value and brand success under this marketing paradigm. This
study thus examined how customer expectations and perceived service quality influence
multidimensional aspects of customer experience (cognitive, emotional, hedonic, and sensory)
in the context of the airline service industry. Data was collected from 400 low-cost airline
travellers in Malaysia and analysed with partial least squares structural equation modelling
(PLS-SEM). The results show that both customer expectations and service quality have a
significant positive effect on all dimensions of customer experience. The findings have
important implications for marketing and consumer behaviour researchers as well as
practitioners in the service sector.
Keywords: Customer Expectation, Service Quality, Customer Experience, Experiential
Marketing
INTRODUCTION
The Marketing 3.0 era (Kotler et al., 2010) entails a ‘value-driven’ and ‘holistic’ approach, wherein
marketing practices are expected to lead to valuable and inspirational product creation driven by
customer interaction, engagement, and brand relationships. In this era, engaging customers to
participate and interact with a company’s multiple touchpoints through their consumer experience
is key to value creation and relationship management (Lemon & Verhoef, 2016; Zhang et al.,
2017). Marketing 3.0 also emphasises a company’s value communication and product positioning
in the market by collaborating with its customers. Therefore, marketers play an important role in
generating more interactive communication with customers by engaging them not just to fulfil
their material, emotional, and spiritual needs through their consumer behaviour but also to share
their consumer experiences. This holistic approach further addresses the complex and multi-
dimensional nature of today’s consumers, who increasingly demand product offerings that match
the values of their mind, heart, and spirit (Kotler et al., 2010). The holistic marketing approach is
thus highly relevant in studying the notion of customer experience by engaging customers’
thoughts, emotions, social interactions, and physical senses to create value.
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In recent years, the term ‘experience’ has been used as a marketing strategy in promoting a variety
of brands, products, and services. The concept of experience has become an economic offering
(Pine & Gilmore, 1998) as well as a vital element of consumer behaviour research (Holbrook &
Hirschman, 1982). The tenet of ‘experience’ enables firms to brand their distinct product or service
in the form of the valuable, memorable, and hedonic experiences of consumers. This practice is
known as experiential marketing (Schmitt & Zarantonello, 2013), whereby firms sell their products
and services with experiential elements created via stimulation factors such as technology. Most
marketing and consumer behaviour researchers share the view that consumers engage themselves
physically, mentally, emotionally, socially, and spiritually in the journey of consumption to gain
meaningful and memorable experiences (Verhoef & Lemon, 2016).
Given the strong association between customer experience and product branding, leading
organisations are now aware that customer experience is the key to strategic synergy in the
consistent delivery of value to consumers. However, firms face challenges in understanding how
to fulfil or improve customer experience in terms of product/service development and innovation.
In particular, issues exist in identifying the product attributes and benefits that customers expect
when experiencing a product or service. Businesses are also concerned about the perceived
importance of experiences to customers in their consumption. In addition, existing methods of
examining customer experience indicate limited reliability and validity in measuring perceptions
of customer experience.
To fill these gaps, the present study researched the roles of customer expectations and perceived
performance in customer experience from a multidimensional perspective. Specifically, it
examined how a firm’s offerings can generate cognitive, emotional, sensory, and hedonic
dimensions of customer experience in the consumption journey. The findings of this study can
drive and enhance well-designed technological developments and innovations that enrich customer
experience.
LITERATURE REVIEW
Consumers’ motivation to fulfil their needs has shifted over time from utilitarian to hedonic drives.
Today, consumers are seeking more than just functional and instrumental benefits (e.g. features
and quality) to satisfy their needs and wants and achieve their desired lifestyle. Experiences are
now viewed as expressions of the hedonic well-being attributes sought by consumers to fulfil their
basic human needs of emotional well-being, pleasure, and self-realisation (Addis & Holbrook,
2001; Schmitt & Zarantonello, 2013). Consequently, numerous efforts have been undertaken by
research scholars and practitioners to examine the importance of customer experience in the
marketplace. Their work has generally emphasised the approaches, methods, tools, ideologies, and
techniques behind improving consumers’ perceptions of value through experiential elements in the
long run.
Experience
The notion of experience has been defined as a mental phenomenon rooted in an individual’s
consciousness when external stimuli affect his or her emotions and senses as expressive or
memorable occurrences (Jantzen et al., 2012; Jüttner et al., 2013). The Cambridge Advanced
Learner’s Dictionary (2010) defines experience as “something that happens to you that affects the
way you feel” while the Compact Oxford English Dictionary (2010) terms it “knowledge or skill
gained over time” or “an event or occurrence which leaves an impression.” In general, these
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definitions explain that phenomena that happen to an individual influence his or her behaviour,
knowledge, skills, and way of thinking. In addition, experience can result from physiological
reactions towards external stimuli and can subsequently be expressed as feelings and memories
towards those particular external stimuli (Cham et al., 2020a; Cham et al., 2020b; Hellén &
Gummerus, 2013; Jaakkola et al., 2015). Individuals’ experiences involve their personal intense
feelings, which they share with their society either orally or behaviourally in their daily life
activities (Coru & Cova, 2003, 2015). After an initial overview of the literature, the present
research examined specific prior work on the concepts of experiential consumption, experiential
marketing, and customer experience.
Experiential Consumption
The concept of experience in consumer behaviour entails the emotional and subconscious natures
of rational information processing in consumption decisions (Berry & Carbone, 2007; Holbrook
& Hirshman, 1982; Meyer & Schwager, 2007). In the literature, experience has been extensively
investigated as a key element for understanding the cognitive, affective, and hedonistic aspects of
consumption (Akaka et al., 2015; Bigne et al., 2008; Gilovich et al., 2015). The findings of prior
research have proposed a new theoretical perspective of behavioural consumption in which
consumers’ perceived experience reflects their subjective state of consciousness carrying symbolic
meanings, hedonic responses, and aesthetic criteria. This builds the view of experiential
consumption as the process that addresses consumer perceptions of an experience through their
behavioural reactions. Consumers do not actually buy products for the latter’s functional benefits
alone; rather, consumers seek to purchase a pleasurable experience via the product consumed.
Schmitt and Zarantonello (2013) provided a detailed explanation that consumers want products,
services, and marketing communication campaigns to dazzle their senses, touch their hearts, and
stimulate their minds. They expect that product usage should not just related to their lifestyle but
also be incorporated into it as their experiences.
Experiential Marketing
Today, customers’ satisfaction with a product’s functional features and quality is no longer the
primary concern, given that they also seek extraordinary experiential benefits to fulfil their human
needs for emotional-wellbeing, sensory pleasure, and self-realisation. The role of experience was
put forth by Pine and Gilmore (1999) as a ‘new economic offering’, while Schmitt’s (1999)
proposition of ‘experiential marketing practices’ inspired numerous subsequent works on the
experiential elements of value creation for customers. In the sectors of retailing, tourism, and
automobile, for example, the development of customer experience involves encouraging
businesses to promote their offerings by creating some stimulation that affects the senses and
feelings of consumers (Gupta & Vajic, 2000; Laming & Mason, 2014; Schmitt & Zarantonello,
2013). Research in this area has predominantly focused on how experiential marketing enables
firms to brand their product offerings in terms of good value and memorable benefits, which in
turn leads to successful competitive advantages and brand positioning (Adhikari & Bhattacharya,
2016; Laming & Mason, 2014; Schmitt & Zarantonello, 2013).
Customer Experience
Based on a review of the literature, the present study examined customers’ perceived experiences
in their consumption journey. Several studies have defined customer experience as a personal
feeling about an occurrence, which reflects customers’ cognitive perception about a particular
consumption activity as well as their involvement and interaction in it (Gupta & Vajic, 2000; Kim
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et al., 2016; Mossberg, 2007; Walls et al., 2011). Customers undergo and interact in a consumption
activity through a series of touchpoints (e.g. service encounter, technological application, product
performance, etc.) which may form sensations and perceptions that arouse their affective feelings
(Krishna, 2012; Meyer & Schwager, 2007; Pullman & Gross, 2004; Schmitt & Zarantonello, 2013;
Shaw & Ivens, 2002). From the experiential consumption perspective, an individual may be
motivated to consume a product or service due to a desire for hedonic and emotional arousal
experiences that elicit affective feelings of pleasure, enjoyment, and even entertainment. Thus,
customer experience is viewed as the expressive feelings people seek through purchasing in order
to fulfil their own goals (Verhoef & Lemon, 2016). Similar findings have been revealed by others
(Bigné et al., 2008; Sundbo, 2015), who indicate that customer experience is a mental phenomenon
within individual consciousness that is triggered by external stimuli. This conceptualisation of
customer experience highlights two main aspects that generate an affective emotional state: first,
the internal cognitive appraisal of a product’s or service’s performance; and second, personal
feelings about the consumption of the product or service. Overall, it is generally accepted among
researchers and practitioners that customer experience positively affects customers’ behavioural
outcomes related to branding, such as satisfaction, loyalty, and word-of-mouth.
Customer experience has been of interest in various studies attempting to conceptualise and
measure this concept (e.g. Brakus et al., 2009; Grewal et al., 2009; Pucinelli et al., 2009; Verhoef
et al., 2009). A systematic review of the literature on how consumers perceive their experience
posits that customer experience can be categorised into cognitive, affective, hedonic, and sensory
responses in the consumption environment. Therefore, the current research adopted this
multidimensional perspective of customer experience, which comprises consumers’ cognitive
evaluation of their expectations and rational buying decisions (Sundbo, 2015), their emotional
affective feelings (Frijda, 2009; Titz, 2008), their hedonic responses (Jantzen et al., 2012; Weijers,
2012) and their sensory feelings (Hulten et al., 2009). Extant research has provided strong and
significant evidence that these dimensions are formative variables in the measurement of customer
experience. However, the effect of customer expectations and perceived service quality on the
formation of customer experience remains complex.
Customer Expectations
Most scholars consider customer expectations about a product or service attribute as the standard
or reference point to justify customer purchasing judgements and evaluations (Ariffin & Maghzi,
2012; Guiry et al., 2013; Gures et al., 2014; Higgs et al., 2005). Accordingly, customer
expectations reflect a functional measurement of the forecasted or predicted quality performance
of a product or service prior to consumption (Boulding et al., 1993; Higgs et al., 2005). Previous
studies have further elaborated customer expectations as consumers’ perceptions of what should
occur, how realistic and feasible a product or service is, as well as the minimum level of tolerance
that should ideally be attained by a product’s or service’s performance.
Prior research on expectations (e.g. Ariffin & Maghzi, 2012; Lim et al., 2020; Motwani &
Shrimali, 2014; Sheng & Chen, 2012) has proven that consumption expectations may impact the
evaluation of customer experiences. This argument is based on the expectancy disconfirmation
theory, which describes that an experienced product or service performance is either better or
worse than expected (Bigné et al., 2008; Hamer, 2006). The contrast between actual perceived
performance and expected performance embodies the cognitive dimension of experience
evaluation (Brown et al., 2008). The cognitive experience explains how perceived product/service
performance compares to customers’ needs and expectations for well-being and pleasurable
feelings (Johnston & Kong, 2011; Kim et al., 2016). Therefore, perceptions of cognitive
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experience result from either confirming or disconfirming one’s expectations based on the
judgment of discrepancy between one’s expectations and product/service performance (Bigné et
al., 2008). The extant literature corroborates that customer experience is shaped by the comparison
between perceived performance attributes and the degree of fulfilment of expected benefits
(Mason & Simmons, 2012; Qazi et al., 2017; Veale & Quester, 2009; Verleye, 2015). Based on
this discussion, the current research postulated that:
H1: Customer expectation has a significant relationship with perceived customer experience.
Perceived Service Quality
Services are consumption activities that prioritise the delivery process and interactions between
individuals, with technological connotations at multiple touchpoints in the consumption process
instead of acquired objects (Cheng et al., 2019; Kumar et al., 2019; Robledo, 2011; Sundbo, 2015).
In the service sector, service attributes can be assessed from the physical context of the
environment and the performance of the service by the service provider at the different points of
interaction. The physical environment is referred to as the ‘servicescape’, which comprises the
elements of ambience, layout, equipment, and facilities. Artifacts and symbols further provide
‘mechanics clues’ on the appearance and image of the service provider (Berry et al., 2006; Cham
et al., 2016; Walls et al., 2011), thereby embodying the tangibility of a service performance setting.
In the service delivery process, functional performance is known as service quality. The concept
of service quality has been examined widely from the perspectives of reliability, responsiveness,
assurance, and empathy, as per the SERVQUAL model developed by Parasuraman et al. (1994,
1988).
Consumers evaluate overall service performance attributes in their journey of consumption
(Laming & Mason, 2014; Sundbo, 2015, Tan et al., 2019). Perceived service quality, as a proxy
for perceived performance, is thus perceived to be highly relevant to customer experience
perceptions (Cham & Easvaralingam, 2012; Cheng et al., 2014; Jaakkola et al., 2015; McColl-
Kennedy et al., 2015). Customers’ perceptions of a service incorporate their judgement of
performance quality, which forms the cognitive and emotional affective dimensions of experiences
(Edvardsson, 2005; Jüttner et al., 2013). This is because perceived quality generates the sense of
reality and feelings about a service’s performance. Further, scholars have argued that the
tangibility of service attributes play an important role in experience as customers respond to
physical stimuli and interactions (Dong & Siu, 2013; Jüttner et al., 2013). Specifically, physical
contact or touchpoints in a tangible surrounding give rise to holistic hedonic and sensory
experiences (Bravo et al., 2019; Pareigis et al., 2012).
In the journey of consumption, customer experience forms over time and across multiple
touchpoints and interactions (Verhoef & Lemon, 2016) that shaped by the perceived service
quality performance. Therefore, customers tend to develop experiential judgements to justify their
cognitive impressions and emotional feelings towards the received service performance (Helkkula,
2011; Laming & Mason, 2014; Sundbo, 2015; Walls et al., 2011). Thus, overall perceived service
quality has a direct relationship with customers’ holistic experience, as it arouses emotional
feelings and satisfaction (Juttner et al., 2013; Laming & Mason, 2014). Moreover, the effect of
perceived service quality is prevalent in multiple dimensions of customer experience (Bosque &
Martin, 2008; Bravo et al., 2019; Edvardsson, 2005). Therefore, this study hypothesised that:
H2: Perceived service quality has a significant relationship with perceived customer experience.
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RESEARCH METHOD
Research Context
The present research examined customer experience in the context of the airline service sector;
that is, in terms of airline traveller experience. In airline services, passengers evaluate their
traveling experiences by comparing the airline service’s perceived quality against their
expectations of the airline service’s multiple performance attributes (Gronroos, 2012). This context
allowed the present research to examine customer experience in its multi-dimensional facets (i.e.
cognitive, affective, hedonic, and sensory) in addition to the performance of service attributes that
were both expected and perceived by airline travellers. Prior research has scarcely paid attention
to the antecedents of airline travel experience, with the most common research stream in the
existing literature being service quality perceptions and satisfaction (Singh, 2015). Merely
examining perceptions of service quality does not reflect the multiple dimensions of experience in
the consumption journey of airline services. Thus, the lack of empirical evidence on traveller
experience in airline services must be addressed. In addition, most commercial airlines are
strategising towards service differentiation to achieve branding excellence, which enhances
customer satisfaction and loyalty (Laming & Mason, 2014). Therefore, the present research’s
investigation of customer experience in airline services also has useful implications in terms of
recommendations on experiential marketing for airlines.
Research Design and Data Collection
The present research was conducted following the positivist paradigm to test the hypothesised
relationships and generate empirical evidence on the study constructs (Penaloza & Venkatesh,
2006). It employed the quantitative research method using statistical tools to develop a structural
model for hypotheses testing. The research process included sampling strategy, item measurement,
data collection, and data analysis. To develop a survey instrument, valid and reliable measurement
scales for customer experience, customer expectations, and perceived service quality were sourced
and adopted from a synthesis of the existing literature. These measurement items were pre-tested
and pilot tested to ensure the applicability and reliability of each scale.
Sampling Strategy and Design
The present study targeted respondents who had travelled regularly with low-cost carries airlines
(LCCs) to examine the perceived customer experience on the airline service performance with
compromise the no-frill services. In addition, the traveller’s decisions on choosing LCCs were
mainly determined by the airfare pricing factor with lower expectation on the service performance
(Curras Perez & Sanchez-Garcia, 2016; Koklic et al., 2017). Therefore, study on the LCCs airline
travelling experience provides the cognitive and affective of evaluation on low-cost determinations
in the quality performance.
As there was no sampling frame available for the study population, the non-probability sampling
technique was used to determine the sampling units. The literature (Evans & Rooney, 2013;
Reynolds et al., 2003) suggests that non-probability sampling does not cause problems in testing
theoretical predictions or hypotheses. Therefore, the present research employed the purposive
sampling method, wherein the selection criterion for respondents was that: First, they were
passengers of low-cost airline carriers who had travelled to and from Malaysia’s Kuala Lumpur
International Airport 2 (KLIA2), and second, the targeted respondents had to have communicated
with airline staff at least one in the journey of travelling. The respondents were drawn randomly
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at the waiting area of lounge and restaurants in KLIA2 with face to face interview. Another source
of targeted respondents were from two local travel agency company who supported the present
study to allow the researcher to conduct the survey interview with their tour visitors.
The total sample size for the present research was 400, based on the rule of thumb that 350 samples
is considered a good and reasonable size to represent a large population (Manning & Munro, 2007;
Saunders et al., 2012). The sample size determination also considered the requirement of the partial
least squares structural equation modelling (PLS-SEM) analysis technique, which calls for an ideal
sample size between 150 and 400 (Hair et al., 2010; Kline, 2005). The targeted sample size of 400
further achieved reliability and validity of the data at the five percent confidence level (Hair et al.,
2010), with outer loadings exceeding the threshold of 0.70 for the measurement model (Cohen,
1992).
Data Analysis
The data collected from the surveys was cleaned and coded before further analyses. The issues of
missing data, outliers, multicollinearity, and data normality were subsequently addressed. In
addition, descriptive analysis was performed with the Statistical Package for the Social Sciences
(SPSS) software to understand the respondents’ demographic characteristics.
Using PLS-SEM as the analytical tool, measurement model assessments of items’ reliability and
validity were determined using internal consistency, convergent validity, and discriminant validity
(Hair et al., 2010; Sekaran & Bouie, 2010). Next, to test the study hypotheses on the
interrelationships between the dependent and independent variables, PLS-SEM was utilised to
evaluate the structural model (Hair et al., 2010). This involved assessments of the path coefficients,
significance, coefficient of determination (R2), effect size (f2), and predictive relevance (Q2). The
results of the PLS-SEM analysis are reported in the next section.
RESULTS
Measurement Model Assessment
The first stage of PLS-SEM analysis is the assessment of the measurement model. The constructs
in this study (i.e. customer expectations, perceived service quality, and perceived customer
experience) comprised both reflective and formative items as well as first (1st) order and second
(2nd) order variables (refer to Table 1). In particular, customer expectation was a 1st order
reflective construct while perceived service quality and customer experience were 2nd order
formative constructs with 1st order reflective dimensions. The four reflective dimensions of
service quality are Tangibility, Responsiveness, Reliability and Assurance, and Empathy.
Similarly, the four reflective dimensions of customer experience are Cognitive, Emotional,
Hedonic, and Sensory experiences. Thus, the measurement model was analysed separately for
these constructs.
Table 1: Types of Measurement Models
Construct
Measurement Model
Reference
Customer Expectation
Reflective
Farooq et al. (2018)
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Service Quality
Reflective (1st order)
1. Tangibility
2. Responsiveness
3. Reliability &
Assurance
4. Empathy
Formative (2nd order)
Tsafarakis et al. (2018);
Suki (2014);
Martinez and Martinez (2010)
Customer Experience
Reflective (1st order)
1. Cognitive
2. Emotional
3. Hedonic
4. Sensory
Formative (2nd order)
Adhikari and Bhattacharya
(2016);
Klaus and Maklan (2012)
Reflective Measurement Model Assessment
The evaluation of the reflective measurement model is based on internal consistency, convergent
validity, and discriminant validity. Specifically, composite reliability (CR) was used to represent
internal consistency by taking into consideration the different outer loadings of the indicators. The
acceptable value of CR is in the range of 0.60 to 0.70 (Nunnally & Bernstein, 1994). Table 2 shows
that the reflective constructs in this study showed satisfactory internal consistency with CR values
between 0.854 and 0.952. Next, average variance extracted (AVE) indicates convergent validity,
which ensures that the variance of a construct’s indicators must positively correlate with each
another. As shown in Table 2, all the AVE values were above the threshold of 0.50 (Hair et al.,
2014), confirming the constructs’ convergent validity.
Table 2: Reflective Measurement Model Results
Construct
Items
Loadings
AVE
CR
Cronbach’
s alpha
Expectation
Expect_1
0.823
0.646
0.901
0.863
Expect_2
0.802
Expect_3
0.800
Expect_5
0.760
Expect_6
0.831
Service Quality
(Tangibility)
SQ_1_T
0.843
0.663
0.854
0.748
SQ_2_T
0.862
SQ_3_T
0.732
Service Quality
(Responsiveness)
SQ_5_RP
0.795
0.687
0.868
0.771
SQ_6_RP
0.879
SQ_8_RP
0.811
Service Quality
(Reliability &
Assurance)
SQ_10_RA
0.894
0.822
0.932
0.892
SQ_11_RA
0.909
SQ_12_RA
0.916
Service Quality
(Empathy)
SQ_7_EM
0.874
0.711
0.880
0.793
SQ_9__EM
0.894
SQ_13_EM
0.755
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Cognitive
Experience
CxCog_3
0.826
0.671
0.891
0.836
CxCog_4
0.794
CxCog_5
0.825
CxCog_6
0.829
Emotional
Experience
CxEmo_1
0.800
0.608
0.903
0.871
CxEmo_2
0.745
CxEmo_3
0.836
CxEmo_4
0.773
CxEmo_5
0.772
CxEmo_7
0.747
Hedonic
Experience
CxHed_1
0.822
0.668
0.923
0.901
CxHed_2
0.741
CxHed_3
0.875
CxHed_4
0.862
CxHed_5
0.819
CxHed_6
0.777
Sensory
Experience
CxSen_1
0.883
0.770
0.952
0.940
CxSen_2
0.847
CxSen_3
0.896
CxSen_4
0.891
CxSen_5
0.873
CxSen_6
0.872
The assessment followed by the Fornell-Lacker criterion analysis to examine discriminant validity
that indicate the construct measurement was distinct from other constructs by the empirical
standards. As per results shown in table 3, the Fornell and Lacker criterion presents the
establishment of discriminant validity, that determine each construct’s AVE values square root is
the highest correlation as compare to any other constructs in all cases than the off-diagonal
elements in their corresponding row and column. In addition, the discriminant validity assessment
was confirmed by Heterotrait-Monotrait (HTMT) Ratio analysis test as the multitrait-multimethod
matrix analysis, where the confidence interval value (as shown in table 4) did not have the value
of 1 in any of the constructs.
Formative Measurement Model Assessment
Service Quality (SQ) and Customer Experience (CX) were measured formatively as 2nd order
reflective–formative models. The dimensions of each construct were assumed as the indicators
that create the construct in the formative measurement model. The assessment of the formative
measurement model in PLS-SEM includes tests for convergent validity, collinearity, and the
significance and relevance of each formative indicator. Based on Table 5, convergent validity was
achieved by the 2nd order formative constructs, as all the indicators’ path coefficients were above
the threshold value of 0.80 (Henseler et al., 2015).
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Table 3: The Fornell and Lacker Criterion Results
Cognitive
Emotion
Empathy
Expectation
Hedonic
Reliability&
Assurance
Responsive
Sensory
Tangible
Cognitive
0.819
Emotion
0.597
0.780
Empathy
0.445
0.383
0.843
Expectation
0.462
0.539
0.342
0.804
Hedonic
0.433
0.522
0.213
0.435
0.817
Reliability
&
Assurance
0.338
0.341
0.610
0.333
0.248
0.906
Responsive
0.593
0.484
0.675
0.524
0.433
0.466
0.829
Sensory
0.339
0.428
0.159
0.416
0.518
0.333
0.327
0.877
Tangible
0.445
0.423
0.417
0.387
0.451
0.458
0.533
0.505
0.814
Note: diagonal (in bold) represent the square root of average variance extracted (AVE) while other entries represent
the correlations.
Table 4: Heterotrait-Monotrait (HTMT) Ratio Analysis Results
Cognitive
Emotion
Empathy
Expectation
Hedonic
Reliability&
Assurance
Responsive
Sensory
Tangible
Cognitive
Emotion
0.683
Empathy
0.544
0.458
Expectation
0.541
0.614
0.410
Hedonic
0.493
0.550
0.246
0.479
Reliability
&
Assurance
0.384
0.382
0.724
0.376
0.262
Responsive
0.737
0.580
0.853
0.648
0.518
0.557
Sensory
0.380
0.465
0.191
0.461
0.558
0.362
0.393
Tangible
0.545
0.498
0.530
0.472
0.554
0.551
0.693
0.616
Table 5: Convergent Validity Results for Formative Indicators
Construct/Indicator
Weight
Path Coefficient
Service Quality (Tangibility)
0.958
SQ_1_T
0.282
SQ_2_T
0.481
SQ_3_T
0.453
Service Quality
(Reliability & Assurance)
0.971
SQ_10_RA
0.322
SQ_11_RA
0.379
SQ_12_RA
0.401
Service Quality
(Responsiveness)
0.949
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SQ_5_Rp
0.426
SQ_6_Rp
0.337
SQ_8_Rp
0.447
Service Quality
(Empathy)
0.959
SQ_13_Em
0.299
SQ_7_Em
0.468
SQ_9_Em
0.406
Customer Experience
(Cognitive)
0.930
CxCog_3
0.317
CxCog_4
0.349
CxCog_5
0.299
CxCog_6
0.257
Customer Experience
(Emotion)
0.950
CxEmo_1
0.255
CxEmo_2
0.278
CxEmo_3
0.143
CxEmo_4
0.191
CxEmo_5
0.199
CxEmo_7
0.216
Customer Experience
(Hedonic)
0.941
CxHed_1
0.267
CxHed_2
0.158
CxHed_3
0.191
CxHed_4
0.239
CxHed_5
0.125
CxHed_6
0.239
Customer Experience
(Sensory)
0.953
CxSen_1
0.202
CxSen_2
0.182
CxSen_3
0.159
CxSen_4
0.239
CxSen_5
0.148
CxSen_6
0.209
In order to detect multicollinearity issues (i.e. high correlations between formative indicators) in
the formative model (Hair et al., 2014), the Variance Inflation Factor (VIF) was used as the
assessment tool. The VIF represents the amount of variance of one formative indicator explained
by the other indicators in the same block. The results in Table 6 reveal that the VIF values for the
formative constructs were all below 5.0, confirming the absence of collinearity issues in this
study’s model (Hair et al., 2011).
Table 6: Variance Inflation Factor (VIF) Results for Formative Indicators
Service Quality
VIF
Customer
Experience
VIF
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Tangibility
1.480
Cognitive
1.561
Reliability and assurance
1.704
Emotional
1.732
Empathy
2.263
Hedonic
1.578
Responsiveness
2.097
Sensory
1.439
The final step of the formative model assessment was testing for significance and relevance of
each indicator to the construct. The formative indicators were assessed via the bootstrapping
technique with 1000 re-samples, whereby t-values were generated to assess the significance of
each indicator’s weight. The results in Table 7 exhibit that the formative indicators of Service
Quality and Customer Experience were significant and relevant, as the indicators for both
constructs reported t-values above the threshold of 1.645 and p-values below 0.05.
Table 7: Significance and Relevance Results for Formative Indicators
Formative Indicators
Beta
Standard
Error
t-value
P value
Empathy → SQ
0.304
0.006
50.273
0.000
Reliability & Assurance → SQ
0.336
0.007
48.188
0.000
Responsive → SQ
0.304
0.007
46.273
0.000
Tangibility → SQ
0.303
0.006
54.642
0.000
Cognitive → CX
0.232
0.006
40.855
0.000
Emotion → CX
0.334
0.007
49.864
0.000
Hedonic → CX
0.347
0.008
45.630
0.000
Sensory → CX
0.369
0.008
45.840
0.000
Note: SQ = service quality, CX = customer experience
Overall, the results of the reflective and formative measurement model assessments established
the satisfactory validity and reliability of the study’s constructs. Thus, the data was deemed fit for
the next stage of PLS-SEM analysis, i.e. structural model assessment.
Structural Model Assessment
The structural model assessment examines the model prediction and the relationships among the
constructs. This includes testing for collinearity issues, path coefficients’ significance, coefficient
of determination (R2), effect size (f2), and predictive relevance (Q2). Collinearity was traced with
Variance Inflation Factor (VIF) values to avoid estimation bias within the predictor constructs.
The VIF values shown in Table 8 range from 1.000 to 2.263 (less than 5.0), indicating that there
was no collinearity issue in the model.
Table 8: Variance Inflation Factor (VIF) Results for Structural Model
Construct / Indicator
VIF
Cognitive experience
1.561
Emotional experience
1.732
Hedonic experience
1.578
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Sensory experience
1.439
Tangibility
1.480
Reliability and assurance
1.704
Empathy
2.263
Responsive
2.097
Expectation
1.413
Service quality
1.777
Next, path coefficients for the hypothesised relationships were evaluated using the bootstrapping
analysis (1000 re-samples). Table 9 shows the path coefficients (β), which were all significant at
p<0.05.
Table 9: Path Coefficient Results of Structural Model
Relationship
Beta
Standard
Error
t-value
P value
H1
Expectations → Customer Experience
0.251
0.034
7.303
0.000
H2
Service Quality → Customer Experience
0.376
0.048
7.893
0.000
The results show that customer expectation and perceived service quality have significant effects
on customer experience with beta values of 0.251 and 0.376, respectively. Therefore, both
hypotheses (H1 and H2) were supported in this study. Moreover, the beta values suggest that
service quality has a stronger effect on customer experience compared to customer expectations.
The structural model also assessed R2, which is the extent to which the endogenous variable is
explained by the exogenous variable (Hair et al, 2011). In this study, the R2 value for customer
experience was 0.590; that is, a substantial 59 percent of the variance in customer experience is
due to variances in customer expectation and perceived service quality.
Next, the effect size, f 2 was tested to measure the changes in R2 when a specific exogenous
construct is withdrawn from the model. Table 10 shows that customer expectation has a small
effect of 0.104 on customer experience while service quality has a medium effect of 0.186 on
customer experience construct. The interpretation of effect size was based on Cohen’s (1992)
guidelines on small (f 2= 0.02), medium (f 2= 0.15), and large (f 2= 0.35) effects.
Table 10: Effect size, f 2, Results
Path
f 2
Effect size
Expectation → Experience
0.104
small
Service Quality → Experience
0.186
medium
Finally, predictive relevance (Q2) measures the prediction accuracy of the indicators. When Q2 is
larger than zero, the exogenous constructs are deemed to have predictive relevance for the
endogenous constructs in the structural model (Hair et al., 2014). Table 11 shows the Q2 value
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obtained using blindfolding procedure with an omission distance (D) of 7, which affirms that this
study’s model demonstrated predictive relevance.
Table 11: Predictive Relevance, Q2
Construct
Q2
Expectation
0.466
Service Quality
0.329
Figure 1: Illustrates the Results of the Structural Model.
DISCUSSIONS
Perceived customer experience is formed by customers’ cognitive, emotional, hedonic, and
sensory responses to their consumption decisions. The present study has provided empirical
evidence that in the service sector, specifically in airline services, perceived customer experience
is influenced by travellers’ (i.e. customers’) expectation and perception of the airline’s service
quality. This finding contributes to the literature on marketing and consumer behaviour by
enhancing the understanding of customer experience development. Through expectations and
service performance attributes, the experiential elements of consumption are important for
consumers, whose individual experiences are expressed as intense feelings and memories to be
shared in their social setting.
MANAGERIAL IMPLICATIONS
In the service sector, customer experience encompasses the interactions and touchpoints between
customers and firms, which require firms to thoroughly understand the preferences, expectations,
Expectation
Q2=0.466
Service
Quality
Q2=0.329
Experience
R2=0.590
Cognitive
Emotion
Hedonic
Sensory
Tangibility
Responsive
Empathy
Reliability &
Assurance
0.251*
0.334*
0.232*
0.369*
0.347*
0.336*
0.304*
0.303*
0.304*
0.376*
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and desired outcomes of consumers so they can create and deliver engaging and memorable
experiences (Teixeira et al., 2011). This study’s findings raise several important considerations for
marketers in terms of understanding the nature of customer experience and its implications for a
holistic approach to experiential marketing practices. Firms must prioritise product and service
attributes (e.g. utilitarian, emotional, and hedonic benefits) that customers expect in their
consumption experience. Furthermore, they must continuously maintain and improve service
quality to enhance customers’ experiences. In addition, experiential marketing activities must be
able to engage customers through unique and enjoyable stimulation of customers’ cognition,
emotions, hedonism, and senses. Ultimately, a holistic experience enriches consumers’ lives by
realising their desired lifestyles; thus, they are more likely to use and relate to brands that provide
such experiences.
FUTURE RESEARCH DIRECTIONS
In the era of Industry 4.0, customer or user experience should be emphasised as the ultimate goal
of all technological developments in consumption. Technological innovations must adopt a
customer-oriented perspective and focus on enhancing consumer experience through product
design. By understanding users’ interactions with technology, user experience can be designed to
fulfil users’ needs based on their mental or emotional state, system characteristics, and user
interaction contexts (Hassenzahl & Tractinsky, 2006). In conclusion, there a growing necessity to
understand and improve customer experience in product and service design from the perspective
of technology development and innovation in Industry 4.0.
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