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A roadmap for the application
of PLS-SEM and IPMA for effective
service quality improvements
Noorjahan Banon Teeluckdharry, Viraiyan Teeroovengadum and
Ashley Keshwar Seebaluck
Faculty of Law and Management, University of Mauritius, Reduit, Mauritius
Abstract
Purpose –The paper provides a step-by-step guide in the guise of a roadmap for service improvement
initiatives using importance performance map analysis (IPMA).
Design/methodology/approach –To empirically illustrate how IPMA can be applied to any service industry,
three sectors are considered; sports and fitness (study A), hospitality (study B) and higher education (study C).
Following the proper selection of measuring instruments and their evaluation using structural equation modeling-
partial least squares (Smart-PLS), IPMA is applied to identify those attributes having strong total effects (high
importance) over the targeted construct (satisfaction) but which also have low average latent variable scores (low
performance).
Findings –For sports and fitness (study A), the physical aspects and programme quality require managerial
attention. For the hospitability sector (study B), it is service commitment, interaction quality and internal sense of
happiness. Whereas for higher education (study C), it is administrative quality as well as the element of
transformative quality, namely the university’s role in adding to its students’emotional stability, which needs the
attention of the top management.
Originality/value –This study provides researchers and practitioners with a roadmap for applying PLS-
SEM and IPMA for continuous service quality improvement. The roadmap extends upon Ringle and Sarstedt’s
(2016) work. It highlights critical decisions that need to be considered in the pre-analytical stages of the IPMA
application, i.e. at the research design phase in selecting the most appropriate service quality measurement
model specifications. It not only contributes to the existing body of knowledge by providing empirical evidence
to advance theory development in the quality management field but also has implications for the practitioners
in any service sector on where to focus their attention for an effective service improvement.
Keywords PLS-SEM, IPMA, Service quality, Service industry, Quality improvement initiatives
Paper type Research paper
1. Introduction
Today’s business environment is ever-demanding and competitive, forcing organisations to
launch quality improvement efforts to strengthen their operational postures and redesign
their processes and systems to be more customer-focused (Ooi et al., 2011). Consistently
improving the quality of services and delivering services that match or exceed the customers’
expectations is at the core of every successful organisation (Adiandari et al., 2020;
Parasuraman et al., 1985;Gilbert and Veloutsou, 2006). Total quality management (TQM)
practices are often adopted in service firms to assist managers in improving service quality
(Talib et al., 2011). The caveat is that successful implementation of TQM necessitates the
presence of all critical TQM practices (Douglas and Judge, 2001). Service quality literature has
been significantly shaped by TQM manufacturing literature (Silvestro, 1998), both sharing a
customer-based perspective on quality management, with customer orientation being the
first principle in TQM, while the need to anticipate consumers’requirements to evaluate
service quality is at the core of the concept (Chen et al., 2022). However, as highlighted by
Silvestro (1998), there exist some key asymmetries between the two pieces of literature,
Application of
PLS-SEM and
IPMA
Ms N Teeluckdharry has been awarded a scholarship by the Higher Education Commission, and the
authors wish to thank the HEC for the support.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1754-2731.htm
Received 29 November 2021
Revised 18 May 2022
3 September 2022
Accepted 26 October 2022
The TQM Journal
© Emerald Publishing Limited
1754-2731
DOI 10.1108/TQM-11-2021-0340
namely in terms of service quality literature being more research-based, focused on
enhancing the academic conceptual knowledge base, being descriptive in nature and geared
toward developing an understanding of contingency in different operational contexts. TQM,
on the other hand, is more consultant-driven, focuses on practical issues, is prescriptive in
nature and tends to adopt a universalistic approach. Scholars in the service industry do
acknowledge the importance of integrating TQM approaches into service quality initiatives
for better service management. Still, they also recognise the gap in the state of knowledge
(Ostrom et al., 2015). Nonetheless, both TQM and service quality are considered crucial
components for achieving business excellence and have a close relationship with one another,
as established by previous studies (e.g. Coo and Verma, 2002;Samat et al., 2006;Sit et al., 2011;
Aburayya et al., 2020).
The adequate measurement of service quality is a vital prerequisite in identifying specific
service quality attributes impactingcustomer satisfaction. Yet, most empirical studies on service
quality and customer satisfaction in the service industries fall short of conducting a
comprehensive evaluation of measurement and structural models by applying all thenecessary
steps (Ghasemy et al., 2020;Sarstedt et al., 2022). Service quality is considered to be not only a
context-specific construct with different meanings according to the stakeholders’perspectives
and cultures (Jayasundara et al., 2009) but also a multi-dimensional one (Brady and Cronin, 2001;
Teeroovengadum et al.,2016,2019). PLS-SEM is particularly useful in circumstances whereby
there are multidimensional constructs (Carranza et al., 2018). The recent PLS-SEM advanced
analysis procedures, such as prediction-oriented model assessment (Shmueli et al., 2016,2019)
and multigroup analysis (Hair et al.,2018), also offer numerous advantages for assessing
measurement and structural models relating to service quality and customer satisfaction.
As attested in TQM literature, service quality is more theoretical and research-oriented, for
instance, developing scale (e.g. Mir et al., 2022) and testing the empirical links between concepts
such as perceived quality and satisfaction (e.g.Ahmed et al., 2022;Famiyeh et al., 2018;Lin, 2021;
Murrar et al., 2021;Park and Kim, 2022). However, effective service quality improvement
pertains to assessing the importance and performance of service quality attributes and using
them in an integrated manner for decision-making. In that respect, theimportance-performance
analysis (IPA) proposed by Martilla and James (1977) has been extensively applied in service
quality research (Ennewet al., 1993;Hudson and Shephard, 1998;Ford et al.,1999;Wu and Hsieh,
2012;Izadi et al., 2017). Despite the substantial contribution of IPA, the research tool has been
criticised for its lack of conceptual, methodological and statistical rigour (Azzopardi and Nash,
2013;Sever, 2015;Lai and Hitchcock, 2015). In the face of those issues, importance performance
mapanalysis(IPMA)ispostulatedasabetteralternative(Ringle and Sarstedt, 2016). Notably
Hair et al. (2017a,b,2018) and Ringle and Sarstedt (2016) advocate the role that IPMA can play in
identifying potential areas of improvement. Data analysed based on IPMA (Ringle’s IPMA) can
provide policymakers and top management with actional results (Ghasemy et al., 2020)asit
identifies key drivers of satisfaction (Elliott and Shin, 2002) and provides practical insights on
where to invest the limited resources to ensure continuous quality improvement (Abalo et al.,
2007;O’Neill and Palmer, 2004). From a decision-making perspective, IPMA can be viewed as a
helpful instrument in enhancing satisfaction and, consequently, corporate performance and
business management (Matzler et al., 2004). The increased financial responsibility placed on
managers for their choices further emphasises how important IPMA is for quality management
in the service industries (Seggie et al., 2007).
A review of the recent TQM literature also shows that studies in service quality
(e.g. Ahmed et al., 2022;Hussein et al., 2022;Lin, 2021;Murrar et al., 2021) merely provide
generic recommendations and fail to look beyond the service quality evaluation and its
significant effects on other variables, to appropriately earmark areas for prioritised resource
allocation and provide accurate recommendations. Existing literature suggests limited
studies applying IPMA in the field of quality management. While there are several papers
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(e.g. Ringle and Sarstedt, 2016;Hair et al., 2019) and books (Hair et al., 2017a,b,2018) that
elaborate in detail on how to carry out an IPMA, this paper endeavours to provide a detailed
roadmap with empirical illustrations on how IPMA can be used for service quality
improvements in any service sector. The roadmap extends upon Ringle and Sarstedt’s (2016)
work. It highlights critical decisions that need to be considered in the pre-analytical stages of
the IPMA application, i.e. at the research design phase in terms of selecting the most
appropriate service quality measurement scales with the correct model specifications. Such
considerations allow for reliable measures of the perceptions of the customers as well as an
increased likelihood of IPMA to accurately identify the service attributes that need to be
improved upon, thus leading to better strategic decision-making.
The heterogeneity of the services sectors and the services themselves make it practically
impossible to have a one-fit-all approach suitable for all service types (Edvardsson et al., 2005;
Becker et al., 2011). It is neither feasible for the study to cover all the various sectors. As
Ottenbacher et al. (2006) point out, the broad range of services within the economy does not
allow for a concise strategic analysis of the entire service industry. As such, to illustrate the
key considerations that need to be taken when measuring service quality for the application
of IPMA in the service industry, three sectors have been selected using Lovelock’s (1983)
classification schemes, namely; sports and fitness (study A), hospitality (study B) and higher
education (study C). The underpinning reason is that they fall on different quadrants within
Lovelock’s (1983) classification schemes. The criteria proposed by Lovelock (1983) were the
nature of the service act, the relationship between the service organisation and the customers,
room for customisation and judgement on the part of the service provider, the nature of
demand and supply, as well as the service delivery. Services in fitness clubs are high contact
(Lovelock, 1996) in which there is high participation of the customer in co-producing the
service (Chelladurai, 1992;Peitzika et al., 2020) and high inter-client interaction (Chang and
Chelladurai, 2003) over prolonged and repeated periods, leading to a membership relationship
between the service organisation and its customers. Likewise, for higher education, though in
this specific context, the difference lies in the nature of the service delivery and the room for
customisation and judgement towards the services and service delivery characteristics.
While teaching staff may exert creativity in the delivery of the lectures, they do not usually
differentiate one student from the other; therefore, the judgement might be high, but the
customisation prospect is low (Lovelock, 1983). When it comes to service quality in higher
education, it is a phenomenon that is highly complex since it captures both institutional and
psychological outcomes (Yeo, 2008). For the hospitality sector, fast-food chain restaurants
have been considered. In a fast-food setting, the role of the employees is somewhat restrained;
the customers may choose from a set of predefined menus. Furthermore, there is no formal
relationship between the service provider and the customers. Service delivery is mostly
discrete transactions as opposed to sectors such as sports and fitness and higher education,
whereby service delivery tends to be continuous. Fast food is mainly a consumer service
(Sasser et al., 1978), where the degree of complexity, the need for tailored services, and the
involvement of knowledge and expertise are relatively low.
Therefore, to ensure continual quality improvement in line with the TQM philosophy and
to assist the senior management in improving the overall customer retention strategy
through enhanced customer satisfaction, the study proposes a roadmap showing the
sequential steps that need to be taken from the proper selection of service quality measures to
the applying the required assessment checks before IPMA is carried out. For illustrative
purposes, the present study considers the application of the PLS-SEM and IPMA
methodology in three service contexts; sports and fitness (study A), hospitality (study B)
and higher education (study C).
The paper makes valuable contributions to the relevant literature by providing a roadmap
for applying IPMA in the service industry, highlighting the crucial pre-analytical
Application of
PLS-SEM and
IPMA
considerations needed when using service quality to enhance customer satisfaction. It also
provides empirical evidence for the utility of IPMA in identifying the drivers of customer
satisfaction in various service sectors while providing practitioners with knowledge on how
to apply IPMA as a useful diagnostic tool for continuous service quality improvement. As
pointed out by Ringle and Sarstedt (2016), IPMA holds the potential to offer crucial insights
into the relevance of antecedent constructs for managerial actions.
The paper is divided as such: section 2 provides a brief review of the literature on IPMA,
service quality, customer satisfaction and the empirical studies in the three sectors selected.
Section 3 discusses the methodology adopted, while section 4 provides the results and
discusses the analysis done. Finally, section 5 contains the concluding remarks along with the
theoretical and practical implications.
2. Literature review
2.1 Importance performance map analysis (IPMA)
Importance-performance analysis (IPA) (Martilla and James, 1977) is a practical business
research technique commonly used to facilitate the interpretation of data by diagnosing
the performance of service attributes, identifying the crucial ones requiring managerial
focus to allow for optimal and strategic resources allocation (Abalo et al., 2007). The
practicality of IPA lies in the fact that it examines both performance and importance of the
attributes in an integrated matrix, given that individual examination might be misleading;
a low-performance score might prompt managerial intervention, but if its importance for
enhancing satisfaction is low, the resource might be better allocated (Sever, 2015). Despite
its longevity as a research tool, IPA presents several drawbacks, namely in terms of the
choice of the cut-off points/discriminating thresholds, which is highly subjective and
depends on the researchers’judgement (Azzopardi and Nash, 2013;Bacon, 2003;Sever,
2015), the lack of accurate definition of what importance constitutes (Dwyer et al.,2016;
Sever, 2015) and, the self-stated rating of importance being subject to biases such as social
desirability and fatigue (Sever, 2015) as well as showing lack of variation with all
attributes being rated as high in terms of importance (Lai and Hitchcock, 2015). The
conceptual and methodological issues question IPA’s validity (Ringle and Sarstedt, 2016;
Sever, 2015).
On the other hand, IPMA (Ringle and Sarstedt, 2016) not only cater to the shortcomings of
IPA but has also been proven to be a more straightforward, user-friendly means for advanced
analysis of a previously tedious process (Ahmad and Afthanorhan, 2014). IPMA offers a
graphical representation of unstandardised total effects of predecessor constructs (derived
from a PLS path model estimation) in the x-axis, which represent the importance in predicting
a target construct, and this is contrasted against average rescaled latent variable scores,
which serve as the performance component of the map in the y-axis (Hair et al., 2017a,b;
Ringle and Sarstedt, 2016). The IPMA extends the PLS-SEM results to provide practical
insights. The rescaling of the latent variable seems to cater to technical issues arising from the
use of different response numbers in the Likert scale (Ringle and Sarstedt, 2016), while the
usage of unstandardised total effects allows for straightforward interpretation in the sense
that if the performance of the predecessor increases by one unit, then the performance of the
target construct witnesses an increase equivalent to the size of the unstandardised total effect
of the predecessor (Hair et al., 2017a,b).
To circumvent the arbitrary selection of cut-off points, which sometimes tend to be scaled-
driven in IPA (i.e. based on the median value of the scale), for IPMA, the discriminating
thresholds are data-driven, i.e. mean scores of performance value and importance values are
used as threshold values for cutting off the map into the various quadrants. Hair et al. (2018)
provide three primary requirement checks before carrying out IPMA:
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(1) For rescaling of the latent variables in a range of 0–100, all indicators in the PLS path
model should either have a metric scale or an equidistant scale, such as an ordinal
scale. Hence, forced-choice scales or nominal scales cannot be used.
(2) All the indicators must have the same scale direction.
(3) The values of the outer weights estimate must be positive regardless of the model’s
specification (formative, reflective, or higher-order model).
The usefulness of IPMA has been demonstrated in several studies, such as in banking
(Ramayah et al., 2014;Tailab, 2020); education (Ting et al., 2020;Wook et al., 2019); tourism
(Ebrahimi et al., 2021), restaurants (Carranza et al., 2018;Fakfare, 2021) and technology (Groβ,
2018;Palos-Sanchez et al., 2018). However, its application for identifying quality attributes is
yet to be fully explored. In this study, IPMA is applied in the context of service quality to
identify indicators for effective prioritisation.
2.2 Service quality
According to Howat and Assaker (2013), service quality is the customers’cognitive
evaluation of service attributes. The concept of service quality is grounded in the transaction-
specific conceptualisation of the expectancy-disconfirmation theory (Gr€
onroos, 1982;
Iacobucci et al., 1995;Parasuraman et al., 1985). This study considers the functional and
technical aspects of service quality as two theoretically distinct concepts and analyses their
influence on satisfaction in line with recommendations for a better and more holistic
evaluation of service quality (Brady and Cronin, 2001;Teeroovengadum et al., 2019).
Functional quality refers to the service delivery process. In contrast, the technical aspect of
service quality also referred to as outcome quality, relates to the outcome of the service
encounter and what the customer receives from the interaction with the service provider
(Brady and Cronin, 2001;Gr€
onroos, 1984).
In line with Teeroovengadum et al. (2019), functional service quality is viewed as a second-
order factor model. Second-order factor models are known to have several advantages, not
limited to parsimoniously explaining the covariance and restricting the number of variables
tested within a structural model to those being statistically significant without the loss in
theoretical rigour (Koufteros et al., 2009;Cenfetelli and Bassellier, 2009;Hair et al., 2017a,b,
2020). On the other hand, outcome quality is considered a unidimensional construct in the
higher education context (Teeroovengadum et al., 2016), and higher-order model in sports and
fitness centres (Lagrosen and Lagrosen, 2007) and fast-food restaurants (Wu and Mohi, 2015;
Cheng et al., 2018).
2.3 Customer satisfaction
There are numerous and often competing theories that have been used for the
conceptualisation of satisfaction, such as the dissonance theory (Festinger, 1957), the
assimilation contrast theories (Sherif and Hovland, 1961), the equity theory (Adams, 1963),
adaptation level theory (Helson, 1964), expectancy-disconfirmation paradigm (Oliver, 1977),
the comparison level theory (LaTour and Peat, 1979), the value-precept theory (Westbrook
and Reilly, 1983), evaluative congruity model (or the social cognition model) (Sirgy, 1984) and
person situation fit concept (Pearce and Moscardo, 1984). This study adopts the value-percept
theory’s definition of satisfaction (Westbrook and Reilly, 1983) since it is more in line with the
study’s objective to find out what the customers truly value. Therefore, according to the
value-percept theory, satisfaction is an emotional response activated by a cognitive
evaluation whereby perceptions are compared against one’s values, needs, wants, or desires
(Oliver, 1977).
Application of
PLS-SEM and
IPMA
Despite the close relationship between service quality and satisfaction, for instance, both can
be explained using the expectancy-disconfirmation paradigm (Oliver, 2010); they are distinct
constructs. While researchers can theoretically discern between service quality and satisfaction
as concepts, it is uncertain whether customers can make substantive differentiation in practice
(Iacobucci et al., 1994). Rust and Oliver (1994) suggest that perceived quality is evaluated based
on the product or service attributes, while satisfaction may result from any dimension.
Furthermore, organisations may control how service quality is perceived to a certain extent. The
same cannot be said for satisfaction since customers may have numerous preferences to which
the organisations cannot cater simultaneously (Griffin and Hauser, 1993). Therefore, when
measuring both concepts within the same study, it is deemed better to use the concept of overall
satisfaction to avoid the issue of multicollinearity. Overall satisfaction is the customer’sgeneral
feeling toward a service after service consumption (Choi and Chu, 2001), and these emotional
responses are usually based on the experiences with the service that culminates over time or over
several encounters (Gustafsson et al.,2005;Homburg et al., 2005;Seiders et al., 2005).
2.4 Service quality dimensions as predictors of customer satisfaction
The delivery of superior service quality and achieving customer satisfaction form the basis of
effective service management (Rust and Oliver, 1994;Brady and Robertson, 2001). The
antecedent of satisfaction often has been identified (e.g. in Brady et al., 2002) using Bagozzi’s
(1992) appraisal-emotional response-coping framework, whereby appraisal of an object leads
to an emotive response which in turn leads to behaviour. In the service context, the
framework was applied by Gotlieb et al. (1994) to justify the causal relationship between
service quality, satisfaction and behavioural intentions. The framework dictates that the
appraisal of service quality leads to the assessment of customer satisfaction which ultimately
triggers behavioural intentions (Gotlieb et al., 1994). Numerous studies have provided
empirical support for the effects of service quality dimensions on customer satisfaction
across various service industries, e.g. education (Alsheyadi and Albalushi, 2020); hospitality
(Nunkoo et al., 2020;Prentice et al., 2020); banking (Raza et al., 2020;Teeroovengadum, 2022);
transport (Wang et al., 2020); and healthcare (Nguyen and Nagase, 2019).
Several studies have established a positive relationship between service quality and
customer satisfaction in sports and fitness settings (Alexandris et al., 2004;Dias et al., 2019;
Nuviala et al.,2012;Tsitskari et al., 2014;Yu et al., 2014). Physical infrastructure has the most
significant impact on satisfaction in Lentell’s (2000) study, while Papadimitriou and Karteroliotis
(2000) showed that the quality of instructors has the most significant contribution, followed by
the facility attraction and operation, the availability of the programmes and the way it was
delivered. On the otherhand, Alexandris et al. (2004) demonstrated physical environment has the
most decisive influence on satisfaction, followed by interaction with staff.
Customer satisfaction is paramount in fast-food restaurants to gain a competitive
advantage and increase the probability of repeated purchases (Chen et al., 2018;Ryu et al.,
2012;Ma et al., 2014;Zhong and Moon, 2020). Catering to their client’s satisfaction is crucial
since dissatisfied customers might easily switch to competitors (Berezina et al., 2012;Ahmad
Shariff et al., 2015) and even convince others to do the same (Gilbert et al., 2004). In a
restaurant setting, a positive link is found between service quality (process quality) and
customer satisfaction (Bufquin et al., 2017;Liu and Jang, 2009;Wu, 2013).
Regarding higher education, several studies (Arif et al., 2013;Chong and Ahmed, 2012;
Clemes et al., 2013;Annamdevula and Bellamkonda, 2016;Khoo et al., 2017) have
demonstrated a significant effect on overall service quality on satisfaction. However, in
studies such as Alves and Raposo (2007) and Teeroovengadum et al. (2019), whereby
functional quality and technical quality were delineated from overall service quality, it was
seen that the relationship between service quality and student satisfaction was insignificant
while there was a positive direct effect of outcome service quality on student satisfaction.
TQM
Therefore, building upon the above arguments and considering that this study views
functional and outcome quality as two theoretically distinct concepts, the conceptual model
used is illustrated in Figure 1.
3. Methodology
Effectively measuring and assessing service quality using an appropriate research
instrument is crucial to enhance the diagnostic utility of measurement scales for
practitioners (Howat and Assaker, 2016). The first step is identifying the selected sectors’
service quality dimensions and attributes. This study suggests following the roadmap (refer
to Figure 2) provided below to identify and analyse the service quality attributes to enhance
the recommendations’reliability, accuracy and specificity based on the IPMA results.
Literature reveals that in higher education, there is already a comprehensive hierarchical
measurement of service quality in the Mauritian context, HESQUAL (Teeroovengadum et al.,
2016), which fits this current purpose study. Therefore, as per theroadmap, we proceed directly
to the confirmatory phase. While for the other two sectors, an exploratory survey was carried
out to assess the scale’s dimensionality and the reliability of each sub-scale. It included steps
such as pretesting the questions, sampling and survey administration, item reduction and factor
extraction. Owing to practical constraints, quota sampling was used in the three sectors to
ensure that we obtained quasi-representativesamples (Neuman, 2011)fromthelocalpopulation
(Mauritius, where the study was conducted). The questionnaires were self-administered, either
face-to-face or online using Google Forms.
Study A:
xStaff quality
xProgram quality
xPhysical aspects
Study B:
xInteraction quality (behaviour of staff, service
commitment)
xPhysical environment quality
Study C:
xAdministrative quality (Attitude and Behaviour,
xAdministrative procedure)
xPhysical environment (Support infrastructure,
learning setting, general infrastructure)
xCore educational Quality (Curriculum,
pedagogy, competence)
xSupport Infrastructures
PROCESS
QUALITY
TECHNICAL
QUALITY
(OUTCOME
QUALITY)
OVERALL
SATISFACTION
Study A:
xSocial outcomes
xPhysical and psychological outcomes
Study B:
xFood quality
xHealthy catering
xInternal sense of happiness
Study C:
xTransformative quality
Context-specific lower order service
quality sub-dimensions
Figure 1.
Conceptual model
adopted for the study
Application of
PLS-SEM and
IPMA
For exploratory factor analysis (EFA), carried out using SPSS, items were retained based on
the guidelines provided by Hair et al. (2006). The complete list of scale items used is presented
in Appendix A.1.Table 1 provides an overview of the data collection techniques and analysis
in the different sectors.
Figure 2.
A proposed roadmap
for the application of
IPMA in the service
quality context
TQM
In the confirmatory phase, explanatory surveys were carried out in each of the identified
contexts to collect data to analyse the psychometric properties of the measures, confirm the
relationship between the set of items and their respective factors, test conceptual structural
models and carry out the IPMA. For sports and fitness, data was collected from 429 sports
and fitness centre members, while the study targeted 426 clients from Subway restaurants in
the hospitality sector. In higher education, data was collected from 405 university students.
The demographic details are listed in Table 2.
In line with existing literature, all the measures of service quality used for the different
contexts are operationalised as higher-order constructs, namely reflective-formative
models, which are widely used in social sciences (Cheah et al.,2018;Ringle et al., 2012). The
lower-order constructs are measured as reflective, while the higher-order constructs
are measured as formative, using the data from the lower-order constructs (Sarstedt
et al., 2019).
A two-stage approach was used to empirically validate the reflective-formative models as
specified by Hair et al. (2018) using Confirmatory Tetrad Analysis (CTA-PLS). As per the two-
stage approach, CTA was initially carried out at the lower-order level only with the items
making up the lower-order constructs. The results showed that the confidence interval of the
tetrads did not include zero, providing support for the original specifications of the
lower-order constructs being reflective. The lower-order constructs’latent variable scores
Study A in sports and fitness centres
Study B in healthy fast-
food restaurants
(subway)
Study C in
higher education
Study A.1 Study B.1 Study C.1
Phase I Purpose Item generation N/A N/A
Respondents 20 gym members, 4 gym trainers,
and 1 gym manager
3 focus groups of 7 gym members
each (n521)
Methods In-depth interviews
Thematic analysis
Study A.2 Study B.2 Study C.2
Purpose Content validity and item reduction N/A N/A
Respondents 2 trainers and one gym manager
Methods Theoretical analysis, calculation of
content validity ratio. Items with a
CVR of 0.78 were retained
Phase
II
Study A.3 Study B.3 Study C.3
Purpose Pretesting of questions
Respondents 30 gym members 30 Subway customers N/A
Methods Face validity, verbal probing, and thinking aloud
Study A.4 Study B.4 Study C.4
Purpose Item reduction and assessment of scale dimensionality N/A
Respondents 272 gym members 275 Subway customers
Methods EFA and reliability analysis EFA and reliability
analysis
Phase
III
Study A.5 Study B.5 Study C.5
Purpose Scale evaluation and testing of the conceptual model Testing of the
conceptual model
Respondents 429 gym members 426 Subway Customers 405 HE students
Methods CTA and CCA using SMART-PLS 3
Discriminant validity using Heterotrait-Monotrait ratio of correlations (HTMT)
IPMA
Table 1.
Overview of data
collection techniques
and analysis in the
different studies
Application of
PLS-SEM and
IPMA
were then used as input to assess the covariance structure of the higher-order construct’s
measurement model. Bollen and Ting (2000) and Gudergan et al. (2008) provide insights on
how to deal with tetrad construction having less than four indicators per measurement model.
However, Hair et al. (2018) strongly recommend using CTA-PLS only on measurement models
with at least four indicators. Therefore CTA-PLS was applied only to those higher-order
models with at least four lower-order models. The results of the CTA-PLS are significantly
different from zero. Hence, the CTA-PLS rejects the null hypothesis of a reflective
measurement model for the higher-order model and provides support for the formative
measurement model.
For the subsequent analyses, a repeated indicator approach, as advocated by Becker et al.
(2012), was then applied to assess the path models before running IPMA (Hair et al., 2014),
namely in terms of the measurement model (Appendix A.3) and the structural model (Hair
et al., 2014). According to the guidelines provided by Hair et al. (2020), the reflective
measurement model is assessed in terms of the indicator loadings (>0.708) and their
significance (t-statistic above ±1.96), indicator reliability (>0.5), composite reliability (>0.7),
convergent validity (AVE > 0.5) and discriminant validity (HTMT < 0.85). Indicators with
values lower than 0.708 were deleted only if their omission increased the composite reliability
Study A in sports and fitness
centres
Study B in healthy fast-food
restaurants (subway) Study C in higher education
Variables (n5429) % (n5426) % (n5405) %
Gender
Female 190 44.3 Female 247 58 Female 256 60.2
Male 239 55.7 Male 179 42 Male 165 39.3
Age group
16–19 60 14 Less than 16 12 2.8 18–22 274 67.6
20–29 235 54.8 16–19 68 16 23–27 68 16.8
30–39 93 21.7 20–29 224 52.6 >27 63 15.6
40–49 33 7.7 30–39 58 13.6
50–59 7 1.6 40–49 30 7
Above 60 1 0.2 50–59 21 4.9
60 or above 13 3.1
Level of education Level of education Level of study
Below
graduate
level
154 35.9 CPE 17 4 Undergraduate 274 67.7
Graduate
Level
174 40.6 Vocational 3 0.7 Postgraduate 131 32.3
Post-
graduate
level
101 23.5 School certificate 45 10.6
Higher School
Certificate
155 36.4 Mode of study
Undergraduate
(diploma/degree)
165 38.7 Full-time 286 67.9
Postgraduate 41 9.6 Part-Time 135 32.1
Frequency of training per week Number of visits in the last 3 months Institutions
Once 47 11 Less than 1 152 35.7 Public
Universities
281 69.4
Twice 70 16.3 Less than 2 103 24.2 Private
Universities
124 30.6
3 times 168 39.2 Less than 3 93 21.8
More than
3 times
144 33.6 More than 3
times
78 18.3
Table 2.
Demographic details
for each sector
TQM
above 0.7 or the Average Variance Extracted (AVE) above the threshold value of 0.5, as
advised by Hair et al. (2017a,b).
For the formative models, the VIF values are below the recommended value of 3, showing
no multicollinearity issues. The first-order factors have significant outer weights, and their
bootstrap confidence intervals do not include zero. The significance of indicators was
checked through a bootstrap procedure.
The structural model for each sector was assessed using relevant metrics such as the R
2
and path coefficients, while their statistical significance was tested using the bootstrapping
procedure with 5,000 iterations.
In addition to the abovementioned checks, PLSpredict (Shmueli et al., 2016,2019)is
recommended as a robust approach for out-of-sample prediction (Hair et al., 2020). Root mean
squared error (RMSE) is advocated as the preferred prediction statistic in most cases (Hair
et al., 2019), except when the prediction error distribution is asymmetric, then MAE (mean
absolute error) instead of RMSE is advised (Shmueli et al., 2019). Therefore, RMSE values
were used to assess the predictive powers of the models.
3.1 Research instruments and measurement scales
The performance-only model (Cronin and Taylor, 1992) was deemed a better choice for this
study’s measurement of perceived service quality, satisfaction and loyalty. All observable
variables were measured on a 5-point Likert scale whereby 1 5“strongly disagree”,
25“disagree”,35“neutral”,45“agree”and 5 5“strongly agree”, to ensure the scales used
are equidistant as per requirements for IPMA.
The number of variables used to capture each construct is summarised in Table 3. 15 items
were used to capture functional quality in sports and fitness centres, namely staff quality (6
items), programme quality (4 items) and physical aspects quality (5 items). For outcome
quality in sports and fitness centres, it was measured using 8 items; social outcomes (3 items)
and physical and psychological outcome quality (5 items).
Study
Sports and fitness
centres
Healthy fast-
food restaurants Higher education
Process quality
All variables were
measured on a 5-point
Likert scale
Staff 6 Service
commitment
5 Attitude and behaviours
of administrative staff
4
Program 4 Staff behaviour 4 Administrative process 3
Physical aspects 5 Physical
environment
5 Support infrastructure 4
Learning setting 3
General infrastructure 4
Attitude and behaviours
of academics
6
Curriculum 4
Pedagogy 4
Competence 3
Support facilities 6
Outcome quality
All variables were
measured on a 5-point,
similar to process quality
Social outcomes 3 Food quality 7 Transformative quality 8
Physical and
psychological
outcomes
5 Internal sense of
happiness
4
Healthy catering 5
Table 3.
Summary of numbers
of indicators for service
quality measurement
in each sector
Application of
PLS-SEM and
IPMA
As far as healthy fast-food restaurants are concerned, process service quality consists of 3
dimensions: service commitment (5 items), staff behaviour (4 items) and physical
environment (5 items), while outcome quality is measured as a higher-order model
consisting of 3 lower factor measurement model, namely, food quality (7 items), internal sense
of happiness (4 items) and health catering (5 items).
Functional service quality in higher education contains a total of 48 items and is grouped into
ten first-order factors; attitude and behaviours (4 items), administrative process (3 items), support
infrastructure (4 items), learning setting (3 items), general infrastructure (3 items), attitude and
behaviours of academics (6 items), curriculum (4 items), pedagogy (4 items) competence (3 items),
support facilities (6 items). For outcome quality, transformative quality is measured using 8 items.
All observable variables were measured on a 5-point Likert scale whereby 1 5“strongly disagree”,
25“disagree”,35“neutral”,45“agree”and 5 5“strongly agree”.
The overall satisfaction was measured on a 5-point Likert scale for sports and fitness centres
and healthy fast-food restaurants. For sports and fitness centres, the adapted indicators were as
follows: “My choice to this fitness centre has been a wise one”,“I am satisfied with my decision to
subscribe to this service provider”,“I feel that my experience with this fitness centre has been
enjoyable”and “I think I did the right thing when I subscribed this service”.Similarly,for
healthy fast-food restaurants, the indicators were “I am satisfied with my decision to visit to this
service provider”,“I feel that my experience with this service provider has been enjoyable”,“I
think I didthe right thing when I purchased this service”and “My choice to purchase at Subway
has been a wise one”. The overall satisfaction of the students of the higher education institutions
was measured using a measurement scale of 6 items whereby the indicators were adapted from
the scale used by Brady et al. (2002). Student satisfaction was measured with the following
indicators: “My choice to enrol at my university was a wise one”,“This university is exactly
what is needed for higher educationstudies”,“I did theright thing by choosing my university”,“I
am pleased to be enrolled as a student at my university”,“I am enjoying studying at my
university”and “I am happy with my experience as a student at my university”.
4. Results and discussions
4.1 Study A: sports and fitness centres
4.1.1 Measurement and structural model results. Figure 3 illustrates the measurement model
used for study A. 75.9% of variance explained in overall satisfaction is due to the combined
effect of process and outcome quality, with a confidence interval of 0.695–0.792 obtained from
the bootstrapping procedure; at the 95% confidence level, the coefficient of determination is
found to be statistically significant.
To get further insight into the model’s predictive power, a PLSpredict analysis is carried
out. In line with the guidelines provided by Hair et al. (2019) and Shmueli et al. (2019), since
none of the constructs in the PLS analysis has a higher RMSE value than in the LM
benchmark, the model has high predictive power.
The examination of the path coefficients (Table 4) shows that process quality had a
statistically significant direct effect on overall satisfaction (β50.612, p<0.05,
BCa 5[0.541–0.671]). All three dimensions measured as first-order factors are also found
to exert statistically significant effects on overall satisfaction, in line with previous studies
(Alexandris et al., 2004;Cizrelio
gullari et al., 2021;Murray and Howat, 2002;Papadimitriou
and Karteroliotis, 2000;Shonk and Chelladurai, 2009) whereby services attributes were found
to positively and significantly impact customer satisfaction.
Outcome quality also displayed a statistically significant direct effect on overall
satisfaction (β50.339, p< 0.05, BCa 5[0.274–0.404]), which is in line with previous studies
on outcome quality in fitness centres (Alexandris et al., 2004;Ko and Pastore, 2004,2007;
Lagrosen and Lagrosen, 2007). Among these, physical aspects had the strongest effect
TQM
Path
coefficients t-values p-values
95%
confidence
intervals Sig
a
(p< 0.05)?2.5% 97.5%
Assessment of direct effects
→Overall satisfaction (OS)
Process quality 0.612 17.744 0.000 0.541 0.671 Yes
Outcome quality 0.339 10.088 0.000 0.274 0.404 Yes
Assessment of total effects
→Overall satisfaction (OS)
Programme quality 0.232 6.478 0.000 0.16 0.303 Yes
Physical aspects 0.281 7.719 0.000 0.211 0.351 Yes
Staff quality 0.181 5.206 0.000 0.114 0.256 Yes
Social outcomes 0.118 5.43 0.000 0.084 0.167 Yes
Psychological and physical
outcomes
0.259 8.586 0.000 0.206 0.324 Yes
Note(s): *Reference is made to the bootstrap bias-corrected confidence interval
Figure 3.
Measurement model
for the sports and
fitness sector
Table 4.
Results of path
coefficients, t-values
and p-values for
study A
Application of
PLS-SEM and
IPMA
(β50.281, p< 0.05, BCa 5[0.211–0.351]), followed by psychological and physical outcomes
(β50.259, p< 0.05, BCa 5[0.206–0.324]).
4.1.2 Importance-performance map analysis. At the construct level, as illustrated in
Figure 4, the IPMA identified programme and physical aspects quality as areas requiring
managerial attention. More specifically, the IPMA carried out at the indicator level (refer to
Figure 5) suggested the following areas for improvement, namely in terms of physical aspects
quality: PaQ1 “There is access to easily available and safe lockers”[β50.067 > 0.057,
PS 564.1 < 74.5], PaQ2 “The environment is pleasant”[β50.072 > 0.057, PS 573.7 < 74.5],
PaQ3 “There is a variety of workout facility/equipment”,[β50.065 > 0.057, PS 571.4 < 74.5],
PaQ4 “The workout facilities and equipment are easily available”[β50.068 > 0.057,
PS 571.6 < 74.5] and PaQ5 “There is a good overall maintenance”[β50.070 > 0.057,
PS 571.4 < 74.5].
In terms of the quality of the programme quality, the indicators PrQ3 “The content and
delivery of the programme are of good quality”[β50.073 > 0.057, PS 573.4 < 74.5] as well as
PrQ4 “The background music used is appropriate”[β50.067 > 0.057, PS 569.2 < 74.5] fell
within the priority quadrant. PrQ1 “There is a good variety of programmes offered at a
suitable level”[β50.075 > 0.057, PS 574.6 > 74.5] is right on the threshold line and, as such,
should be considered as an element to be prioritised as well. As far as the items of staff quality
are concerned, all βare below 0.057, while performance scores are within the range of
72.6–77.3.
When it comes to the two dimensions of outcome quality, all the items within physical and
psychological outcomes while being important, all of the βvalues are greater than 0 .065; they
appear to be well catered by at the sports and fitness centres with all performance scores
being greater than 74.5. On the other hand, all the performance scores for the three items of
Physical and Psychological outcomes,
0.259, 81.6
Physical Aspect Quality,
0.281, 70.6
Program Quality,
0.232, 73.4
Social outcomes, 0.118,
71.3
Staff Quality, 0.181, 74.8
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
0 0.02 0.04 0.06 0.08 0.1 0.1 2 0.14 0.16 0.18 0.2 0.22 0.24 0.26 0.28 0.3
Performance
Importance
Importance-Performance Map
[Constructs]
Figure 4.
IPMA at the construct
level for the sports and
fitness sector
TQM
social outcomes fell below the threshold of 74.5, but their importance values were consistently
below 0.057.
It appears that social outcomes are not necessary from the members’perspectives.
Though the fitness centres do not appear to offer many socialisation opportunities, this is not
an issue given their low importance. Staff quality elements appear to be lingering around the
mean value for performance. Still, the associated importance is relatively low. Therefore, it
does not seem to be of great significance to fitness customers.
4.2 Study B: hospitality
4.2.1 Measurement and structural model results. Figure 6 shows the measurement model used
for the hospitality sector in study B. 59.1% of variance explained in overall satisfaction was
due to the combined effect of process and outcome quality. With a confidence interval of
0.491–0.637 obtained from the bootstrapping procedure, at the 95% confidence level, the
coefficient of determination is found to be statistically significant. Since only a few of the
constructs in the PLS analysis has a higher RMSE value than in the LM benchmark (Hair
et al., 2019;Shmueli et al., 2019), the model has medium predictive power.
The examination of the path coefficients (Table 5) shows that process quality has a
statistically significant direct effect on overall satisfaction (β50.409, p< 0.05, BCa 5[0.292–
0.509]). All the two dimensions measured as first-order factors are also found to exert
statistically significant effects on overall satisfaction. Interaction quality has the highest
effect (β50.357, p< 0.05, BCa 5[0.246–0.460]) followed by physical environment (β50.081,
p< 0.05, BCa 5[0.038–0.134]).
In line with previous studies in a restaurant setting (Bufquin et al., 2017;Liu and Jang,
2009;Wu, 2013), there is a positive link between service quality (process quality) and
customer satisfaction. Similar to previous studies such as Johns and Howard (1998),Izogo and
Ogba (2015), and Qin and Prybutok (2009), the behaviour of staff and service commitment
was found to be the most important dimension of service quality for the customers.
PPo1, 0.069, 77.9
PPo2, 0.065, 79.0
PPo3, 0.061, 83.5
PPo4, 0.063, 85.1
PPo5, 0.068, 81.9
PaQ1, 0.067, 64.1
PaQ2, 0.072, 73.3
PaQ3, 0.065, 71.4
PaQ4, 0.068, 71.6
PaQ5, 0.070, 71.4
PrQ1, 0.075, 74.6
PrQ2, 0.067, 75.8
PrQ3, 0.073, 73.4
PrQ4, 0.067, 69.2
So1, 0.047, 69.9
So2, 0.048, 70.9
So3, 0.043, 73.1
StQ1, 0.036, 77.3
StQ2, 0.034, 74.7
StQ3, 0.035, 77.3
StQ4, 0.037, 72.2
StQ5, 0.037, 74.3
StQ6, 0.039, 72.6
63.0
64.0
65.0
66.0
67.0
68.0
69.0
70.0
71.0
72.0
73.0
74.0
75.0
76.0
77.0
78.0
79.0
80.0
81.0
82.0
83.0
84.0
85.0
86.0
87.0
0.030 0.035 0.040 0.045 0.050 0. 055 0.060 0.065 0.070 0.075 0.08 0
Performance
Importance
Importance-Performance Map [Indicators]
Figure 5.
IPMA at indicator level
for the sports and
fitness sector
Application of
PLS-SEM and
IPMA
Path coefficients t-values p-values
95%
confidence
intervals Sig
a
(p< 0.05)?2.5% 97.5%
Assessment of direct effects
→Overall satisfaction
Process quality 0.409 7.558 0.000 0.292 0.509 yes
Outcome quality 0.426 7.817 0.000 0.301 0.519 yes
Assessment of total effects
→Process quality
Service commitment 0.721 11.084 0.000 0.581 0.84 yes
Behaviour of staff 0.224 3.983 0.000 0.121 0.338 yes
Service commitment 0.295 5.273 0.000 0.189 0.413 yes
Behaviour of staff 0.092 4.124 0.000 0.052 0.141 yes
Physical environment 0.081 3.25 0.000 0.038 0.134 yes
Interaction quality 0.357 6.492 0.000 0.246 0.46 yes
Internal sense of happiness 0.159 4.029 0.000 0.075 0.229 yes
Food quality 0.232 5.363 0.000 0.134 0.31 yes
Healthy catering 0.126 5.057 0.000 0.08 0.177 yes
Note(s): * Reference is made to the bootstrap bias-corrected confidence interval
Figure 6.
Measurement model
for the hospitality
sector (healthy fast-
food restaurants)
Table 5.
Results of path
coefficients, t-values
and p-values for
study B
TQM
Outcome Quality also displayed a statistically significant direct effect on overall satisfaction
(β50.425, p< 0.05, BCa 5[0.301–0.519]). Among these, food quality had the strongest effect
(β50.232, p< 0.05, BCa 5[0.134–0.310]), followed by internal sense of happiness (β50.159,
p< 0.05, BCa 5[0.075–0.229]) and then by healthy catering. The statistically significant
relationship between food quality and customer satisfaction is in line with previous findings
(Al-Tit, 2015;Namin, 2017;Haghighi et al., 2012;Gagi
cet al., 2013).
4.2.2 Importance-performance map analysis. At the construct level, as shown in Figure 7,
the IPMA results reveal service commitment and interaction quality as areas that require
managerial attention. From the IPMA at the indicator level, as illustrated in Figure 8, the
elements which fell into the prioritisation quadrants are all the indicators for service
commitment, namely SC1 “Staff are able to provide services in a delightful manner”
[β50.074 > 0.038, PS 560.2 < 62.4], SC2 “Staff immediately make corrections when they
discover mistakes”[β50.068 > 0.038, PS 557.7 < 62.4], SC3 “Subway offers a reliable
service”[β50.076 > 0.038, PS 562.0 < 62.4], SC4 “Staff are happy to assist customers”
[β50.078 > 0.038, PS 562.36 < 62.4] and SC5 “There are proactive services at Subway”
[β50.073 > 0.038, PS 557.7 < 62.4].
In terms of food quality, the indicator of Fq6, “There is a variety in the side dishes
available”[β50.038, PS 560.7 < 62.4], is also within the prioritisation quadrant. The other
indicators of food quality were rated as important (β> 0.038), but their performance level was
above the mean value of 62.4. Therefore, in line with previous studies (Namin, 2017;Namkung
and Jang, 2007;Han and Ryu, 2009), customers associate high importance with this
dimension.
When it comes to the internal sense of happiness, the IPMA reveals their importance with
all of them having βvalues greater than 0.038, but their performance is lesser than the
average 62.4; Ih 1 “Subway enables customers to experience pleasure and joy”
Food quality, 0.232,
65.09
Interaction Quality, 0.357, 60.31
Internal sense of
happiness, 0.159, 58.82
Physical Environmental
Quality, 0.081, 65.92
Behaviour of Staff,
0.092, 60.10
Healthy catering, 0.126,
62.37
Service Commitment,
0.295, 60.08
58.0
58.5
59.0
59.5
60.0
60.5
61.0
61.5
62.0
62.5
63.0
63.5
64.0
64.5
65.0
65.5
66.0
66.5
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4
Performance
Importance
Importance-Performance Map [Constructs]
Figure 7.
IPMA at the construct
level for the hospitality
sector
Application of
PLS-SEM and
IPMA
[β50.042 > 0.038, PS 561.3 < 62.4], Ih 2 “Subway enables customers to be filled with
positive energies”[β50.043 > 0.038, PS 558.6 < 62.4], Ih 3 “Subway enables customers to
feel that “they are valued”[β50.043 > 0.038, PS 558.7 < 62.4], and Ih 4 “Subway enables
customers to preserve happy memories”[β50.039 > 0.038, PS 556.4 < 62.4]. They called
forth managerial attention since their performance level is below average. The importance of
emotional service elements, such as an internal sense of happiness when it comes to food
consumption in restaurants, has been mentioned in previous studies (Cheng et al., 2018;
Zhong and Moon, 2020).
4.3 Study C: higher education
4.3.1 Measurement and structural model results. The measurement model used for the higher
education sector is shown in Figure 9. 43.4% of variance explained in overall satisfaction was
due to the combined effect of process and transformative quality. With a confidence interval
of 0.333–0.484 obtained from the bootstrapping procedure, at the 95% confidence level, the
coefficient of determination is found to be statistically significant. PLSpredict results show
that very few of the constructs in the PLS analysis have higher RMSE values compared to the
LM, thus establishing the medium predictive power of the model (Hair et al., 2019;Shmueli
et al., 2019).
When it comes to student satisfaction, it can be seen from Table 6 that outcome quality
had a marginally higher contribution to student satisfaction with a statistically significant
direct effect (β50.399, p< 0.05, BCa 5[0.337–0.495]) as compared to process quality
(β50.355, p< 0.05, BCa 5[0.229–0.391]). This again reinforces the importance of
incorporating the element of transformative quality in service quality, especially in the higher
education context, as highlighted by Teeroovengadum et al. (2019). As suggested by
Teeroovengadum et al. (2019), universities should pull their focus on the overall development
of students and provide them with the tools to face the reality of the job market.
Fq1, 0.046, 66.6
Fq2, 0.05, 66.8
Fq3, 0.046, 66.9
Fq4, 0.046, 63.6
Fq5, 0.041, 64.5
Fq6, 0.041, 60.7
Fq7, 0.041, 66.0
Hc1, 0.033, 57.5
Hc2, 0.033, 62.9
Hc3, 0.034, 61.7
Hc4, 0.029, 66.0
Hc5, 0.034, 64.6
ICI1, 0.079, 60.2
ICI2, 0.074, 57.7
ICI3, 0.072, 62.0
ICI4, 0.078, 62.4
ICI5, 0.074, 57.7
Ih1, 0.049, 61.3
Ih2, 0.05, 58.6
Ih3, 0.048, 58.7
Ih4, 0.046, 56.4
PeQ1, 0.019, 64.2
PeQ2, 0.021, 62.2
PeQ4, 0.026, 68.8
PeQ5, 0.022, 69.1
PeQ6, 0.023, 63.4
Ps1, 0.027, 61.0
Ps2, 0.03, 60.7
Ps3, 0.027, 59.9
Ps4, 0.031, 58.8
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
0 0.01 0 .02 0.03 0.04 0.0 5 0.06 0.07 0.08 0.09
Performance
Importance
Importance-Performance Map [ Indicators]
Figure 8.
IPMA at indicator level
for the hospitality
sector
TQM
From the assessment of total effects, it can be seen that the functional quality dimensions
which have significant positive effects on satisfaction are administrative quality (both
behaviour and attitude of admin staff, as well as administrative procedures, have a
substantial impact) and core educational quality under which curriculum, pedagogy and
competence are seen to be having a significant positive effect on satisfaction. The findings
highlight that these dimensions must be catered to since they directly affect student
satisfaction.
Therefore, with both functional and outcome (technical) service quality having a
significant positive effect on satisfaction, the findings join these studies (Kasiri et al., 2017;
Silvestri et al., 2017) that demonstrated a similar result in a retail and tourism context,
respectively. Furthermore, both functional and outcome quality in higher education are
strong predictors of student satisfaction. This study further adds to the empirical support for
embracing the aspect of technical quality in similar studies.
Figure 9.
Measurement model
for the higher
education sector
Application of
PLS-SEM and
IPMA
4.3.2 Importance-performance map analysis. At the construct level, as can be seen from
Figure 10, no construct directly falls into the priority quadrant. In terms of importance,
curriculum and transformative quality are two constructs that can be quantified as highly
important. Performance-wise they are doing satisfactorily, and their performance needs to be
maintained. Competence, pedagogy and attitude, and behaviours of academics perform
pretty well, though they are not rated as highly important. General infrastructure,
administrative procedures, attitude and behaviour (of administrative staff), support
infrastructure, learning setting and support facilities all perform poorly; the IPMA
Figure 10 shows that they are not rated as critical.
At the indicator level, as shown in Figure 11, the IPMA suggests that the only indicators
requiring managerial attention are TQ1, “My university has enabled me to be more
emotionally stable”[β50.063 > 0.025, PS 549.9 < 51.3], and AP2 “, Clear and well-structured
administrative procedures so that service delivery times are minimum”[β50.027 > 0.025,
PS 538.8 < 51.3]. Interestingly, the other two indicators of administrative procedures, AP1
“Well standardised administrative processes so that there is not much bureaucracy and
useless difficulties”[β50.025, PS 551.2 < 51.3] and AP3 “Transparency of official
procedures and regulations”[β50.025, PS 544.9 < 51.3], fall precisely on the threshold line
for importance with both their βvalues being 0.025. The results show that students do
consider the impact of administrative procedures in their evaluation of the service offered by
their universities, which is in line with previous studies such as Abbas (2020),
Teeroovengadum et al. (2016) and Douglas et al. (2015).
On the other hand, elements of the curriculum, CC3 “Challenging academic standards of
programmes to ensure students’overall development”[β50.032 > 0.025, PS 551.9 > 51.3]
and CC4 “Relevance of course content to the future/current job of students”[β50.03 > 0.025,
Path
coefficients t-values p-values
95% confidence
intervals Sig
a
(p< 0.05)?2.5% 97.5%
Assessment of direct effects
→Satisfaction
Process quality 0.355 7.952 0.000 0.229 0.391 Yes
Outcome quality 0.399 9.017 0.000 0.337 0.495 Yes
Assessment of total effects
→Satisfaction
Administrative quality 0.104 2.691 0.007 0.032 0.181 Yes
Physical environment quality 0.082 1.761 0.079 0.009 0.17 No
Core educational quality 0.209 4.717 0.000 0.105 0.263 Yes
Support facilities 0.053 1.498 0.134 0.008 0.131 No
Attitude and behaviour 0.056 2.262 0.024 0.016 0.112 Yes
Administrative process 0.063 2.5 0.013 0.021 0.119 Yes
Support infrastructure 0.022 1.619 0.106 0.001 0.055 No
Learning setting 0.038 1.636 0.102 0.002 0.09 No
General infrastructure 0.042 1.545 0.123 0.004 0.098 No
Attitude and behaviour of
academics
0.033 1.12 0.263 0.026 0.087 No
Curriculum 0.101 2.719 0.007 0.033 0.158 Yes
Pedagogy 0.066 2.287 0.022 0.014 0.125 Yes
Competence 0.055 1.999 0.046 0.0 0.102 Yes
Note(s): * Reference is made to the bootstrap bias-corrected confidence interval
Table 6.
Results of path
coefficients, t-values
and p-values for
study C
TQM
PS 551.6 > 51.3] while not being in the prioritisation quadrant, do fall relatively close to the
latter. Therefore, their performance must be maintained above the average.
4.4 Discussion of findings
4.4.1 Sector A: sports and fitness centres. The significance of physical facility in the service
evaluation is acknowledged in all the service-quality models under consideration (Chelladurai
et al., 1987;Kim and Kim, 1995;Howat et al., 1996,1999;Padadimitriou and Karteroliotis, 2000;
Chang and Chelladurai, 2003;Alexandris et al., 2004;Ko and Pastore, 2005,Lam et al., 2005,
Yıldız, 2011:Yildiz and Kara, 2012;Freitas and Lacerda, 2019). The present study also shows
that all elements of the physical environment fell within the priority quadrant of the IPMA, i.e.
gym customers perceived these as essential determining elements of satisfaction.
Nevertheless, under current situations, they are performing poorly. Gyms are facilities
driven, and equipment has been found to play an essential role in the servicescape. Kim and
Kim (2016) recommended that equipment be included in any servicescape examination.
Furthermore, regular equipment maintenance and safety checks are likely to identify faulty
machines before any breakdown, prevent accidents and deliver the message that the fitness
centres care for the members’wellbeing. Physical aspects, including adequate equipment,
must be seriously considered for quality improvement initiatives.
The current study also shows that three elements of programme quality fell within the
priority quadrant. The concerns of the gym patrons were related to the variety of
programmes being offered, the content and delivery of the programme being of good quality
and the class sizes being reasonable. Exercise adherence is more likely to be supported
through task mastery, perceived progress in goal achievement, and positive changes in
Administrative
Processes, 0.063,
44.762
Attitude and Behaviour, 0.056, 44.383
Attitude and Behaviour of Academics, 0.033, 52.471
Competence, 0.055,
64.185
Curriculum, 0.101,
54.299
General Infrastructure,
0.042, 49.023
Learning Setting, 0.038, 42.397
Transformative
Quality, 0.399, 64.05
Pedagogy, 0.066,
54.472
Support Facilities, 0.053, 41.422
Support Infrastructure, 0.022, 44.446
38
40
42
44
46
48
50
52
54
56
58
60
62
64
66
0 0.05 0.1 0.15 0 .2 0.25 0.3 0.35 0 .4 0.45
Performance
Importance
Importance-Peformance Map (Constructs)
Figure 10.
IPMA at the construct
level for the higher
education sector
Application of
PLS-SEM and
IPMA
emotional states and self-image (Butler and Hardy, 1992;Annesi, 2000;Steinhardt and
Dishman, 1989). Therefore, a suitable workout programme must strike the optimum balance
between psychological well-being and tangible progress. Due to their various fitness levels
and personal fitness preferences, customers’demands for fitness centres are growing and
diversifying. To keep their interest and prevent exercise boredom, programmes must be
ABA1, 0.007, 55.4
ABA2, 0.006, 49.4
ABA3, 0.007, 54.9
ABA4, 0.007, 51.0
ABA5, 0.007, 53.6
ABA6, 0.007, 49.3
ABS1, 0.017, 46.3
ABS2, 0.017, 44.9
ABS3, 0.017, 44.0
ABS4, 0.017, 42.2
AP1, 0.025, 51.2
AP2, 0.027, 38.5
AP3, 0.025, 44.9
CC1, 0.034, 60.0
CC2, 0.032, 52.7
CC3, 0.032, 51.9
CC4, 0.03, 51.6
CP1, 0.022, 64.2
CP2, 0.023, 62.9
CP3, 0.022, 65.5
GI1, 0.018, 46.4
GI2, 0.017, 52.6
GI3, 0.018, 48.3
LS1, 0.016, 47.4
LS2, 0.014, 38.2
LS3, 0.017, 4 0.8
P1, 0.022, 63.8
P2, 0.021, 52.7
P3, 0.022, 43.5
P4, 0.024, 57.7
SF1, 0.012, 33.9
SF2, 0.013, 41.3
SF3, 0.014, 42.1
SF4, 0.011, 42.0
SF5, 0.013, 44.1
SF6, 0.01, 46.6
SI1, 0.006, 38.5
SI2, 0.007, 50.6
SI3, 0.008, 43.0
SI4, 0.006, 44.8
TQ1, 0.063, 49.9
TQ2, 0.073, 62.5
TQ3, 0.075, 67.6
TQ4, 0.07, 67.5
TQ5, 0.067 , 67.6
TQ6, 0.056, 58.7
TQ7, 0.069 , 63.1
TQ8, 0.067, 71.9
32.0
33.0
34.0
35.0
36.0
37.0
38.0
39.0
40.0
41.0
42.0
43.0
44.0
45.0
46.0
47.0
48.0
49.0
50.0
51.0
52.0
53.0
54.0
55.0
56.0
57.0
58.0
59.0
60.0
61.0
62.0
63.0
64.0
65.0
66.0
67.0
68.0
69.0
70.0
71.0
72.0
73.0
0 0.010.020.030.040.050.060.070.08
Performance
Importance
Importance-Performance Map [ Indicators]
Figure 11.
IPMA at indicator level
for the higher
education sector
TQM
appropriate with adequate feedback, emotional support, and continuous inclusion of new
exercises and treatments (Annesi, 2002,2003).
4.4.2 Sector B: hospitality (healthy fast-food restaurants). The behaviour of the staff and
service commitment was found to be the most crucial aspects of service quality for the clients,
which is consistent with other studies by Johns and Howard (1998),Izogo and Ogba (2015),
and Qin and Prybutok (2009). Yet the performances of these attributes are below average. The
customers want the staff to be able to provide services more delightfully, appear happy and
enthusiastic to assist the customers, be proactive in their approaches, immediately correct
any mistakes and offer a reliable service. Hence, to satisfy the customers and retain them,
there is a need to emphasise the quality of interactions between the personnel and the
customers.
In terms of outcome quality, in line with previous studies (Law et al.,2004;Namin, 2017;
Namkung and Jang, 2007;Han and Ryu, 2009), the findings show that customers are very much
concerned with food quality and they associate high importance to this dimension, as seen in
the IPMA. After all, people’s primary purpose when visiting a restaurant is to satisfy a need,
hunger (Johns and Howard. 1998). Related to the aspect of food offering is that aspect of healthy
catering. Healthy catering is what demarks Subway from other traditional fast-food outlets.
The study shows that customers associate healthy catering with their satisfaction, as seen by
the significant positive relationship between healthy catering and satisfaction. Likewise, an
internal sense of happiness is critical, as seen in the IPMA results. However, they call forth the
managerial attention since their performance level is below average. The importance of
emotional service elements, such as an internal sense of happiness when it comes to food
consumption in restaurants, has been mentioned in previous studies (Cheng et al.,2018;Zhong
and Moon, 2020). Hence increasing the internal sense of happiness of the customers during the
service experience islikely to improve theirsatisfaction level with the brandas they would want
to re-engage in such consumption activities (Zhong and Moon, 2020).
4.4.3 Sector C: higher education. In line with previous studies (e.g. Abbas, 2020:
Teeroovengadum et al., 2016;O’Neill and Palmer, 2004;Abdullah, 2006;Douglas et al., 2008;
Douglas et al., 2015;Narang, 2012) which established students’perceptions of the level of
service their universities provide is influenced by the attitudes and behaviours of
administrative staff as well as administrative procedures, the IPMA data showed that
students consider the effect of administrative procedures while evaluating the service, their
universities provide. Therefore, higher education institutions must invest in boosting the
quality of the administrative system.
Furthermore, the IPMA result suggests that one of the items, namely the ability of the
university to make the students feel emotionally stable, is highly important. It needs the
management’s attention since the performance is not up the average threshold. As rightly put
forth by Harvey and Green (1993), transformative quality is all about enhancing and
empowering and adding value to them in terms of knowledge, abilities and skills. Universities
must make the transformative aspect of service quality one of their main goals to promote
satisfaction among their existing students. The university’s top administrators should ensure
that the academic programmes implemented enable a smooth transition from students to
qualified scholars and employees. Students should be encouraged to reflect critically
(Mezirow, 1981) in order to reassess their mindset and beliefs in preparation for personal and
academic growth. Another crucial aspect of transformative quality at the higher education
level is the empowerment of students. To achieve this, universities should constantly make
sure there are adequate tools and resources available and that the teaching methods and
pedagogy are reviewed (Barnett, 2009) so that students can feel that their universities are
assisting them in developing their emotional stability and metacognitive skills, such as self-
efficacy, emotional intelligence and self-confidence (Johnes, 2006;Pool and Sewell, 2007;
Harvey and Green, 1993;Knight and Yorke, 2002).
Application of
PLS-SEM and
IPMA
5. Conclusion
This study’s main objective is to provide a roadmap for the application of IPMA using PLS-
SEM to gain perspective on the constructs’importance and performance to identify which
attributes should be prioritised for improvement and resource allocation in the service
sectors. As highlighted in the roadmap, irrespective of the service sector, whenever IPMA is
applied in the service quality context, specific considerations need to be taken before the
application of IPMA, especially concerning selecting the appropriate and reliable measuring
instruments. It is crucial to holistically capture service quality using the right measuring
instrument to improve the likelihood of adequately identifying attributes for prioritisation
that can lead to enhanced strategic decision-making. To showcase those pre-analytical
considerations before conducting an IPMA, Lovelock’s (1983) classification scheme was used
to select three different sectors: sports and fitness, hospitality and higher education.
The results provide empirical evidence for the usefulness of IPMA in better understanding the
customer satisfaction drivers in the quest for quality improvement initiatives. While using PLS-
SEM allows for testing the theoretical relationships among the studied constructs, the follow-up
IPMA proposed by Ringle and Sarstedt (2016) provides a means to single out the service attributes
worth investing in additional resources to enhance the level of customer satisfaction. The order of
importance of the service attributes and their actual perceived performance is identified from the
customers’perspectives. It thus contributes to the organisations being more customer-focused,
taking evidence-based decisions based on the IPMA results and aiming for continual
improvements, all of which are core principles of the TQM philosophy.
The following deductions were made for the different sectors considered: For the sports
and fitness sector, the physical aspects and the programme quality require managerial
attention. For the hospitability sector, it is service commitment, interaction quality and the
internal sense of happiness. Whereas for higher education, it is the administrative quality, as
well as an element of transformative quality, namely the role of the university in adding to the
emotional stability of its students, that needs the attention of the top management.
The results of IPMA further add to the increasing empirical evidence that illustrates the
importance of outcome quality in positively impacting customer satisfaction
(Teeroovengadum et al., 2019). They highlight the necessity of having a holistic service
quality evaluation, comprising both functional and outcome quality, for a better diagnostic
utility of such scales for practitioners (Howat and Assaker, 2016).
While the present study considered only three sectors, the roadmap can be utilised for
applying PLS-SEM and IPMA to any service sector to identify critical service quality
dimensions and attributes for enhancing customer satisfaction.
5.1 Theoretical implications
Service evaluation is usually made using the perspective of functional service quality only,
causing the existing measurement scales to be incomplete and theoretically limited
(Teeroovengadum et al., 2019). There are instances whereby such studies lack conceptual,
methodological, and statistical rigour regarding reliability and validity (Lai and Hitchcock,
2015). This study integrates the notion of outcome quality to offer a more holistic measurement
of service quality in three service sectors. As highlighted in the paper, many of the limitations,
for instance, not using context-specific comprehensive scales or not extending PLS-SEM results
using IPMA, persists, as can be seen in the recent TQM literature (e.g. Ahmed et al., 2022;
Hussein et al., 2022;Lin, 2021;Murrar et al., 2021).Therefore, by considering outcome quality and
using a comprehensive hierarchical model for service quality measurement, the study makes
significant contributions at a theoretical level with a better assessment of service quality
measurement. This study provides a step-by-step guide in the form of a roadmap, with
illustrative examples that help identify the appropriate context-specific scales and summarises
TQM
the various necessary assessments needed to correctly identify service quality attributes to be
prioritised for improvements, using the customers’perspectives. As such, the roadmap resides
on a customer focus approach and can be used for continuous improvements of the services
offered, thus contributing to the TQM philosophy.
The findings also add empirical support to the limited studies in quality management, which
have applied IPMA to identify critical drivers of satisfaction to provide the policymaker with
specific practical insights on which attributes need to be prioritised to improve customer
satisfaction. This highlights the significance of the proposed roadmap in ensuring that future
studies employ more reliable metrics and extend the PLS-SEM results using advanced analysis
such as IPMA rather than stopping at generic recommendations.
Since there are significant differences in service quality evaluation across service
industries and socio-cultural settings (Ahmed et al., 2022), this study also contributes to the
literature addressing service quality improvement initiatives within a developing economy
like Mauritius. The empirical findings confirm that customers attach diverse values to the
different service quality attributes, which aligns with the value-percept theory. The high
explanatory powers of the models also confirm the theoretical link between service quality
and customer satisfaction in line with Bagozzi’s (1992) framework.
5.2 Practical implications
While several existing studies discuss advanced model assessment individually, such as CTA-
PLS (Chin, 2010;Hair et al.,2018;Cheah et al., 2018)orIPMA(Ringle and Sarstedt, 2016;Hair
et al., 2019) individually, it can be difficult for practitioners to know which assessments are
needed to have an accurate evaluation of the servicebeing offered or to accurately identify areas
of improvement with the potential for return of investments in the form of satisfied customers.
Therefore, the roadmap is of crucial help to practitioners to understand the necessary steps
needed to ensure the specificity and accuracy of recommendations.
While the study’s present evaluation of prioritised attributes can help practitioners in
those sectors decide where to concentrate their efforts for effective service improvement, the
roadmap proposed in this study can guide service quality improvement initiatives in any
service sector. By extension, the roadmap can assist in enhancing any service organisation’s
overall quality management endeavours.
5.3 Limitations and recommendations
Practical limitations limit the scope of this study in terms of feasibility. For instance, the contexts
and time durations have been determined based on practical reasons. On the other hand, other
demarcations, such as sample population, type of sampling methods and sample size, are
influenced by methodology. Since customers’requirements are likely to be country-specific, the
generalisation concerning the indicators identified in the IPMA findings should be undertaken
with due care.
It is recommended that studies that empirically assess service quality and customer
satisfaction and identify key service quality attributes for improving customer satisfaction
use the PLS-SEM and IPMA. This study demonstrates that the IPMA tool provides
researchers and practitioners with a useful diagnostic tool for continuous service quality
improvement. Future research could also integrate PLS-SEM and IPMA findings into the
quality function deployment (QFD) process for systematically processing and deploying this
information throughout the service design.
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TQM
Appendix
A.1: Research instrument and measurement scales
A.1.1. Study A-sports and fitness
Sources
Staff quality
StQ1 The “staffs are well-trained and possess required
knowledge/skills/qualifications”
Alexandris et al. (2004),Freitas and
Lacerda (2019),Lam et al., (2005)
StQ3 The “staffs are polite and friendly”
StQ4 The “staffs are good at communicating with the
members”
StQ5 The “staffs are patient and provide individual attention to
members (e.g. correcting the postures)”
StQ6 The “staffs are always willing to help and are responsive
to any complaint”
StQ7 There “is a consistency in level of service provided”
Programme quality
PrQ1 There is a good variety of programmes offered at a
suitable level
Lam et al. (2005)
PrQ2 The time schedules of the programmes are very
appropriate
PrQ3 The content and delivery of the programmes are of good
quality
PrQ4 The class sizes are reasonable
Physical aspects quality
PaQ1 There is access to easily available and safe lockers Freitas and Lacerda (2019),Lam et al.
(2005)PaQ2 The environment is pleasant
PaQ3 There is a variety of workout facilities/equipment
PaQ4 The workout facilities and equipment are easily available
PaQ5 There is a good overall maintenance
Table A1.
Scale for service
quality in sports and
fitness centres (process
quality)
Application of
PLS-SEM and
IPMA
A.1.2: Study B-hospitability
Sources
Social outcomes
So1 I find a sense of group belonging within the fitness centre Alexandris et al. (2004),Ko and
Pastore (2005)
þ*Interviews and Focus
groups
So2 By attending the gym, I have the opportunity to socialise
So3 I feel accepted by others within the fitness centre*
Physical and psychological outcomes
PPo1 The programmes help me keep fit and healthy
PPo2 The programmes contribute to my psychological well-being (better
emotional health, ability to relax, reduce and manage stress)
Alexandris et al. (2004),Ko and
Pastore (2005)
PPo3 I am pleased with my self-improvement (aesthetic, endurance,
flexibility, etc.)*
PPo4 Exercising in the fitness centre boost positively my self-concept
(e.g. self-confidence and self-esteem)*
þ*Interviews and Focus
groups
PPo5 Exercising in the centre offers me the opportunity to increase my
knowledge*
Interaction quality Sources
Service commitment
SC1 Staff are able to provide services in a delightful manner Chen et al. (2018)
SC2 Staff immediately make corrections when they discover mistakes
SC3 Subway offers a reliable service
SC4 Staff are happy to assist customers
SC5 There are proactive services at Subway
Behaviour of staff
Bs1 Staff passionately respond to customers Chen et al. (2018),Wu and Mohi
(2015)Bs2 Staff possess professional knowledge and can answer questions
raised by customers
Bs3 Staff is pleased to introduce the menu content and meal
characteristics of the menus
Bs4 Staff are well-trained and rich in experience
Physical environment
Pe1 There is ease of access Chen et al. (2018),Raajpoot
(2002)Pe2 The facilities at Subway have an attractive appearance
Pe3 The facilities are clean
Pe4 The uniforms of the staff are tidy
Pe5 The dining environment is comfortable
Table A2.
Scale for service
quality in sports and
fitness centres
(outcome quality)
Table A3.
Scale for service
quality (process
quality) in the healthy
fast-food restaurant
TQM
A.1.3: Study C-higher education
As for service quality in higher education, HESQUAL (Teeroovengadum et al., 2016) is used.
Sources
Food quality
Fq1 The overall quality of food is good Chen et al. (2018),Kim et al. (2009),Wu and Mohi
(2015)Fq2 The taste of food is good
Fq3 The food is visually appealing
Fq4 The food served is fresh
Fq5 There is a variety of main courses available
Fq6 There is a variety in the side dishes available
Fq7 There is a variety of sauces available
Internal sense of happiness
Lh1 Subway enables customers to experience pleasure
and joy
Chen et al. (2018)
Lh2 Subway enables customers to be filled with positive
energies
Lh3 Subway enables customers to feel that “they are
valued”
Lh4 Subway enables customers to preserve happy
memories
Healthy catering
Hc1 There is use of organic products Chen et al. (2018)
Hc2 There is use of local ingredients
Hc3 Natural ingredients are utilised
Hc4 Subway provides the concept of a balanced diet
Hc5 Subway provides healthy meals
Administrative quality
Attitude and behaviours (of admin staff)
ABS1 Willingness of administrative staff members to help students
ABS2 Ability of administrative staff members to solve students’problems
ABS3 Politeness of administrative staff
ABS4 Behaviour of administrative staff members imparting confidence in students
Administrative procedure
AP1 Well standardised administrative processes so that there is not much bureaucracy and useless
difficulties
AP2 Clear and well-structured administrative procedures so that service delivery times are minimum
AP3 Transparency of official procedures and regulations
Physical environment quality
Support infrastructure
SI1 Availability of adequate cafeteria infrastructure
SI2 Availability of adequate library infrastructure
SI3 Availability of adequate recreational infrastructure
SI4 Availability of adequate sports infrastructure
(continued )
Table A4.
Scale for service
quality (outcome
quality) in healthy fast-
food restaurants
Table A5.
Scale for service
quality (process
quality) in higher
education
Application of
PLS-SEM and
IPMA
Physical environment quality
Learning setting
LS1 Having adequate lecture rooms
LS2 Having quiet places to study within campus
LS3 Availability of up-to-date teaching tools and equipment
General infrastructure
GI1 Favourable ambient condition (ventilation, noise, odour etc)
GI2 Safety on campus
GI3 Appearance of buildings and grounds
Core educational quality
Attitude and behaviours of academics
ABA1 Lecturers understanding students’needs
ABA2 Lecturers giving personal attention to students
ABA3 Availability of lecturers to guide and advise students
ABA4 Prevalence of a culture of sharing and collaboration among lecturers
ABA5 Behaviour of lecturers instilling confidence in students
ABA6 Lecturers appearing to have students’best interest at heart
Curriculum
CC1 Clearly defined course content and course objectives
CC2 Usefulness of module content and design to cater for the personal needs of students
CC3 Challenging academic standards of programmes to ensure students’overall development
CC4 Relevance of course content to the future/current job of students
Pedagogy
P1 Usage of multimedia in teaching (e.g. use of overhead projector, power-point presentations
P2 Allowing active participation of students in their learning process
P3 Provision of regular feedback to students with respect to their academic performance
P4 Well-designed examinations and continuous assessment to promote the enhancement of knowledge
and skills
Competence of academics
CP1 Theoretical knowledge, qualifications and practical knowledge of lecturers
CP2 Communication skills of lecturers
CP3 Lecturers being up to date in their area of expertise
Support facilities
SF1 Availability of quality food and refreshments on campus
SF2 Availability of adequate IT facilities
SF3 Availability and adequacy of photocopy and printing facilities
SF4 Availability of adequate transport facilities
SF5 Availability of adequate sports and recreational facilities
SF6 Availability and adequacy of extra-curricular activities including those through clubs and societies
Table A5.
TQM
A.2 Results for testing the higher-order formative measurement models and lower-order
reflective models
A.2.1 Study A: sports and fitness
Transformative quality
TQ1 My university has enabled me to be more emotionally stable
TQ2 My university has enabled me to be more self-confident
TQ3 My university has helped me to think more critically
TQ4 My university has enabled me to have a higher level of self-awareness
TQ5 My university has helped me to develop problem-solving skills with respect to my field of study
TQ6 My university has allowed me to transcend my prejudices
TQ7 My university has allowed me to acquire adequate technical knowledge and skills to perform my future
job
TQ8 My university has enabled me to increase my knowledge and skills in general
Outer weights
95% confidence
intervals
VIF2.5% 97.5%
Process quality Staff Quality (S) 0.310 0.201 0.420 2.147
Programme Quality (Pr) 0.387 0.275 0.503 2.174
Physical Aspects Quality (Pa) 0.439 0.317 0.556 1.965
Outcome quality Social outcomes 0.436 0.306 0.573 1.421
Physical and Psychological outcomes 0.691 0.561 0.799 1.421
Table A6.
Scale for service
quality (outcome
quality) in higher
education
Table A7.
Higher-order formative
measures for service
quality
Application of
PLS-SEM and
IPMA
A.2.2. Study B: hospitability
Latent variable Indicators
Convergent validity
Internal
consistency
reliability
DV MSDLoadings
Indicator
reliability AVE CR CA
>0.70 >0.50 >0.50 >0.70 >0.70 HTMT CI*
Process quality
Staff quality StQ1 0.783 0.613 0.695 0.932 0.912 Yes 0.78 0.02
StQ3 0.828 0.684 0.83 0.02
StQ4 0.849 0.717 0.85 0.02
StQ5 0.873 0.762 0.87 0.01
StQ6 0.831 0.692 0.82 0.02
StQ7 0.837 0.702 0.84 0.01
Program
quality
PrQ1 0.846 0.711 0.672 0.891 0.836 Yes 0.84 0.02
PrQ2 0.738 0.551 0.74 0.03
PrQ3 0.854 0.728 0.85 0.02
PrQ4 0.836 0.701 0.84 0.02
Physical
aspects
PaQ1 0.762 0.581 0.672 0.911 0.877 Yes 0.76 0.02
PaQ2 0.819 0.674 0.82 0.02
PaQ3 0.816 0.667 0.82 0.02
PaQ4 0.854 0.714 0.85 0.02
PaQ5 0.853 0.724 0.85 0.02
Outcome quality
Social
outcomes
So1 0.862 0.741 0.738 0.894 0.822 Yes 0.86 0.02
So2 0.897 0.808 0.90 0.01
So3 0.817 0.664 0.81 0.02
Physical and
psychological
outcomes
PPo1 0.794 0.626 0.629 0.894 0.853 Yes 0.79 0.02
PPo2 0.807 0.648 0.81 0.03
PPo3 0.785 0.618 0.79 0.02
PPo4 0.796 0.637 0.80 0.02
PPo5 0.784 0.618 0.78 0.03
Outer weights
95% confidence
interval
VIF2.5% 97.5%
Interaction quality Behaviour of staff 0.257 0.128 0.391 1.504
Service commitment 0.827 0.716 0.922 1.504
Process quality Interaction quality 0.872 0.777 0.945 1.472
Physical environment quality 0.198 0.092 0.319 1.472
Outcome quality Food quality 0.547 0.415 0.661 1.660
Healthy catering 0.295 0.188 0.457 1.525
Internal sense of happiness 0.372 0.239 0.492 1.322
Table A8.
Lower-order reflective
measures
Table A9.
Higher-order formative
measurement models
TQM
Latent variable Indicators
Convergent validity
Internal
consistency
reliability
DV MSDLoadings
Indicator
reliability AVE CR CA
>0.70 >0.50 >0.50 >0.70 >0.70
HTMT
CI*
Interaction quality
Behaviour of
staff
Bs1 0.762 0.581 0.63 0.848 0.775 Yes 0.76 0.03
Bs2 0.829 0.687 0.83 0.02
Bs3 0.796 0.634 0.79 0.03
Bs4 0.801 0.642 0.80 0.03
Service
commitment
SC1 0.806 0.650 0.613 0.843 0.842 Yes 0.81 0.02
SC2 0.783 0.613 0.78 0.02
SC3 0.729 0.531 0.73 0.03
SC4 0.809 0.654 0.81 0.03
SC5 0.785 0.616 0.79 0.03
Outcome quality
Food quality Fq1 0.767 0.588 0.553 0.896 0.865 Yes 0.77 0.03
Fq2 0.791 0.626 0.79 0.02
Fq3 0.758 0.575 0.76 0.02
Fq4 0.752 0.566 0.75 0.03
Fq5 0.732 0.536 0.73 0.03
Fq6 0.699 0.489 0.70 0.03
Fq7 0.702 0.493 0.70 0.03
Healthy
catering
Hc1 0.746 0.557 0.597 0.881 0.831 Yes 0.75 0.03
Hc2 0.757 0.573 0.76 0.03
Hc3 0.812 0.659 0.81 0.02
Hc4 0.782 0.612 0.78 0.03
Hc5 0.764 0.584 0.77 0.03
Internal sense
of happiness
Ih1 0.833 0.694 0.679 0.894 0.871 Yes 0.83 0.02
Ih2 0.865 0.748 0.86 0.02
Ih3 0.793 0.629 0.79 0.02
Ih4 0.804 0.646 0.81 0.02
Latent variable Indicators
Convergent validity
Internal
consistency
reliability
DV MSDLoadings
Indicator
reliability AVE CR CA
>0.70 >0.50 >0.50 >0.70 >0.70
HTMT
CI*
Physical
environment
PeQ1 0.646 0.417 0.528 0.848 0.775 Yes 0.65 0.04
PeQ2 0.741 0.549 0.74 0.03
PeQ3 0.785 0.616 0.79 0.03
PeQ4 0.772 0.596 0.77 0.03
PeQ5 0.679 0.461 0.68 0.04
Satisfaction OS1 0.854 0.724 0.751 0.924 0.890 Yes 0.85 0.02
OS2 0.868 0.757 0.87 0.02
OS3 0.893 0.799 0.89 0.01
OS4 0.850 0.726 0.85 0.02
Table A10.
Lower-order reflective
measurement models
Table A11.
Lower-order reflective
measures
(unidimensional
models)
Application of
PLS-SEM and
IPMA
A.2.3. Study C: higher education
Outer weights
95%
confidence
interval
VIF2.5% 97.5%
Second-order models for process quality
Process quality Administrative quality 0.294 0.138 0.471 1.266
Physical environment quality 0.231 0.002 0.462 2.006
Core educational quality 0.589 0.349 0.755 1.524
Support facilities 0.151 0.005 0.368 1.565
First-order models for process quality
Administrative quality Attitude and behaviour 0.539 0.305 0.742 1.356
Administrative procedures 0.608 0.400 0.808 1.356
Physical environment quality Support infrastructure 0.269 0.058 0.456 1.319
Learning setting 0.458 0.284 0.619 1.476
General infrastructure 0.509 0.341 0.666 1.407
Core educational quality Attitude and behaviour 0.158 0.065 0.413 1.707
Curriculum 0.482 0.248 0.673 1.730
Pedagogy 0.317 0.111 0.554 1.767
Competence 0.259 0.062 0.470 1.734
Latent variable Indicators
Convergent validity
Internal
consistency
reliability
DV MSDLoadings
Indicator
reliability AVE CR CA
>0.70 >0.50 >0.50 >0.70 >0.70
HTMT
CI*
Administrative quality
Attitude and
behaviour
ABS1 0.839 0.70 0.687 0.898 0.848 Yes 0.84 0.02
ABS2 0.836 0.70 0.84 0.02
ABS3 0.838 0.70 0.84 0.02
ABS4 0.802 0.64 0.80 0.02
Administrative
processes
AP1 0.823 0.68 0.683 0.866 0.767 Yes 0.82 0.02
AP2 0.868 0.75 0.87 0.01
AP3 0.786 0.62 0.79 0.02
Physical environment quality
Support
infrastructure
SI1 0.733 0.54 0.531 0.871 0.823 Yes 0.73 0.03
SI2 0.794 0.63 0.80 0.02
SI3 0.898 0.81 0.90 0.01
SI4 0.794 0.63 0.79 0.03
Learning setting LS1 0.809 0.65 0.656 0.851 0.739 Yes 0.81 0.02
LS2 0.783 0.61 0.78 0.03
LS3 0.837 0.70 0.84 0.02
(continued )
Table A12.
Higher-order formative
measurement model
for process quality
Table A13.
Lower-order reflective
measures for process
quality
TQM
Latent variable Indicators
Convergent validity
Internal
consistency
reliability
DV MSDLoadings
Indicator
reliability AVE CR CA
>0.70 >0.50 >0.50 >0.70 >0.70
HTMT
CI*
General
infrastructure
GI1 0.761 0.58 0.605 0.822 0.674 Yes 0.76 0.03
GI2 0.780 0.61 0.78 0.03
GI3 0.793 0.63 0.79 0.02
Core educational quality
Attitude and
behaviour of
academics
ABA1 0.784 0.61 0.642 0.915 0.889 Yes 0.79 0.02
ABA2 0.767 0.59 0.77 0.03
ABA3 0.820 0.67 0.82 0.02
ABA4 0.810 0.66 0.81 0.02
ABA5 0.822 0.68 0.82 0.02
ABA6 0.804 0.65 0.80 0.02
Curriculum CC1 0.798 0.64 0.625 0.869 0.799 Yes 0.80 0.02
CC2 0.828 0.69 0.83 0.02
CC3 0.800 0.64 0.80 0.02
CC4 0.734 0.54 0.73 0.04
Pedagogy P1 0.707 0.50 0.551 0.83 0.728 Yes 0.71 0.03
P2 0.744 0.55 0.74 0.03
P3 0.737 0.54 0.74 0.03
P4 0.778 0.61 0.78 0.03
Competence CP1 0.814 0.66 0.674 0.861 0.757 Yes 0.81 0.02
CP2 0.853 0.73 0.85 0.02
CP3 0.794 0.63 0.80 0.02
Support facilities
Support facilities SF1 0.645 0.42 0.531 0.871 0.823 Yes 0.64 0.04
SF2 0.744 0.55 0.74 0.03
SF3 0.767 0.59 0.77 0.03
SF4 0.724 0.52 0.72 0.03
SF5 0.779 0.61 0.78 0.03
SF6 0.705 0.50 0.70 0.03 Table A13.
Application of
PLS-SEM and
IPMA
Corresponding author
Viraiyan Teeroovengadum can be contacted at: v.teeroovengadum@uom.ac.mu
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Latent variable Indicators
Convergent validity
Internal
consistency
reliability
DV MSDLoadings
Indicator
reliability AVE CR CA
>0.70 >0.50 >0.50 >0.70 >0.70
HTMT
CI*
Transformative
quality
TQ1 0.631 0.40 0.546 0.905 0.880 Yes 0.63 0.04
TQ2 0.775 0.60 0.77 0.03
TQ3 0.808 0.65 0.81 0.02
TQ4 0.816 0.67 0.82 0.02
TQ5 0.741 0.55 0.74 0.03
TQ6 0.709 0.50 0.71 0.04
TQ7 0.695 0.48 0.69 0.03
TQ8 0.717 0.51 0.72 0.03
Satisfaction S1 0.853 0.73 0.71 0.936 0.918 Yes 0.85 0.02
S2 0.779 0.61 0.78 0.02
S3 0.895 0.80 0.90 0.01
S4 0.897 0.80 0.90 0.01
S5 0.837 0.70 0.84 0.02
S6 0.790 0.62 0.79 0.03
Table A14.
Lower-order reflective
measures
(unidimensional
models)
TQM
